36 research outputs found
Application of Knowledge Sharing at UPT Puskesmas Desa Medan Krio
Objective: This review aims to find a relationship between the increasing number of monkeypox cases, especially in LGBT community, based on the literature study approach and case reports. One of the non-medicamentous ways is to reduce travelling abroad, especially in countries with many monkeypox cases. Preventing direct contact, such as abstaining from sex and using condoms, is also a way to reduce the morbidity of monkeypox infection, especially in LGBT community. The treatment has not proven effective, and the available vaccines, especially in Indonesia, are still limited. Only a few cases in Indonesia have been reported. Method: This research is based on a literature study approach and case reports with article sources obtained from Eurosurveillance and Elsevier. It restricts articles using the keywords "cases that occurred in patients in the LGBT / MSM community" to get relevant data following the current monkeypox outbreak conditions. Result: The appearance of the monkeypox virus in 2022 in 58 countries and Indonesia is no exception, which confirmed 1 case in patients post travelling abroad. The current monkeypox infection is still present, with symptoms that vary from person to person and are typically characterized by reddish rashes. Still, it is primarily confined to the genital, perigenital, and perianal areas. It manifests at various stages of development, in addition to transmission from animals caused by hunting activities or in LGBT ( Lesbian, Gay, Bisexual, and Transgender) communities that have unprotected sex and can be infected through semen and saliva. Conclusion: Monkeypox cases that occur globally in 2022 need further research to reduce and prevent, especially in the LGBT community and those at risk of infection, pregnant women and babies, as well as medical personnel in close contact with patients. One of the non-medical procedures is reducing travel abroad, especially in countries with many cases of monkeypox, and reducing direct contact with sufferers, such as abstinence from sex and using condoms in the LGBT community, is also one way to reduce the morbidity of monkeypox infection. The treatment has not been proven effective, and the existing vaccines, especially in Indonesia, are still limited. Only a few cases in Indonesia have been reported
Differentiable VQ-VAE's for Robust White Matter Streamline Encodings
Given the complex geometry of white matter streamlines, Autoencoders have
been proposed as a dimension-reduction tool to simplify the analysis
streamlines in a low-dimensional latent spaces. However, despite these recent
successes, the majority of encoder architectures only perform dimension
reduction on single streamlines as opposed to a full bundle of streamlines.
This is a severe limitation of the encoder architecture that completely
disregards the global geometric structure of streamlines at the expense of
individual fibers. Moreover, the latent space may not be well structured which
leads to doubt into their interpretability. In this paper we propose a novel
Differentiable Vector Quantized Variational Autoencoder, which are engineered
to ingest entire bundles of streamlines as single data-point and provides
reliable trustworthy encodings that can then be later used to analyze
streamlines in the latent space. Comparisons with several state of the art
Autoencoders demonstrate superior performance in both encoding and synthesis.Comment: 5 pages, 4 figures, 1 tabl
SHEPHERD SCHOOL SYMPHONY ORCHESTRA LARRY RACHLEFF, Music Director WOMEN OF THE RICE CHORALE THOMAS JABER, Music Director and the HOUSTON CHILDREN'S CHORUS CONCERT CHOIR Stephen Roddy, Director LARRY RACHLEFF, Conductor Monday, April 25, 2011 8:00 p.m. Stude Concert Hall
SHEPHERD SCHOOL SYMPHONY ORCHESTRA LARRY RACHLEFF, music director Friday, March 18, 2011 8:00 p.m. Stude Concert Hall
RocketQAv2: A Joint Training Method for Dense Passage Retrieval and Passage Re-ranking
In various natural language processing tasks, passage retrieval and passage
re-ranking are two key procedures in finding and ranking relevant information.
Since both the two procedures contribute to the final performance, it is
important to jointly optimize them in order to achieve mutual improvement. In
this paper, we propose a novel joint training approach for dense passage
retrieval and passage re-ranking. A major contribution is that we introduce the
dynamic listwise distillation, where we design a unified listwise training
approach for both the retriever and the re-ranker. During the dynamic
distillation, the retriever and the re-ranker can be adaptively improved
according to each other's relevance information. We also propose a hybrid data
augmentation strategy to construct diverse training instances for listwise
training approach. Extensive experiments show the effectiveness of our approach
on both MSMARCO and Natural Questions datasets. Our code is available at
https://github.com/PaddlePaddle/RocketQA.Comment: EMNLP 202
ChatGPT and Simple Linguistic Inferences: Blind Spots and Blinds
This paper sheds light on the limitations of ChatGPT's understanding
capabilities, focusing on simple inference tasks that are typically easy for
humans but appear to be challenging for the model. Specifically, we target (i)
grammatically-specified entailments, (ii) premises with evidential adverbs of
uncertainty, and (iii) monotonicity entailments. We present expert-designed
evaluation sets for these inference types and conduct experiments in a
zero-shot setup. Our results show that the model struggles with these types of
inferences, exhibiting moderate to low accuracy. Moreover, while ChatGPT
demonstrates knowledge of the underlying linguistic concepts when prompted
directly, it often fails to incorporate this knowledge to make correct
inferences. Even more strikingly, further experiments show that embedding the
premise under presupposition triggers or non-factive verbs causes the model to
predict entailment more frequently {regardless} of the correct semantic label.
Overall these results suggest that, despite GPT's celebrated language
understanding capacity, ChatGPT has blindspots with respect to certain types of
entailment, and that certain entailment-cancelling features act as ``blinds''
overshadowing the semantics of the embedded premise. Our analyses emphasize the
need for further research into the linguistic comprehension and reasoning
capabilities of LLMs, in order to improve their reliability, and establish
their trustworthiness for real-world applications