Miracle-dz/UniFAR-FTUs huggingface dataset:
Ocular Biometrics by Score-Level Fusion of Disparate Experts

The concept of periocular biometrics emerged to improve the robustness of iris recognition to degraded data. Being a relatively recent topic, most of the periocular recognition algorithms work in a holistic way and apply a feature encoding/matching strategy without considering each biological component in the periocular area. This not only augments the correlation between the components in the resulting biometric signature, but also increases the sensitivity to particular data covariates. The main novelty in this paper is to propose a periocular recognition ensemble made of two disparate components: 1) one expert analyses the iris texture and exhaustively exploits the multispectral information in visible-light data and 2) another expert parameterizes the shape of eyelids and defines a surrounding dimensionless region-of-interest, from where statistics of the eyelids, eyelashes, and skin wrinkles/furrows are encoded. Both experts work on disjoint regions of the periocular area and meet three important properties. First, they produce practically independent responses, which is behind the better performance of the ensemble when compared to the best individual recognizer. Second, they do not share particularly sensitivity to any image covariate, which accounts for augmenting the robustness against degraded data. Finally, it should be stressed that we disregard information in the periocular region that can be easily forged (e.g., shape of eyebrows), which constitutes an active anticounterfeit measure. An empirical evaluation was conducted on two public data sets (FRGC and UBIRIS.v2), and points for consistent improvements in performance of the proposed ensemble over the state-of-the-art periocular recognition algorithms.
Community Detection Metrics and Algorithms in Social Networks

Community detection is one of the key areas of social network analysis. There are various community detection algorithms available in the literature. Numerous community metrics are also available to evaluate the detected communities. In our study, by using synthetic networks, we compare between four well known community metrics, namely; modularity, conductance, coverage and performance. We also compare seven different community detection algorithms based on above mentioned parameters.
A low-mass triple system with a wide L/T transition brown dwarf component: NLTT 51469AB/SDSS 2131−0119

We demonstrate that the previously identified L/T transition brown dwarf SDSS J213154.43−011939.3 (SDSS 2131−0119) is a widely separated (82${^{\prime\prime}_{.}}$3, ∼3830 au) common proper motion companion to the low-mass star NLTT 51469, which we reveal to be a close binary itself, separated by 0${^{\prime\prime}_{. }}$64 ± 0${^{\prime\prime}_{. }}$01 (∼30 au). We find the proper motion of SDSS 2131−0119 of μαcos δ = −100 ± 20 mas yr−1 and μδ = −230 ± 20 mas yr−1 consistent with the proper motion of the primary provided by Gaia DR2: μαcos δ = −95.49 ± 0.96 mas yr−1 and μδ = −239.38 ± 0.96 mas yr−1. Based on optical and near-infrared spectroscopy, we classify the primary NLTT 51469A as an M3 ± 1 dwarf, estimate photometrically the spectral type of its close companion NLTT 51469B at ∼M6, and confirm the spectral type of the brown dwarf to be L9 ± 1. Using radial velocity, proper motion, and parallax, we derived the UVW Galactic space velocities of NLTT 51469A, showing that the system does not belong to any known young stellar moving group. The high V, W velocities, lack of a 670.8 nm Li i absorption line, and absence of H α emission, detected X-rays, or UV excess, indicate that the system is likely a member of the thin disc population and is older than 1 Gyr. For the parallactic distance of 46.6 ± 1.6 pc from Gaia DR2, we determined luminosities of $-1.50^{+0.02}_{-0.04}$ and −4.4 ± 0.1 dex of the M3 and L9, respectively. Considering the spectrophotometric estimation, which yields a slightly lower distance of $34^{+10}_{-13}$ pc, the obtained luminosities are $-1.78^{+0.02}_{-0.04}$ and $-4.7^{+0.3}_{-0.5}$ dex. We also estimated their effective temperatures and masses, and obtained 3410$^{+140}_{-210}$ K and 0.42 ± 0.02 M⊙ for the primary, and 1400–1650 K and 0.05–0.07 M⊙ for the wide companion. For the ∼M6 component, we estimated Teff = 2850 ± 200 K and m = 0.10$^{+0.06}_{-0.01}$ M⊙.
Puerto Rican Wannabes

