Miracle-dz/UniFAR-FTUs huggingface dataset:
Down-regulation of CYLD expression by Snail promotes tumor progression in malignant melanoma

High malignancy and early metastasis are hallmarks of melanoma. Here, we report that the transcription factor Snail1 inhibits expression of the tumor suppressor CYLD in melanoma. As a direct consequence of CYLD repression, the protooncogene BCL-3 translocates into the nucleus and activates Cyclin D1 and N-cadherin promoters, resulting in proliferation and invasion of melanoma cells. Rescue of CYLD expression in melanoma cells reduced proliferation and invasion in vitro and tumor growth and metastasis in vivo. Analysis of a tissue microarray with primary melanomas from patients revealed an inverse correlation of Snail1 induction and loss of CYLD expression. Importantly, tumor thickness and progression-free and overall survival inversely correlated with CYLD expression. Our data suggest that Snail1-mediated suppression of CYLD plays a key role in melanoma malignancy.
An energy efficient multicast algorithm for an Adhoc network using network coding and MAC scheduling

The paper envisages the network modeling of a wireless multicast network by approach of optimized joint MAC scheduling and network coding. A heuristic method of designing a network is achieved by scheduling algorithm which is based on network parameters such as interference free transmissions, and optimization of avoidable scheduling sets. We derive a joint linear optimization problem which is used in the designing of the wireless network. Further when the scheduling is done, the network coding is adapted. The code construction algorithms are used and the unequal timeshare is incorporated in designing network codes. The paper demonstrates the variation in the network performance parameters like Throughput, Energy efficiency, Delay, Packet Delivery Ratio, Overhead, and Normalized Overhead. The statistics is madeby varying the network modeling parameters with different node densities. After the network coding, based on the traffic flow, the sleep state is introduced where the network goes to the hibernated mode. A unique joint optimized structure is required for MAC scheduling and network coding. A heuristic approach of scheduling is needed to avoid hidden station problem. Also to provide an energy and throughput optimization for multi hop wireless network.
Financial Incentives for Reducing Smoking and Promoting Other Health-Related Behavior Change in Vulnerable Populations

Substantial reductions in U.S. cigarette smoking and associated chronic diseases over the past 50 years have benefited health. Unfortunately, those reductions have distributed unevenly throughout the population. Smoking remains prevalent and even increasing among certain vulnerable populations: economically disadvantaged groups, those with other substance-use disorders or mental illness, certain ethnic and racial minorities, and gender and sexual minorities. Moreover, other unhealthy behavior patterns (physical inactivity, unhealthy food choices, risky sexual behavior, poor adherence to medical preventive regimens) and associated chronic diseases are also overrepresented in many of these same populations. Disparities in unhealthy behavior patterns contribute to health disparities and escalating health care costs, underscoring the need for more effective behavior-change strategies. This report reviews research on the efficacy of financial incentives for reducing smoking in vulnerable populations, while also touching on applications of that behavior-change strategy for promoting other health-related behavior changes in vulnerable populations.
Predictive Property of Hidden Representations in Recurrent Neural Network Language Models

The hidden representation of a recurrent neural network language model (RNNLM) is regarded as a summary of the past input sequence. In this study, we propose that the hidden representation also consists of the expectation about upcoming inputs. A RNNLM is originally trained to predict the next word or character, but we experimentally discover, even for an unmodified RNNLM, the farther sequences can also be predicted given the activation of the hidden neurons. This property makes the hidden activation a summary of the local context covering both the past and the near future, which may benefit some language processing tasks which did not previously take advantage of language models. Dimensionality reduction approach is also briefly considered to facilitate the practical application of the predictive property.
Evaluating 'Cash-for-Clunkers': Program Effect on Auto Sales and the Environment

