Miracle-dz/UniFAR-FTUs huggingface dataset:
A new insight into land use classification based on aggregated mobile phone data

Land-use classification is essential for urban planning. Urban land-use types can be differentiated either by their physical characteristics (such as reflectivity and texture) or social functions. Remote sensing techniques have been recognized as a vital method for urban land-use classification because of their ability to capture the physical characteristics of land use. Although significant progress has been achieved in remote sensing methods designed for urban land-use classification, most techniques focus on physical characteristics, whereas knowledge of social functions is not adequately used. Owing to the wide usage of mobile phones, the activities of residents, which can be retrieved from the mobile phone data, can be determined in order to indicate the social function of land use. This could bring about the opportunity to derive land-use information from mobile phone data. To verify the application of this new data source to urban land-use classification, we first construct a vector of aggregated mobile phone data to characterize land-use types. This vector is composed of two aspects: the normalized hourly call volume and the total call volume. A semi-supervised fuzzy c-means clustering approach is then applied to infer the land-use types. The method is validated using mobile phone data collected in Singapore. Land use is determined with a detection rate of 58.03%. An analysis of the land-use classification results shows that the detection rate decreases as the heterogeneity of land use increases, and increases as the density of cell phone towers increases.
Semantic similarity measure based on multiple ressources

The ability to accurately judge the semantic similarity between words is critical to the performance of several applications such as Information Retrieval and Natural Language Processing. Therefore, in this paper we propose a semantic similarity measure that uses in one hand, an online English dictionary provided by the Semantic Atlas project of the French National Centre for Scientific Research (CNRS) and on the other hand, a page counts based metric returned by a social website whose content is generated by users.
An evaluation of dating violence risk factors for Division III student-athletes

v List of Tables . vii Chapter 1: Introduction . 1 Chapter 2: Methodology . 8 Participants . 8 Procedure . 9 Measures . 9 Sociodemographics . 9 Revised Conflict Tactics Scale . 10 Ambivalent Sexism Inventory . 11 Alcohol Use Disorders Identification Test . 12 Chapter 3: Results . 13 Analysis Strategy . 13 Frequency of DV Perpetration and Victimization . 13 Gender Differences in Risk Factors . 17 Bivariate Correlations Among Primary Variables . 18 Generalized Linear Models . 19 Factors Associated with Perpetration.20 Factors Associated with Victimization . 20 Chapter 4: Discussion . 22 References . 28
An Insight into the Architecture of Condor - A Distributed Scheduler

This article gives a detailed description of the Condor system—a distributed scheduler. A typical Condor system consists of four components: A Condor pool, the central manager, Submitting machine and Execution machine. The ClassAd is a flexible representation of the characteristics and constraints of both machines and jobs in the Condor system. Matchmaking is the mechanism by which Condor matches an idle job with an available machine. Condor plays the role of matchmaker by continuously reading all job ClassAds and all machine ClassAds, matching and ranking job ads with machine ads. Condor ensures that the requirements in both ClassAds are satisfied. The architecture of Condor depends on the interaction of the following Condor daemons: condor_master, condor_collector, condor_negotiator, condor_schedd, condor_shadow, condor_startd, and condor_starter. By integrating the basic Condor commands into a GUI-based program, Condor becomes more user-friendly and more convenient to use. Keywords-scheduler; Condor; Condor daemons; Condor Architecture; GUI-based Condor
Identification, isolation, and overexpression of the gene encoding the psi subunit of DNA polymerase III holoenzyme

The gene encoding the psi subunit of DNA polymerase III holoenzyme, holD, was identified and isolated by an approach in which peptide sequence data were used to obtain a DNA hybridization probe. The gene, which maps to 99.3 centisomes, was sequenced and found to be identical to a previously uncharacterized open reading frame that overlaps the 5' end of rimI by 29 bases, contains 411 bp, and is predicted to encode a protein of 15,174 Da. When expressed in a plasmid that also expressed holC, holD directed expression of the psi subunit to about 3% of total soluble protein.
Compression-based similarity in EEG signals

