P-ICL: Point In-Context Learning for Named Entity Recognition with Large Language Models

In recent years, the rise of large language models (LLMs) has made it possible to directly achieve named entity recognition (NER) without any demonstration samples or only using a few samples through in-context learning (ICL). However, standard ICL only helps LLMs understand task instructions, format and input-label mapping, but neglects the particularity of the NER task itself. In this paper, we propose a new prompting framework P-ICL to better achieve NER with LLMs, in which some point entities are leveraged as the auxiliary information to recognize each entity type. With such significant information, the LLM can achieve entity classification more precisely. To obtain optimal point entities for prompting LLMs, we also proposed a point entity selection method based on K-Means clustering. Our extensive experiments on some representative NER benchmarks verify the effectiveness of our proposed strategies in P-ICL and point entity selection

Semi-Markov jump linear systems with bi-boundary sojourn time: Anti-modal-asynchrony control

This paper investigates the problem of control synthesis for a class of discrete-time semi-Markov jump linear systems, in which the sojourn time of each mode is bi-boundary (with upper and lower bounds). The system is subject to modal asynchrony, which means that the switchings of the mode-dependent controller to be designed lag behind the ones of the controlled plant, and the lag is mode-dependent. In contrast with the traditional mode-independent lag commonly assumed in the existing studies, not only is the modal lag more practical and general, but also it yields less conservatism of the controller design. By virtue of the semi-Markov kernel approach, the conditions on the existence of the anticipated stabilizing controllers capable of overcoming the modal asynchrony are derived. Illustrative examples including a class of vertical take-off and landing (VTOL) helicopter models are presented to demonstrate the necessity and the validity of the designed anti-modal-asynchrony controllers

Effect of Doping on the phase stability and Superconductivity in LaH10

We present a computational investigation into the effects of chemical doping with 15 different elements on phase stability and superconductivity in the LaH10 structure. Most doping elements were found to induce softening of phonon modes, enhancing electron-phonon coupling and improving critical superconducting temperature while weakening dynamical stability. Unlike these dopants, Ce was found to extend the range of dynamical stability for LaH10 by eliminating the van Hove singularity near the Fermi level. The doped compound, La0.75Ce0.25H10, maintains high-temperature superconductivity. We also demonstrate that different Ce doping configurations in the LaH10 structure have a minimal effect on energetic stability and electron-phonon coupling strength. Our findings suggest that Ce is a promising dopant to stabilize LaH10 at lower pressures while preserving its high-temperature superconductivity

Constraining the Woods-Saxon potential in fusion reactions based on the neural network

The accurate determination of the nuclear interaction potential is essential for predicting the fusion cross sections and understanding the reaction mechanism, which plays an important role in the synthesis of superheavy elements. In this work, the neural network, which combines with the calculations of the fusion cross sections via the Hill-Wheeler formula, is developed to optimize the parameters of the Woods-Saxon potential by comparing the experimental values. The correlations between the parameters of Woods-Saxon potential and the reaction partners, which can be quantitatively fitted to a sigmoid-like function with the mass numbers, have been displayed manifestly for the first time. This study could promote the accurate estimation of nucleus-nucleus interaction potential in low energy heavy-ion collisions.Comment: 6 pages, 5 figure

Serum protein N-glycome patterns reveal alterations associated with endometrial cancer and its phenotypes of differentiation

BackgroundAberrant N-glycosylation and its involvement in pathogenesis have been reported in endometrial cancer (EC). Nevertheless, the serum N-glycomic signature of EC remains unknown. Here, we investigated serum N-glycome patterns of EC to identify candidate biomarkers.MethodsThis study enrolled 34 untreated EC patients and 34 matched healthy controls (HC) from Peking Union Medical College Hospital. State-of-the-art MS-based methods were employed for N-glycans profiling. Multivariate and univariate statistical analyses were used to identify discriminative N-glycans driving classification. Receiver operating characteristic analyses were performed to evaluate classification accuracy.ResultsEC patients displayed distinct differences in serum N-glycome and had abnormal high-mannose and hybrid-type N-glycans, fucosylation, galactosylation, and linkage‐specific sialylation compared with HC. The glycan panel built with the four most discriminative and biologically important derived N-glycan traits could accurately identify EC (random forest model, the area under the curve [AUC]=0.993 [95%CI 0.955-1]). The performance was validated by two other models. Total hybrid-type N-glycans significantly associated with the differentiation types of EC could effectively stratify EC into well- or poorly-differentiated subgroups (AUC>0.8).ConclusionThis study provides the initial evidence supporting the utility of serum N-glycomic signature as potential markers for the diagnosis and phenotyping of EC

Quantifying angular distributions in multinucleon transfer reactions with a semi-classical method

The multinucleon transfer (MNT) process in low-energy heavy ion collisions can be utilized to produce unknown nuclei far beyond the stability line. However, the reaction products exhibit broad angular and energy distributions, which could lower the experimental detection efficiency. We present a classical approach that employs a parameterized angular distribution to describe the complex issue. By analyzing limited experimental data on angular distribution, we proposed a three-parameter formula to calculate the angular distribution and identified the dependencies of the parameters. We also discuss the sensitivity of these parameters within this method. A comprehensive comparison with microscopic models and experimental data across a wide range of conditions is conducted. The proposed formula offers an efficient and straightforward way to determine the angular distribution of MNT products.6 pages, 6 figur

E2CFD\mathrm{E^{2}CFD}: Towards Effective and Efficient Cost Function Design for Safe Reinforcement Learning via Large Language Model