“Cash-for-Clunkers” was a $3 billion program that attempted to stimulate the U.S. economy and improve the environment by encouraging consumers to retire older vehicles and purchase fuel-efficient new vehicles. We investigate the effects of this program on new vehicle sales and the environment. Using Canada as the control group in a difference-in-differences framework, we find that, of the 0.68 million transactions that occurred under the program, the program increased new vehicle sales only by about 0.37 million during July and August of 2009, implying that approximately 45 percent of the spending went to consumers who would have purchased a new vehicle anyway. Our results cannot reject the hypothesis that there is little or no gain in sales beyond 2009. The program will reduce CO2 emissions by only 9–28.2 million tons based on upper and lower bounds of the estimate of the program effect on sales, implying a cost per ton ranging from $92 to $288 even after accounting for reduced criteria pollutants.
Wireless , Continuous Wave Near Infrared Spectroscopy System for Monitoring Brain Activity

Near Infrared Spectroscopy is a noninvasive optical method to monitor hemodynamic activity in tissue. Various techniques and devices have been developed to utilize this method in a variety of fields such as, neuroscience, neuropsychology, education, muscle physiology and training. However, current devices do not enable complete freedom of motion; therefore they limit the fields where this technology can be used. In this paper, design of a 16 channel wireless, continuous wave near infrared brain imager is described. The aim of this design is to produce a miniaturized, wearable, affordable, negligibly intrusive system that can monitor hemodynamic response of prefrontal cortex activity. The system consists of a sensor pad, a circuit board, a battery, and a pocket pc with a data acquisition (DAQ) card. The pocket pc provides control signals to the circuit board, acquires the data and sends it to a distant computer displaying the temporal and spatial information in real time as concentration changes in oxy-hemoglobin (HbO2) and deoxy-hemoglobin (Hb). Custom pocket pc application that has low level access to DAQ hardware has been developed by using eMbedded Visual C++. To test the system performance, experiments have been conducted with a dynamic phantom that simulates optical properties of human tissue and changes in Hb and HbO2 concentrations.
Regularized Common Spatial Pattern With Aggregation for EEG Classification in Small-Sample Setting

Common spatial pattern (CSP) is a popular algorithm for classifying electroencephalogram (EEG) signals in the context of brain-computer interfaces (BCIs). This paper presents a regularization and aggregation technique for CSP in a small-sample setting (SSS). Conventional CSP is based on a sample-based covariance-matrix estimation. Hence, its performance in EEG classification deteriorates if the number of training samples is small. To address this concern, a regularized CSP (R-CSP) algorithm is proposed, where the covariance-matrix estimation is regularized by two parameters to lower the estimation variance while reducing the estimation bias. To tackle the problem of regularization parameter determination, R-CSP with aggregation (R-CSP-A) is further proposed, where a number of R-CSPs are aggregated to give an ensemble-based solution. The proposed algorithm is evaluated on data set IVa of BCI Competition III against four other competing algorithms. Experiments show that R-CSP-A significantly outperforms the other methods in average classification performance in three sets of experiments across various testing scenarios, with particular superiority in SSS.
Target Gene and Function Prediction of Differentially Expressed MicroRNAs in Lactating Mammary Glands of Dairy Goats

MicroRNAs are small noncoding RNAs that can regulate gene expression, and they can be involved in the regulation of mammary gland development. The differential expression of miRNAs during mammary gland development is expected to provide insight into their roles in regulating the homeostasis of mammary gland tissues. To screen out miRNAs that should have important regulatory function in the development of mammary gland from miRNA expression profiles and to predict their function, in this study, the target genes of differentially expressed miRNAs in the lactating mammary glands of Laoshan dairy goats are predicted, and then the functions of these miRNAs are analyzed via bioinformatics. First, we screen the expression patterns of 25 miRNAs that had shown significant differences during the different lactation stages in the mammary gland. Then, these miRNAs are clustered according to their expression patterns. Computational methods were used to obtain 215 target genes for 22 of these miRNAs. Combining gene ontology annotation, Fisher's exact test, and KEGG analysis with the target prediction for these miRNAs, the regulatory functions of miRNAs belonging to different clusters are predicted.
Diffusion and Random Walk Processes

