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In the pursuit of artificial general intelligence (AGI), we tackle Abstraction and Reasoning Corpus (ARC) tasks using a novel two-pronged approach. We employ the Decision Transformer in an imitation learning paradigm to model human problem-solving, and introduce an object detection algorithm, the Push and Pull clustering method. This dual strategy enhances AI's ARC problem-solving skills and provides insights for AGI progression. Yet, our work reveals the need for advanced data collection tools, robust training datasets, and refined model structures. This study highlights potential improvements for Decision Transformers and propels future AGI research.

This review researched the effectiveness and safety of Shugan jieyu capsule for the treatment of psychiatric symptoms in epilepsy.

In this paper, we introduce SurvRev, a next-generation revisit prediction model that can be tested directly in business. The SurvRev model offers many advantages. First, SurvRev can use partial observations which were considered as missing data and removed from previous regression frameworks. Using deep survival analysis, we could estimate the next customer arrival from unknown distribution. Second, SurvRev is an event-rate prediction model. It generates the predicted event rate of the next k days rather than directly predicting revisit interval and revisit intention. We demonstrated the superiority of the SurvRev model by comparing it with diverse baselines, such as the feature engineering model and state-of-the-art deep survival models.

The Cox proportional hazards model is a canonical method in survival analysis for prediction of the life expectancy of a patient given clinical or genetic covariates -- it is a linear model in its original form. In recent years, several methods have been proposed to generalize the Cox model to neural networks, but none of these are both numerically correct and computationally efficient. We propose FastCPH, a new method that runs in linear time and supports both the standard Breslow and Efron methods for tied events. We also demonstrate the performance of FastCPH combined with LassoNet, a neural network that provides interpretability through feature sparsity, on survival datasets. The final procedure is efficient, selects useful covariates and outperforms existing CoxPH approaches.

A method using deep reinforcement learning (DRL) to non-iteratively generate an optimal mesh for an arbitrary blade passage is developed. Despite automation in mesh generation using either an empirical approach or an optimization algorithm, repeated tuning of meshing parameters is still required for a new geometry. The method developed herein employs a DRL-based multi-condition optimization technique to define optimal meshing parameters as a function of the blade geometry, attaining automation, minimization of human intervention, and computational efficiency. The meshing parameters are optimized by training an elliptic mesh generator which generates a structured mesh for a blade passage with an arbitrary blade geometry. During each episode of the DRL process, the mesh generator is trained to produce an optimal mesh for a randomly selected blade passage by updating the meshing parameters until the mesh quality, as measured by the ratio of determinants of the Jacobian matrices and the skewness, reaches the highest level. Once the training is completed, the mesh generator create an optimal mesh for a new arbitrary blade passage in a single try without an repetitive process for the parameter tuning for mesh generation from the scratch. The effectiveness and robustness of the proposed method are demonstrated through the generation of meshes for various blade passages., Comment: 53 pages and 11 figures

This review researched the effectiveness and safety of Shugan jieyu capsule for the treatment of psychiatric symptoms in epilepsy.

An integrated framework of computational fluid-structural dynamics (CFD-CSD) and deep reinforcement learning (deep-RL) is developed for control of a fly-scale flexible-winged flyer in complex flow. Dynamics of the flyer in complex flow is highly unsteady and nonlinear, which makes modeling the dynamics challenging. Thus, conventional control methodologies, where the dynamics is modeled, are insufficient for regulating such complicated dynamics. Therefore, in the present study, the integrated framework, in which the whole governing equations for fluid and structure are solved, is proposed to generate a control policy for the flyer. For the deep-RL to successfully learn the control policy, accurate and ample data of the dynamics are required. However, satisfying both the quality and quantity of the data on the intricate dynamics is extremely difficult since, in general, more accurate data are more costly. In the present study, two strategies are proposed to deal with the dilemma. To obtain accurate data, the CFD-CSD is adopted for precisely predicting the dynamics. To gain ample data, a novel data reproduction method is devised, where the obtained data are replicated for various situations while conserving the dynamics. With those data, the framework learns the control policy in various flow conditions and the learned policy is shown to have remarkable performance in controlling the flyer in complex flow fields., 53 pages, 13 figures, 1 algorithm, 1 table

Accurate prognosis for an individual patient is a key component of precision oncology. Recent advances in machine learning have enabled the development of models using a wider range of data, including imaging. Radiomics aims to extract quantitative predictive and prognostic biomarkers from routine medical imaging, but evidence for computed tomography radiomics for prognosis remains inconclusive. We have conducted an institutional machine learning challenge to develop an accurate model for overall survival prediction in head and neck cancer using clinical data etxracted from electronic medical records and pre-treatment radiological images, as well as to evaluate the true added benefit of radiomics for head and neck cancer prognosis. Using a large, retrospective dataset of 2,552 patients and a rigorous evaluation framework, we compared 12 different submissions using imaging and clinical data, separately or in combination. The winning approach used non-linear, multitask learning on clinical data and tumour volume, achieving high prognostic accuracy for 2-year and lifetime survival prediction and outperforming models relying on clinical data only, engineered radiomics and deep learning. Combining all submissions in an ensemble model resulted in improved accuracy, with the highest gain from a image-based deep learning model. Our results show the potential of machine learning and simple, informative prognostic factors in combination with large datasets as a tool to guide personalized cancer care., 27 pages, 7 figures, under review

