Miracle-dz/UniFAR-FTUs huggingface dataset:
A fast method to estimate speciation parameters in a model of isolation with an initial period of gene flow and to test alternative evolutionary scenarios

We consider a model of "isolation with an initial period of migration" (IIM), where an ancestral population instantaneously split into two descendant populations which exchanged migrants symmetrically at a constant rate for a period of time but which are now completely isolated from each other. A method of Maximum Likelihood estimation of the parameters of the model is implemented, for data consisting of the number of nucleotide differences between two DNA sequences at each of a large number of independent loci, using the explicit analytical expressions for the likelihood obtained in Wilkinson-Herbots (2012). The method is demonstrated on a large set of DNA sequence data from two species of Drosophila, as well as on simulated data. The method is extremely fast, returning parameter estimates in less than 1 minute for a data set consisting of the numbers of differences between pairs of sequences from 10,000s of loci, or in a small fraction of a second if all loci are trimmed to the same estimated mutation rate. It is also illustrated how the maximized likelihood can be used to quickly distinguish between competing models describing alternative evolutionary scenarios, either by comparing AIC scores or by means of likelihood ratio tests. The present implementation is for a simple version of the model, but various extensions are possible and are briefly discussed.
An Algorithm for Nonintrusive In Situ Efficiency Estimation of Induction Machines Operating With Unbalanced Supply Conditions

The development of an in situ efficiency estimation technique is a challenging task where the lowest level of intrusion and the highest possible accuracy are required. In this paper, a new algorithm is discussed for the in situ efficiency estimation of induction machines under unbalanced power supplies. Prior work in the literature has concentrated on balanced supplies. In addition, to have a nonintrusive speed measurement, a specific adaptive nonlinear algorithm is applied for the extraction of the speed-dependent current harmonics from the measured current signal. A similar algorithm is used to extract the symmetrical components from the current and voltage signals to handle the unbalanced supply conditions. Experimental results with two different machines are used to prove the effectiveness and generality of the proposed method. Measurement error analysis, as well as repeatability tests, has been done to determine the credibility of the proposed method.
1 PRODUCTION / COMPREHENSION ASYMMETRIES IN LANGUAGE ACQUISITION

The theme of this Special Issue is asymmetries in language acquisition, and the contributions present evidence for either the presence or the absence of a production/comprehension asymmetry in child language. The authors focus on various areas of linguistics, ranging from phonology and syntax to semantics and pragmatics, using a variety of empirical methods. Although the contributions do not agree on the existence of asymmetries or on the explanations for observed asymmetries, they all address the relation between children’s production of a particular linguistic form and their comprehension of this form. As this Special Issue shows, the relation between children’s performance in language production, their performance in language comprehension, and their knowledge of grammar may not be as straightforward as has often been assumed.
Approximation algorithms for the metric labeling problem via a new linear programming formulation

We consider approximation algorithms for the metric labeling problem. This problem was introduced in a recent paper by Kleinberg and Tardos [20], and captures many classification problems that arise in computer vision and related fields. They gave an &Ogr;(log k log log k) approximation for the general case where k is the number of labels and a 2-approximation for the uniform metric case. More recently, Gupta and Tardos [15] gave a 4-approximation for the truncated linear metric, a natural non-uniform metric motivated by practical applications to image restoration and visual correspondence. In this paper we introduce a natural integer programming formulation and show that the integrality gap of its linear relaxation either matches or improves the ratios known for several cases of the metric labeling problem studied until now, providing a unified approach to solving them. In particular, we show that the integrality gap of our LP is bounded by &Ogr;(log k log log k) for general metric and 2 for the uniform metric thus matching the ratios in [20]. We also develop an algorithm based on our LP that achieves a ratio of 2 + √2 ≃ 3.414 for the truncated linear metric improving the ratio given in [15]. Our algorithm uses the fact that the integrality gap of our LP is 1 on a linear metric. We believe that our formulation has the potential to provide improved approximation algorithms for the general case and other useful special cases. Finally, our formulation admits general non-metric distance functions. This leads to a non-trivial approximation guarantee for a non-metric case that arises in practice [21], namely the truncated quadratic distance function. We note here that there are non-metric distance functions for which no bounded approximation ratio is achievable.
Using the Triarchic model of psychopathy to replicate ``Greed is good? Student disciplinary choice and self-reported psychopathy''

