104 research outputs found
Radiative corrections to leptonic decays using infinite-volume reconstruction
Lattice QCD calculations of leptonic decay constants have now reached
sub-percent precision so that isospin-breaking corrections, including QED
effects, must be included to fully exploit this precision in determining
fundamental quantities, in particular the elements of the
Cabibbo-Kobayashi-Maskawa (CKM) matrix, from experimental measurements. A
number of collaborations have performed, or are performing, such computations.
In this paper we develop a new theoretical framework, based on Infinite-Volume
Reconstruction (IVR), for the computation of electromagnetic corrections to
leptonic decay widths. In this method, the hadronic correlation functions are
first processed theoretically in infinite volume, in such a way that the
required matrix elements can be determined non-perturbatively from lattice QCD
computations with finite-volume uncertainties which are exponentially small in
the volume. The cancellation of infrared divergences in this framework is
performed fully analytically. We also outline how this IVR treatment can be
extended to determine the QED effects in semi-leptonic kaon decays with a
similar degree of accuracy
Microglia, TREM2, and Therapeutic Methods of Alzheimer’s Disease
Alzheimer’s disease (AD) is one of the most common causes of dementia all around the world. It is characterized by the deposition of amyloid-β protein (Aβ) and the formation of neurofibrillary tangles (NFTs), which contribute to neuronal loss and cognitive decline. Microglia, as innate immune cells in brain, plays dual roles in the pathological process of AD. Expression in different subtypes of microglia is diverse in AD genes. Triggering receptor expressed on myeloid cells 2 (TREM2) is a transmembrane glycoprotein mainly expressed on microglia in the central nervous system (CNS). Soluble TREM2 (sTREM2), a proteolytic product of TREM2, which is abundant in the cerebrospinal fluid, shows a dynamic change in different stages and ameliorates the pathological process of AD. The interplay between the different subtypes of apolipoprotein and TREM2 is closely related to the mechanism of AD and serves as important regulatory sites. Moreover, several therapeutic strategies targeting TREM2 have shown positive outcomes during clinical trials and some novel therapies at different points are in progress. In this review, we mainly talk about the interrelationships among microglia, TREM2, and AD, and hope to give an overview of the strategies of AD
Inferring Tabular Analysis Metadata by Infusing Distribution and Knowledge Information
Many data analysis tasks heavily rely on a deep understanding of tables
(multi-dimensional data). Across the tasks, there exist comonly used metadata
attributes of table fields / columns. In this paper, we identify four such
analysis metadata: Measure/dimension dichotomy, common field roles, semantic
field type, and default aggregation function. While those metadata face
challenges of insufficient supervision signals, utilizing existing knowledge
and understanding distribution. To inference these metadata for a raw table, we
propose our multi-tasking Metadata model which fuses field distribution and
knowledge graph information into pre-trained tabular models. For model training
and evaluation, we collect a large corpus (~582k tables from private
spreadsheet and public tabular datasets) of analysis metadata by using diverse
smart supervisions from downstream tasks. Our best model has accuracy = 98%,
hit rate at top-1 > 67%, accuracy > 80%, and accuracy = 88% for the four
analysis metadata inference tasks, respectively. It outperforms a series of
baselines that are based on rules, traditional machine learning methods, and
pre-trained tabular models. Analysis metadata models are deployed in a popular
data analysis product, helping downstream intelligent features such as insights
mining, chart / pivot table recommendation, and natural language QA...Comment: 13pages, 7 figures, 9 table
MusicAOG: an Energy-Based Model for Learning and Sampling a Hierarchical Representation of Symbolic Music
In addressing the challenge of interpretability and generalizability of
artificial music intelligence, this paper introduces a novel symbolic
representation that amalgamates both explicit and implicit musical information
across diverse traditions and granularities. Utilizing a hierarchical and-or
graph representation, the model employs nodes and edges to encapsulate a broad
spectrum of musical elements, including structures, textures, rhythms, and
harmonies. This hierarchical approach expands the representability across
various scales of music. This representation serves as the foundation for an
energy-based model, uniquely tailored to learn musical concepts through a
flexible algorithm framework relying on the minimax entropy principle.
Utilizing an adapted Metropolis-Hastings sampling technique, the model enables
fine-grained control over music generation. A comprehensive empirical
evaluation, contrasting this novel approach with existing methodologies,
manifests considerable advancements in interpretability and controllability.
