Miracle-dz/UniFAR-FTUs huggingface dataset:
Spatial Compressive Sensing for Direction-of-Arrival Estimation With Bias Mitigation Via Expected Likelihood

This work addresses the problem of direction-of-arrival (DOA) estimation using spatial compressive sensing (SCS) with bias mitigation via an expected likelihood (EL) approach. Compressive sensing (CS)-based estimation approaches such as SCS suffer from two main bias sources: a) a grid-bias resulting from the discretization of the azimuth bearing space and b) an inherent-bias which is the result of regularized L1 optimization underpinning sparse signal recovery. This work investigates the SCS bias sources and proposes a novel application of the EL approach to mitigate both SCS bias sources to produce two competitive maximum likelihood (ML) surrogate algorithms for DOA estimation. The DOA estimation performance and practical suitability of the proposed approaches are demonstrated via simulation. Simulations demonstrate that SCS with the EL-based bias mitigation is able to provide improved DOA estimation accuracy without the need for intensive regularization parameter tuning.
The reliability of Big Data

The era of Big Data is underway. Compared to Small Data, machine learning, computerized databases and other modern technologies makes it possible for Big Data to do handle massive data and reveal information in a way that individual bits of data can't. Random sampling's accuracy depends on ensuring randomness when collecting data from samples. Analyzing only a limited number of data points means errors may get amplified, which may reduce the accuracy of the overall results potentially. Meanwhile, Big Data gathers and analyzes massive data to produce some excellent results that we could never know when we are limited to smaller quantities, like address various societal ills, offer potential of new insights into diverse fields. But as the data comes from an environment of uncertainty and rapid change, bigger data may not a better data. Increasing the volume of data may lead to inaccuracy, but in return for relaxing the standards of allowable errors, which produces more valuable information and better results. This article elaborates the reliability of Big Data. Based on our analysis we have constructed a model to analyze the reliability of Big Data.
HD2C interacts with HDA6 and is involved in ABA and salt stress response in Arabidopsis

HD2 proteins are plant specific histone deacetylases. Four HD2 proteins, HD2A, HD2B, HD2C, and HD2D, have been identified in Arabidopsis. It was found that the expression of HD2A, HD2B, HD2C, and HD2D was repressed by ABA and NaCl. To investigate the function of HD2 proteins further, two HD2C T-DNA insertion lines of Arabidopsis, hd2c-1 and hd2c-3 were identified. Compared with wild-type plants, hd2c-1 and hd2c-3 plants displayed increased sensitivity to ABA and NaCl during germination and decreased tolerance to salt stress. These observations support a role of HD2C in the ABA and salt-stress response in Arabidopsis. Moreover, it was demonstrated that HD2C interacted physically with a RPD3-type histone deacetylase, HDA6, and bound to histone H3. The expression of ABA-responsive genes, ABI1 and ABI2, was increased in hda6, hd2c, and hda6/hd2c-1 double mutant plants, which was associated with increased histone H3K9K14 acetylation and decreased histone H3K9 dimethylation. Taken together, our results suggested that HD2C functionally associates with HDA6 and regulates gene expression through histone modifications.
From Rags to Riches: On Constant Proportions Investment Strategies

This paper studies the performance of self-financing constant proportions trading strategies, i.e. dynamic asset allocation strategies that keep a fixed constant proportion of wealth invested in each asset in all periods in time. We prove that any self-financing constant proportions strategy yields a strictly positive exponential rate of growth of investor's wealth in a financial market in which prices are described by stationary stochastic processes and the price ratios are non-degenerate. This result might be regarded as being counterintuitive because any such strategy yields no increase of wealth under constant prices. We further show that the result also holds under small transaction costs, which is important for the viability of this approach, since constant proportions strategies require frequent rebalancing of the portfolio.
Experiences with Shared Virtual Memory on System Area Interconnection Networks : System Simulation , Implementation , and Emulation

Recently there has been a lot of effort in providing costeffective Shared Memory systems by employing software only solutions on clusters of high-end workstations coupled with high–bandwidth, low–latency commodity networks. The related results have generated a lot of interest as well as many questions about how future SVM clusters will look and what are the related issues in building them. In this work we use system simulation to identify system bottlenecks related to architectural features of SVM clusters, actual system implementation to demonstrate how the most important bottlenecks can be alleviated, and finally system emulation to examine all aspects related to building future SVM clusters that may be inadvertently overlooked by simulation studies, e.g., including operating system and data placement effects.
Diffusion Tensor Weighted Harmonic Fields for Feature Classification

