(Z)-1-(2,4-Difluoro­phen­yl)-3-(4-fluoro­phen­yl)-2-(1H-1,2,4-triazol-1-yl)prop-2-en-1-one

In the title mol­ecule, C17H10F3N3O, the C=C bond connecting the triazole ring and 4-fluoro­phenyl groups adopts a Z conformation. The triazole ring forms dihedral angles of 15.3 (1) and 63.5 (1)°, with the 2,4-difluoro-substituted and 4-fluoro-substituted benzene rings, respectively. The dihedral angle between the two benzene rings is 51.8 (1)°

Mastering Autonomous Assembly in Fusion Application with Learning-by-doing: a Peg-in-hole Study

Robotic peg-in-hole assembly is an essential task in robotic automation research. Reinforcement learning (RL) combined with deep neural networks (DNNs) lead to extraordinary achievements in this area. However, current RL-based approaches could hardly perform well under the unique environmental and mission requirements of fusion applications. Therefore, we have proposed a new designed RL-based method. Furthermore, unlike other approaches, we focus on innovations in the structure of DNNs instead of the RL model. Data from the RGB camera and force/torque (F/T) sensor as the input are fed into a multi-input branch network, and the best action in the current state is output by the network. All training and experiments are carried out in a realistic environment, and from the experiment result, this multi-sensor fusion approach has been shown to work well in rigid peg-in-hole assembly tasks with 0.1mm precision in uncertain and unstable environments

The role of EGFR mutation as a prognostic factor in survival after diagnosis of brain metastasis in non-small cell lung cancer: A systematic review and meta-analysis

Abstract Background The brain is a common site for metastasis in non-small-cell lung cancer (NSCLC). This study was designed to evaluate the relationship between the mutational of the epidermal growth factor receptor (EGFR) and overall survival (OS) in NSCLC patients with brain metastases. Methods Searches were performed in PubMed, EmBase, and the Cochrane Library to identify studies evaluating the association of EGFR mutation with OS in NSCLC patients through September 2017. Results 4373 NSCLC patients with brain metastases in 18 studies were involved. Mutated EGFR associated with significantly improved OS compared with wild type. Subgroup analyses suggested that this relationship persisted in studies conducted in Eastern, with retrospective design, with sample size ≥500, mean age of patients ≥65.0 years, percentage male < 50.0%, percentage of patients receiving tyrosine kinase inhibitor ≥30.0%. Finally, although significant publication bias was observed using the Egger test, the results were not changed after adjustment using the trim and fill method. Conclusions This meta-analysis suggests that EGFR mutation is an important predictive factor linked to improved OS for NSCLC patients with brain metastases. It can serve as a useful index in the prognostic assessment of NSCLC patients with brain metastases

Dynamic Programming and Heuristic for Stochastic Uncapacitated Lot-Sizing Problems with Incremental Quantity Discount

The stochastic uncapacitated lot-sizing problems with incremental quantity discount have been studied in this paper. First, a multistage stochastic mixed integer model is established by the scenario analysis approach and an equivalent reformulation is obtained through proper relaxation under the decreasing unit order price assumption. The proposed reformulation allows us to extend the production-path property to this framework, and furthermore we provide a more accurate characterization of the optimal solution. Then, a backward dynamic programming algorithm is developed to obtain the optimal solution and considering its exponential computation complexity in term of time stages, we design a new rolling horizon heuristic based on the proposed property. Comparisons with the commercial solver CPLEX and other heuristics indicate better performance of our proposed algorithms in both quality of solution and run time

(Z)-1,3-Bis(4-chloro­phen­yl)-2-(1H-1,2,4-triazol-1-yl)prop-2-en-1-one

In the title mol­ecule, C17H11Cl2N3O, the C=C bond connecting the triazole and 4-chloro­phenyl groups adopts a Z geometry. The dihedral angles formed by the triazole ring and the 4-chloro substituted benzene rings are 67.3 (1) and 59.1 (1)°. The dihedral angle between the two benzene rings is 73.5 (1)°

Learning from Easy to Complex: Adaptive Multi-curricula Learning for Neural Dialogue Generation

Current state-of-the-art neural dialogue systems are mainly data-driven and are trained on human-generated responses. However, due to the subjectivity and open-ended nature of human conversations, the complexity of training dialogues varies greatly. The noise and uneven complexity of query-response pairs impede the learning efficiency and effects of the neural dialogue generation models. What is more, so far, there are no unified dialogue complexity measurements, and the dialogue complexity embodies multiple aspects of attributes---specificity, repetitiveness, relevance, etc. Inspired by human behaviors of learning to converse, where children learn from easy dialogues to complex ones and dynamically adjust their learning progress, in this paper, we first analyze five dialogue attributes to measure the dialogue complexity in multiple perspectives on three publicly available corpora. Then, we propose an adaptive multi-curricula learning framework to schedule a committee of the organized curricula. The framework is established upon the reinforcement learning paradigm, which automatically chooses different curricula at the evolving learning process according to the learning status of the neural dialogue generation model. Extensive experiments conducted on five state-of-the-art models demonstrate its learning efficiency and effectiveness with respect to 13 automatic evaluation metrics and human judgments.Comment: Accepted to AAAI 202