The “Puerto Rican wannabe” is one contemporary, local expression of contested racial identities—identities that are also inflected with class and gender meanings. This study uses interviews with local youth and young adults to explore their use of the caricature of the wannabe to create and contest race, class, and gender boundaries. The wannabe’s challenge to racially designated categories provides a symbol onto which nonwannabe kids project their own stereotypes, anxieties, and desires. The stories told about the wannabe in this study reveal both the persistence and the fragility of race, class, and gender identities and underline the centrality of sexuality in bolstering and undermining them. Boundary negotiations in one category rely on and affect other categories: In this study, the contestation of racial boundaries reestablishes heteronormative and hierarchical gender relations.
Optimal Power Flow Solution Including Stochastic Renewable Resources

This paper proposes a probabilistic power flow multiobjective optimization for solving a hybrid wind and solar power system. In this study, some conventional generators are replaced with renewable sources as wind and photovoltaic (PV) power for IEEE 30-bus standard test system. The randomness of power generated by wind and PV is modeled. The problem is solved based on expected values of generated renewable power using the Probability Density Function (PDF) and the Cumulative Density Function (CDF). Weibull and lognormal probability density function are used to calculate available wind and solar power, respectively. Two cases (with and without renewable sources) were considered in the evaluation. The simulation results show that the proposed algorithm ensures better performance than other optimization approaches found in the literature.
Exploiting sparseness in deep neural networks for large vocabulary speech recognition

Recently, we developed context-dependent deep neural network (DNN) hidden Markov models for large vocabulary speech recognition. While reducing errors by 33% compared to its discriminatively trained Gaussian-mixture counterpart on the switchboard benchmark task, DNN requires much more parameters. In this paper, we report our recent work on DNN for improved generalization, model size, and computation speed by exploiting parameter sparseness. We formulate the goal of enforcing sparseness as soft regularization and convex constraint optimization problems, and propose solutions under the stochastic gradient ascent setting. We also propose novel data structures to exploit the random sparseness patterns to reduce model size and computation time. The proposed solutions have been evaluated on the voice-search and switchboard datasets. They have decreased the number of nonzero connections to one third while reducing the error rate by 0.2-0.3% over the fully connected model on both datasets. The nonzero connections have been further reduced to only 12% and 19% on the two respective datasets without sacrificing speech recognition performance. Under these conditions we can reduce the model size to 18% and 29%, and computation to 14% and 23%, respectively, on these two datasets.
Comparison of the Pharmacodynamics of Biapenem in Bronchial Epithelial Lining Fluid in Healthy Volunteers Given Half-Hour and Three-Hour Intravenous Infusions

ABSTRACT The time above the MIC (T>MIC) is the pharmacokinetic/pharmacodynamic (PK/PD) parameter that correlates with the therapeutic efficacy of beta-lactam antibiotics. A prolonged infusion can provide plasma drug concentrations that remain above the MIC for a long period. The objective of this study was to compare the PK/PD parameters in bronchial epithelial lining fluid (ELF) of biapenem given as 0.5-h and 3-h infusions by using bronchoscopic microsampling (BMS). Six healthy adult volunteers received 0.5-h and 3-h infusions of 0.3 g of biapenem with a washout interval. BMS was performed repeatedly from 0.5 to 24 h after biapenem administration in order to determine the pharmacokinetics in bronchial ELF. The subjects received intravenous biapenem with the same regimens again and then underwent bronchoalveolar lavage (BAL) at the end of infusion in order to determine the concentration of the drug in alveolar ELF. The percentages (means ± standard deviations) of T>MIC in bronchial ELF at MICs from 0.25 to 4 μg/ml ranged from zero to 34.6% ± 5.2% after the 0.5-h infusion and from 5.1% ± 5.6% to 52.2% ± 17.0% after the 3-h infusion. The percentage of T>MIC in bronchial ELF after the 3-h infusion tended to be higher than that after the 0.5-h infusion. The concentrations of the drug in alveolar ELF after 0.5-h and 3-h infusions were 3.5 ± 1.2 μg/ml and 1.3 ± 0.3 μg/ml, respectively. The present results support the use of prolonged infusions of beta-lactam antibiotics and may provide critical information for successful treatment of lower respiratory tract infections based on PK/PD parameters in bronchial ELF.
Second-order particle MCMC for Bayesian parameter inference

