Miracle-dz/UniFAR-FTUs huggingface dataset:
Faster Is Not Always Better: Selection on Growth Rate Fluctuates across Life History and Environments

Growth rate is increasingly recognized as a key life-history trait that may affect fitness directly rather than evolve as a by-product of selection on size or age. An ongoing challenge is to explain the abundant levels of phenotypic and genetic variation in growth rates often seen in natural populations, despite what is expected to be consistently strong selection on this trait. Such a paradox suggests limits to how contemporary growth rates evolve. We explored limits arising from variation in selection, based on selection differentials for age-specific growth rates expressed under different ecological conditions. We present results from a field experiment that measured growth rates and reproductive output in wild individuals of a colonial marine invertebrate (Hippopodina iririkiensis), replicated within and across the natural range of succession in its local community. Colony growth rates varied phenotypically throughout this range, but not all such variation was available for selection, nor was it always targeted by selection as expected. While the maintenance of both phenotypic and genetic variation in growth rate is often attributed to costs of growing rapidly, our study highlights the potential for fluctuating selection pressures throughout the life history and across environments to play an important role in this process.
A three-phase 10 kVAC-750 VDC power electronic transformer for smart distribution grid

In this paper, a power electronic transformer (PET) topology for distribution grid is presented based on modular multilevel converter (MMC). The presented topology is of three-phase type and have two dc voltage terminals, i.e. the high-voltage dc terminal and the low-voltage dc terminal. So, it can be connected directly to high-voltage direct current (HVDC) systems and renewable energy systems simultaneously. This feature makes it possible to perform as an “energy router” in smart grids. What is more important, the proposed PET can substantially reduce (by 44.4% for the studied case) the number of medium frequency transformers (MFTs), compared with the existing solutions. Computer simulation and experimental results on a 10 kV prototype show validity of this PET.
Rational Addiction, Peer Externalities and Long Run Effects of Public Policy

The main purpose of this research is to understand the patterns of consumption of addictive goods, their economic and welfare consequences for society and the long-run effect of tax policy in a dynamic general equilibrium model of rational addiction. In contrast to prior research, we allow individuals to make their consumption decisions simultaneous with savings and labor supply. When addictive goods have a stronger habit formation effect (an addiction effect'), individuals choose to save less due to the anticipated adverse health consequences of addiction (a detrimental health effect'). This is particularly important since total savings pins down future productivity in the economy. We also consider the role of peer influence in the choice of addiction and find that more peer pressure' raises addictive consumption, lowers savings and reduces productivity. In light of the various distortions associated with addiction, we conclude by studying the long-run effects of an excise tax on addictive goods. Our calibration exercises suggest that incorporating capital formation and peer effects in a model of rational addiction are crucial for the design of public policy. In particular, accounting for peer externalities increases the optimal sin tax rate by more than 50 percent.
Automatic Evaluation Metric for Machine Translation that is Independent of Sentence Length

We propose new automatic evaluation metric to evaluate machine translation. Different from most similar metrics, our proposed metric does not depend heavily on sentence length. In most metrics based on f-measure comparisons of reference and candidate translations, the relative weight of each mismatched word in short sentences is larger than it in long sentences. Therefore, the evaluation score becomes disproportionally low in short sentences even when only one non-matching word exists. In our metric, the weight of each mismatched word is kept small even in short sentences. We designate our metric as Automatic Evaluation Metric that is Independent of Sentence Length (AILE). Experimental results indicate that AILE has the highest correlation with human judgments among some leading metrics.
A microfluidic approach towards hybridoma generation for cancer immunotherapy

Dendritic cells/tumor fusions have shown to elicit anti-cancer immunity in different cancer types. However, the application of these vaccines for human cancer immunotherapy are limited by the instable quality and insufficient quanity of fusion cells. We present a cell electrofusion chip fabricated using soft lithography technique, which combines the rapid and precise cell pairing microstructures and the high yield electrofusion micro-electrodes to improve the cell fusion. The design uses hydrodynamic trapping in combination with positive dielectrophoretic force (pDEP) to achieve cell fusion. The chip consists of total 960 pairs of trapping channels, which are capable of pairing and fusing both homogeneous and heterogeneous types of cells. The fused cells can be easily taken out of the chip that makes this device a distinguishable from other designs. We observe pairing efficiency of 68% with fusion efficiency of 64%.
Supporting Knowledge Sharing in Heterogeneous Social Network Thematic Groups

