Miracle-dz/UniFAR-FTUs huggingface dataset:
Inference for stochastic volatility model using time change transformations

We address the problem of parameter estimation for diffusion driven stochastic volatility models through Markov chain Monte Carlo (MCMC). To avoid degeneracy issues we introduce an innovative reparametrisation defined through transformations that operate on the time scale of the diffusion. A novel MCMC scheme which overcomes the inherent difficulties of time change transformations is also presented. The algorithm is fast to implement and applies to models with stochastic volatility. The methodology is tested through simulation based experiments and illustrated on data consisting of US treasury bill rates.
Knowledge Representation and Learning Mechanism Based on Networks of Spiking Neurons

Knowledge representation is very important in intelligent systems -e.g. for knowledge discovery, data mining, and machine learning. The human brain, a significant intelligent system, works with a huge number of spiking neurons. Based on spiking neuron models a new generation of spiking neural networks (SNNs) has been developed for artificial intelligence systems. SNNs are computationally more powerful than conventional artificial neural networks. In this paper, the spiking neuron model is applied to represent logic rules and fuzzy rules. Based on the STDP (Spike Timing Dependent Plasticity) principle, a new SNN model is proposed for pattern recognition. An efficient learning rule derived from the STDP is applied for self-organizing the input training set efficiently. An example, Animal-Growth-Record, is used to explain the principle of the SNN model. Benchmark data sets are applied to compare the proposed approach with other approaches. As there are very efficient learning rules in the SNN model, the model can be applied not only for fusion of multi-sensory data, but also for data mining in large databases with large numbers of attributes.
LACK OF CALBINDIN-D28K ALTERS RESPONSE OF THE MURINE CIRCADIAN CLOCK TO LIGHT

A strong stimulus adjusting the circadian clock to the prevailing light-dark cycle is light. However, the circadian clock is reset by light only at specific times of the day. The mechanisms mediating such gating of light input to the CNS are not well understood. There is evidence that Ca2+ ions play an important role in intracellular signaling mechanisms, including signaling cascades stimulated by light. Therefore, Ca2+ is hypothesized to play a role in the light-mediated resetting of the circadian clock. Calbindin-D28k (CB; gene symbol: Calb1) is a Ca2+ binding protein implicated in Ca2+ homeostasis and sensing. The absence of this protein influences Ca2+ buffering capacity of a cell, alters spatio-temporal aspects of intracellular Ca2+ signaling, and hence might alter transmission of light information to the circadian clock in neurons of the suprachiasmatic nuclei (SCN). We tested mice lacking a functional Calb1 gene (Calb1−/−) and found an increased phase-delay response to light applied at circadian time (CT) 14 in these animals. This is accompanied by elevated induction of Per2 gene expression in the SCN. Period length and circadian rhythmicity were comparable between Calb1−/− and wild-type animals. Our findings indicate an involvement of CB in the signaling pathway that modulates the behavioral and molecular response to light. (Author correspondence: urs.albrecht@unifr.ch)
A target recognition matching method with high reliability

Grayness correlation is a method with high reliability for target recognition. However, there is a lack of reliable guidelines for matching results. Basing on normalized correlation, this thesis uses the SURF (Speed up Robust Features) algorithm of local features of an image to verify matching results. By fully utilizing the global and local information of the image, this method ensures the reliability of the matching results. Emulation tests how that this method is hardly interfered by outside factors and can greatly save time by using parallel calculations. With a high accuracy of matching results, it is applicable for targets recognition.
Probing a composite spin-boson environment