The electrical activity of brain or EEG signal is very complex data system that may be used to many different applications such as device control using mind. It is not easy to understand and detect useful signals in continuous EEG data stream. In this paper, we are describing an application of data compression which is able to recognize important patterns in this data. The proposed algorithm uses Lampel-Ziv complexity for complexity measurement and it is able to successfully detect patterns in EEG signal.
A Novel Gain Invariant Quantization-Based Watermarking Approach

This paper presents a novel quantization-based information hiding approach, which is invariant to gain attack. In the presented scheme, the host signal vector is first divided into two parts, then lp-norm of each vector is calculated. The watermark bits are embedded by quantizing the ratio of the lp-norm of each part. The decoding scheme is performed blindly using the Euclidean distance. Due to the use of division function, the proposed method is robust to scaling attacks. The analytical error probability and embedding distortion are derived and assessed by simulations on artificial signals. The optimum parameter in the lp-norm function is obtained based on minimizing the error probability. Experimental results confirm the superiority of the proposed technique against common attacks in comparison with the existing state-of-the-art methods.
MLLR adaptation for hidden semi-Markov model based speech synthesis

This paper describes an extension of maximum likelihood linear regression (MLLR) to hidden semi-Markov model (HSMM) and presents an adaptation technique of phoneme/state duration for an HMM-based speech synthesis system using HSMMs. The HSMM-based MLLR technique can realize the simultaneous adaptation of output distributions and state duration distributions. We focus on describing mathematical aspect of the technique and derive an algorithm of MLLR adaptation for HSMMs.
Overexpression of MicroRNA miR-30a or miR-191 in A549 Lung Cancer or BEAS-2B Normal Lung Cell Lines Does Not Alter Phenotype

Background MicroRNAs (miRNAs) are small, noncoding RNAs (ribonucleic acids) that regulate translation. Several miRNAs have been shown to be altered in whole cancer tissue compared to normal tissue when quantified by microarray. Based on previous such evidence of differential expression, we chose to study the functional significance of miRNAs miR-30a and -191 alterations in human lung cancer. Methodology/Principal Findings The functional significance of miRNAs miR-30a and -191 was studied by creating stable transfectants of the lung adenocarcinoma cell line A549 and the immortalized bronchial epithelial cell line BEAS-2B with modest overexpression of miR-30a or -191 using a lentiviral system. When compared to the corresponding controls, both cell lines overexpressing miR-30a or -191 do not demonstrate any significant changes in cell cycle distribution, cell proliferation, adherent colony formation, soft agar colony formation, xenograft formation in a subcutaneous SCID mouse model, and drug sensitivity to doxorubicin and cisplatin. There is a modest increase in cell migration in cell lines overexpressing miR-30a compared to their controls. Conclusions/Significance Overexpression of miR-30a or -191 does not lead to an alteration in cell cycle, proliferation, xenograft formation, and chemosensitivity of A549 and BEAS-2B cell lines. Using microarray data from whole tumors to select specific miRNAs for functional study may be a suboptimal strategy.
Synthesis of Moore finite state machine with nonstandard presentation of state codes

The method is proposed for reduction of hardware amount in logic circuit of Moore finite st ate machine. The method is oriented on CPLD technology. It is based on representation of the next state code a s a concatenation of codes for class of pseudoequivalen t states and collection of microoperations. Such an approach allows elimination of dependence among states and microoperations. As a result, both circuits for gen eration of input memory functions and microoperations are optimized. An example of the proposed method application is given.
Estimator Design for Discrete-Time Switched Neural Networks With Asynchronous Switching and Time-Varying Delay

