303 research outputs found
Fatigue in patients with Sjögren’s syndrome and intervention of traditional herbal medicine
Background: Fatigue is the main complaint exiting in patients with primary Sjögren’s Syndrome (pSS) but rarely addressed. Patients described it as an uncontrollable symptom of lack of energy, which has negative impacts on health related quality of life. Todate, many studies have demonstrated that cytokines, depression, sleep and endocrine disturbance interrelated with pSS-related fatigue. However, the pathogenesis remains unclear. With a long history, Traditional Chinese Medicine (TCM) as alternative therapy has become increasingly popular among patients with various kinds of disease, especially in pSS. Based on the unique principle of therapy, practitioners have achieved a satisfactory effect on relieving disease related symptoms with Chinese Herbal Medicine (CHM).Materials and Methods: In this article, we succinctly reviewed the highly correlated factors to pSS-related fatigue from the standpoint of western medicine. Then, from TCM perspective, we illustrated that theoretic mechanisms lead to fatigue in patients with pSS.Results: According to the theory of TCM, we concluded that CHM as complementary and alternative medicines are attractive options to alleviate pSS-related fatigue.Conclusion: In clinic, physicians should remember to inquire whether their patients are worn out easily. Combination of Yin-tonifying and Qi-tonifying CHM may be the optimal options to pSS-related fatigue.Keywords:Sjögren’s Syndrome, fatigue, Traditional Chinese Medicine, Chinese Herbal Medicine, rheumatic disease
Deconvolutional Latent-Variable Model for Text Sequence Matching
A latent-variable model is introduced for text matching, inferring sentence
representations by jointly optimizing generative and discriminative objectives.
To alleviate typical optimization challenges in latent-variable models for
text, we employ deconvolutional networks as the sequence decoder (generator),
providing learned latent codes with more semantic information and better
generalization. Our model, trained in an unsupervised manner, yields stronger
empirical predictive performance than a decoder based on Long Short-Term Memory
(LSTM), with less parameters and considerably faster training. Further, we
apply it to text sequence-matching problems. The proposed model significantly
outperforms several strong sentence-encoding baselines, especially in the
semi-supervised setting.Comment: Accepted by AAAI-201
Towards More Efficient Insertion Transformer with Fractional Positional Encoding
Auto-regressive neural sequence models have been shown to be effective across
text generation tasks. However, their left-to-right decoding order prevents
generation from being parallelized. Insertion Transformer (Stern et al., 2019)
is an attractive alternative that allows outputting multiple tokens in a single
generation step. Nevertheless, due to the incompatibility between absolute
positional encoding and insertion-based generation schemes, it needs to refresh
the encoding of every token in the generated partial hypothesis at each step,
which could be costly. We design a novel reusable positional encoding scheme
for insertion transformers called Fractional Positional Encoding (FPE), which
allows reusing representations calculated in previous steps. Empirical studies
on various text generation tasks demonstrate the effectiveness of FPE, which
leads to floating-point operation reduction and latency improvements on batched
decoding
Learning a Hybrid Architecture for Sequence Regression and Annotation
When learning a hidden Markov model (HMM), sequen- tial observations can
often be complemented by real-valued summary response variables generated from
the path of hid- den states. Such settings arise in numerous domains, includ-
ing many applications in biology, like motif discovery and genome annotation.
In this paper, we present a flexible frame- work for jointly modeling both
latent sequence features and the functional mapping that relates the summary
response variables to the hidden state sequence. The algorithm is com- patible
with a rich set of mapping functions. Results show that the availability of
additional continuous response vari- ables can simultaneously improve the
annotation of the se- quential observations and yield good prediction
performance in both synthetic data and real-world datasets.Comment: AAAI 201
The Entity-Deduction Arena: A playground for probing the conversational reasoning and planning capabilities of LLMs
Large language models (LLMs) are effective at answering questions that are
clearly asked. However, when faced with ambiguous queries they can act
unpredictably and produce incorrect outputs. This underscores the need for the
development of intelligent agents capable of asking clarification questions to
resolve ambiguities effectively. This capability requires complex
understanding, state tracking, reasoning and planning over multiple
conversational turns. However, directly measuring this can be challenging. In
this paper, we offer a surrogate problem which assesses an LLMs's capability to
deduce an entity unknown to itself, but revealed to a judge, by asking the
judge a series of queries. This entity-deducing game can serve as an evaluation
framework to probe the conversational reasoning and planning capabilities of
language models. We systematically evaluate various LLMs and discover
significant differences in their performance on this task. We find that strong
LLMs like GPT-4 outperform human players by a large margin. We further employ
Behavior Cloning (BC) to examine whether a weaker model is capable of imitating
a stronger model and generalizing to data or domains, using only the
demonstrations from a stronger model. We finally propose to use Reinforcement
Learning to enhance reasoning and planning capacity of Vicuna models through
episodes of game playing, which lead to significant performance improvement. We
hope that this problem offers insights into how autonomous agents could be
trained to behave more intelligently in ambiguous circumstances.Comment: 22 page
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