We propose an improved proposal distribution in the Particle Metropolis-Hastings (PMH) algorithm for Bayesian parameter inference in nonlinear state space models. This proposal incorporates second-order information about the parameter posterior distribution, which can be extracted from the particle filter already used within the PMH algorithm. The added information makes the proposal scale-invariant, simpler to tune and can possibly also shorten the burn-in phase. The proposed algorithm has a computational cost which is proportional to the number of particles, i.e. the same as the original marginal PMH algorithm. Finally, we provide two numerical examples that illustrates some of the possible benefits of adding the second-order information.
Single- and multiobjective evolutionary optimization assisted by Gaussian random field metamodels

This paper presents and analyzes in detail an efficient search method based on evolutionary algorithms (EA) assisted by local Gaussian random field metamodels (GRFM). It is created for the use in optimization problems with one (or many) computationally expensive evaluation function(s). The role of GRFM is to predict objective function values for new candidate solutions by exploiting information recorded during previous evaluations. Moreover, GRFM are able to provide estimates of the confidence of their predictions. Predictions and their confidence intervals predicted by GRFM are used by the metamodel assisted EA. It selects the promising members in each generation and carries out exact, costly evaluations only for them. The extensive use of the uncertainty information of predictions for screening the candidate solutions makes it possible to significantly reduce the computational cost of singleand multiobjective EA. This is adequately demonstrated in this paper by means of mathematical test cases and a multipoint airfoil design in aerodynamics
Bringing the Best of Two Worlds Together for Social Capital Research in Education

This article proposes an analytical consideration for social capital research in education by exploring a pragmatic combination of social network analysis (SNA) and symbolic interactionism (SI) as a research method. The article first delineates the theoretical linkages of social capital theory with SNA and SI. The article then discusses how SNA and SI can be utilized for social capital research in education by illuminating analytically complementary characteristics between SNA and SI. Based on this, the article argues that such a combined approach could pave a route to explore social capital in an analytically enhanced way.
KSAC, a Defined Leishmania Antigen, plus Adjuvant Protects against the Virulence of L. major Transmitted by Its Natural Vector Phlebotomus duboscqi

Background Recombinant KSAC and L110f are promising Leishmania vaccine candidates. Both antigens formulated in stable emulsions (SE) with the natural TLR4 agonist MPL® and L110f with the synthetic TLR4 agonist GLA in SE protected BALB/c mice against L. major infection following needle challenge. Considering the virulence of vector-transmitted Leishmania infections, we vaccinated BALB/c mice with either KSAC+GLA-SE or L110f+GLA-SE to assess protection against L. major transmitted via its vector Phlebotomus duboscqi. Methods Mice receiving the KSAC or L110f vaccines were challenged by needle or L. major-infected sand flies. Weekly disease progression and terminal parasite loads were determined. Immunological responses to KSAC, L110f, or soluble Leishmania antigen (SLA) were assessed throughout vaccination, three and twelve weeks after immunization, and one week post-challenge. Results Following sand fly challenge, KSAC-vaccinated mice were protected while L110f-vaccinated animals showed partial protection. Protection correlated with the ability of SLA to induce IFN-γ-producing CD4+CD62LlowCCR7low effector memory T cells pre- and post-sand fly challenge. Conclusions This study demonstrates the protective efficacy of KSAC+GLA-SE against sand fly challenge; the importance of vector-transmitted challenge in evaluating vaccine candidates against Leishmania infection; and the necessity of a rapid potent Th1 response against Leishmania to attain true protection.
Protocatechuic Acid, a Phenolic from Sansevieria roxburghiana Leaves, Suppresses Diabetic Cardiomyopathy via Stimulating Glucose Metabolism, Ameliorating Oxidative Stress, and Inhibiting Inflammation

