A Hybrid Framework for Sequential Data Prediction with End-to-End Optimization

We investigate nonlinear prediction in an online setting and introduce a hybrid model that effectively mitigates, via an end-to-end architecture, the need for hand-designed features and manual model selection issues of conventional nonlinear prediction/regression methods. In particular, we use recursive structures to extract features from sequential signals, while preserving the state information, i.e., the history, and boosted decision trees to produce the final output. The connection is in an end-to-end fashion and we jointly optimize the whole architecture using stochastic gradient descent, for which we also provide the backward pass update equations. In particular, we employ a recurrent neural network (LSTM) for adaptive feature extraction from sequential data and a gradient boosting machinery (soft GBDT) for effective supervised regression. Our framework is generic so that one can use other deep learning architectures for feature extraction (such as RNNs and GRUs) and machine learning algorithms for decision making as long as they are differentiable. We demonstrate the learning behavior of our algorithm on synthetic data and the significant performance improvements over the conventional methods over various real life datasets. Furthermore, we openly share the source code of the proposed method to facilitate further research

Analysis of Turkish Prospective Science Teachers’ Perceptions on Technology in Education

Purpose of this study was to determine and analyze Turkish pre-service science teachers\u27 perceptions on technology in terms of learning style, computer competency level, possession of a computer, and gender. The study involved 264 Turkish pre-service science teachers. Analyses were conducted through four-way ANOVA, t-tests, Mann Whitney U test and one-way ANOVAs and the results showed there were one main effect for gender and one interaction effect between gender and computer competency level. The interaction effect pointed out that the male pre-service science teachers who were weak in computer competency held more positive perceptions toward instructional technology than their counterparts

Teaching Machines to Read and Comprehend

Teaching machines to read natural language documents remains an elusive challenge. Machine reading systems can be tested on their ability to answer questions posed on the contents of documents that they have seen, but until now large scale training and test datasets have been missing for this type of evaluation. In this work we define a new methodology that resolves this bottleneck and provides large scale supervised reading comprehension data. This allows us to develop a class of attention based deep neural networks that learn to read real documents and answer complex questions with minimal prior knowledge of language structure.Comment: Appears in: Advances in Neural Information Processing Systems 28 (NIPS 2015). 14 pages, 13 figure

Hybrid State Space-based Learning for Sequential Data Prediction with Joint Optimization

We investigate nonlinear prediction/regression in an online setting and introduce a hybrid model that effectively mitigates, via a joint mechanism through a state space formulation, the need for domain-specific feature engineering issues of conventional nonlinear prediction models and achieves an efficient mix of nonlinear and linear components. In particular, we use recursive structures to extract features from raw sequential sequences and a traditional linear time series model to deal with the intricacies of the sequential data, e.g., seasonality, trends. The state-of-the-art ensemble or hybrid models typically train the base models in a disjoint manner, which is not only time consuming but also sub-optimal due to the separation of modeling or independent training. In contrast, as the first time in the literature, we jointly optimize an enhanced recurrent neural network (LSTM) for automatic feature extraction from raw data and an ARMA-family time series model (SARIMAX) for effectively addressing peculiarities associated with time series data. We achieve this by introducing novel state space representations for the base models, which are then combined to provide a full state space representation of the hybrid or the ensemble. Hence, we are able to jointly optimize both models in a single pass via particle filtering, for which we also provide the update equations. The introduced architecture is generic so that one can use other recurrent architectures, e.g., GRUs, traditional time series-specific models, e.g., ETS or other optimization methods, e.g., EKF, UKF. Due to such novel combination and joint optimization, we demonstrate significant improvements in widely publicized real life competition datasets. We also openly share our code for further research and replicability of our results.Comment: Submitted to the IEEE TNNLS journa