This paper presents an efﬁcient method for feature deﬁnition and classiﬁcation on shapes. We tackle this challenge by exploring the weighted harmonic ﬁeld (WHF), which is also the stable state of a heat diffusion regulated by an anisotropic diffusion tensor. The technical merit of our method is highlighted by the elegant integration of locally-deﬁned diffusion tensor and globally-deﬁned harmonic ﬁeld in an anisotropic manner. At the computational front, the partial differential equation of heat diffusion becomes a linear system with Dirichlet boundary condition at heat sources (also called seeds). We develop an algorithm for automatic seed selection, enhanced by a fast update procedure in a high dimensional space. Various experiments are conducted to demonstrate the ease of manipulation and high performance of our method.
Generalizing foraging theory for analysis and design

Foraging theory has been the inspiration for several decision-making algorithms for task-processing agents facing random environments. As nature selects for foraging behaviors that maximize lifetime calorie gain or minimize starvation probability, engineering designs are favored that maximize returned value (e.g. profit) or minimize the probability of not reaching performance targets. Prior foraging-inspired designs are direct applications of classical optimal foraging theory (OFT). Here, we describe a generalized optimization framework that encompasses the classical OFT model, a popular competitor, and several new models introduced here that are better suited for some task-processing applications in engineering. These new models merge features of rate maximization, efficiency maximization, and risk-sensitive foraging while not sacrificing the intuitive character of classical OFT. However, the central contributions of this paper are analytical and graphical methods for designing decision-making algorithms guaranteed to be optimal within the framework. Thus, we provide a general modeling framework for solitary agent behavior, several new and classic examples that apply to it, and generic methods for design and analysis of optimal task-processing behaviors that fit within the framework. Our results extend the key mathematical features of optimal foraging theory to a wide range of other optimization objectives in biological, anthropological, and technological contexts.
Hedgehog signaling regulates prosensory cell properties during the basal-to-apical wave of hair cell differentiation in the mammalian cochlea

Mechanosensory hair cells and supporting cells develop from common precursors located in the prosensory domain of the developing cochlear epithelium. Prosensory cell differentiation into hair cells or supporting cells proceeds from the basal to the apical region of the cochleae, but the mechanism and significance of this basal-to-apical wave of differentiation remain to be elucidated. Here, we investigated the role of Hedgehog (Hh) signaling in cochlear development by examining the effects of up- and downregulation of Hh signaling in vivo. The Hh effector smoothened (Smo) was genetically activated or inactivated specifically in the developing cochlear epithelium after prosensory domain formation. Cochleae expressing a constitutively active allele of Smo showed only one row of inner hair cells with no outer hair cells (OHCs); abnormal undifferentiated prosensory-like cells were present in the lateral compartment instead of OHCs and their adjacent supporting cells. This suggests that Hh signaling inhibits prosensory cell differentiation into hair cells or supporting cells and maintains their properties as prosensory cells. Conversely, in cochlea with the Smo conditional knockout (Smo CKO), hair cell differentiation was preferentially accelerated in the apical region. Smo CKO mice survived after birth, and exhibited hair cell disarrangement in the apical region, a decrease in hair cell number, and hearing impairment. These results indicate that Hh signaling delays hair cell and supporting cell differentiation in the apical region, which forms the basal-to-apical wave of development, and is required for the proper differentiation, arrangement and survival of hair cells and for hearing ability.
SN and CR for free-style LKtq: linear decorations and simulation of normalization

Abstract The present report is a, somewhat lengthy, addendum to [4], where the elimination of cuts from derivations in sequent calculus for classical logic was studied ‘from the point of view of linear logic’. To that purpose a formulation of classical logic was used, that - as in linear logic - distinguishes between multiplicative and additive versions of the binary connectives. The main novelty here is the observation that this type-distinction is not essential: we can allow classical sequent derivations to use any combination of additive and multiplicative introduction rules for each of the connectives, and still have strong normalization and confluence of tq-reductions. We give a detailed description of the simulation of tq-reductions by means of reductions of the interpretation of any given classical proof as a proof net of PN2 (the system of second order proof nets for linear logic), in which moreover all connectives can be taken to be of one type, e.g., multiplicative. We finally observe that dynamically the different logical cuts, as determined by the four possible combinations of introduction rules, are independent: it is not possible to simulate them internally, i.e. by only one specific combination, and structural rules.
CAP 6415 – Computer Vision – Project Report Version 2 Face Detection Using Skin Color and Haar-like features – Implementation and Evaluation

From a research point of view, well-established problems need standard databases, evaluation protocols and scoring methods available. Evaluating algorithms lets researchers know the strengths and weaknesses of a particular approach and identifies aspects of a problem where further research is needed. In this report, two face detection algorithms (one based on Haar-like features and the other based on skin color modeling) have been implemented. They have been empirically evaluated and the strengths and weaknesses of each method are identified. Also, with the changing values of the measures a global setting of parameter for the skin-color based face detector, which is optimal for most of the images, is identified. Since the performance has been quantified, a conclusion as to which algorithm outperforms the other is made. Experimental results of both the face detection algorithms and empirical evaluation have been produced.
SIDekICk: SuspIcious DomaIn Classification in the .nl zone