The present study is a replication ofWilson andMcCarthy (2011) [Greed is good? Student disciplinary choice and self-reported psychopathy. Personality and Individual Differences, 51(7), 873–876]. We replicated the previous findings, supporting higher levels of primary psychopathic traits, as measured by Meanness, in commerce students in comparison to students from other disciplines. Our results also indicate that law students display higher levels of boldness than students from any other field, and that science students display higher levels of disinhibition than students in arts or social sciences.
DESIGN PATTERN BASED ANALYSIS IN MULTI-AGENT FRAMEWORK

A modern machine or software can deal with some inbuilt programs which represent a solution to a problem statically where the inputs and the outputs given are constant. During a machine gets inputs at run - time the program must be able to work depending on new situations at that time it needs some intelligence or knowledge which are developed and implemented by agents. When a single agent running in an environment it may identify only some of the states but when multiple agents are running each individual state can be judged easily. Hence in our proposed system a multi agent environment is created and each set of actions are done by individual agent concurrently for a health based system. Thus the actions identified by each agent are communicated through communication languages and justify the solution for that given problem using Evolutionary algorithm and it is stored as a pattern for further use. When there is a solution for a problem in the knowledge base it can be identified through pattern based ontology which is applied on multi agent environment. Thus it may decrease the time to find new solutions.
PERFORMANCE MEASUREMENT OF DISTRIBUTION CENTRE COMBINING DATA ENVELOPMENT ANALYSIS AND ANALYTIC HIERARCHY PROCESS

Abstract: Performance measurement has received significant interest during the present industrial scenario. Researchers are emphasizing the development of newer exotic cutting edge tools and more effective methods that can be applied to solve organisational problems. Data Envelopment Analysis (DEA) model measure the efficiencies of a set of homogenous systems or decision making units (DMUs) by incorporating multiple input and output factors, can be applied for the purpose. Performances of all the DMUs, Distribution centres (DCs) as envisaged in this paper are evaluated by calculating efficiencies. DEA being a nonparametric technique, the output generated by DEA should be viewed with caution and should be used only after conducting appropriate sensitivity analysis. Analytic Hierarchy Process (AHP) which is a powerful and flexible decision making tool for complex, multicriteria problems overcomes the deficiencies of DEA. In the next step again performances of all the DCs are evaluated using the AHP. On the other hand DEA guides the non performing DCs about the level of improvement required for any given criteria to meet the performance level of performing DCs. Where, AHP has virtually no contribution to sort out the said problem. Hence in the third step a hybrid methodology has been proposed combining performance suggested by both DEA and AHP, to develop an overall performance measure and to derive the benefit of both the models. Sensitivity analysis has also been reported for making an eclectic decision.
The scaling limit of the incipient infinite cluster in high-dimensional percolation. II. Integrated super-Brownian excursion

For independent nearest-neighbor bond percolation on Zd with d≫6, we prove that the incipient infinite cluster’s two-point function and three-point function converge to those of integrated super-Brownian excursion (ISE) in the scaling limit. The proof is based on an extension of the new expansion for percolation derived in a previous paper, and involves treating the magnetic field as a complex variable. A special case of our result for the two-point function implies that the probability that the cluster of the origin consists of n sites, at the critical point, is given by a multiple of n−3/2, plus an error term of order n−3/2−e with e>0. This is a strong version of the statement that the critical exponent δ is given by δ=2.
Economies of Scale : The Case of REITs