Empirical evidence demonstrates the superiority of interleaved over blocked learning schedules in inductive learning, yet many students believe the opposite. We investigated the effect of metacognitive activities on learners’ awareness of interleaved benefits. Participants experienced two learning schedules for different painting styles and took a transfer test. Then we manipulated the provision of post-test feedback for each schedule and metacognitive prompts to reflect on the learning schedules. Finally, participants were asked to choose a more effective schedule between the two. Although there were no significant effects of either the post-test feedback or metacognitive prompts, the performance difference between the two schedules significantly predicted participants’ schedule selection. The greater benefits from the interleaved schedule the participants experienced, the more likely they chose the interleaved schedule. Furthermore, participants’ responses to metacognitive prompts revealed that those who acknowledged spacing benefits from the interleaved schedule were more likely to choose the interleaved schedule.

Accurate survival prediction is crucial for development of precision cancer medicine, creating the need for new sources of prognostic information. Recently, there has been significant interest in exploiting routinely collected clinical and medical imaging data to discover new prognostic markers in multiple cancer types. However, most of the previous studies focus on individual data modalities alone and do not make use of recent advances in machine learning for survival prediction. We present Deep-CR MTLR -- a novel machine learning approach for accurate cancer survival prediction from multi-modal clinical and imaging data in the presence of competing risks based on neural networks and an extension of the multi-task logistic regression framework. We demonstrate improved prognostic performance of the multi-modal approach over single modality predictors in a cohort of 2552 head and neck cancer patients, particularly for cancer specific survival, where our approach achieves 2-year AUROC of 0.774 and $C$-index of 0.788., Accepted at AAAI Spring Symposium 2021 (SP-ACA)

Developing polymeric membranes with propylene/propane separation performance surpassing the upper bound has been received much attention. Herein, a highly controlled thermal treatment process yielding microporous polyimide membranes made from dense brominated-polyimide membrane precursor via thermal debromination/cross-linking approach is demonstrated. We showed that facile thermal treatment could generate a narrow size distribution of micro-voids as well as covalent linkages between inter/intra polymer chains while the polyimide backbone structure remained intact. The thermally treated membranes were well-characterized by FTIR, TGA, WAXD, solid-state 13C NMR and N2 adsorption/desorption isotherm. Single gas propylene/propane (C3H6/C3H8) separation performance greatly improved in terms of both selectivity and permeability, surpassing the trade-off line while maintaining flexible membrane properties. A thermal cross-linking treatment at 380 °C for 5 h exhibited the best C3H6 permeability of 84.70 barrer and C3H6/C3H8 selectivity of 14.6 with high plasticization resistance.

Objective: Due to rotatability of the forearm, open reduction and internal fixation with plate is a gold standard treatment of forearm fracture. Our hypothesis was that if it does not make any problems on alignment and rotation, minimally invasive plate osteosynthesis (MIPO) technique could be also applied for forearm fracture. It is easy to align when only one of forearm bone is fractured because reference point exists. The purpose of this study is to introduce MIPO technique for comminuted one bone fracture of forearm shaft. Methods: Between May 2011 and January 2014, 17 patients with fractures of forearm shaft were treated by MIPO technique. MIPO technique was applied to single forearm bone shaft fractures or to the more severe comminuted bone when both forearm bones are fractured. The other less sever bone fracture is treated with open reduction and internal fixation. There were 5 cases of forearm both bone fracture (radius 3, ulna 2), 5 cases of radius shaft fracture, and 7 cases of ulnar shaft fracture. MIPO technique was applied in total 8 cases of radius fracture and 9 cases of ulna fracture. The mean follow-up period was 26 months (range, 24-44 months). Bone union period was determined by radiographs taken upon follow up, and clinical function was evaluated by range of movement, Grace and Eversmann criteria measured at the last follow up. Results: Bony union was achieved in all patients. The mean time to bone union was 10.8 weeks in radius and 13.4 weeks in ulna. The average range of motion of forearm was 80.6° in supination and 83.2° in pronation. Both groups showed mean percentage of 97 compared with the contralateral side. Functional results assessed by Grace and Eversmann criteria were rated excellent in 11 cases and good in 6 cases. Conclusion: In our series, MIPO technique for comminuted one bone fractures in forearm shaft fracture was safe and effective, which had advantages, including a small skin incision, in bone biology.