The Domain Name System (DNS) plays a central role in the Internet. It allows the translation of human-readable domain names to (alpha-) numeric IP addresses in a fast and reliable manner. However, domain names not only allow Internet users to access benign services on the Internet but are used by hackers and other criminals as well, for example to host phishing campaigns, to distribute malware, and to coordinate botnets. Registry operators, which are managing top-level domains (TLD) like . com, . net or . nl, disapprove theses kinds of usage of their domain names because they could harm the reputation of their zone and would consequentially lead to loss of income and an insecure Internet as a whole. Up to today, only little research has been conducted with the intention to fight malicious domains from the view of a TLD registry. This master thesis focuses on the detection of malicious domain names for the . nl country code TLD. Therefore, we analyse the characteristics of known malicious . nl domains which have been used for phishing and by botnets. We confirm findings from previous research in . com and . net and evaluate novel characteristics including query patterns for domains in quarantine and recursive resolver relations. Based on this analysis, we have developed a prototype of a detection system called SIDekICk. It is able to detect newly registered phishing domains and other online scams as soon as they propagate through the Internet with a false positive rate of 0,3 percent. It relies solely on features that can be collected from the vantage point of any TLD registry like DNS query patterns, geographic features of querying resolvers, and domain registration information. A second component of SIDekICk reports suspicious domain names that were formerly used for benign purposes but might have been compromised to become part of a malware infection chain or a phishing campaign. This component demonstrates that DNS traffic analysis has the potential to detect compromised domains as well and in this thesis, we suggest additional features to improve the detection rate.
Stochastic Steady-State Replenishable Resource Management Policies

The purpose of this paper is to investigate the generalization of deterministic golden rule results to the case of resource uncertainty. It is shown that within a diffusion framework, the optimal steady-state management policy is equivalent to a management policy that is optimal outside the stochastic steady state. The results contained in this paper provide a useful link between steady-state optimal management policies in the deterministic resource economics and biology literatures and the quickly developing stochastic resource literature.
Functional Alterations in Brain Activation and Deactivation in Mild Cognitive Impairment in Response to a Graded Working Memory Challenge

Aim: To investigate dynamic changes in functional brain activity in mild cognitive impairment (MCI) in response to a graded working memory (WM) challenge with increasing memory load. Methods: In an event-related functional magnetic resonance imaging (fMRI) study, 35 MCI and 22 cognitively normal subjects performed a visuospatial associative WM task with 3 load levels. Potential performance differences were controlled for by individually calibrating the number of items presented at each load. Results: An interaction between group and WM load was observed during stimulus encoding. At lower loads, greater activity in the right anterior cingulate and right precuneus was observed in MCI subjects. As the load increased to higher levels, reduced activation in these regions and greater deactivation in the posterior cingulate-medial precuneus were observed in MCI compared to control subjects. Stronger expression of load-related patterns of activation and deactivation in MCI subjects was associated with greater clinical severity and a more abnormal pattern of performance variability. Conclusion: Patterns of overactivation, underactivation and deactivation during successful encoding in MCI subjects were dependent on WM load. This type of graded cognitive challenge may operate like a ‘memory stress test’ in MCI and may be a useful biomarker of disease at the predementia stage.
Axisymmetric black hole accretion in the Kerr metric as an autonomous dynamical system

In a stationary, general relativistic, axisymmetric, inviscid and rotational accretion flow, described within the Kerr geometric framework, transonicity has been examined by setting up the governing equations of the flow as a first-order autonomous dynamical system. The consequent linearised analysis of the critical points of the flow leads to a comprehensive mathematical prescription for classifying these points, showing that the only possibilities are saddle points and centre-type points for all ranges of values of the fixed flow parameters. The spin parameter of the black hole influences the multitransonic character of the flow, as well as some of its specific critical properties. The special case of a flow in the space-time of a non-rotating black hole, characterised by the Schwarzschild metric, has also been studied for comparison and the conclusions are compatible with what has been seen for the Kerr geometric case.
How long before the end of inflation were observable perturbations produced

We reconsider the issue of the number of e-foldings before the end of inflation at which observable perturbations were generated. We determine a plausible upper limit on that number for the standard cosmology which is around 60, with the expectation that the actual value will be up to 10 below this. We also note a special property of the $\ensuremath{\lambda}{\ensuremath{\varphi}}^{4}$ model which reduces the uncertainties in that case and favors a higher value, giving a fairly definite prediction of 64 e-foldings for that model. We note an extreme (and highly implausible) situation where the number of e-foldings can be even higher, possibly up to 100, and discuss the shortcomings of quantifying inflation by e-foldings rather than by the change in $\mathrm{aH}. $ Finally, we discuss the impact of nonstandard evolution between the end of inflation and the present, showing that again the expected number of e-foldings can be modified, and in some cases significantly increased.
A Class of Nonparametric Volatility Models : Applications to Financial Time Series

