71 research outputs found
Spectroscopic data de-noising via training-set-free deep learning method
De-noising plays a crucial role in the post-processing of spectra. Machine
learning-based methods show good performance in extracting intrinsic
information from noisy data, but often require a high-quality training set that
is typically inaccessible in real experimental measurements. Here, using
spectra in angle-resolved photoemission spectroscopy (ARPES) as an example, we
develop a de-noising method for extracting intrinsic spectral information
without the need for a training set. This is possible as our method leverages
the self-correlation information of the spectra themselves. It preserves the
intrinsic energy band features and thus facilitates further analysis and
processing. Moreover, since our method is not limited by specific properties of
the training set compared to previous ones, it may well be extended to other
fields and application scenarios where obtaining high-quality multidimensional
training data is challenging
OT-Attack: Enhancing Adversarial Transferability of Vision-Language Models via Optimal Transport Optimization
Vision-language pre-training (VLP) models demonstrate impressive abilities in
processing both images and text. However, they are vulnerable to multi-modal
adversarial examples (AEs). Investigating the generation of
high-transferability adversarial examples is crucial for uncovering VLP models'
vulnerabilities in practical scenarios. Recent works have indicated that
leveraging data augmentation and image-text modal interactions can enhance the
transferability of adversarial examples for VLP models significantly. However,
they do not consider the optimal alignment problem between dataaugmented
image-text pairs. This oversight leads to adversarial examples that are overly
tailored to the source model, thus limiting improvements in transferability. In
our research, we first explore the interplay between image sets produced
through data augmentation and their corresponding text sets. We find that
augmented image samples can align optimally with certain texts while exhibiting
less relevance to others. Motivated by this, we propose an Optimal
Transport-based Adversarial Attack, dubbed OT-Attack. The proposed method
formulates the features of image and text sets as two distinct distributions
and employs optimal transport theory to determine the most efficient mapping
between them. This optimal mapping informs our generation of adversarial
examples to effectively counteract the overfitting issues. Extensive
experiments across various network architectures and datasets in image-text
matching tasks reveal that our OT-Attack outperforms existing state-of-the-art
methods in terms of adversarial transferability
Superconducting fluctuations and charge-4 plaquette state at strong coupling
Recent experiments indicate that superconducting fluctuations also play an
important role in overdoped cuprates. Here we apply the static auxiliary field
Monte Carlo approach to study phase correlations of the pairing fields in a
microscopic model with spin-singlet pairing interaction. We find that the
short- and long-range phase correlations are well captured by the phase mutual
information, which allows us to construct a theoretical phase diagram
containing the uniform -wave superconducting region, the phase fluctuating
region, the local pairing region, and the disordered region. We show that the
gradual development of phase coherence has a number of consequences on
spectroscopic measurements, such as the development of the Fermi arc and the
anisotropy in the angle-resolved spectra, scattering rate, entropy, specific
heat, and quasiparticle dispersion, in good agreement with experimental
observations. For strong coupling, our Monte Carlo simulation reveals an
unexpected charge-4 plaquette state with -wave bonds, which competes with
the uniform -wave superconductivity and exhibits a U-shaped density of
states
Rapid and Efficient Extraction and HPLC Analysis of Sesquiterpene Lactones from Aucklandia lappa Root.
The root of Aucklandia lappa Decne, family Asteraceae, is widely used in Asian traditional medicine due to its sesquiterpene lactones. The aim of this study was the development and optimization of the extraction and analysis of these sesquiterpene lactones. The current Chinese Pharmacopoeia reports a monograph for "Aucklandiae Radix", but the extraction method is very long and tedious including maceration overnight and ultrasonication. Different extraction protocols were evaluated with the aim of optimizing the maceration period, solvent, and shaking and sonication times. The optimized method consists of only one hour of shaking plus 30 minutes of sonication using 100% MeOH as solvent. 1H NMR spectroscopy was used as a complementary analytical tool to monitor the residual presence of sesquitepene lactones in the herbal material. A suitable LC-DAD method was set up to quantify the sesquiterpene lactones. Recovery was ca. 97%, but a very high instability of constituents was found after powdering the herbal drug. A loss of about 20% of total sesquiterpenes was found after 15–20 days; as a consequence, it is strongly endorsed to use fresh powdered herbal material to avoid errors in the quantification
Spectrum-Guided Adversarial Disparity Learning
It has been a significant challenge to portray intraclass disparity precisely
in the area of activity recognition, as it requires a robust representation of
the correlation between subject-specific variation for each activity class. In
this work, we propose a novel end-to-end knowledge directed adversarial
learning framework, which portrays the class-conditioned intraclass disparity
using two competitive encoding distributions and learns the purified latent
codes by denoising learned disparity. Furthermore, the domain knowledge is
incorporated in an unsupervised manner to guide the optimization and further
boosts the performance. The experiments on four HAR benchmark datasets
demonstrate the robustness and generalization of our proposed methods over a
set of state-of-the-art. We further prove the effectiveness of automatic domain
knowledge incorporation in performance enhancement
Germanium-lead perovskite light-emitting diodes.
