104 research outputs found
Traceable and Authenticable Image Tagging for Fake News Detection
To prevent fake news images from misleading the public, it is desirable not
only to verify the authenticity of news images but also to trace the source of
fake news, so as to provide a complete forensic chain for reliable fake news
detection. To simultaneously achieve the goals of authenticity verification and
source tracing, we propose a traceable and authenticable image tagging approach
that is based on a design of Decoupled Invertible Neural Network (DINN). The
designed DINN can simultaneously embed the dual-tags, \textit{i.e.},
authenticable tag and traceable tag, into each news image before publishing,
and then separately extract them for authenticity verification and source
tracing. Moreover, to improve the accuracy of dual-tags extraction, we design a
parallel Feature Aware Projection Model (FAPM) to help the DINN preserve
essential tag information. In addition, we define a Distance Metric-Guided
Module (DMGM) that learns asymmetric one-class representations to enable the
dual-tags to achieve different robustness performances under malicious
manipulations. Extensive experiments, on diverse datasets and unseen
manipulations, demonstrate that the proposed tagging approach achieves
excellent performance in the aspects of both authenticity verification and
source tracing for reliable fake news detection and outperforms the prior
works
When does In-context Learning Fall Short and Why? A Study on Specification-Heavy Tasks
In-context learning (ICL) has become the default method for using large
language models (LLMs), making the exploration of its limitations and
understanding the underlying causes crucial. In this paper, we find that ICL
falls short of handling specification-heavy tasks, which are tasks with
complicated and extensive task specifications, requiring several hours for
ordinary humans to master, such as traditional information extraction tasks.
The performance of ICL on these tasks mostly cannot reach half of the
state-of-the-art results. To explore the reasons behind this failure, we
conduct comprehensive experiments on 18 specification-heavy tasks with various
LLMs and identify three primary reasons: inability to specifically understand
context, misalignment in task schema comprehension with humans, and inadequate
long-text understanding ability. Furthermore, we demonstrate that through
fine-tuning, LLMs can achieve decent performance on these tasks, indicating
that the failure of ICL is not an inherent flaw of LLMs, but rather a drawback
of existing alignment methods that renders LLMs incapable of handling
complicated specification-heavy tasks via ICL. To substantiate this, we perform
dedicated instruction tuning on LLMs for these tasks and observe a notable
improvement. We hope the analyses in this paper could facilitate advancements
in alignment methods enabling LLMs to meet more sophisticated human demands.Comment: Under revie
Use of non-steroidal anti-inflammatory drugs and adverse outcomes during the COVID-19 pandemic: A systematic review and meta-analysis.
Background
There are concerns that the use of non-steroidal anti-inflammatory drugs (NSAIDs) may increase the risk of adverse outcomes among patients with coronavirus COVID-19. This study aimed to synthesize the evidence on associations between the use of NSAIDs and adverse outcomes.
Methods
A systematic search of WHO COVID-19 Database, Medline, the Cochrane Library, Web of Science, Embase, China Biology Medicine disc, China National Knowledge Infrastructure, and Wanfang Database for all articles published from January 1, 2020, to November 7, 2021, as well as a supplementary search of Google Scholar. We included all comparative studies that enrolled patients who took NSAIDs during the COVID-19 pandemic. Data extraction and quality assessment of methodology of included studies were completed by two reviewers independently. We conducted a meta-analysis on the main adverse outcomes, as well as selected subgroup analyses stratified by the type of NSAID and population (both positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) or not).
Findings
Forty comparative studies evaluating 4,867,795 adult cases were identified. Twenty-eight (70%) of the included studies enrolled patients positive to SARS-CoV-2 tests. The use of NSAIDs did not reduce mortality outcomes among people with COVID-19 (number of studies [N]Â =Â 29, odds ratio [OR]Â =Â 0.93, 95% confidence interval [CI]: 0.75 to 1.14, I2 Â =Â 89%). Results suggested that the use of NSAIDs was not significantly associated with higher risk of SARS-CoV-2 infection in patients with or without COVID-19 (NÂ =Â 10, ORÂ =Â 0.96, 95% CI: 0.86 to 1.07, I2 Â =Â 78%; NÂ =Â 8, aORÂ =Â 1.01, 95% CI: 0.94 to 1.09, I2 Â =Â 26%), or an increased probability of intensive care unit (ICU) admission (NÂ =Â 12, ORÂ =Â 1.28, 95% CI: 0.94 to 1.75, I2 Â =Â 82% ; NÂ =Â 4, aORÂ =Â 0.89, 95% CI: 0.65 to 1.22, I2 Â =Â 60%), requiring mechanical ventilation (NÂ =Â 11, ORÂ =Â 1.11, 95% CI: 0.79 to 1.54, I2 Â =Â 63%; NÂ =Â 5, aORÂ =Â 0.80, 95% CI: 0.52 to 1.24, I2 Â =Â 66%), or administration of supplemental oxygen (NÂ =Â 5, ORÂ =Â 0.80, 95% CI: 0.52 to 1.24, I2 Â =Â 63%; NÂ =Â 2, aORÂ =Â 1.00, 95% CI: 0.89 to 1.12, I2 Â =Â 0%). The subgroup analysis revealed that, compared with patients not using any NSAIDs, the use of ibuprofen (NÂ =Â 5, ORÂ =Â 1.09, 95% CI: 0.50 to 2.39; NÂ =Â 4, aORÂ =Â 0.95, 95% CI: 0.78 to 1.16) and COX-2 inhibitor (NÂ =Â 4, ORÂ =Â 0.62, 95% CI: 0.35 to 1.11; NÂ =Â 2, aORÂ =Â 0.73, 95% CI: 0.45 to 1.18) were not associated with an increased risk of death.