Random walk and diffusion models form one of the major classes of models in the response time domain. They best apply in situations in which subjects make two-choice decisions that are based on a single, ‘one shot’, cognitive process, decisions for which response times do not average much over one second. The basic assumption of the models is that a stimulus test item provides information that is accumulated over time towards one of two decision criteria, each criterion representing one of the two response alternatives. A response is initiated when one of the decision criteria is reached. The researchers who have developed random walk and diffusion models take the approach that all aspects of experimental data need to be accounted for by a model. This means that models should deal with both correct and error response times, with the shapes of the full distributions of response times, and with the probabilities of correct versus error responses. It should be stressed that dealing with all these aspects of data is much more of a challenge than dealing with only one dependent variable. Random walk models have been prominent since the 1960s (Laming 1968, Link and Heath, 1975). Diffusion models, continuous versions of the random walk, appeared in the late 1970s (Ratcliff 1978, 1980, 1981). The random walk and diffusion models are close cousins and not competitors of each other as they are with other classes of models (e.g., accumulator models and counter models, e.g., LaBerge 1962, Smith and Vickers 1988). The earliest random walk models assumed that the accumulation of information occurred at discrete points in time, each piece of information either fixed or variable in size (e.g., Laming 1968, Link and Heath 1975). The models succeeded in accounting for mean correct response time and accuracy, and were sometimes successful with mean error response times, but they rarely addressed the shapes of response time distributions. In diffusion models, the accumulation of information is continuous over time. Ratcliff’s (1978) diffusion model is illustrated in Fig. 1. One parameter of the model is the mean rate of accumulation of Figure 1 Illustration of Ratcliff’s diffusion model
4D gravity localized in non Z_2-symmetric thick branes

We present a comparative analysis of localization of 4D gravity on a non Z_2-symmetric scalar thick brane in both a 5-dimensional Riemannian space time and a pure geometric Weyl integrable manifold. This work was mainly motivated by the hypothesis which claims that Weyl geometries mimic quantum behaviour classically. We start by obtaining a classical 4-dimensional Poincare invariant thick brane solution which does not respect Z_2-symmetry along the (non-)compact extra dimension. The scalar energy density of our field configuration represents several series of thick branes with positive and negative energy densities centered at y_0. The only qualitative difference we have encountered when comparing both frames is that the scalar curvature of the Riemannian manifold turns out to be singular for the found solution, whereas its Weylian counterpart presents a regular behaviour. By studying the transverse traceless modes of the fluctuations of the classical backgrounds, we recast their equations into a Schroedinger's equation form with a volcano potential of finite bottom (in both frames). By solving the Schroedinger equation for the massless zero mode m^2=0 we obtain a single bound state which represents a stable 4-dimensional graviton in both frames. We also get a continuum gapless spectrum of KK states with positive m^2>0 that are suppressed at y_0, turning into continuum plane wave modes as"y"approaches spatial infinity. We show that for the considered solution to our setup, the potential is always bounded and cannot adopt the form of a well with infinite walls; thus, we do not get a discrete spectrum of KK states, and we conclude that the claim that Weylian structures mimic, classically, quantum behaviour does not constitute a generic feature of these geometric manifolds.
Polarization Mode Dispersion in High-Speed Optical Communication Systems

Abstract We review the research progress concerning some fundamental issues related to polarization-mode dispersion (PMD) in high-speed fiber-optic transmission systems. We pay particular attention to issues such as the PMD-induced pulse broadening, PMD measurement and emulation, as well as PMD compensation. An electrical equalization technique based on a transversal filter and an optical technique based on a nonlinear chirped fiber Bragg gratings for PMD compensation will be discussed.
A primal-dual semi-smooth N ewton method for nonlinear L 1 data fitting problems