Significant controversy exists surrounding the hypothesis of consolidation in the real estate industry. Central to the debate is whether REITs can generate economies of scale through asset growth. To date, empirical evidence is limited. For example, Ambrose, Ehrlich, Hughes, and Wachter (2000) find indirect evidence for scale economies but the effect dissipates over time. This finding appears to contradict Bers and Springer (1998) who find that REIT general and administrative (G&A) expenses exhibit substantial economies of scale. G&A, however, is a relatively small part of total expenses with the cost of capital being the primary expense in a capital-intensive industry. This study builds on previous empirical work by focusing on the cost of capital.
A Batched GPU Algorithm for Set Intersection

Intersection of inverted lists is a frequently used operation in search engine systems. Efficient CPU and GPU intersection algorithms for large problem size are well studied. We propose an efficient GPU algorithm for high performance intersection of inverted index lists on CUDA platform. This algorithm feeds queries to GPU in batches, thus can take full advantage of GPU processor cores even if problem size is small. We also propose an input preprocessing method which alleviate load imbalance effectively. Our experimental results based on a real world test set show that the batched algorithm is much faster than the fastest CPU algorithm and plain GPU algorithm.
Perturbation of self-similar sets and some regular configurations and comparison of fractals

We consider several distances between two sets of points, which are modifications of the Hausdorff metric, and apply them to describe some fractals such as $\delta$-quasi-self-similar sets, and some other geometric notions in Euclidean space, such as tilings with quasi-prototiles and patterns with quasi-motifs. For the $\delta$-quasi-self-similar sets satisfying the open set condition we obtain the same result as a classical theorem due to P. A. P. Moran. In this paper we try to gaze on fractals in an aspect of their "form" and suggest a few of related questions. Finally, we attempt to inquire an issue -- what nature and behavior do non-crystalline solids that approximate to crystals show?
Improving Arabic Diacritization through Syntactic Analysis

We present an approach to Arabic automatic diacritization that integrates syntactic analysis with morphological tagging through improving the prediction of case and state features. Our best system increases the accuracy of word diacritization by 2.5% absolute on all words, and 5.2% absolute on nominals over a state-of-theart baseline. Similar increases are shown on the full morphological analysis choice.
Intermediate language design of high-level language virtual machines: towards comprehensive concurrency support

Today's major high-level language virtual machines (VMs) are becoming successful in being multi-language execution platforms, hosting a wide range of languages. With the transition from few-core to many-core processors, we argue that VMs will also have to abstract from concrete concurrency models at the hardware level, to be able to support a wide range of abstract concurrency models on a language level. To overcome the lack of sufficient abstractions for concurrency concepts in VMs, we proposed earlier to extend VM intermediate languages by special concurrency constructs [Marr et al. 2009].
MASSIVE PRIMORDIAL BLACK HOLES IN HYBRID INFLATION

It is well known that energy density fluctuations at the early Universe give rise primordial black holes (BH) formation [1]. Widespread opinion is that these BHs are small enough with masses somewhere in the range MBH ∼ 10−5 − 10gram depending on specific model. What could be said about BHs with masses in the range 10 − 10gram? There exist a few number of inflationary models that predict BH production at some conditions [2], [3].
SoF: Soft-Cluster Matrix Factorization for Probabilistic Clustering

We propose SoF (Soft-cluster matrix Factorization), a probabilistic clustering algorithm which softly assigns each data point into clusters. Unlike model-based clustering algorithms, SoF does not make assumptions about the data density distribution. Instead, we take an axiomatic approach to define 4 properties that the probability of co-clustered pairs of points should satisfy. Based on the properties, SoF utilizes a distance measure between pairs of points to induce the conditional co-cluster probabilities. The objective function in our framework establishes an important connection between probabilistic clustering and constrained symmetric Nonnegative Matrix Factorization (NMF), hence providing a theoretical interpretation for NMF-based clustering algorithms. To optimize the objective, we derive a sequential minimization algorithm using a penalty method. Experimental results on both synthetic and real-world datasets show that SoF significantly outperforms previous NMF-based algorithms and that it is able to detect non-convex patterns as well as cluster boundaries.
A duality between Schrödinger operators on graphs and certain Jacobi matrices