Interpretation
Data suggests that NSAIDs such as ibuprofen, aspirin and COX-2 inhibitor, can be used safely among patients positive to SARS-CoV-2. However, for some of the analyses the number of studies were limited and the quality of evidence was overall low, therefore more research is needed to corroborate these findings.
Funding
There was no funding source for this study
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Engineering grain boundaries at the 2D limit for the hydrogen evolution reaction
Atom-thin transition metal dichalcogenides (TMDs) have emerged as fascinating materials and key structures for electrocatalysis. So far, their edges, dopant heteroatoms and defects have been intensively explored as active sites for the hydrogen evolution reaction (HER) to split water. However, grain boundaries (GBs), a key type of defects in TMDs, have been overlooked due to their low density and large structural variations. Here, we demonstrate the synthesis of wafer-size atom-thin TMD films with an ultra-high-density of GBs, up to ~1012 cm−2. We propose a climb and drive 0D/2D interaction to explain the underlying growth mechanism. The electrocatalytic activity of the nanograin film is comprehensively examined by micro-electrochemical measurements, showing an excellent hydrogen-evolution performance (onset potential: −25 mV and Tafel slope: 54 mV dec−1), thus indicating an intrinsically high activation of the TMD GBs
Estimate of Leaf Area Index in an Old-Growth Mixed Broadleaved-Korean Pine Forest in Northeastern China
Leaf area index (LAI) is an important variable in the study of forest ecosystem processes, but very few studies are designed to monitor LAI and the seasonal variability in a mixed forest using non-destructive sampling. In this study, first, true LAI from May 1st and November 15th was estimated by making several calibrations to LAI as measured from the WinSCANOPY 2006 Plant Canopy Analyzer. These calibrations include a foliage element (shoot, that is considered to be a collection of needles) clumping index measured directly from the optical instrument, TRAC (Tracing Radiation and Architecture of Canopies); a needle-to-shoot area ratio obtained from shoot samples; and a woody-to-total area ratio. Second, by periodically combining true LAI (May 1st) with the seasonality of LAI for deciduous and coniferous species throughout the leaf-expansion season (from May to August), we estimated LAI of each investigation period in the leaf-expansion season. Third, by combining true LAI (November 15th) with litter trap data (both deciduous and coniferous species), we estimated LAI of each investigation period during the leaf-fall season (from September to mid-November). Finally, LAI for the entire canopy then was derived from the initial leaf expansion to the leaf fall. The results showed that LAI reached its peak with a value of 6.53 m2 m−2 (a corresponding value of 3.83 m2 m−2 from optical instrument) in early August, and the mean LAI was 4.97 m2 m−2 from May to November using the proposed method. The optical instrument method underestimated LAI by an average of 41.64% (SD = 6.54) throughout the whole study period compared to that estimated by the proposed method. The result of the present work implied that our method would be suitable for measuring LAI, for detecting the seasonality of LAI in a mixed forest, and for measuring LAI seasonality for each species
A reference-grade wild soybean genome
Wild relatives of crop plants are invaluable germplasm for genetic improvement. Here, Xie et al. report a reference-grade wild soybean genome and show that it can be used to identify structural variation and refine quantitative trait loci
A reference-grade wild soybean genome
Efficient crop improvement depends on the application of accurate genetic information contained in diverse germplasm resources. Here we report a reference-grade genome of wild soybean accession W05, with a final assembled genome size of 1013.2 Mb and a contig N50 of 3.3 Mb. The analytical power of the W05 genome is demonstrated by several examples. First, we identify an inversion at the locus determining seed coat color during domestication. Second, a translocation event between chromosomes 11 and 13 of some genotypes is shown to interfere with the assignment of QTLs. Third, we find a region containing copy number variations of the Kunitz trypsin inhibitor (KTI) genes. Such findings illustrate the power of this assembly in the analysis of large structural variations in soybean germplasm collections. The wild soybean genome assembly has wide applications in comparative genomic and evolutionary studies, as well as in crop breeding and improvement programs
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