This brief deals with the estimator design problem for discrete-time switched neural networks with time-varying delay. One main problem is the asynchronous-mode switching between the neuron state and the estimator. Our goal is to design a mode-dependent estimator for the switched neural networks under average dwell time switching such that the estimation error system is exponentially stable with a prescribed l2 gain (in the H∞ sense) from the noise signal to the estimation error. A new Lyapunov functional is constructed that may increase during the mismatched switchings. New results on the stability and l2 gain analysis are then obtained. The admissible estimator gains are computed by solving a set of linear matrix inequalities. The relations among the switching law, the maximal delay upper bound, and the optimal H∞ disturbance attenuation level are established. The effectiveness of the proposed design method is finally illustrated by a numerical example.
Performance comparison between slotted IEEE 802.15.4 and IEEE 802.1 lah in IoT based applications

In this paper, we present a performance comparison between IEEE 802.15. 4 which specifies the physical and media access control layers for low-rate wireless personal area networks (LR-WPANs) like ZigBee, and IEEE 802.11ah, a new global WLAN standard using sub-1 GHz frequency band, in terms of throughput and energy consumption. Both standards are targeting low power applications with relatively high number of nodes as in IoT and M2M applications. The simulations results demonstrate the better performance of IEEE 802.11ah throughput mainly in congested networks. However in particular cases, the IEEE 802.15. 4 outperforms the IEEE 802.11ah from energy consumption point of view.
Graphical models for functional connectivity

A lack of functional integration has been proposed as a signature of many degenerative and developmental disorders, including schizophrenia and autism (Just et al., 2004). These connectivity patterns are often assessed with ad-hoc techniques based on pairwise correlations. The procedures may lead to a misleading characterization of the underlying neural connectivity pattern, because these techniques cannot distinguish direct connections from mediated connections. More powerful statistical techniques exist which can sometimes uncover a model reflecting these distinctions. Although only a handful of papers to date have applied graphical model techniques to fMRI, they are often used in bioinformatics, which shares some statistical issues with fMRI data, including a small number of observations (i.e. hundreds) with a large number of variables (thousands). The proposed project will examine existing techniques for learning functional structure from fMRI time series, focusing on methods from the graphical models framework. The major part of the project will compromise a review of existing techniques, evaluation using real and simulated fMRI datasets, and work on algorithms which can scale up to a large number of regions than usually considered in functional connectivity analysis. The remainder of this proposal is organized as follows: Section 2 explains the problem with correlation-based approaches. Section 3 contains a brief overview of graphical models and algorithms for structure learning. Section 4 summarizes previous literature in structure learning for fMRI. Section 5 summarizes work already done on this project and Section 6 outlines proposed extensions and comparisons to existing methods. Section 7 contains predictions and open questions to be addressed during this project.
Reversal of docosahexaenoic acid deficiency in the rat brain, retina, liver, and serum.

The loss of docosahexaenoic acid (DHA) from the retina or brain has been associated with a loss in nervous-system function in experimental animals, as well as in human infants fed vegetable oil-based formulas. The reversibility of the loss of DHA and the compensation by an increase in the n-6 docosapentaenoic acid (DPAn-6) was studied in young adult rats. Long-Evans rats were subjected to a very low level of n-3 fatty acids through two generations. The F2 generation, n-3-deficient animals at 7 weeks of age were provided a repletion diet containing both alpha-linolenate and DHA. A separate group of F2 generation rats had been maintained on an n-3-adequate diet of the same composition. Tissues from the brain, retina, liver, and serum were collected on weeks 0, 1, 2, 4, and 8 from both groups of animals. The concentrations of DHA, DPAn-6, and other fatty acids were determined and the rate of recovery and length of time needed to complete DHA recovery were determined for each tissue. The DHA level in the brain at 1 and 2 weeks after diet reversal was only partially recovered, rising to approximately 20% and 35%, respectively, of the n-3-adequate group level. Full recovery was not obtained until 8 weeks after initiation of the repletion diet. Although the initial rate of retinal DHA accretion was greater than that of brain DHA, the half-time for DHA recovery was only marginally greater. On the other hand, the levels of DHA in the serum and liver were approximately 90% and 100% replaced, respectively, within 2 weeks of diet reversal. A consideration of the total amounts and time courses of DHA repleted in the nervous system compared with the liver and circulation suggests that transport-related processes may limit the rate of DHA repletion in the retina and brain. -- Moriguchi, T. , J. Loewke, M. Garrison, J. N. Catalan, N. Salem, Jr. Reversal of docosahexaenoic acid deficiency in the rat brain, retina, liver, and serum. J. Lipid Res. 2001. 42: 419--427.
Simulation of confined magnetohydrodynamic flows using a pseudo-spectral method with volume penalization