Different classes of safe reinforcement learning algorithms have shown satisfactory performance in various types of safety requirement scenarios. However, the existing methods mainly address one or several classes of specific safety requirement scenario problems and cannot be applied to arbitrary safety requirement scenarios. In addition, the optimization objectives of existing reinforcement learning algorithms are misaligned with the task requirements. Based on the need to address these issues, we propose $\mathrm{E^{2}CFD}$, an effective and efficient cost function design framework. $\mathrm{E^{2}CFD}$ leverages the capabilities of a large language model (LLM) to comprehend various safety scenarios and generate corresponding cost functions. It incorporates the \textit{fast performance evaluation (FPE)} method to facilitate rapid and iterative updates to the generated cost function. Through this iterative process, $\mathrm{E^{2}CFD}$ aims to obtain the most suitable cost function for policy training, tailored to the specific tasks within the safety scenario. Experiments have proven that the performance of policies trained using this framework is superior to traditional safe reinforcement learning algorithms and policies trained with carefully designed cost functions

FastShrinkage: Perceptually-aware retargeting toward mobile platforms

© 2017 ACM. Retargeting aims at adapting an original high-resolution photo/video to a low-resolution screen with an arbitrary aspect ratio. Conventional approaches are generally based on desktop PCs, since the computation might be intolerable for mobile platforms (especially when retargeting videos). Besides, only low-level visual features are exploited typically, whereas human visual perception is not well encoded. In this paper, we propose a novel retargeting framework which fast shrinks photo/video by leveraging human gaze behavior. Specifically, we first derive a geometry-preserved graph ranking algorithm, which efficiently selects a few salient object patches to mimic human gaze shifting path (GSP) when viewing each scenery. Afterward, an aggregation-based CNN is developed to hierarchically learn the deep representation for each GSP. Based on this, a probabilistic model is developed to learn the priors of the training photos which are marked as aesthetically-pleasing by professional photographers. We utilize the learned priors to efficiently shrink the corresponding GSP of a retargeted photo/video to be maximally similar to those from the training photos. Extensive experiments have demonstrated that: 1) our method consumes less than 35ms to retarget a 1024 × 768 photo (or a 1280 × 720 video frame) on popular iOS/Android devices, which is orders of magnitude faster than the conventional retargeting algorithms; 2) the retargeted photos/videos produced by our method outperform its competitors significantly based on the paired-comparison-based user study; and 3) the learned GSPs are highly indicative of human visual attention according to the human eye tracking experiments

Divergent Evolution of TRC Genes in Mammalian Niche Adaptation

Mammals inhabit a wide variety of ecological niches, which in turn can be affected by various ecological factors, especially in relation to immunity. The canonical TRC repertoire (TRAC, TRBC, TRGC, and TRDC) codes C regions of T cell receptor chains that form the primary antigen receptors involved in the activation of cellular immunity. At present, little is known about the correlation between the evolution of mammalian TRC genes and ecological factors. In this study, four types canonical of TRC genes were identified from 37 mammalian species. Phylogenetic comparative methods (phyANOVA and PGLS) and selective pressure analyses among different groups of ecological factors (habitat, diet, and sociality) were carried out. The results showed that habitat was the major ecological factor shaping mammalian TRC repertoires. Specifically, trade-off between TRGC numbers and positive selection of TRAC and the balanced evolutionary rates between TRAC and TRDC genes were speculated as two main mechanisms in adaption to habitat and sociality. Overall, our study suggested divergent mechanisms for the evolution of TRCs, prompting mammalian immunity adaptions within diverse niches

Mechanistic insights into phosphorus transformation mediated by Arthrobacter and Sordariomycetes under long-term high-volume swine manure application in a wheat-rice rotation system

IntroductionUnderstanding the impacts of sustained high-input swine manure on soil phosphorus (P), along with identifying and functionally characterizing P-associated microorganisms, can provide a scientific foundation for effective management of soil P in relation to swine manure application. This study provides novel insights into the functional roles of P-associated microorganisms in mediating phosphorus dynamics under long-term excessive swine manure application.MethodsThe study investigated the prolonged impact of high-volume swine manure application on soil P fractions over an 8-year continuous, randomized field trial involving rotating wheat (wet conditions) and rice (flooded conditions) crops. And the soil treated with the prolonged high- volume swine manure application was selected to isolate and identify specific microorganisms, which were subsequently inoculated into soil previously treated with long-term NPK fertilizer (F) and swine manure application (M) for indoor cultivation and functional characterization verification.ResultsThe sustained high input of swine manure markedly enhanced soil P activity and microbial P content (P < 0.05), specifically extracting P-associated microorganisms, namely Arthrobacter sp. M4 bacteria and Sordariomycetes 2 MS-M4 fungi. Upon separate inoculation of these microorganisms into high-Carbon (C) and high-P soils (M soil, Olsen P > 70 mg kg–1, ROC > 150 mg kg–1), it was observed that both microorganisms effectively converted available P sources (Ca2-P, Ca8-P) into organic P reserves through biological immobilization. Conversely, under conditions of low C and low P (F soil, Olsen P < 10 mg kg–1, ROC < 75 mg kg–1), there was an enhancement in the decomposition and utilization of soil organic C which resulted in increased effective P content via the breakdown of organic phosphates—demonstrating a robust capacity for P transformation. Furthermore, when these phosphate-related microorganisms were introduced to long-term fertilized soils enriched with NPK fertilizer (F), they exhibited a significantly greater enhancement in soil P availability compared to those inoculated into soils subjected to prolonged high inputs of swine manure.DiscussionThe P-related microorganisms Arthrobacter sp. M4 and Sordariomycetes 2 MS-M4 extracted from soils with high P availability were confirmed to have the key functions of enhancing the fixation of inorganic P into organic P (high-C and high-P condition) or promoting the activation of organic P into rapidly available P (low C and low P level). Which may plays an important role in the management of agricultural P nutrients