Design rules for dislocation filters

The efficacy of strained layer threading dislocation filter structures in single crystal epitaxial layers is evaluated using numerical modeling for (001) face-centred cubic materials, such as GaAs or Si(1-x)Ge(x), and (0001) hexagonal materials such as GaN. We find that threading dislocation densities decay exponentially as a function of the strain relieved, irrespective of the fraction of threading dislocations that are mobile. Reactions between threading dislocations tend to produce a population that is a balanced mixture of mobile and sessile in (001) cubic materials. In contrast, mobile threading dislocations tend to be lost very rapidly in (0001) GaN, often with little or no reduction in the immobile dislocation density. The capture radius for threading dislocation interactions is estimated to be approx. 40nm using cross section transmission electron microscopy of dislocation filtering structures in GaAs monolithically grown on Si. We find that the minimum threading dislocation density that can be obtained in any given structure is likely to be limited by kinetic effects to approx. 1.0e+04 to 1.0e+05 per square cm.Comment: 18 pages, 9 figure

Downregulation of protease activated receptor expression and cytokine production in P815 cells by RNA interference

Abstract Background Protease-activated receptors (PAR) are seven transmembrane G-coupled receptors comprising four genes (PAR-1 ~ PAR-4). Mast cell has been identified to be able to express PARs and release an array of cytokines upon activation. Recently, it was reported that interleukin (IL)-12 could regulate the expression of PARs in mast cells, and tryptase could induce IL-4 and IL-6 release from mast cells. In order to further investigate the issues, RNA interference (RNAi) technique was employed and small interfering RNAs (siRNA) of PARs were transfected in P815 cells. Results The results showed that siRNAs for PAR-1, PAR-2 and PAR-4 significantly downregulated expression of PAR-1, PAR-2 and PAR-4 mRNAs and proteins in P815 cells at 24, 48 and 72 h following transfection. siRNA PAR-1.2 and siRNA PAR-4.2 significantly reduced IL-12 induced upregulation of PAR-1 and PAR-4 expression, respectively when P815 cells were transfected with them for 48 h. siRNA PAR-2.3 blocked IL-12 induced downregulation of PAR-2 expression on both mRNA and protein levels. It was also observed that siRNA PAR-2.3 and siRNA PAR-1.2 reduced trypsin induced IL-4 release by approximately 92.6% and 65.3%, and SLIGKV-NH2 induced IL-4 release by 82.1% and 60.1%, respectively. Similarly, siRNA PAR-2.3 eliminated tryptase-induced IL-4 release by 75.3%, and siRNA PAR-1.2 diminished SFLLR-NH2 induced IL-4 release by 79.3%. However, siRNA PAR-1.2, siRNA PAR-2.3 and siRNA PAR-4.3 at 10 nM did not show any effect on tryptase-induced IL-6 release from P815 cells. Conclusion In conclusion, siRNAs of PARs can modulate PAR expression and PAR related cytokine production in mast cells, confirming that PARs are likely to play a role in allergic reactions.</p

Practical Probabilistic Model-based Deep Reinforcement Learning by Integrating Dropout Uncertainty and Trajectory Sampling

This paper addresses the prediction stability, prediction accuracy and control capability of the current probabilistic model-based reinforcement learning (MBRL) built on neural networks. A novel approach dropout-based probabilistic ensembles with trajectory sampling (DPETS) is proposed where the system uncertainty is stably predicted by combining the Monte-Carlo dropout and trajectory sampling in one framework. Its loss function is designed to correct the fitting error of neural networks for more accurate prediction of probabilistic models. The state propagation in its policy is extended to filter the aleatoric uncertainty for superior control capability. Evaluated by several Mujoco benchmark control tasks under additional disturbances and one practical robot arm manipulation task, DPETS outperforms related MBRL approaches in both average return and convergence velocity while achieving superior performance than well-known model-free baselines with significant sample efficiency. The open source code of DPETS is available at https://github.com/mrjun123/DPETS

Effective Multi-Agent Deep Reinforcement Learning Control with Relative Entropy Regularization

In this paper, a novel Multi-agent Reinforcement Learning (MARL) approach, Multi-Agent Continuous Dynamic Policy Gradient (MACDPP) was proposed to tackle the issues of limited capability and sample efficiency in various scenarios controlled by multiple agents. It alleviates the inconsistency of multiple agents' policy updates by introducing the relative entropy regularization to the Centralized Training with Decentralized Execution (CTDE) framework with the Actor-Critic (AC) structure. Evaluated by multi-agent cooperation and competition tasks and traditional control tasks including OpenAI benchmarks and robot arm manipulation, MACDPP demonstrates significant superiority in learning capability and sample efficiency compared with both related multi-agent and widely implemented signal-agent baselines and therefore expands the potential of MARL in effectively learning challenging control scenarios

FOAL: Fine-grained Contrastive Learning for Cross-domain Aspect Sentiment Triplet Extraction