A volume penalization approach to simulate magnetohydrodynamic (MHD) flows in confined domains is presented. Here the incompressible visco-resistive MHD equations are solved using parallel pseudo-spectral solvers in Cartesian geometries. The volume penalization technique is an immersed boundary method which is characterized by a high flexibility for the geometry of the considered flow. In the present case, it allows to use other than periodic boundary conditions in a Fourier pseudo-spectral approach. The numerical method is validated and its convergence is assessed for two- and three-dimensional hydrodynamic (HD) and MHD flows, by comparing the numerical results with results from literature and analytical solutions. The test cases considered are two-dimensional Taylor-Couette flow, the z-pinch configuration, three dimensional Orszag-Tang flow, ohmic-decay in a periodic cylinder, three-dimensional Taylor-Couette flow with and without axial magnetic field and three-dimensional Hartmann-instabilities in a cylinder with an imposed helical magnetic field.
A Toolset for Program

Program understanding is associated with the hierarchy of abstractions and interpretations that are deduced from the code 16]. Therefore, the understanding process parallels that of the bottom-up programming process in that maintainers begin by associating small groupings of individual instructions with higher-level interpretations. The understanding process is repeated until the desired level of understanding is attained. Program understanding in this context requires the identiication and study of the numerous complex interrelationships that are induced by the data ow, calling, and functional dependencies that exist in the software. Therefore, an environment is needed in order aid the programmer in understanding software. The internal program representation (IPR) plays a critical role in the nature of that environment. In an earlier paper, we discussed both an internal program representation and an environment that conforms to the requirements stated above.11] The internal program representation, the system dependence graph (SDG), is a directed labeled multigraph that captures all control and data dependences, as well as the calling context of procedures; it is based on the one proposed in 8]. The toolset is referred to as Ghinsu and it supports a number of tasks over a program written in a subset of ANSI C such as slicing, dicing, and ripple analysis. In this paper we will present some background on the problems associated with program understanding and show how the Ghinsu toolset can aid the programmer in understanding software.
Matrix Multiplication and Binary Space Partitioning Trees : An Exploration

Herein we explore a dual tree algorithm for matrix multiplication of $A\in \mathbb{R}^{M\times D}$ and $B\in\mathbb{R}^{D\times N}$, very narrowly effective if the normalized rows of $A$ and columns of $B$, treated as vectors in $\mathbb{R}^{D}$, fall into clusters of order proportionate to $\Omega(D^{\tau})$ with radii less than $\arcsin(\epsilon/\sqrt{2})$ on the surface of the unit $D$-ball. The algorithm leverages a pruning rule necessary to guarantee $\epsilon$ precision proportionate to vector magnitude products in the resultant matrix. \textit{ Unfortunately, if the rows and columns are uniformly distributed on the surface of the unit $D$-ball, then the expected points per required cluster approaches zero exponentially fast in $D$; thus, the approach requires a great deal of work to pass muster. }
Statistical surface-based morphometry using a nonparametric approach