We consider non-interacting multi-qubit systems as controllable probes of an environment of defects/impurities modelled as a composite spin-boson environment. The spin-boson environment consists of a small number of quantum-coherent two-level fluctuators (TLFs) damped by independent bosonic baths. A master equation of the Lindblad form is derived for the probe-plus-TLF system. We discuss how correlation measurements in the probe system encode information about the environment structure and could be exploited to efficiently discriminate between different experimental preparation techniques, with particular focus on the quantum correlations (entanglement) that build up in the probe as a result of the TLF-mediated interaction. We also investigate the harmful effects of the composite spin-boson environment on initially prepared entangled bipartite qubit states of the probe and on entangling gate operations. Our results offer insights in the area of quantum computation using superconducting devices, where defects/impurities are believed to be a major source of decoherence.
Impact of Tumor Blood Flow Modulation on Tumor Sensitivity to the Bioreductive Drug Banoxantrone

We investigated the hypoxia-dependent cytotoxicity of AQ4N (banoxantrone) using a panel of 13 cancer cell lines and studied its relationship to the expression of the quinone reductase DT-diaphorase (NQO1), which is widely found in cancer cells. We also investigated pharmacologic treatments that increase tumor hypoxia in vivo and their impact on AQ4N chemosensitivity in a solid tumor xenograft model. AQ4N showed ≥8-fold higher cytotoxicity under hypoxia than normoxia in cultures of 9L rat gliosarcoma and H460 human non-small-cell lung carcinoma cells but not for 11 other human cancer cell lines. DT-diaphorase protein levels and AQ4N chemosensitivity were poorly correlated across the cancer cell line panel, and AQ4N chemosensitivity was not affected by DT-diaphorase inhibitors. The vasodilator hydralazine decreased tumor perfusion and increased tumor hypoxia in 9L tumor xenografts, and to a lesser extent in H460 tumor xenografts. However, hydralazine did not increase AQ4N-dependent antitumor activity. Combination of AQ4N with the angiogenesis inhibitor axitinib, which increases 9L tumor hypoxia, transiently increased antitumor activity but with an increase in host toxicity. These findings indicate that the capacity to bioactivate AQ4N is not dependent on DT-diaphorase and is not widespread in cultured cancer cell lines. Moreover, the activation of AQ4N cytotoxicity in vivo requires tumor hypoxia that is more extensive or prolonged than can readily be achieved by vasodilation or by antiangiogenic drug treatment.
Inflation-Output Gap Trade-Off With a Dominant Oil Supplier

An exogenous oil price shock raises inflation and contracts output, similar to a negative productivity shock. In the standard New Keynesian model, however, this does not generate any trade-off between inflation and output gap volatility: under a strict inflation-targeting policy, the output decline is exactly equal to the efficient output contraction in response to the shock. Modeling the oil sector from optimizing first principles rather than assuming an exogenous oil price, we show that the presence of a dominant oil supplier (OPEC) leads to inefficient fluctuations in the oil price markup. The latter reflects a dynamic distortion of the production process, and as a result, stabilizing inflation does not automatically stabilize the distance of output from first-best. Our model is a step away from discussing the effects of exogenous oil price changes and toward analyzing the implications of the underlying shocks that cause the oil price to change in the first place.
Quantitative FRET Imaging to Visualize the Invasiveness of Live Breast Cancer Cells

Matrix metalloproteinases (MMPs) remodel tumor microenvironment and promote cancer metastasis. Among the MMP family proteases, the proteolytic activity of the pro-tumorigenic and pro-metastatic membrane-type 1 (MT1)-MMP constitutes a promising and targetable biomarker of aggressive cancer tumors. In this study, we systematically developed and characterized several highly sensitive and specific biosensors based on fluorescence resonant energy transfer (FRET), for visualizing MT1-MMP activity in live cells. The sensitivity of the AHLR-MT1-MMP biosensor was the highest and five times that of a reported version. Hence, the AHLR biosensor was employed to quantitatively profile the MT1-MMP activity in multiple breast cancer cell lines, and to visualize the spatiotemporal MT1-MMP activity simultaneously with the underlying collagen matrix at the single cell level. We detected a significantly higher level of MT1-MMP activity in invasive cancer cells than those in benign or non-invasive cells. Our results further show that the high MT1-MMP activity was stimulated by the adhesion of invasive cancer cells onto the extracellular matrix, which is precisely correlated with the cell’s ability to degrade the collagen matrix. Thus, we systematically optimized a FRET-based biosensor, which provides a powerful tool to detect the pro-invasive MT1-MMP activity at single cell levels. This readout can be applied to profile the invasiveness of single cells from clinical samples, and to serve as an indicator for screening anti-cancer inhibitors.
Improvement of Adaptive Cruise Control Performance

