A Game Based, Financial Literacy Oriented Approach to Improving Programming Education

Every year, two thirds of college seniors (about 1.8 million) in the US graduate with significant debts, but most of them are poorly equipped with essential financial knowledge to manage their debts and make intelligent financial decisions. Programming courses are uniquely positioned to offer opportunities to help students improve financial literacy. However, there have been no integrated courses to exploit the synergy. Meanwhile, computing disciplines face continued challenges of getting students interested in computing and finding ways to improve learning effectiveness. To address these challenges, we are developing an innovative teaching strategy that infuses financial literacy into four computing courses and engages students to develop financial literacy games. Students’ interests and learning outcomes will be improved because they enjoy computer games and are motivated when they use computing skills to address issues closely related to their daily lives

Extended First-Principles Molecular Dynamics Method From Cold Materials to Hot Dense Plasmas

An extended first-principles molecular dynamics (FPMD) method based on Kohn-Sham scheme is proposed to elevate the temperature limit of the FPMD method in the calculation of dense plasmas. The extended method treats the wave functions of high energy electrons as plane waves analytically, and thus expands the application of the FPMD method to the region of hot dense plasmas without suffering from the formidable computational costs. In addition, the extended method inherits the high accuracy of the Kohn-Sham scheme and keeps the information of elec- tronic structures. This gives an edge to the extended method in the calculation of the lowering of ionization potential, X-ray absorption/emission spectra, opacity, and high-Z dense plasmas, which are of particular interest to astrophysics, inertial confinement fusion engineering, and laboratory astrophysics

Manifold Regularized Correlation Object Tracking

In this paper, we propose a manifold regularized correlation tracking method with augmented samples. To make better use of the unlabeled data and the manifold structure of the sample space, a manifold regularization-based correlation filter is introduced, which aims to assign similar labels to neighbor samples. Meanwhile, the regression model is learned by exploiting the block-circulant structure of matrices resulting from the augmented translated samples over multiple base samples cropped from both target and nontarget regions. Thus, the final classifier in our method is trained with positive, negative, and unlabeled base samples, which is a semisupervised learning framework. A block optimization strategy is further introduced to learn a manifold regularization-based correlation filter for efficient online tracking. Experiments on two public tracking data sets demonstrate the superior performance of our tracker compared with the state-of-the-art tracking approaches

Hypothermia treatment ameliorated cyclin-dependent kinase 5-mediated inflammation in ischemic stroke and improved outcomes in ischemic stroke patients

OBJECTIVES: The inflammatory response is a key mechanism of neuronal damage and loss during acute ischemic stroke. Hypothermia has shown promise as a treatment for ischemic stroke. In this study, we investigated the molecular signaling pathways in ischemic stroke after hypothermia treatment. METHODS: Cyclin-dependent kinase 5 (CDK5) was overexpressed or silenced in cultured cells. Nuclear transcription factor-kB (NF-kB) activity was assessed by measurement of the luciferase reporter gene. An ischemic stroke model was established in Sprague–Dawley (SD) rats using the suture-occluded method. Animals were assigned to three groups: sham operation control, ischemic stroke, and ischemic stroke + hypothermia treatment groups. Interleukin 1b (IL-1b) levels in the culture supernatant and blood samples were assessed by ELISA. Protein expression was measured by Western blotting. RESULTS: In HEK293 cells and primary cortical neuronal cultures exposed to hypothermia, CDK5 overexpression was associated with increased IL-1b, caspase 1, and NF-kB levels. In both a murine model of stroke and in patients, increased IL-1b levels were observed after stroke, and hypothermia treatment was associated with lower IL-1b levels. Furthermore, hypothermia-treated patients showed significant improvement in neurophysiological functional outcome. CONCLUSIONS: Overall, hypothermia offers clinical benefit, most likely through its effects on the inflammatory response

DeepPsych: Harnessing Market Psychology with Deep Learning

Investor psychology provides an important avenue for modeling non-fundamental behaviors in financial analysis. Yet, whether market psychological information has a practical application in predicting asset returns is still under debate. Thus, a burgeoning number of machine learning algorithms have been developed to test the effectiveness of investor psychology in financial predictions. With all the merits of machine learning approach, the drawbacks are prediction biases, data overfitting issues and poor performance. To address these issues, we developed a DeepPsych system to harness the power of high frequency TRMI psychology data for market prediction. In a “hybridization–generalization–dual-channel-fusion” three-stage experiment, we evaluate each proposed module and the entire framework against the state-of-art machine learning benchmarks on investor psychology and trading data of the SPY (SP500 ETF). Results demonstrate that our deep learning framework can automatically identify features that are more effective than fundamental factors and support profitable trading

Analytical model for the photocurrent-voltage characteristics of bilayer MEH-PPV/TiO2 photovoltaic devices

The photocurrent in bilayer polymer photovoltaic cells is dominated by the exciton dissociation efficiency at donor/acceptor interface. An analytical model is developed for the photocurrent-voltage characteristics of the bilayer polymer/TiO2 photovoltaic cells. The model gives an analytical expression for the exciton dissociation efficiency at the interface, and explains the dependence of the photocurrent of the devices on the internal electric field, the polymer and TiO2 layer thicknesses. Bilayer polymer/TiO2 cells consisting of poly[2-methoxy-5-(2-ethylhexyloxy)-1,4-phenylenevinylene] (MEH-PPV) and TiO2, with different thicknesses of the polymer and TiO2 films, were prepared for experimental purposes. The experimental results for the prepared bilayer MEH-PPV/TiO2 cells under different conditions are satisfactorily fitted to the model. Results show that increasing TiO2 or the polymer layer in thickness will reduce the exciton dissociation efficiency in the device and further the photocurrent. It is found that the photocurrent is determined by the competition between the exciton dissociation and charge recombination at the donor/acceptor interface, and the increase in photocurrent under a higher incident light intensity is due to the increased exciton density rather than the increase in the exciton dissociation efficiency

SCCAM: Supervised Contrastive Convolutional Attention Mechanism for Ante-hoc Interpretable Fault Diagnosis with Limited Fault Samples

In real industrial processes, fault diagnosis methods are required to learn from limited fault samples since the procedures are mainly under normal conditions and the faults rarely occur. Although attention mechanisms have become popular in the field of fault diagnosis, the existing attention-based methods are still unsatisfying for the above practical applications. First, pure attention-based architectures like transformers need a large number of fault samples to offset the lack of inductive biases thus performing poorly under limited fault samples. Moreover, the poor fault classification dilemma further leads to the failure of the existing attention-based methods to identify the root causes. To address the aforementioned issues, we innovatively propose a supervised contrastive convolutional attention mechanism (SCCAM) with ante-hoc interpretability, which solves the root cause analysis problem under limited fault samples for the first time. The proposed SCCAM method is tested on a continuous stirred tank heater and the Tennessee Eastman industrial process benchmark. Three common fault diagnosis scenarios are covered, including a balanced scenario for additional verification and two scenarios with limited fault samples (i.e., imbalanced scenario and long-tail scenario). The comprehensive results demonstrate that the proposed SCCAM method can achieve better performance compared with the state-of-the-art methods on fault classification and root cause analysis