La correspondance bien connue entre le modele de Kronig-Penney et certaines matrices de Jacobi est generalisee a un large ensemble d'operateurs de Schrodinger sur les graphes. Nous presentons des exemples de reseaux rectangulaires avec et sans champ magnetique, ou des graphes en forme de peigne qui produisent un modele de type Maryland.
Effect of restrictions on smoking at home, at school, and in public places on teenage smoking: cross sectional study

Abstract Objective: To determine the relation between extent of restrictions on smoking at home, at school, and in public places and smoking uptake and smoking prevalence among school students. Design: Cross sectional survey with merged records of extent of restrictions on smoking in public places. Setting: United States. Participants: 17 287 high school students. Main outcome measures: Five point scale of smoking uptake; 30 day smoking prevalence. Results: More restrictive arrangements on smoking at home were associated with a greater likelihood of being in an earlier stage of smoking uptake (P<0.05) and a lower 30 day prevalence (odds ratio 0.79 (95% confidence interval 0.67 to 0.91), P<0.001). These findings applied even when parents were smokers. More pervasive restrictions on smoking in public places were associated with a higher probability of being in a earlier stage of smoking uptake (P<0.05) and lower 30 day prevalence (0.91 (0.83 to 0.99), P=0.03). School smoking bans were related to a greater likelihood of being in an earlier stage of smoking uptake (0.89 (0.85 to 0.99), P<0.05) and lower prevalence (0.86 (0.77 to 0.94), P<0.001) only when the ban was strongly enforced, as measured by instances when teenagers perceived that most or all students obeyed the rule. Conclusions: These findings suggest that restrictions on smoking at home, more extensive bans on smoking in public places, and enforced bans on smoking at school may reduce teenage smoking.
Accuracy of non-invasive frequency estimation during atrial fibrillation

Ablation procedures have become one of the most efficient treatments for termination of atrial fibrillation (AF). The aim of this study is to evaluate the accuracy of dominant frequency (DF) maps on the epicardium computed from non-invasive recordings as a clinical tool for the identification of AF sources. Spherical and realistic atrial models were used. Four fibrillation patterns with varying dominant frequency distributions were obtained. Surface potentials were computed by solving the forward problem and adding noise at signal-to-noise ratios (SNR) from 10 to 20 dB. For the spherical model, with 80% of the surface with a DF of 14 Hz and 20% with a DF of 21.5 Hz, RDM* between generated and inverse computed potentials was 0.56 without added noise and 1.05 with SNR=10 dB. However, for the same conditions, the RDM* of DF maps were 0.02 and 0.09, with DF errors ofO.0l+0.31 Hz and 0.26+1.39 Hz, respectively. For the realistic model, frequency reconstruction was consistent with generated electrograms, allowing an accurate estimation of the DF distribution with a maximum RDM* of 0.19 (SNR=10dB). Inverse computed DF maps reconstructed during insilico AF were more accurate than voltage maps. Noninvasive estimation of DF maps during AF is feasible and may help in procedure planning.
An Efficient Convolutional Framework for Multitask Learning

Structured prediction of multiple outputs (commonly referred to as multi-task learning) presents a problem for kernel methods: how do we best compute the kernel between the different outputs? Several solutions have been suggested (e.g. [1, 2, 3, 4]), but many of them can be seen as corresponding to an affine transformation of the target data followed by independent modelling of outputs. Such linear transformations are clearly limiting. A potentially more powerful class of approximations involves convolutions [5, 6]. Temporal or spatial convolutions contain affine transformations of outputs as a (trivial) special case, providing a much broader class of models. The major problem with these models is their computational complexity, prohibitive for systems involving thousands of data points or outputs. We approach the problem from a Gaussian process (GP) perspective. A way to model multiple correlated outputs with GPs is to assume that they are generated from a small set of R latent GP functions analogously to the linear latent variable models commonly used in machine learning [3, 4]. A significant generalization of this approach is to combine it with a convolution-based framework as proposed in [5, 6, 7]. Thus, the value yq(x) for the q-th output at input x is generated according to
Variational inference of latent state sequences using Recurrent Networks