This paper describes the Adaptive Cruise Control system (ACC), a system which reduces the driving burden on the driver. The ACC system primarily supports four driving modes on the road and controls the acceleration and deceleration of the vehicle in order to maintain a set speed or to avoid a crash. This paper proposes more accurate methods of detecting the preceding vehicle by radar while cornering, with consideration for the vehicle sideslip angle, and also of controlling the distance between vehicles. By making full use of the proposed identification logic for preceding vehicles and path estimation logic, an improvement in driving stability was achieved.
Theory and Flight-Test Validation of a Concurrent-Learning Adaptive Controller

Theory and results of flight-test validation are presented for a novel adaptive law that concurrently uses current as well as recorded data for improving the performance of model reference adaptive control architectures. This novel adaptive law is termed concurrent learuing. This adaptive law restricts the weight updates based on stored data to the null-space of the weight updates based on current data for ensuring that learning on stored data does not affect responsiveness to current data. This adaptive law alleviates the rank-1 condition on weight updates in adaptive control, thereby improving weight convergence properties and improving tracking performance. Lyapunov-like analysis is used to show that the new adaptive law guarantees uniform ultimate boundedness of all system signals in the framework of model reference adaptive control. Flight-test results confirm expected improvements in performance.
Fast spatially variant deconvolution for optical microscopy via iterative shrinkage thresholding

Deconvolution offers an effective way to improve the resolution of optical microscopy data. While fast algorithms are available when the point spread function (PSF) is shift-invariant (SI), they are not directly applicable in thick samples, where the problem is shift-variant (SV). Here, we propose a fast iterative shrinkage/thresholding 3D deconvolution method that uses different PSFs at every depth. This is realized by modeling the imaging system as a multi-rate filter-bank, with each channel corresponding to a distinct 3D PSF dependent on the position along the optical axis. The complexity associated with the thresholded Landweber update in each iteration of our SV algorithm is equivalent to that of an iteration in an SI algorithm, multiplied by the number of channels in the filter-bank. We simulated images of a set of beads embedded in an aqueous gel, using varying PSFs along the optical axis, to illustrate the effectiveness of our algorithm.
Automated web application testing using search based software engineering

This paper introduces three related algorithms and a tool, SWAT, for automated web application testing using Search Based Software Testing (SBST). The algorithms significantly enhance the efficiency and effectiveness of traditional search based techniques exploiting both static and dynamic analysis. The combined approach yields a 54% increase in branch coverage and a 30% reduction in test effort. Each improvement is separately evaluated in an empirical study on 6 real world web applications.
Robust Wi-Fi based indoor positioning with ensemble learning

This paper proposes a new Wi-Fi based indoor positioning method that is robust over unstable Wi-Fi access points (APs). Because Wi-Fi based indoor positioning relies on unstable and uncontrollable infrastructure (Wi-Fi APs), the positioning performance significantly decreases when such unstable APs are included in the localization system. This paper proposes a indoor positioning method by employing ensemble of weak position estimators, which permits us to construct a robust positioning model. Our proposed boosted position estimator has the following features. 1) The estimator does not overfit the training data and thus it is robust over unstable signals from APs. 2) Because each weak estimator employs a small number of APs for positioning, the estimator is not affected by the curse of dimensionality. 3) Our model can adaptively change the weight (importance) of each weak estimator according to a user's position in order to achieve a position-aware precise localization.
Learning to Detect Pitch Accents Using a Naïve Bayes Classifier