Thematic groups are gaining a lot of attention and high centrality in OSNs, as users share opinions and/or mutually collaborate for reaching their targets. Since users can be affiliated with groups belonging to different social networks, they can be supported by personal software agents able to perform activities aimed at supporting relationships and mutual cooperation among heterogeneous OSNs thematic groups. Basically software agents can encode users profiles with detailed information to be related with specific groups. This work discusses a multi-agent framework whose structure focuses on the role of software agents and the use of a common, shared dictionary for each group. Personal software agents are associated to OSN users to share knowledge for a specific theme for a number of groups related to that topic. Group agents are defined to support each group on each OSN by interacting with personal agents to manage group affiliation and enrich the common dictionaries of their own groups. The common dictionary of the group is a key element to provide knowledge sharing and interoperability between personal and group agents. In the proposed approach each user agent is able to personalize its own dictionary and enrich that of its own groups by means of selected categories.
Pricing of Information Products on Online Servers: Issues, Models, and Analysis

Online information servers that provide access to diverse databases where users can search for, browse through, and download the information they need have been rapidly increasing in number in the past few years. Online vendors have traditionally charged users for information on the based on the length of the time they were connected to the databases. With hardware and software advances, many online servers have recently started changing their pricing strategies to search-based and/or subscription-fee pricing. This paper examines the various issues involved in pricing these information products, and presents an economic approach to analyze conditions under which the various pricing schemes may prove optimal for the online servers. Our results show that the variation in consumer expertise and valuation of information affects the choice of a pricing strategy by the server. We present general conditions under which subscription-fee pricing is optimal even when consumer demand is inelastic. We also find that, given the cost structures characterizing the market, undifferentiated online servers can compete and coexist in the market each making positive profits. We show that in a competitive setting an increase in costs of online servers can sometimes benefit them by enabling them to differentiate themselves. Our results offer insights into the trends in pricing strategies and may provide an explanation as to why many servers may persist with connect-time strategies.
Trojan-horse attacks threaten the security of practical quantum cryptography

A quantum key distribution (QKD) system may be probed by an eavesdropper Eve by sending in bright light from the quantum channel and analyzing the back-reflections. We propose and experimentally demonstrate a setup for mounting such a Trojan-horse attack. We show it in operation against the quantum cryptosystem Clavis2 from ID Quantique, as a proof-of-principle. With just a few back-reflected photons, Eve discerns Bobʼs (secret) basis choice, and thus the raw key bit in the Scarani–Acín–Ribordy–Gisin 2004 protocol, with higher than 90% probability. This would clearly breach the security of the cryptosystem. Unfortunately, Eveʼs bright pulses have a side effect of causing a high level of afterpulsing in Bobʼs single-photon detectors, resulting in a large quantum bit error rate that effectively protects this system from our attack. However, in a Clavis2-like system equipped with detectors with less-noisy but realistic characteristics, an attack strategy with positive leakage of the key would exist. We confirm this by a numerical simulation. Both the eavesdropping setup and strategy can be generalized to attack most of the current QKD systems, especially if they lack proper safeguards. We also propose countermeasures to prevent such attacks.
Structured pursuits for geometric super-resolution

Super-resolution image zooming is possible when the image has some geometric regularity. We introduce a general class of non-linear inverse estimators, which combines linear estimators with mixing weights in a frame providing a sparse representation. Mixing weights are computed with a block decomposition, which minimizes a Tikhonov energy penalized by an 11 norm of the mixing weights. A fast orthogonal matching pursuit algorithm computes the mixing weights. Adaptive directional image interpolations are calculated with mixing weights in a wavelet frame.
Lateral Temporal Lobe: An Early Imaging Marker of the Presymptomatic GRN Disease?