We present a novel method of statistical surface-based morphometry based on the use of nonparametric permutation tests. In order to evaluate morphological differences of brain structures, we compare anatomical structures acquired at different times and/or from different subjects. Registration to a common coordinate system establishes corresponding locations and the differences between such locations are modeled as a displacement vector field (DVF). The analysis of DVFs involves testing thousands of hypothesis for signs of statistically significant effects. We randomly permute the surface data among two groups to determine thresholds that control the family wise (type 1) error rate. These thresholds are based on the maximum distribution of the amplitude of the vector fields, which implicitly accounts for spatial correlation of the fields. We propose two normalization schemes for achieving uniform spatial sensitivity. We demonstrate their application in a shape similarity study of the lateral ventricles of monozygotic twins and nonrelated subjects.
A connectionist inductive learning system for modal logic programming

Neural-Symbolic integration has become a very active research area in the last decade. In this paper, we present a new massively parallel model for modal logic. We do so by extending the language of Modal Prolog to allow modal operators in the head of the clauses. We then use an ensemble of C-IL/sup 2/p neural networks to encode the extended modal theory (and its relations), and show that the ensemble computes a fixpoint semantics of the extended theory. An immediate result of our approach is the ability to perform learning from examples efficiently using each network of the ensemble. Therefore, one can adapt the extended C-IL/sup 2/P system by training possible world representations.
Patient-specific seizure onset detection

This work presents an automated, patient-specific method for the detection of epileptic seizure onsets from noninvasive EEG. We adopt a patient-specific approach to exploit the consistency of an individual patient's seizure and non-seizure EEG. Our method uses a wavelet decomposition to construct a feature vector that captures the morphology and spatial distribution of an EEG epoch, and then determines whether that vector is representative of a patient's seizure or non-seizure EEG using the support-vector machine classification algorithm. Our completely automated method was tested on non-invasive EEG from thirty-six pediatric subjects suffering from a variety of seizure types. It detected 131 of 139 seizure events within 8.0/spl plusmn/3.2 seconds following electrographic onset, and declared 15 false-detections in 60 hours of clinical EEG. Our patient-specific method can be used to initiate delay-sensitive clinical procedures following seizure onset; for example, the injection of an imaging radiopharmaceutical or stimulation of the vagus nerve.
Characterization and Expression of the Zebrafish qki Paralogs

Quaking (QKI) is an RNA-binding protein involved in post-transcriptional mRNA processing. This gene is found to be associated with several human neurological disorders. Early expression of QKI proteins in the developing mouse neuroepithelium, together with neural tube defects in Qk mouse mutants, suggest the functional requirement of Qk for the establishment of the nervous system. As a knockout of Qk is embryonic lethal in mice, other model systems like the zebrafish could serve as a tool to study the developmental functions of qki. In the present study we sought to characterize the evolutionary relationship and spatiotemporal expression of qkia, qki2, and qkib; zebrafish homologs of human QKI. We found that qkia is an ancestral paralog of the single tetrapod Qk gene that was likely lost during the fin-to-limb transition. Conversely, qkib and qki2 are orthologs, emerging at the root of the vertebrate and teleost lineage, respectively. Both qki2 and qkib, but not qkia, were expressed in the progenitor domains of the central nervous system, similar to expression of the single gene in mice. Despite having partially overlapping expression domains, each gene has a unique expression pattern, suggesting that these genes have undergone subfunctionalization following duplication. Therefore, we suggest the zebrafish could be used to study the separate functions of qki genes during embryonic development.
A Heart Disease Prediction Model using SVM-Decision Trees-Logistic Regression (SDL)

The early prognosis of cardiovascular diseases can aid in making decisions to lifestyle changes in high risk patients and in turn reduce their complications. Research has attempted to pinpoint the most influential factors of heart disease as well as accurately predict the overall risk using homogenous data mining techniques. Recent research has delved into amalgamating these techniques using approaches such as hybrid data mining algorithms. This paper proposes a rule based model to compare the accuracies of applying rules to the individual results of support vector machine, decision trees, and logistic regression on the Cleveland Heart Disease Database in order to present an accurate model of predicting heart disease.