Labeling of pitch accents is the first step in most prosodic analyses. Speeding this process up through automation can make both prosodic research and development of speech technology much more efficient. We built a Gaussian Naive Bayes Classifier to classify words as accented or non-accented based on their F0 range, RMS energy, and duration. We trained and tested the system on a subset of the Boston University Radio News Corpus, a corpus of high-quality recordings with word-aligned transcriptions and ToBI labels. Our system accurately predicted an average of 71.3% of pitch accents in our test corpus, compared to the average of 54.4% it would correctly predict by always guessing the most common classification.
Design and Implementation of an Efficient Image Retrieval System using Spatial Feature Reduction

Image segmentation is a very emerging and important area due to a large number of real life applications. Bag-of-features (BoFs) model is the one of the most successful algorithm used for the image classification. BoF methods are based on order-less collections of quantized local image descriptors; they discard spatial information and are therefore conceptually and computationally simpler than many alternative methods.Bag-of-features algorithm is used for the feature extraction from the image. BoFs model has several advantages like,scalability,simplicity, and generality. But BoFs have some disadvantages too and some of them are Time and Accuracy of the image classification process. In this paper Bag-of-features model is extended by using spatial pooling to improve the time and accuracy of the image classification model. In proposed method first the system is trained by creating the database of the image feature for the evaluation process and then the evolution of the features is done for the input image by using clustering algorithm. In the proposed method KKN algorithm is used to find out the similar features from the database. Spatial pooling is then used to improve the performance of the existing system.
On Quality Assessment of Early Childhood Programs in the International Context and Reconstructing the Related Conceptions in China

This article firsdy summarizes and sorts through the related important research literatures in the international context in recent decades from the 1970s.Then,based on the reflective critique on the differences concerning quality assessment between China and the West,the authors propose that the conceptions on the quality assessment of early childhood programs in China should be reconstructed.And some related problems are discussed in the end of the article.
Linear precoder designs for K-user interference channels

This paper studies linear precoding and decoding schemes for K-user interference channel systems. It was shown by Cadambe and Jafar that the interference alignment (IA) algorithm achieves a theoretical bound on degrees of freedom (DOF) for interference channel systems. Based on this, we first introduce a non-iterative solution for the precoding and decoding scheme. To this end, we determine the orthonormal basis vectors of each user's precoding matrix to achieve the maximum DOF, then we optimize precoding matrices in the IA method according to two different decoding schemes with respect to individual rate. Second, an iterative processing algorithm is proposed which maximizes the weighted sum rate. Deriving the gradient of the weighted sum rate and applying the gradient descent method, the proposed scheme identifies a local-optimal solution iteratively. Simulation results show that the proposed iterative algorithm outperforms other existing methods in terms of sum rate. Also, we exhibit that the proposed non-iterative method approaches a local optimal solution at high signal-to-noise ratio with reduced complexity.
Finding group interactions in social gathering videos

In this paper, we propose a framework for recognizing the group dynamics embedded in social gathering videos. We aim to identify interacting sub-groups present in a scene. In particular we are interested in identifying the participants converging to form a group, the participants dispersing from a group and the ones in static groups. We use the linear cyclic pursuit (LCP) based framework to model the collective motion. The proposed algorithm employs trajectories of individuals over a period of time to estimate the model parameters. We show that the details of group dynamics are hidden in the eigenvalues and eigenvectors of the pursuit matrix. The experiments are done on the simulated data as well as on the real gathering videos. The results show a promising scope of the proposed approach.
Simulation of Physical Semiconductor Devices under Large and Small Signal Conditions ( Invited Paper )

