96 research outputs found
Recommended from our members
Combination antiretroviral therapy improves cognitive performance and functional connectivity in treatment-naïve HIV-infected individuals.
Our study aimed to investigate the short-term effect of combination antiretroviral therapy (cART) on cognitive performance and functional and structural connectivity and their relationship to plasma levels of antiretroviral (ARV) drugs. Seventeen ARV treatment-naïve HIV-infected individuals (baseline mean CD4 cell count, 479 ± 48 cells/mm3) were age matched with 17 HIV-uninfected individuals. All subjects underwent a detailed neurocognitive and functional assessment and magnetic resonance imaging. HIV-infected subjects were scanned before starting cART and 12 weeks after initiation of treatment. Uninfected subjects were assessed once at baseline. Functional connectivity (FC) was assessed within the default mode network while structural connectivity was assessed by voxel-wise analysis using tract-based spatial statistics (TBSS) and probabilistic tractography within the DMN. Tenofovir and emtricitabine blood concentration were measured at week 12 of cART. Prior to cART, HIV-infected individuals had significantly lower cognitive performance than control subjects as measured by the total Z-score from the neuropsychological tests assessing six cognitive domains (p = 0.020). After 12 weeks of cART treatment, there remained only a weak cognitive difference between HIV-infected and HIV-uninfected subjects (p = 0.057). Mean FC was lower in HIV-infected individuals compared with those uninfected (p = 0.008), but FC differences became non-significant after treatment (p = 0.197). There were no differences in DTI metrics between HIV-infected and HIV-uninfected individuals using the TBSS approach and limited evidence of decreased structural connectivity within the DMN in HIV-infected individuals. Tenofovir and emtricitabine plasma concentrations did not correlate with either cognitive performance or imaging metrics.ConclusionsTwelve weeks of cART improves cognitive performance and functional connectivity in ARV treatment-naïve HIV-infected individuals with relatively preserved immune function. Longer periods of observation are necessary to assess whether this effect is maintained
UniPCM: Universal Pre-trained Conversation Model with Task-aware Automatic Prompt
Recent research has shown that multi-task pre-training greatly improves the
model's robustness and transfer ability, which is crucial for building a
high-quality dialog system. However, most previous works on multi-task
pre-training rely heavily on human-defined input format or prompt, which is not
optimal in quality and quantity. In this work, we propose to use Task-based
Automatic Prompt generation (TAP) to automatically generate high-quality
prompts. Using the high-quality prompts generated, we scale the corpus of the
pre-trained conversation model to 122 datasets from 15 dialog-related tasks,
resulting in Universal Pre-trained Conversation Model (UniPCM), a powerful
foundation model for various conversational tasks and different dialog systems.
Extensive experiments have shown that UniPCM is robust to input prompts and
capable of various dialog-related tasks. Moreover, UniPCM has strong transfer
ability and excels at low resource scenarios, achieving SOTA results on 9
different datasets ranging from task-oriented dialog to open-domain
conversation. Furthermore, we are amazed to find that TAP can generate prompts
on par with those collected with crowdsourcing. The code is released with the
paper
Self-Explanation Prompting Improves Dialogue Understanding in Large Language Models
Task-oriented dialogue (TOD) systems facilitate users in executing various
activities via multi-turn dialogues, but Large Language Models (LLMs) often
struggle to comprehend these intricate contexts. In this study, we propose a
novel "Self-Explanation" prompting strategy to enhance the comprehension
abilities of LLMs in multi-turn dialogues. This task-agnostic approach requires
the model to analyze each dialogue utterance before task execution, thereby
improving performance across various dialogue-centric tasks. Experimental
results from six benchmark datasets confirm that our method consistently
outperforms other zero-shot prompts and matches or exceeds the efficacy of
few-shot prompts, demonstrating its potential as a powerful tool in enhancing
LLMs' comprehension in complex dialogue tasks
Improving Factual Consistency of Text Summarization by Adversarially Decoupling Comprehension and Embellishment Abilities of LLMs
Despite the recent progress in text summarization made by large language
models (LLMs), they often generate summaries that are factually inconsistent
with original articles, known as "hallucinations" in text generation. Unlike
previous small models (e.g., BART, T5), current LLMs make fewer silly mistakes
but more sophisticated ones, such as imposing cause and effect, adding false
details, overgeneralizing, etc. These hallucinations are challenging to detect
through traditional methods, which poses great challenges for improving the
factual consistency of text summarization. In this paper, we propose an
adversarially DEcoupling method to disentangle the Comprehension and
EmbellishmeNT abilities of LLMs (DECENT). Furthermore, we adopt a probing-based
efficient training to cover the shortage of sensitivity for true and false in
the training process of LLMs. In this way, LLMs are less confused about
embellishing and understanding; thus, they can execute the instructions more
accurately and have enhanced abilities to distinguish hallucinations.
