Existence of solutions for fractional differential equations with three-point boundary conditions at resonance in Rn\mathbb{R}^n

In this paper, by applying the coincidence degree theory which was first introduced by Mawhin, we obtain an existence result for the fractional three-point boundary value problems in $\mathbb{R}^n$, where the dimension of the kernel of fractional differential operator with the boundary conditions can take any value in $\{1,2,\ldots,n\}$. This is our novelty. Several examples are presented to illustrate the result

Observation of electric current induced by optically injected spin current

A normally incident light of linear polarization injects a pure spin current in a strip of 2-dimensional electron gas with spin-orbit coupling. We report observation of an electric current with a butterfly-like pattern induced by such a light shed on the vicinity of a crossbar shaped InGaAs/InAlAs quantum well. Its light polarization dependence is the same as that of the spin current. We attribute the observed electric current to be converted from the optically injected spin current caused by scatterings near the crossing. Our observation provides a realistic technique to detect spin currents, and opens a new route to study the spin-related science and engineering in semiconductors.Comment: 15 pages, 4 figure

Prediction of chlorophyll a concentration using HJ-1 satellite imagery for Xiangxi Bay in Three Gorges Reservoir

AbstractSince the impoundment of the Three Gorges Reservoir in 2003, algal blooms have frequently been observed in it. The chlorophyll a concentration is an important parameter for evaluating algal blooms. In this study, the chlorophyll a concentration in Xiangxi Bay, in the Three Gorges Reservoir, was predicted using HJ-1 satellite imagery. Several models were established based on a correlation analysis between in situ measurements of the chlorophyll a concentration and the values obtained from satellite images of the study area from January 2010 to December 2011. Chlorophyll a concentrations in Xiangxi Bay were predicted based on the established models. The results show that the maximum correlation is between the reflectance of the band combination of B4/(B2+B3) and in situ measurements of chlorophyll a concentration. The root mean square errors of the predicted values using the linear and quadratic models are 18.49 mg/m3 and 18.52 mg/m3, respectively, and the average relative errors are 37.79% and 36.79%, respectively. The results provide a reference for water bloom prediction in typical tributaries of the Three Gorges Reservoir and contribute to large-scale remote sensing monitoring and water quality management

AutoAgents: A Framework for Automatic Agent Generation

Large language models (LLMs) have enabled remarkable advances in automated task-solving with multi-agent systems. However, most existing LLM-based multi-agent approaches rely on predefined agents to handle simple tasks, limiting the adaptability of multi-agent collaboration to different scenarios. Therefore, we introduce AutoAgents, an innovative framework that adaptively generates and coordinates multiple specialized agents to build an AI team according to different tasks. Specifically, AutoAgents couples the relationship between tasks and roles by dynamically generating multiple required agents based on task content and planning solutions for the current task based on the generated expert agents. Multiple specialized agents collaborate with each other to efficiently accomplish tasks. Concurrently, an observer role is incorporated into the framework to reflect on the designated plans and agents' responses and improve upon them. Our experiments on various benchmarks demonstrate that AutoAgents generates more coherent and accurate solutions than the existing multi-agent methods. This underscores the significance of assigning different roles to different tasks and of team cooperation, offering new perspectives for tackling complex tasks. The repository of this project is available at https://github.com/Link-AGI/AutoAgents

ABSent: Cross-Lingual Sentence Representation Mapping with Bidirectional GANs

A number of cross-lingual transfer learning approaches based on neural networks have been proposed for the case when large amounts of parallel text are at our disposal. However, in many real-world settings, the size of parallel annotated training data is restricted. Additionally, prior cross-lingual mapping research has mainly focused on the word level. This raises the question of whether such techniques can also be applied to effortlessly obtain cross-lingually aligned sentence representations. To this end, we propose an Adversarial Bi-directional Sentence Embedding Mapping (ABSent) framework, which learns mappings of cross-lingual sentence representations from limited quantities of parallel data

On the Metallicities of Kepler Stars

We use 12000 stars from Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) spectroscopic data to show that the metallicities of Kepler field stars as given in the Kepler Input Catalog (KIC) systematically underestimate both the true metallicity and the dynamic range of the Kepler sample. Specifically, to the first order approximation, we find [Fe/H]_KIC = -0.20 + 0.43 [Fe/H]_LAMOST, with a scatter of ~0.25 dex, due almost entirely to errors in KIC. This relation is most secure for -0.3<[Fe/H]_LAMOST<+0.4 where we have >200 comparison stars per 0.1 dex bin and good consistency is shown between metallicities determined by LAMOST and high-resolution spectra. It remains approximately valid in a slightly broader range. When the relation is inverted, the error in true metallicity as derived from KIC is (0.25 dex)/0.43~0.6 dex. We thereby quantitatively confirm the cautionary note by Brown et al. (2011) that KIC estimates of [Fe/H] should not be used by "anyone with a particular interest in stellar metallicities". Fortunately, many more LAMOST spectroscopic metallicities will be available in the near future