Panel Data Models with Interactive Fixed Effects and Multiple Structural Breaks

In this paper we consider estimation of common structural breaks in panel data models with interactive fixed effects which are unobservable. We introduce a penalized principal component (PPC) estimation procedure with an adaptive group fused LASSO to detect the multiple structural breaks in the models. Under some mild conditions, we show that with probability approaching one the proposed method can correctly determine the unknown number of breaks and consistently estimate the common break dates. Furthermore, we estimate the regression coefficients through the post-LASSO method and establish the asymptotic distribution theory for the resulting estimators. The developed methodology and theory are applicable to the case of dynamic panel data models. The Monte Carlo simulation results demonstrate that the proposed method works well in finite samples with low false detection probability when there is no structural break and high probability of correctly estimating the break numbers when the structural breaks exist. We finally apply our method to study the environmental Kuznets curve for 74 countries over 40 years and detect two breaks in the data

Boosting Feedback Efficiency of Interactive Reinforcement Learning by Adaptive Learning from Scores

Interactive reinforcement learning has shown promise in learning complex robotic tasks. However, the process can be human-intensive due to the requirement of large amount of interactive feedback. This paper presents a new method that uses scores provided by humans, instead of pairwise preferences, to improve the feedback efficiency of interactive reinforcement learning. Our key insight is that scores can yield significantly more data than pairwise preferences. Specifically, we require a teacher to interactively score the full trajectories of an agent to train a behavioral policy in a sparse reward environment. To avoid unstable scores given by human negatively impact the training process, we propose an adaptive learning scheme. This enables the learning paradigm to be insensitive to imperfect or unreliable scores. We extensively evaluate our method on robotic locomotion and manipulation tasks. The results show that the proposed method can efficiently learn near-optimal policies by adaptive learning from scores, while requiring less feedback compared to pairwise preference learning methods. The source codes are publicly available at https://github.com/SSKKai/Interactive-Scoring-IRL.Comment: Accepted by IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023

Association between ERCC1 and TS mRNA levels and disease free survival in colorectal cancer patients receiving oxaliplatin and fluorouracil (5-FU) adjuvant chemotherapy

BACKGROUND: Aim was to explore the association of ERCC1 and TS mRNA levels with the disease free survival (DFS) in Chinese colorectal cancer (CRC) patients receiving oxaliplatin and 5-FU based adjuvant chemotherapy. METHODS: Total 112 Chinese stage II-III CRC patients were respectively treated by four different chemotherapy regimens after curative operation. The TS and ERCC1 mRNA levels in primary tumor were measured by real-time RT-PCR. Kaplan–Meier curves and log-rank tests were used for DFS analysis. The Cox proportional hazards model was used for prognostic analysis. RESULTS: In univariate analysis, the hazard ratio (HR) for the mRNA expression levels of TS and ERCC1 (logTS: HR = 0.820, 95% CI = 0.600 - 1.117, P = 0.210; logERCC1: HR = 1.054, 95% CI = 0.852 - 1.304, P = 0.638) indicated no significant association of DFS with the TS and ERCC1 mRNA levels. In multivariate analyses, tumor stage (IIIc: reference, P = 0.083; IIb: HR = 0.240, 95% CI = 0.080 - 0.724, P = 0.011; IIc: HR < 0.0001, P = 0.977; IIIa: HR = 0.179, 95% CI = 0.012 - 2.593, P = 0.207) was confirmed to be the independent prognostic factor for DFS. Moreover, the Kaplan-Meier DFS curves showed that TS and ERCC1 mRNA levels were not significantly associated with the DFS (TS: P = 0.264; ERCC1: P = 0.484). CONCLUSION: The mRNA expression of ERCC1 and TS were not applicable to predict the DFS of Chinese stage II-III CRC patients receiving 5-FU and oxaliplatin based adjuvant chemotherapy

Remote Sensing Monitoring System of Land Coverage Change in Mining Area

Based on remote sensing images, the panoramic views of land coverage distribution across a large geographic area can be accessed conveniently. Remote sensing monitoring system of land coverage change in mining area, which is a complex information system based on spatial database to manage multi-source heterogeneous data, was proposed in this article. The system structure, function and development strategy were studied in this paper. Remote sensing image fusion and classification are the key technologies in this system. The remote sensing image fusion method which is based on multi-band wavelet was discussed. Based on remote sensing image, the Chaos Immune Algorithm was proposed to improve the accuracy of land coverage classification. The results showed that this system can integrate the multi-source heterogeneous spatial data, including remote sensing image, vector data and related properties data into the whole body, also demonstrate graphical visualization and analyze compositely

Molecular characterization and expression of DgZFP1, a gene encoding a single zinc finger protein in chrysanthemum

A single zinc finger protein gene was isolated from chrysanthemum by rapid amplification of cDNA ends (RACE) approach and was designated as DgZFP1. The DgZFP1 encodes a protein of 168 amino acids residues with a calculated molecular mass of 18.1 kDa and theoretical isoelectric point is 4.71. DgZFP1 contains one single zinc finger motif and one ethylene-responsive element-binding factor (ERF)-associated amphiphilic repression (EAR) domain. The transcripts of DgZFP1 was enriched in nodes and ray petal than in disc petal, disc stamen, disc pistil and ray pistil, but not detected in other tissues. Subcellular localization revealed that DgZFP1 was preferentially distributed to nucleus. We argued that DgZFP1 is a new member of the single zinc finger protein genes and it may be the ortholog of LIF

Activity and expression of ADP-glucose pyrophosphorylase during rhizome formation in lotus (Nelumbo nucifera Gaertn.)

Additional file 7: Figure S6. Comparison of NnAGPS against AGPS of other species