This study marks a substantial contribution to the fields of music analysis,
composition, and computational musicology
Influences of Canopy Nitrogen and Water Addition on AM Fungal Biodiversity and Community Composition in a Mixed Deciduous Forest of China
Nitrogen (N) deposition and precipitation could profoundly influence the structure and function of forest ecosystems. However, conventional studies with understory additions of nitrogen and water largely ignored canopy-associated ecological processes and may have not accurately reflected the natural situations. Additionally, most studies only made sampling at one time point, overlooked temporal dynamics of ecosystem response to environmental changes. Here we carried out a field trial in a mixed deciduous forest of China with canopy addition of N and water for 4 years to investigate the effects of increased N deposition and precipitation on the diversity and community composition of arbuscular mycorrhizal (AM) fungi, the ubiquitous symbiotic fungi for the majority of terrestrial plants. We found that (1) in the 1st year, N addition, water addition and their interactions all exhibited significant influences on AM fungal community composition; (2) in the 2nd year, only water addition significantly reduced AM fungal alpha-diversity (richness and Shannon index); (3) in the next 2 years, both N addition and water addition showed no significant effect on AM fungal community composition or alpha-diversity, with an exception that water addition significantly changed AM fungal community composition in the 4th year; (4) the increment of N or water tended to decrease the abundance and richness of the dominant genus Glomus and favored other AM fungi. (5) soil pH was marginally positively related with AM fungal community composition dissimilarity, soil NH4+-N and N/P showed significant/marginal positive correlation with AM fungal alpha-diversity. We concluded that the effect of increased N deposition and precipitation on AM fungal community composition was time-dependent, mediated by soil factors, and possibly related to the sensitivity and resilience of forest ecosystem to environmental changes
Tumor Tissue-Derived Formaldehyde and Acidic Microenvironment Synergistically Induce Bone Cancer Pain
Background: There is current interest in understanding the molecular mechanisms of tumor-induced bone pain. Accumulated evidence shows that endogenous formaldehyde concentrations are elevated in the blood or urine of patients with breast, prostate or bladder cancer. These cancers are frequently associated with cancer pain especially after bone metastasis. It is well known that transient receptor potential vanilloid receptor 1 (TRPV1) participates in cancer pain. The present study aims to demonstrate that the tumor tissue-derived endogenous formaldehyde induces bone cancer pain via TRPV1 activation under tumor acidic environment. Methodology/Principal Findings: Endogenous formaldehyde concentration increased significantly in the cultured breast cancer cell lines in vitro, in the bone marrow of breast MRMT-1 bone cancer pain model in rats and in tissues from breast cancer and lung cancer patients in vivo. Low concentrations (1 similar to 5 mM) of formaldehyde induced pain responses in rat via TRPV1 and this pain response could be significantly enhanced by pH 6.0 (mimicking the acidic tumor microenvironment). Formaldehyde at low concentrations (1 mM to 100 mM) induced a concentration-dependent increase of [Ca(2+)]i in the freshly isolated rat dorsal root ganglion neurons and TRPV1-transfected CHO cells. Furthermore, electrophysiological experiments showed that low concentration formaldehyde-elicited TRPV1 currents could be significantly potentiated by low pH (6.0). TRPV1 antagonists and formaldehyde scavengers attenuated bone cancer pain responses. Conclusions/Significance: Our data suggest that cancer tissues directly secrete endogenous formaldehyde, and this formaldehyde at low concentration induces metastatic bone cancer pain through TRPV1 activation especially under tumor acidic environment.Multidisciplinary SciencesSCI(E)PubMed24ARTICLE4e10234
Understanding LLMs: A Comprehensive Overview from Training to Inference
The introduction of ChatGPT has led to a significant increase in the
utilization of Large Language Models (LLMs) for addressing downstream tasks.
There's an increasing focus on cost-efficient training and deployment within
this context. Low-cost training and deployment of LLMs represent the future
development trend. This paper reviews the evolution of large language model
training techniques and inference deployment technologies aligned with this
emerging trend. The discussion on training includes various aspects, including
data preprocessing, training architecture, pre-training tasks, parallel
training, and relevant content related to model fine-tuning. On the inference
side, the paper covers topics such as model compression, parallel computation,
memory scheduling, and structural optimization. It also explores LLMs'
utilization and provides insights into their future development.Comment: 30 pages,6 figure
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