Femtosecond reaction dynamics in the gas-to-liquid transition region: Observation of a three-phase density dependence

We report the observation of a striking density dependence in the coherencedynamics of an elementary reaction, solute iodine in solvent rare gases (density from 0 to 50 mol/l). With the help of MD simulations, the time scales of slow and fast solvent‐force fluctuations are resolved and the underlying mechanism is revealed

Meta-Stock: Task-Difficulty-Adaptive Meta-learning for Sub-new Stock Price Prediction

Sub-new stock price prediction, forecasting the price trends of stocks listed less than one year, is crucial for effective quantitative trading. While deep learning methods have demonstrated effectiveness in predicting old stock prices, they require large training datasets unavailable for sub-new stocks. In this paper, we propose Meta-Stock: a task-difficulty-adaptive meta-learning approach for sub-new stock price prediction. Leveraging prediction tasks formulated by old stocks, our meta-learning method aims to acquire the fast generalization ability that can be further adapted to sub-new stock price prediction tasks, thereby solving the data scarcity of sub-new stocks. Moreover, we enhance the meta-learning process by incorporating an adaptive learning strategy sensitive to varying task difficulties. Through wavelet transform, we extract high-frequency coefficients to manifest stock price volatility. This allows the meta-learning model to assign gradient weights based on volatility-quantified task difficulty. Extensive experiments on datasets collected from three stock markets spanning twenty-two years prove that our Meta-Stock significantly outperforms previous methods and manifests strong applicability in real-world stock trading. Besides, we evaluate the reasonability of the task difficulty quantification and the effectiveness of the adaptive learning strategy

Masked Diffusion with Task-awareness for Procedure Planning in Instructional Videos

A key challenge with procedure planning in instructional videos lies in how to handle a large decision space consisting of a multitude of action types that belong to various tasks. To understand real-world video content, an AI agent must proficiently discern these action types (e.g., pour milk, pour water, open lid, close lid, etc.) based on brief visual observation. Moreover, it must adeptly capture the intricate semantic relation of the action types and task goals, along with the variable action sequences. Recently, notable progress has been made via the integration of diffusion models and visual representation learning to address the challenge. However, existing models employ rudimentary mechanisms to utilize task information to manage the decision space. To overcome this limitation, we introduce a simple yet effective enhancement - a masked diffusion model. The introduced mask acts akin to a task-oriented attention filter, enabling the diffusion/denoising process to concentrate on a subset of action types. Furthermore, to bolster the accuracy of task classification, we harness more potent visual representation learning techniques. In particular, we learn a joint visual-text embedding, where a text embedding is generated by prompting a pre-trained vision-language model to focus on human actions. We evaluate the method on three public datasets and achieve state-of-the-art performance on multiple metrics. Code is available at https://github.com/ffzzy840304/Masked-PDPP.Comment: 7 pages (main text excluding references), 3 figures, 7 table

PDCD1 genes may protect against extraocular manifestations in Chinese Han patients with Vogt-Koyanagi-Harada syndrome

Purpose: To analyze the potential association of programmed cell death 1 (PDCD1) with Vogt-Koyanagi-Harada (VKH) syndrome in a Chinese Han population. Methods: Three single nucleotide polymorphism (SNPs), PD-1.3G/A, PD-1.5C/T, and PD-1.6G/A, were genotyped in 247 VKH patients and 289 age-, sex-, and ethnically-matched healthy controls using polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) assay. The associations of genotypes and alleles with VKH syndrome were analyzed. Results: All genotype distributions in healthy controls were in Hardy-Weinberg equilibrium. The genotype and allele frequencies of PD-1.3, PD-1.5, and PD-1.6 were not different between patients with VKH syndrome and healthy controls. No significant difference was observed according to the status of human leukocyte antigen (HLA)-DR4 and HLA-DRw53. Compared to the controls, lower frequencies of the PD-1.5C genotype and allele frequencies were observed in VKH patients with extraocular findings. Conclusions: PD-1.3 and PD-1.6 polymorphisms are not associated with the susceptibility to VKH syndrome in the Chinese Han population. However, PD-1.5 may be negatively associated with the occurrence of extraocular manifestations of VKH syndrome

Femtosecond reaction dynamics in the gas-to-liquid transition region: Observation of a three-phase density dependence

We report the observation of a striking density dependence in the coherencedynamics of an elementary reaction, solute iodine in solvent rare gases (density from 0 to 50 mol/l). With the help of MD simulations, the time scales of slow and fast solvent‐force fluctuations are resolved and the underlying mechanism is revealed