Persistent hyperglycemia, impairment of redox status and establishment of inflammatory pathophysiology integrally play important role in the pathogenesis of diabetic cardiomyopathy (DC). Present study examined the therapeutic potential of protocatechuic acid isolated from the Sansevieria roxburghiana rhizomes against DC employing rodent model of type 2 diabetes (T2D). T2D was induced by high fat diet + a low-single dose of streptozotocin (35 mg/kg, i.p.). T2D rats exhibited significantly (p < 0.01) high fasting blood glucose level. Alteration in serum lipid profile (p < 0.01) and increased levels of lactate dehydrogenase (p < 0.01) and creatine kinase (p < 0.01) in the sera of T2D rats revealed the occurrence of hyperlipidemia and diabetic pathophysiology. A significantly (p < 0.01) high levels of serum C-reactive protein and pro-inflammatory mediators revealed the establishment of inflammatory occurrence in T2D rats. Besides, significantly high levels of troponins in the sera revealed the establishment of cardiac dysfunctions in T2D rats. However, protocatechuic acid (50 and 100 mg/kg, p.o.) treatment could significantly reverse the changes in serum biochemical parameters related to cardiac dysfunctions. Molecular mechanism studies demonstrated impairment of signaling cascade, IRS1/PI3K/Akt/AMPK/p 38/GLUT4, in glucose metabolism in the skeletal muscle of T2D rats. Significant (p < 0.01) activation of polyol pathway, enhanced production of AGEs, oxidative stress and up-regulation of inflammatory signaling cascades (PKC/NF-κB/PARP) were observed in the myocardial tissue of T2D rats. However, protocatechuic acid (50 and 100 mg/kg, p.o.) treatment could significantly (p < 0.05–0.01) stimulate glucose metabolism in skeletal muscle, regulated glycemic and lipid status, reduced the secretion of pro-inflammatory cytokines, and restored the myocardial physiology in T2D rats near to normalcy. Histological assessments were also in agreement with the above findings. In silico molecular docking study again supported the interactions of protocatechuic acid with different signaling molecules, PI3K, IRS, Akt, AMPK PKC, NF-κB and PARP, involved in glucose utilization and inflammatory pathophysiology. In silico ADME study predicted that protocatechuic acid would support the drug-likeness character. Combining all, results would suggest a possibility of protocatechuic acid to be a new therapeutic agent for DC in future.
A Loser Can Be a Winner: Comparison of Two Instance-based Learning Models in a Market Entry Competition

This paper presents a case of parsimony and generalization in model comparisons. We submitted two versions of the same cognitive model to the Market Entry Competition (MEC), which involved four-person and two-alternative (enter or stay out) games. Our model was designed according to the Instance-Based Learning Theory (IBLT). The two versions of the model assumed the same cognitive principles of decision making and learning in the MEC. The only difference between the two models was the assumption of homogeneity among the four participants: one model assumed homogeneous participants (IBL-same) while the other model assumed heterogeneous participants (IBL-different). The IBL-same model involved three free parameters in total while the IBL-different involved 12 free parameters, i.e., three free parameters for each of the four participants. The IBL-different model outperformed the IBL-same model in the competition, but after exposing the models to a more challenging generalization test (the Technion Prediction Tournament), the IBL-same model outperformed the IBL-different model. Thus, a loser can be a winner depending on the generalization conditions used to compare models. We describe the models and the process by which we reach these conclusions.
On the use of Early-To-Late Reverberation ratio for ASR in reverberant environments

This work presents an analysis of distant-talking speech recognition in a variety of reverberant conditions, correlating ASR performance to the acoustic characteristics of a given propagation channel. In particular we show how, for a digit recognition task, the ASR accuracy is directly related to the Early-to-Late Reverberation ratio of the room impulse response, capturing in a single parameter the reverberation properties of a given channel independently of the setup. Consequently, this measure can be successfully considered for acoustic model training either selecting the most suitable model for a given spatial configuration, or defining the subset of RIRs to be used for the creation of multi-condition models. Experimental results on simulated data as well as on data generated with real impulse responses support our claims.
Interactions of protons with single open L-type calcium channels. Location of protonation site and dependence of proton-induced current fluctuations on concentration and species of permeant ion