This work is concerned with L 1 data fitting for nonlinear inverse problems. This formulation is advantageous if the data is corrupted by impulsive noise. However, the problem is not differentiable and lacks local uniqueness, which makes its efficient solution challenging. By considering a regularized primal-dual formulation of this problem, local uniqueness can be shown under a second order sufficient condition and a semi-smooth Newton method becomes applicable. In particular, its super-linear convergence is proved for the discretized optimality system. The convergence of the regularized formulation as the regularization and discretization parameters go to zero is shown. Additionally, approximation properties of the minimizers to nonlinear functionals with L 1 data fitting are analyzed and a strategy for selecting the regularization parameter based on a balancing principle is suggested. The efficiency is illustrated through the model problem of recovering the potential in an elliptic boundary value problem from distributed observational data, for which one-and two-dimensional numerical examples are presented.
Marketing Strategy and Wall Street: Nailing down Marketing's Impact

The article provides an overview of research on marketing management and its relationship to a corporation's stock price, the subject of the articles published in this issue. Although the aim of marketing, like any corporate action, is ultimately to increase the company's market value, it has traditionally been thought to be a contribution which takes time, as successful marketing increases revenues through attracting and retaining customers. Areas of research considered in this issue and elsewhere are discussed, including whether relations with investors are governed by the same principles as customer relations, and the tension between marketing techniques such as market segmentation, which assume discrepancies in markets, and finance's efficient market hypothesis.
Key destination attributes of behavioural intention: An application of neural networks

In the tourism literature artificial neural networks (ANNs) are mainly employed in forecasting and market segmentation. Hitherto, no existing research applies ANNs in assessing the effect of destination attributes on customer behavioural intention. Thus, the aim of this study is to identify the main destination attributes, and to evaluate the effects of these attributes, customer satisfaction and perceived value on behavioural intention by using ANNs. Data obtained from 332 foreign tourists visiting Antalya-Turkey were analyzed for exploring the relationships among the research variables. ANN results show that five explanatory variables (shopping, transportation, accommodation, customer satisfaction, perceived value), which are the input nodes, correspond to output node of behavioural intention. Findings indicate the role of value perception and the importance of the basic functional attributes in the formation of behavioural intention, and the strength of ANNs in terms of exploration and prediction of tourist behaviour against regression analysis.
Vasovagal syncope in the older person: differences in presentation between older and younger patients.

BACKGROUND vasovagal syncope (VVS) has been diagnosed with increasing frequency in older people since the description of the head-up tilt table test (HUTT). There is, however, a paucity of research describing the clinical features of VVS in this group. To address this issue, we investigated the age distribution and differences in clinical characteristics associated with age in patients diagnosed with VVS by HUTT at our tertiary referral centre. METHODS 1,060 consecutive patients with tilt-positive VVS were identified from a prospective database containing the demographic and clinical information of individuals assessed in our unit over a 10-year period. VVS was diagnosed with appropriate haemodynamic changes during HUTT and accompanying symptom reproduction. RESULTS we found a bimodal age distribution with a small peak at 20-29 years and a larger peak at 70-79 years. Patients aged > or =60 years were less likely to report total loss of consciousness [odds ratio (OR) 0.50, 95% confidence interval (CI) = 0.38-0.64], near loss of consciousness (OR 0.53, 95% CI = 0.40-0.70) or palpitations (OR 0.45, 95% CI = 0.28-0.72) and more likely to present with unexplained falls (OR 2.33, 95% CI = 1.36-4.32). The typical provoking factors of prolonged standing (OR 0.55, 95% CI = 0.40-0.72), posture change (OR 0.61, 95% CI = 0.46-0.82) and hot environments (OR 0.57, 95% CI = 0.42-0.78) were also less common in older patients. CONCLUSION in our large study population, VVS was more common in older patients. The clinical presentation differed significantly between the two groups. Older patients were less likely to give a typical history and therefore clinicians need to have a high index of suspicion when evaluating the older patient presenting with collapse or unexplained falls.
MUSIC GENRE / MOOD CLASSIFICATION : MIREX 2012