Aspect Sentiment Triplet Extraction (ASTE) has achieved promising results while relying on sufficient annotation data in a specific domain. However, it is infeasible to annotate data for each individual domain. We propose to explore ASTE in the cross-domain setting, which transfers knowledge from a resource-rich source domain to a resource-poor target domain, thereby alleviating the reliance on labeled data in the target domain. To effectively transfer the knowledge across domains and extract the sentiment triplets accurately, we propose a method named Fine-grained cOntrAstive Learning (FOAL) to reduce the domain discrepancy and preserve the discriminability of each category. Experiments on six transfer pairs show that FOAL achieves 6% performance gains and reduces the domain discrepancy significantly compared with strong baselines. Our code will be publicly available once accepted

An EGFR and AKT Signaling Pathway was Identified with Mediation Model in Osteosarcomas Clinical Study

Identification of correlation pattern and signal pathway among biomarkers in patients has become increasingly interesting for its potential values in diagnosis, treatment and prognosis. EGFR and p-AKT signaling in osteosarcoma (OS) patients were analyzed for its relationship with cancer cell proliferation maker, Ki-67, using causal procedures and statistical tests. A total of 69 patients were collected who present to Vanderbilt University Medical Center with newly diagnosed, previously untreated osteosarcomas during the clinical study period 1994 through 2003. Tissue microarrays were constructed for EGFR, p-AKT and Ki-67. The mediation model was constructed with structural equation model (SEM) for the causal analysis of the three biomarkers in osteosarcoma patients. The results suggested a mediating effect of p-AKT for the causal relationship between EGFR and Ki-67. The study also found significant associations between EGFR and Ki-67 (p = 0.002), EGFR and p-AKT (p = 0.027), and p-AKT and Ki-67 controlling EGFR (p = 0.004). After the impact of EGFR on Ki-67 was accounted for by p-AKT, the relation between EGFR and Ki-67 was no longer significant (p = 0.381). The mediating effect was confirmed with Sobel test (p < 0.001) and Goodman (I) test (p < 0.001). The study indicated that a mediation model could be an approach to exploring the correlation pattern of EGFR and AKT signal pathway for cancer cell proliferation in OS patients in clinical study

A Bayesian Approach for the Cox Proportional Hazards Model with Covariates Subject to Detection Limit

The research on biomarkers has been limited in its effectiveness because biomarker levels can only be measured within the thresholds of assays and laboratory instruments, a challenge referred to as a detection limit (DL) problem. In this paper, we propose a Bayesian approach to the Cox proportional hazards model with explanatory variables subject to lower, upper, or interval DLs. We demonstrate that by formulating the time-to-event outcome using the Poisson density with counting process notation, implementing the proposed approach in the OpenBUGS and JAGS is straightforward. We have conducted extensive simulations to compare the proposed Bayesian approach to the other four commonly used methods and to evaluate its robustness with respect to the distribution assumption of the biomarkers. The proposed Bayesian approach and other methods were applied to an acute lung injury study, in which a panel of cytokine biomarkers was studied for the biomarkers' association with ventilation-free survival

Chinese basic education and experience from three regions

Basic education is the foundation for people to gain more knowledge in the process of growing up and living. High buildings rise from the ground. What does basic education exert to cultivate the people is the foundation for building a house. Therefore, basic education is such an impor-tant and basic project to improve the quality of people. Since China’s reform and opening and the re-introduction of the college entrance examination in the late 1970s, basic education has con-tinuously improved and developed with more and more attention. China started to participate in the Program for International Students Assessment PISA5 in 2009. Up to now, China has par-ticipated in four sessions of PISA with relatively good grades6. The results of the PISA can help to examine the education quality, fairness and development efficiency, establish and improve an education monitoring indicator system, and promote education reforms for both China and the other countries in the world. The progress of China’s basic education and education with Chinese characteristics has contributed to China’s all-round development, which also provided references for other countries. In the meantime, PISA’s analysis of China and other countries also reflect the parts of China’s basic education that need to be promoted and emphasized

Dislocation filters in GaAs on Si

Cross section transmission electron microscopy has been used to analyse dislocation filter layers (DFLs) in five similar structures of GaAs on Si that had different amounts of strain in the DFLs or different annealing regimes. By counting threading dislocation (TD) numbers through the structure we are able to measure relative changes, even though the absolute density is not known. The DFLs remove more than 90% of TDs in all samples. We find that the TD density in material without DFLs decays as the inverse of the square root of the layer thickness, and that DFLs at the top of the structure are considerably more efficient than those at the bottom. This indicates that the interaction radius, the distance that TDs must approach to meet and annihilate, is dependent upon the TD density

Measuring Your ASTE Models in The Wild: A Diversified Multi-domain Dataset For Aspect Sentiment Triplet Extraction

Aspect Sentiment Triplet Extraction (ASTE) is widely used in various applications. However, existing ASTE datasets are limited in their ability to represent real-world scenarios, hindering the advancement of research in this area. In this paper, we introduce a new dataset, named DMASTE, which is manually annotated to better fit real-world scenarios by providing more diverse and realistic reviews for the task. The dataset includes various lengths, diverse expressions, more aspect types, and more domains than existing datasets. We conduct extensive experiments on DMASTE in multiple settings to evaluate previous ASTE approaches. Empirical results demonstrate that DMASTE is a more challenging ASTE dataset. Further analyses of in-domain and cross-domain settings provide promising directions for future research. Our code and dataset are available at https://github.com/NJUNLP/DMASTE.Comment: 15pages, 5 figures, ACL202