Hierarchical Ensemble-Based Feature Selection for Time Series Forecasting

We study a novel ensemble approach for feature selection based on hierarchical stacking in cases of non-stationarity and limited number of samples with large number of features. Our approach exploits the co-dependency between features using a hierarchical structure. Initially, a machine learning model is trained using a subset of features, and then the model's output is updated using another algorithm with the remaining features to minimize the target loss. This hierarchical structure allows for flexible depth and feature selection. By exploiting feature co-dependency hierarchically, our proposed approach overcomes the limitations of traditional feature selection methods and feature importance scores. The effectiveness of the approach is demonstrated on synthetic and real-life datasets, indicating improved performance with scalability and stability compared to the traditional methods and state-of-the-art approaches

Effect of taxifolin on acrylamide-induced oxidative and proinflammatory lung injury in rats: Biochemical and histopathological studies

Purpose: To examine the probable beneficial effects of taxifolin against acrylamide damage in lung tissue.Methods: 18 male albino Wistar rats were divided into healthy (HG), acrylamide (AG) and taxifolin + acrylamide (TAG) groups. Once a day for 30 days, acrylamide was orally administered to the AG group (50 mg/kg), while ACL (50 mg/kg) and TAX (20 mg/kg) were orally administered to TAG group. Protein concentration, malondialdehyde (MDA), and total glutathione (tGSH) levels as well as oxidant and antioxidant molecules concentrations of the rat lung tissues were measured. In addition, degree of mononuclear (MN) cell infiltration and bronchial-associated lymphoid tissue (BALT) hyperplasia was evaluated by the degree of hyperplasia (absent, mild, moderate, severe). The histopathological andbiochemical data the groups were compared.Results: When compared in terms of MDA levels, it was found that the AG group had high MDA levels, and the TAG group had low MDA levels. (p < 0.001). TAG group was found to have a higher tGSH level than the AG group (p < 0.001). Compared to the AG group, lower TOS and higher TAS levels were obtained in the TAG group (p < 0.001). In addition, when TOS levels of TAG and HG groups were compared, the TOS levels between the two groups were statistically insignificant (p = 0.213). It has been observed that TAX administration prevents the increase in NF-ƘB level. When the NF-ƘB levels of the AG and TAG groups were compared with each other, there was a statistically significant difference (p = 0.001). In the AG group, severe MN cell hyperplasia and BALT hyperplasia were observed histopathologically. It was determined that these findings were alleviated in the TAG group. A histopathologically significant difference was found between AG and TAG groups (p < 0.05).Conclusion: Taxifolin has beneficial effects against lung injury caused by acrylamide, a healthdamaging environmental factor. Regular use of taxifolin can be recommended, especially in people who are known to have intense contact with acrylamide. There is a need for research studies on this subject

Outcomes and effectiveness of bilateral percutaneous transluminal renal artery stenting in patients with critical bilateral renal artery stenosis

Background: The aim of this study was to assess the effects of percutaneous bilateral renal artery stenting in patients with atherosclerotic renal artery stenosis and in-hospital and 4 month outcome of the procedure, focusing on the changes in renal function and blood pressure. Methods: Five consecutive patients (mean age: 64.8 ± 9.7 years, 1 women) with bilateral renal artery stenoses underwent percutaneous interventions. We compared blood pressure, number of oral antihypertensive medications, and renal function changes preprocedure and postprocedure at 4 months follow-up. Results: A total of 5 bilateral atherosclerotic renal artery stenosis patients underwent percutaneous transluminal renal angioplasty and 10 stents were placed. Although systolic and diastolic blood pressures were significantly decreased in follow-up period, glomerular filtration rates were not significantly changed as compared with baseline data (p = 0.009, p = 0.008, p = 1.00, respectively). Also, the number of oral antihypertensive medications were significantly decreased at follow-up period (p = 0.03). Conclusions: Bilateral renal artery stenting provides a beneficial outcome such as stabilization of renal functions, significant improvement in blood pressure control and reduction in the number of oral antihypertensive medications at follow-up

Potentiometric titrations of some mono methyl substituted and 5-keto mono methyl substituted hexadecanoic acids in nonaqueous media

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