408 research outputs found
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
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