Reducing environmental impact is a key challenge for perovskite optoelectronics, as most high-performance devices are based on potentially toxic lead-halide perovskites. For photovoltaic solar cells, tin-lead (Sn-Pb) perovskite materials provide a promising solution for reducing toxicity. However, Sn-Pb perovskites typically exhibit low luminescence efficiencies, and are not ideal for light-emitting applications. Here we demonstrate highly luminescent germanium-lead (Ge-Pb) perovskite films with photoluminescence quantum efficiencies (PLQEs) of up to ~71%, showing a considerable relative improvement of ~34% over similarly prepared Ge-free, Pb-based perovskite films. In our initial demonstration of Ge-Pb perovskite LEDs, we achieve external quantum efficiencies (EQEs) of up to ~13.1% at high brightness (~1900 cd m-2), a step forward for reduced-toxicity perovskite LEDs. Our findings offer a new solution for developing eco-friendly light-emitting technologies based on perovskite semiconductors
Clinical M2 Macrophage-Related Genes Can Serve as a Reliable Predictor of Lung Adenocarcinoma
BackgroundNumerous studies have found that infiltrating M2 macrophages play an important role in the tumor progression of lung adenocarcinoma (LUAD). However, the roles of M2 macrophage infiltration and M2 macrophage-related genes in immunotherapy and clinical outcomes remain obscure.MethodsSample information was extracted from TCGA and GEO databases. The TIME landscape was revealed using the CIBERSORT algorithm. Weighted gene co-expression network analysis (WGCNA) was used to find M2 macrophage-related gene modules. Through univariate Cox regression, lasso regression analysis, and multivariate Cox regression, the genes strongly associated with the prognosis of LUAD were screened out. Risk score (RS) was calculated, and all samples were divided into high-risk group (HRG) and low-risk group (LRG) according to the median RS. External validation of RS was performed using GSE68571 data information. Prognostic nomogram based on risk signatures and other clinical information were constructed and validated with calibration curves. Potential associations of tumor mutational burden (TMB) and risk signatures were analyzed. Finally, the potential association of risk signatures with chemotherapy efficacy was investigated using the pRRophetic algorithm.ResultsBased on 504 samples extracted from TCGA database, 183 core genes were identified using WGCNA. Through a series of screening, two M2 macrophage-related genes (GRIA1 and CLEC3B) strongly correlated with LUAD prognosis were finally selected. RS was calculated, and prognostic risk nomogram including gender, age, T, N, M stage, clinical stage, and RS were constructed. The calibration curve shows that our constructed model has good performance. HRG patients were suitable for new ICI immunotherapy, while LRG was more suitable for CTLA4-immunosuppressive therapy alone. The half-maximal inhibitory concentrations (IC50) of the four chemotherapeutic drugs (metformin, cisplatin, paclitaxel, and gemcitabine) showed significant differences in HRG/LRG.ConclusionsIn conclusion, a comprehensive analysis of the role of M2 macrophages in tumor progression will help predict prognosis and facilitate the advancement of therapeutic techniques
Large expert-curated database for benchmarking document similarity detection in biomedical literature search
Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe
Quantitative staging of alluvial fan geomorphic surfaces in arid areas based on SAR imagery: A case study of the Shule River alluvial fan in the western desert region of the Hexi Corridor
The alluvial fans and river terraces formed by river processes effectively record past tectonic activities, climate changes, and geomorphic evolution processes. Accurately dividing the alluvial fan into stages is the basis for the subsequent research. Previous researchers used L-band SAR backscatter coefficient values as a substitute parameter for geomorphic roughness to achieve quantitative zoning of geomorphic surfaces. However, these studies did not consider the impact of different time data sources on the geomorphic surface results. This study selects the Shule River alluvial fan as the research object. It determines the optimal data source by analyzing the posterior statistical indicators of multi-temporal L-band SAR data and evaluating atmospheric conditions. The maximum likelihood classification method is used to complete the classification of backscatter intensity values and achieve quantitative staging of the geomorphic surface. The results indicate that the posterior statistical indicators of staging can be used as the standard for selecting the best temporal image data to obtain better staging results. L-band HH monopolarization data provides better staging results, demonstrating advantages in distinguishing landforms of different ages compared to C-band data. Moreover, L-band data is more accessible and holds potential for automated staging. SAR image quality and staging results are closely related to imaging atmospheric conditions but show minimal seasonal dependence. Therefore, the study recommends prioritizing images with low surface water content during imaging, such as in high-evaporation intensity summer seasons. The proposed method for analyzing remote sensing data quality and staging landforms can be applied to rapidly and quantitatively stage large-scale alluvial fans in arid regions, providing valuable information for studies on tectonics and climate
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