In this paper we demonstrate the use of a physics-based device simulation approach for RF applications. We describe the modeling frame for physical semiconductor devices given by PDE-based transport model equations and illustrate the extraction of relevant technical parameters. I. INTRODUCTION Modern electronic systems require various levels of modeling depending on the design tasks. In the RF area, most simulations are performed on the level of compact modeling of nonlinear circuits, both by time-and, especially, frequency-domain analysis methods. For small building blocks consisting of only one or two nonlinear devices, physics-based simulation offers several advantages and possibly new insights into the device. The major advantage of such a physics-based approach is that only one model is used which is valid for the whole range of operating conditions, is independent of the applied frequencies, covers both small and large signal excitations, includes technology and material parameters, and abandons the quasi-static assumption. On the other side, this approach requires much more computer resources than compact model-ing, and hence, becomes feasible only for networks where the optimization of one or two kernel devices improves the overall performance.
AC: An Integrated Source Code Plagiarism Detection Environment

Plagiarism detection in educational programming assignments is still a problematic issue in terms of resource waste, ethical controversy, legal risks, and technical complexity. This paper presents AC, a modular plagiarism detection system. The design is portable across platforms and assignment formats and provides easy extraction into the internal assignment representation. Multiple similarity measures have been incorporated, both existing and newly-developed. Statistical analysis and several graphical visualizations aid in the interpretation of analysis results. The system has been evaluated with a survey that encompasses several academic semesters of use at the authors' institution.
A CASE STUDY OF BRAIN VOLUME REDUCTION IN MULTIPLE SCLEROSIS

The development of sophisticated magnetic resonance imaging techniques and software for medical imaging processing and analysis has led to a significant progress in multiple sclerosis research and clinical care. The measurement of brain volumes provides a quantitative representation of damage, thus facilitating the objective follow-up process. The parameters obtained, though not being used routinely in clinical practice, are more and more often applied in clinical studies. The amount of whole brain and regional atrophy, estimated from serial scans, is considered important not only for disease progression, but also for cognitive dysfunction which is common in multiple sclerosis. In this paper we describe a volumetric study of two magnetic resonance scans of a patient with relapsingremitting multiple sclerosis, performed 16 months one after the other, and analyzed using FSL SIENA software. Analysis demonstrated brain volume reduction of 1.7% between the two scans. We discuss the advantages of the method and its possible clinical applications.
Flux phase of the half-filled band.

The conjecture is verified that the optimum, energy minimizing, magnetic flux for a half-filled band of electrons hopping on a planar, bipartite graph is π per square plaquette. We require only that the graph has periodicity in one direction and the result includes the hexagonal lattice (with flux 0 per hexagon) as a special case. The theorem goes beyond previous conjectures in several ways: (1) It does not assume, a priori, that all plaquettes have the same flux (as in Hofstadter’s model). (2) A Hubbard-type on-site interaction of any sign, as well as certain longer range interactions, can be included. (3) The conclusion holds for positive temperature as well as the ground state. (4) The results hold in D ≥ 2 dimensions if there is periodicity in D — 1 directions (e.g., the cubic lattice has the lowest energy if there is flux π in each square face).
Empirically Evaluating Multiagent Learning Algorithms

There exist many algorithms for learning how to play repeated bimatrix games. Most of these algorithms are justified in terms of some sort of theoretical guarantee. On t he other hand, little is known about the empirical performance of these algorithms. Most such claims in the literature are been based on small experiments, which has hampered understanding as well as the development of new multiagent learning (MAL) algorithms. We have developed a new suite of tools for running multiagent experiments: the MultiAgent Learning Testbed (MALT). These tools are designed to facilitate larger and more comprehensive experiments by removing the need to build one-off experimental code. MALT also provides baseline implementations of many MAL algorithms, hopefully eliminating or reducing differences between algorithm implementations and increasing the reproducibility of results. Using this test suite, we ran an experiment unprecedented in size. We analyzed the results according to a variety of performance metrics including reward, maxmin distance, regret, and several notions of equilibrium conve rgence. We confirmed several pieces of conventional wisdom, but also discovered some surprising results. For example, we found that single-agent Q-learning outperformed many more complicated and more modern MAL algorithms.