In this submission system, the spectro-temporal features are extracted based on the timbre features such as melfrequency cepstral coefficients. The feature-selection algorithm such as a support vector machine (SVM) ranker is applied for the spectro-temporal features to reduce the computational complexity. An SVM is used as a classifier.
Comm Engine Sync Thread SCSP Queue Comm Thread Request Container Threading Model Sync Container Worker Worker . . . Worker Shepherd Shepherd

We describe a novel runtime system that integrates a task model with RDMA communication and software caching. For evaluating the runtime system, we design two microbenchmarks and implement three applications: Barnes-Hut, Sparse triangular linear solver, and Monte Carlo particle tracking. The resulting codes are simpler, since load balancing across cores and latency hiding are hidden in the runtime. This also results in them performing better than state-of-the-art implementations of the same algorithms by up to 13, 10.8, and 3 times, respectively.
Modeling Students Self-Studies Behaviors

In this work we propose a decision-theoretic approach to Intelligent Tutoring Systems (ITSs) that seeks to alleviate the need for extensive development and hand-tuning in the design of such systems. Given a set of available learning materials, our approach enables the ITS to track the students' difficulties and provide the right material at the right time. We model the learning process as a Partially Observable Markov Decision Process (POMDP), where the hidden information corresponds to the student's familiarity with each of the topics to be learned. The student's progress is monitored from his/her performance in different test exercises and, depending on this performance, the ITS actively determines which type of materials should be provided to the student. We deploy our proposed approach in a learning scenario and compare the ability of our system to model the students' self-study behaviors of the learning materials. Our initial results show that such behaviors are not trivial to model and that our proposed POMDP approach better matched the observed student behaviors in comparison with a baseline teaching policy that corresponds to a fix set of actions hand-designed by a human expert.
Reflective Navigation

In the expanding domain of mobile service robots deployed in natural, everyday environments, this assumption does not hold, since humans (which are the moving obstacles in this context) do perceive the robot and its motion and adapt their own motion accordingly. Therefore, reflective navigation approaches which include reasoning about other agents’ navigational decision processes become increasingly interesting.
Spicy stonehenge: Proposing a SOA case study

Maintenance research in the context of Service Oriented Architecture (SOA) is currently lacking a suitable standard case study that can be used by scientists in order to (1) develop and assess their research ideas and (2) compare and benchmark their solution(s). It is also well established in different fields that having such a standard case study system brings many benefits, in that it helps determine which approaches work best for specific problems. For this reason, we decided to build upon an existing open-source system and make it available for other researchers to use. This system is Spicy Stonehenge.
Wide‐azimuth angle gathers for anisotropic wave‐equation migration

Extended common‐image‐point gathers (CIP) constructed by wide‐azimuth TI wave‐equation migration contain all the necessary information for angle decomposition as a function of the reflection and azimuth angles at selected locations in the subsurface. The aperture and azimuth angles are derived from the extended images using analytic relations between the space‐ and time‐lag extensions using information which is already available at the time of migration, i.e. the anisotropic model parameters. CIPs are cheap to compute because they can be distributed in the image at the most relevant positions, as indicated by the geologic structure. If the reflector dip is known at the CIP locations, then the computational cost can be reduced by evaluating only two components of the space‐lag vector. The transformation from extended images to angle gathers is a planar Radon transform which depends on the local medium parameters. This transformation allows us to separate all illumination directions for a given experiment, or between different experiments. We do not need to decompose the reconstructed wavefields or to choose the most energetic directions for decomposition. Applications of the method include illumination studies in complex areas where ray‐based methods fail, and assuming that the subsurface illumination is sufficiently dense, the study of amplitude variation with aperture and azimuth angles.