In this paper, we first examine several volatility models in the literature. We then estimate financial volatility using multivariate adaptive regression splines (MARS) by logarithmic transformation as a preliminary analysis to examine a nonparametric volatility model. Despite its popularity, MARS has never been applied to model financial volatility. To implement the MARS methodology in a time series setting, we let the predictor variables to be lagged values which results in a model referred to as adaptive spline threshold autoregression (ASTAR). The estimation is illustrated through simulations and empirical examples. We compare the performance of the MARS volatility model with the existing parametric and nonparametric models in the literature by using several out-of-sample goodness-of-fit measures.
On the diversity of physicochemical environments experienced by identical ligands in binding pockets of unrelated proteins

Most function prediction methods that identify cognate ligands from binding site analyses work on the assumption of molecular complementarity. These approaches build on the conjectured complementarity of geometrical and physicochemical properties between ligands and binding sites so that similar binding sites will bind similar ligands. We found that this assumption does not generally hold for protein–ligand interactions and observed that it is not the chemical composition of ligand molecules that dictates the complementarity between protein and ligand molecules, but that the ligand's share within the functional mechanism of a protein determines the degree of complementarity. Here, we present for a set of cognate ligands a descriptive analysis and comparison of the physicochemical properties that each ligand experiences in various nonhomologous binding pockets. The comparisons in each ligand set reveal large variations in their experienced physicochemical properties, suggesting that the same ligand can bind to distinct physicochemical environments. In some protein ligand complexes, the variation was found to correlate with the electrochemical characteristic of ligand molecules, whereas in others it was disclosed as a prerequisite for the biochemical function of the protein. To achieve binding, proteins were observed to engage in subtle balancing acts between electrostatic and hydrophobic interactions to generate stabilizing free energies of binding. For the presented analysis, a new method for scoring hydrophobicity from molecular environments was developed showing high correlations with experimental determined desolvation energies. The presented results highlight the complexities of molecular recognition and underline the challenges of computational structural biology in developing methods to detect these important subtleties. Proteins 2010. © 2009 Wiley‐Liss, Inc.
Runge-Kutta Discontinuous Galerkin method for the Boltzmann equation

In this thesis we investigate the ability of the Runge-Kutta Discontinuous Galerkin (RKDG) method to provide accurate and efficient solutions of the Boltzmann equation. Solutions of the Boltzmann equation are desirable in connection to small scale science and technology because when characteristic flow length scales become of the order of, or smaller than, the molecular mean free path, the Navier-Stokes description fails. The prevalent Boltzmann solution method is a stochastic particle simulation scheme known as Direct Simulation Monte Carlo (DSMC). Unfortunately, DSMC is not very effective in low speed flows (typical of small scale devices of interest) because of the high statistical uncertainty associated with the statistical sampling of macroscopic quantities employed by this method. This work complements the recent development of an efficient low noise method for calculating the collision integral of the Boltzmann equation, by providing a highorder discretization method for the advection operator balancing the collision integral in the Boltzmann equation. One of the most attractive features of the RKDG method is its ability to combine high-order accuracy, both in physical space and time, with the ability to capture discontinuous solutions. The validity of this claim is thoroughly investigated in this thesis. It is shown that, for a model collisionless Boltzmann equation, high-order accuracy can be achieved for continuous solutions; whereas for discontinuous solutions, the RKDG method, with or without the application of a slope limiter such as a viscosity limiter, displays high-order accuracy away from the vicinity of the discontinuity. Given these results, we developed a RKDG solution method for the Boltzmann equation by formulating the collision integral as a source term in the advection equation. Solutions of the Boltzmann equation, in the form of mean velocity and shear stress, are obtained for a number of characteristic flow length scales and compared to DSMC solutions. With a small number of elements and a low order of approximation in physical space, the RKDG method achieves similar results to the DSMC method. When the characteristic flow length scale is small compared to the mean free path (i.e. when the effect of collisions is small), oscillations are present in the mean velocity and shear stress profiles when a coarse velocity space discretization is used. With a finer velocity space discretization, the oscillations are reduced, but the method becomes approximately five times more computationally expensive. We show that these oscillations (due to the presence of propagating discontinuities in the distribution function) can be removed using a viscosity limiter at significantly smaller computational cost. Thesis Supervisor: Nicolas G. Hadjiconstantinou Title: Associate Professor