Quambalaria species associated with eucalypt diseases in southern China

The genus Quambalaria includes several important pathogens of species of Eucalyptus and Corymbia, mainly causing leaf and shoot blight. Recently, extensive shoot and leaf dieback and stem cankers suspected to be Quambalaria diseases have been found on young Eucalyptus urophylla E. grandis trees in Guangdong and Hainan Provinces. The occurrence of Quambalaria species and their association with eucalypt hosts within China needs to be investigated for tree diseases management. The isolates from the diseased samples were identified based on their morphological structures and phylogenetic analyses with DNA sequence data for the internal transcribed spacer region and large ribosome subunit RNA of the nuclear rDNA. This work revealed that three species of Quambalaria were present: Quambalaria pitereka from Corymbia citriodora, Q. eucalypti from E. urophylla E. grandis, both isolated from young eucalypt leaves and shoots in Guangdong Province, and Quambalaria simpsonii, which was isolated from stem cankers of E. urophylla E. grandis at four different sites across Guangdong and Hainan Provinces. These results confirmed that Quambalaria agents were associated with the diseases occurring on eucalypt hosts in South China. This is the first report of Q. eucalypti in Asia and the first report of Q. simpsonii in China on Eucalyptus trees.The Fundamental Research Funds for the Central Non-profit Research Institution of CAF (CAFYBB2014MA018), and the Overseas Outstanding Scholars Lecture Program, the Fundamental Research Funds for the Central Non-profit Research Institution of CAF (CAFYBB2017ZF005).http://journal.hep.com.cn/faseam2018Forestry and Agricultural Biotechnology Institute (FABI

Rubidium Chloride Targets Jnk/p38-Mediated NF-κB Activation to Attenuate Osteoclastogenesis and Facilitate Osteoblastogenesis

The unbalanced crosstalk between osteoclasts and osteoblasts could lead to disruptive bone homeostasis. Herein, we investigated the therapeutic effects of rubidium chloride (RbCl) on ovariectomized (OVX) and titanium (Ti) particle-induced calvaria osteolysis mouse models, showing that non-toxic RbCl attenuated RANKL-stimulated osteoclast formation and functionality while significantly enhancing osteogenesis in vitro. The expressions of osteoclast-specific genes were downregulated considerably by RbCl. Despite the direct inhibition of RANKL-induced activation of MAPK signaling, RbCl was able to target NF-κB directly and indirectly. We found that after the co-stimulation of the c-Jun N-terminal kinase (Jnk)/p38 activator and RANKL, RbCl inhibited the elevated expression of p-IKKα and the degradation of IκBα in osteoclast precursors, indicating indirect NF-κB inhibition via MAPK suppression. Furthermore, the two animal models demonstrated that RbCl attenuated tartrate-resistant acid phosphate (TRAP)-positive osteoclastogenesis and rescued bone loss caused by the hormonal dysfunction and wear particle in vivo. Altogether, these findings suggest that RbCl can target Jnk/p38-mediated NF-κB activation to attenuate osteoclastogenesis, while facilitating osteoblastogenesis both in vivo and in vitro, suggesting the possible future use of RbCl for surface coating of orthopedic implant biomaterials to protect against osteoporosis

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

GFlowOut: Dropout with Generative Flow Networks

Bayesian Inference offers principled tools to tackle many critical problems with modern neural networks such as poor calibration and generalization, and data inefficiency. However, scaling Bayesian inference to large architectures is challenging and requires restrictive approximations. Monte Carlo Dropout has been widely used as a relatively cheap way for approximate Inference and to estimate uncertainty with deep neural networks. Traditionally, the dropout mask is sampled independently from a fixed distribution. Recent works show that the dropout mask can be viewed as a latent variable, which can be inferred with variational inference. These methods face two important challenges: (a) the posterior distribution over masks can be highly multi-modal which can be difficult to approximate with standard variational inference and (b) it is not trivial to fully utilize sample-dependent information and correlation among dropout masks to improve posterior estimation. In this work, we propose GFlowOut to address these issues. GFlowOut leverages the recently proposed probabilistic framework of Generative Flow Networks (GFlowNets) to learn the posterior distribution over dropout masks. We empirically demonstrate that GFlowOut results in predictive distributions that generalize better to out-of-distribution data, and provide uncertainty estimates which lead to better performance in downstream tasks