We further investigated the rapid fluctuations between two different conductance levels promoted by protons when monovalent ions carry current through single L-type Ca channels. We tested for voltage dependence of the proton-induced current fluctuations and for accessibility of the protonation site from both sides of the membrane patch. The results strongly suggest an extracellular location of the protonation site. We also studied the dependence of the kinetics of the fluctuations and of the two conductance levels on the concentration of permeant ion and on external ionic strength. We find that saturation curves of channel conductance vs. [K] are similar for the two conductance levels. This provides evidence that protonation does not appreciably change the surface potential near the entry of the permeation pathway. The proton-induced conduction change must therefore result from an indirect interaction between the protonation site and the ion-conducting pathway. Concentration of permeant ion and ionic strength also affect the kinetics of the current fluctuations, in a manner consistent with our previous hypothesis that channel occupancy destabilizes the low conductance channel conformation. We show that the absence of measurable fluctuations with Li and Ba as charge carriers can be explained by significantly higher affinities of these ions for permeation sites. Low concentrations of Li reduce the Na conductance and abbreviate the lifetimes of the low conductance level seen in the presence of Na. We use whole-cell recordings to extrapolate our findings to the physiological conditions of Ca channel permeation and conclude that in the presence of 1.8 mM Ca no proton-induced fluctuations occur between pH 7.5 and 6.5. Finally, we propose a possible physical interpretation of the formal model of the protonation cycle introduced in the companion paper.
‘Governance without governance’ 1 : how nature policy was democratized in the Netherlands

Trends in governance, including a changing role for the state and increasing civil society participation, are often seen as promising ways to achieve democratic legitimacy. The prominent presence of these claims and intentions in the new Dutch nature policy plan, ‘Nature for People, People for Nature’, stimulated us to look more closely into how this plan came about. Our analysis shows that the process started with the organization of several informal participatory processes, which involved not only traditional but also new actors. However, it ended in a fairly traditional way, with limited participation, which involved mostly traditional actors, and which was strictly orchestrated by central government. Based on these findings, we argue that although the plan itself was clearly intended to achieve participatory governance, the participatory characteristics of the process can be questioned. For this reason, the case may be seen as one of ‘governance without governance’. The article ends by discussing the implications of these findings for democratic legitimacy.
TOPNET: A Tool for the Visual Simulation of Communication Networks

TOPNET is a software package for the simulation of communication networks that is being developed with the aim of exploiting as much as possible the visual aspects of a simulation experiment. The communication network topology and architecture are described by drawings, the system dynamics are represented with a class of timed Petri nets, the simulation experiment is controlled through menus and buttons activated with a mouse, and results are graphically displayed on the screen. With the help of an example, the main characteristics of the user interface and the internal architecture of TOPNET are described. >
Alias Detection in Malicious Environments

Alias detection is a challenging task critical in several areas such as the intelligence community, social network analysis, databases, biology, and marketing. Problem domains can be as simple as datasets containing accidentally replicated data, or as complex as populations containing criminals or terrorists wielding multiple identities. Teasing out aliases or near aliases in the later case is a serious and challenging problem. We propose an unsupervised information theoretic approach for automatically detecting aliases in malicious environments by observing the behaviors of the entities. Our model discovers the most informative observations (e.g. emails, phone calls, relational data) between entities and then compares them to identify entities exhibiting similar behaviors. We test our model by applying it to the task of discovering aliases in a standard synthetic world of interrelated individuals. Given our system’s top-20 guesses, we extracted with 80% accuracy the true aliases of a given entity.
Molecular cytogenetic findings of acute leukemia included in the Brazilian Collaborative Study Group of Infant acute leukemia

Chromosome abnormalities often occur prenatally in childhood leukemia, characterizing an early event in leukemogenesis. The majority of the abnormalities occurring in infants involve the MLL gene on chromosome band 11q23. We describe the molecular cytogenetic findings of 207 infant acute leukemia (IAL) cases included in the Brazilian Collaborative Study Group of Infant acute leukemia.