Experimental results show that DECENT significantly improves the reliability of
text summarization based on LLMs
SpokenWOZ: A Large-Scale Speech-Text Benchmark for Spoken Task-Oriented Dialogue Agents
Task-oriented dialogue (TOD) models have made significant progress in recent
years. However, previous studies primarily focus on datasets written by
annotators, which has resulted in a gap between academic research and
real-world spoken conversation scenarios. While several small-scale spoken TOD
datasets are proposed to address robustness issues such as ASR errors, they
ignore the unique challenges in spoken conversation. To tackle the limitations,
we introduce SpokenWOZ, a large-scale speech-text dataset for spoken TOD,
containing 8 domains, 203k turns, 5.7k dialogues and 249 hours of audios from
human-to-human spoken conversations. SpokenWOZ further incorporates common
spoken characteristics such as word-by-word processing and reasoning in spoken
language. Based on these characteristics, we present cross-turn slot and
reasoning slot detection as new challenges. We conduct experiments on various
baselines, including text-modal models, newly proposed dual-modal models, and
LLMs, e.g., ChatGPT. The results show that the current models still have
substantial room for improvement in spoken conversation, where the most
advanced dialogue state tracker only achieves 25.65% in joint goal accuracy and
the SOTA end-to-end model only correctly completes the user request in 52.1% of
dialogues. The dataset, code, and leaderboard are available:
https://spokenwoz.github.io/SpokenWOZ-github.io/
Americans do not select their doctors based on race
To what extent do Americans racially discriminate against doctors? While a large literature shows that racial biases pervade the American healthcare system, there has been no systematic examination of these biases in terms of who patients select for medical treatment. We examine this question in the context of the ongoing global COVID-19 pandemic, where a wealth of qualitative evidence suggests that discrimination against some historically marginalized communities, particularly Asians, has increased throughout the United States. Conducting a well-powered conjoint experiment with a national sample of 1,498 Americans, we find that respondents do not, on average, discriminate against Asian or doctors from other systematically minoritized groups. We also find no consistent evidence of treatment effect heterogeneity; Americans of all types appear not to care about the racial identity of their doctor, at least in our study. This finding has important implications for the potential limits of American prejudice
Histopathological Observation of Immunized Rhesus Macaques with Plague Vaccines after Subcutaneous Infection of Yersinia pestis
In our previous study, complete protection was observed in Chinese-origin rhesus macaques immunized with SV1 (20 µg F1 and 10 µg rV270) and SV2 (200 µg F1 and 100 µg rV270) subunit vaccines and with EV76 live attenuated vaccine against subcutaneous challenge with 6×106 CFU of Y. pestis. In the present study, we investigated whether the vaccines can effectively protect immunized animals from any pathologic changes using histological and immunohistochemical techniques. In addition, the glomerular basement membranes (GBMs) of the immunized animals and control animals were checked by electron microscopy. The results show no signs of histopathological lesions in the lungs, livers, kidneys, lymph nodes, spleens and hearts of the immunized animals at Day 14 after the challenge, whereas pathological alterations were seen in the corresponding tissues of the control animals. Giemsa staining, ultrastructural examination, and immunohistochemical staining revealed bacteria in some of the organs of the control animals, whereas no bacterium was observed among the immunized animals. Ultrastructural observation revealed that no glomerular immune deposits on the GBM. These observations suggest that the vaccines can effectively protect animals from any pathologic changes and eliminate Y. pestis from the immunized animals. The control animals died from multi-organ lesions specifically caused by the Y. pestis infection. We also found that subcutaneous infection of animals with Y. pestis results in bubonic plague, followed by pneumonic and septicemic plagues. The histopathologic features of plague in rhesus macaques closely resemble those of rodent and human plagues. Thus, Chinese-origin rhesus macaques serve as useful models in studying Y. pestis pathogenesis, host response and the efficacy of new medical countermeasures against plague
Spatiotemporal Clustering Analysis of Bicycle Sharing System with Data Mining Approach
The main objective of this study is to explore the spatiotemporal activities pattern of bicycle sharing system by combining together temporal and spatial attributes variables through clustering analysis method. Specifically, three clustering algorithms, i.e., hierarchical clustering, K-means clustering, expectation maximization clustering, are chosen to group the bicycle sharing stations. The temporal attributes variables are obtained through the statistical analysis of bicycle sharing smart card data, and the spatial attributes variables are quantified by point of interest (POI) data around bicycle sharing docking stations, which reflects the influence of land use on bicycle sharing system. According to the performance of the three clustering algorithms and six cluster validation measures, K-means clustering has been proven as the better clustering algorithm for the case of Ningbo, China. Then, the 477 bicycle sharing docking stations were clustered into seven clusters. The results show that the stations of each cluster have their own unique spatiotemporal activities pattern influenced by people’s travel habits and land use characteristics around the stations. This analysis will help bicycle sharing operators better understand the system usage and learn how to improve the service quality of the existing system
- …