Strong aftershocks traffic light system: A case study of the 8 January 2022 MS6.9 Menyuan earthquake, Qinghai Province, China

Strong aftershocks, especially the disaster-causing M≥5.0 kind, are a key concern for mitigation of seismic risks because they often lead to superimposed earthquake damage. However, the real-time forecasting results of the traditional probability prediction models based on statistics are usually far from accurate and therefore unsatisfactory. Borrowing an idea from the foreshock traffic light system (FTLS), which is based on observations of decreasing b-values or increasing differential stress just before a strong aftershock, we constructed a strong aftershock traffic light system (SATLS) that uses data-driven technology to improve the reliability of time sequence b-value calculations, and analyzed the b-value variations of strong aftershocks in the China continent. We applied this system to the MS6.9 Menyuan earthquake occurred on 8 January 2022. The earthquake occurrence rates before the largest aftershock (MS5.2) forecast by the Omi-R-J model were too low, although the model could accurately forecast aftershock rates for each magnitude interval in most time-periods. However, reliable b-values can be calculated using the time-sequence b-value data-driven (TbDD) method, and the results showed that the b-values continued declining from 1.3 days before the MS5.2 aftershock and gradually recovered afterward. This would suggest that the stress evolution in the focal area can provide data for deciding when to post risk alerts of strong aftershocks. In the process of building the SATLS, we studied thirty-four M≥6.0 intraplate earthquake sequences in the China continent and concluded that the differences between the b-values of the aftershock sequences and of the background events, △b = bafter - bbg = ±0.1, could be used as thresholds to determine whether M≥5.0 aftershocks would occur. The △b value obtained using the events before the MS5.2 aftershock of the MS6.9 Menyuan sequence was about -0.04, which would have caused the SATLS to declare a yellow alert, but there would have been some gap expected before a red alert was triggered by the b-value difference derived from the events associated with this strong aftershock. To accurately forecast a strong aftershock of M≥5.0, a deeper understanding of the true b-value and a detailed description of the stress evolution state in the source area is necessary

Molecular Targeted Therapy in the Treatment of Chordoma: A Systematic Review

Objectives: Chordoma is a rare bone malignancy that affects the spine and skull base. Treatment dilemma leads to a high rate of local relapse and distant metastases. Molecular targeted therapy (MTT) is an option for advanced chordoma, but its therapeutic efficacy and safety have not been investigated systematically. Therefore, a systematic review was conducted on studies reporting MTT regimens for chordoma.Methods: Clinical trials, case series and case reports on chordoma MTT were identified using MEDLINE, Cochrane library and EMBASE, and systematically reviewed. Data on clinical outcomes, such as median overall survival, progression-free survival, response rate and adverse events (AEs) were extracted and analyzed.Results: Thirty-three eligible studies were selected for the systematic review, which indicated that imatinib and erlotinib were the most frequently used molecular targeted inhibitors (MTIs) for chordoma. For PDGFR-positive and/or EGFR-positive chordoma, clinical benefits were achieved with acceptable AEs. Monotherapy is preferred as the first-line of treatment, and combined drug therapy as the second-line treatment. In addition, the brachyury vaccine has shown promising results.Conclusions: The selection of MTIs for patients with advanced or relapsed chordoma should be based on gene mutation screening and immunohistochemistry (IHC). Monotherapy of TKIs is recommended as the first-line management, and combination therapy (two TKIs or TKI plus mTOR inhibitor) may be the choice for drug-resistant chordoma. Brachyury vaccine is a promising therapeutic strategy and requires more clinical trials to evaluate its safety and efficacy

InterFormer: Interactive Local and Global Features Fusion for Automatic Speech Recognition

The local and global features are both essential for automatic speech recognition (ASR). Many recent methods have verified that simply combining local and global features can further promote ASR performance. However, these methods pay less attention to the interaction of local and global features, and their series architectures are rigid to reflect local and global relationships. To address these issues, this paper proposes InterFormer for interactive local and global features fusion to learn a better representation for ASR. Specifically, we combine the convolution block with the transformer block in a parallel design. Besides, we propose a bidirectional feature interaction module (BFIM) and a selective fusion module (SFM) to implement the interaction and fusion of local and global features, respectively. Extensive experiments on public ASR datasets demonstrate the effectiveness of our proposed InterFormer and its superior performance over the other Transformer and Conformer models.Comment: Accepted by Interspeech 202