Recent advances in the estimation of deep directed graphical models and recurrent networks let us contribute to the removal of a blind spot in the area of probabilistc modelling of time series. The proposed methods i) can infer distributed latent state-space trajectories with nonlinear transitions, ii) scale to large data sets thanks to the use of a stochastic objective and fast, approximate inference, iii) enable the design of rich emission models which iv) will naturally lead to structured outputs. Two different paths of introducing latent state sequences are pursued, leading to the variational recurrent auto encoder (VRAE) and the variational one step predictor (VOSP). The use of independent Wiener processes as priors on the latent state sequence is a viable compromise between efficient computation of the Kullback-Leibler divergence from the variational approximation of the posterior and maintaining a reasonable belief in the dynamics. We verify our methods empirically, obtaining results close or superior to the state of the art. We also show qualitative results for denoising and missing value imputation.
Multimodal Communication from Multimodal Thinking - towards an Integrated Model of Speech and Gesture Production

A computational model for the automatic production of combined speech and iconic gesture is presented. The generation of multimodal behavior is grounded in processes of multimodal thinking, in which a propositional representation interacts and interfaces with an imagistic representation of visuo-spatial imagery. An integrated architecture for this is described, in which the planning of content and the planning of form across both modalities proceed in an interactive manner. Results from an empirical study are reported that inform the on-the-spot formation of gestures.
The Chk1/Cdc25A Pathway as Activators of the Cell Cycle in Neuronal Death Induced by Camptothecin

Cell cycle regulators appear to play a paradoxical role in neuronal death. We have shown previously that cyclin-dependent kinases (CDKs), along with their downstream effectors, Rb (retinoblastoma) and E2F/DP1 (E2 promoter binding factor/deleted in polyposis 1), regulate neuronal death evoked by the DNA damaging agent camptothecin. However, the mechanism by which CDKs are activated in this model is unclear. The cell division cycle 25A (Cdc25A) phosphatase is a critical regulator of cell cycle CDKs in proliferating cells. In cortical neurons, we presently show that expression of Cdc25A promotes death even in the absence of DNA damage. Importantly, Cdc25A activity is rapidly increased during DNA damage treatment. Inhibition of Cdc25A blocks death and reduces cyclin D1-associated kinase activity and Rb phosphorylation. This indicates that endogenous Cdc25A activity is important for regulation of cell cycle-mediated neuronal death. We also examined how Cdc25A activity is regulated after DNA damage. Cultured embryonic cortical neurons have a significant basal activity of checkpoint kinase 1 (Chk1), a kinase that regulates cell cycle arrest. During camptothecin treatment of neurons, this activity is rapidly downregulated with a concomitant increase in Cdc25A activity. Importantly, expression of wild-type Chk1, but not kinase-dead Chk1, inhibits the camptothecin-induced increase in Cdc25A activity. In addition, Chk1 expression also promotes survival in the presence of the DNA-damaging agent. Together, our data suggest that a Chk1/Cdc25A activity participates in activation of a cell cycle pathway-mediated death signal in neurons. These data also define how a proliferative signal may be abnormally activated in a postmitotic environment.
Program Readability: Procedures Versus Comments

A 3*2 factorial experiment was performed to compare the effects of procedure format (none, internal, or external) with those of comments (absent or present) on the readability of a PL/1 program. The readability of six editions of the program, each having a different combination of these factors, was inferred from the accuracy with which students could answer questions about the program after reading it. Both extremes in readability occurred in the program editions having no procedures: without comments the procedureless program was the least readable and with comments it was the most readable. >