Abstract The preclinical stage of frontotemporal lobar degeneration (FTLD) is not well characterized. We conducted a brain metabolism (FDG-PET) and structural (cortical thickness) study to detect early changes in asymptomatic GRN mutation carriers (aGRN+) that were evaluated longitudinally over a 20-month period. At baseline, a left lateral temporal lobe hypometabolism was present in aGRN+ without any structural changes. Importantly, this is the first longitudinal study and, across time, the metabolism more rapidly decreased in aGRN+ in lateral temporal and frontal regions. The main structural change observed in the longitudinal study was a reduction of cortical thickness in the left lateral temporal lobe in carriers. A limit of this study is the relatively small sample (n = 16); nevertheless, it provides important results. First, it evidences that the pathological processes develop a long time before clinical onset, and that early neuroimaging changes might be detected approximately 20 years before the clinical onset of disease. Second, it suggests that metabolic changes are detectable before structural modifications and cognitive deficits. Third, both the baseline and longitudinal studies provide converging results implicating lateral temporal lobe as early involved in GRN disease. Finally, our study demonstrates that structural and metabolic changes could represent possible biomarkers to monitor the progression of disease in the presymptomatic stage toward clinical onset.
Daily Family Interactions among Young Adults from Latino, Asian, and European Backgrounds

A total of 220 young adults (Mage=25.5 years) from Latin American, East Asian, Filipino, and European backgrounds reported their family interactions, daily activities, and psychological well being for fourteen days. Although young adults reported a lower frequency of family interactions than what has been observed during adolescence, leisure time and conflict with parents and siblings continued to have significance for psychological well being. Time spent in work, school, and other relationships made it difficult for young adults to spend time being with or helping family members. Other findings suggested a potentially higher level of family importance and connectedness among young adults from Filipino backgrounds as compared to their peers.
Improving 3D Facial Action Unit Detection with Intrinsic Normalization

Data normalization techniques are commonly used to reduce intra-class variance while preserving inter-class differences that aid in classification and regression tasks. Such practices bring all data examples to similar scale and range, and help to decrease the dimensionality of categorization problems. In this paper we describe a novel use of a geometric framework to extend the concept of data normalization to the domain of functions that lie on surfaces. In this context, normalization is posed as an embedding of all example functions into Riemannian manifolds nearly isometric to one another. Using geometric tools, we propose an implementation for the case of discretized functions on triangulated meshes. To demonstrate the proposed framework we apply it as a preprocessing step to the task of automatic facial action unit detection for depth maps from a 3D scanner. We show how this subject-independent representation produces more accurate action unit classification results. We also empirically demonstrate that utilizing this intrinsic normalization technique indeed improves action unit recognition performance.
Improving DPOP with function filtering

DPOP is an algorithm for distributed constraint optimization which has, as main drawback, the exponential size of some of its messages. Recently, some algorithms for distributed cluster tree elimination have been proposed. They also suffer from exponential size messages. However, using the strategy of cost function filtering, in practice these algorithms obtain important reductions in maximum message size and total communication cost. In this paper, we explain the relation between DPOP and these algorithms, and show how cost function filtering can be combined with DPOP. We present experimental evidence of the benefits of this new approach.
Modeling Broadcasting in IEEE 802.11p/WAVE Vehicular Networks

IEEE 802.11p/WAVE (Wireless Access in Vehicular Environments) is an emerging family of standards intended to support wireless access in Vehicular Ad Hoc Networks (VANETs). Broadcasting of data and control packets is expected to be crucial in this environment. Both safety-related and non-safety applications rely on broadcasting for the exchange of data or status and advertisement messages. Most of the broadcasting traffic is designed to be delivered on a given frequency during the control channel (CCH) interval set by the WAVE draft standard. The rest of the time, vehicles switch over to one of available service channels (SCHs) for non-safety related data exchange. Although broadcasting in VANETs has been analytically studied, related works neither consider the WAVE channel switching nor its effects on the VANET performance. In this letter, a new analytical model is designed for evaluating the broadcasting performance on CCH in IEEE 802.11p/WAVE vehicular networks. This model explicitly accounts for the WAVE channel switching and computes packet delivery probability as a function of contention window size and number of vehicles.
Change management through leadership: the mediating role of organizational culture

Purpose This paper aims to examine the influence of change leadership on organizational culture and change management practices in public-sector firms in the United Arab Emirates (UAE). It also examines the mediating role of organizational culture on the interactions between leadership and change management programmes in the organization. Design/methodology/approach An empirical test of the hypotheses using confirmatory factor analysis and structural equation modelling were applied to data collected from 210 middle-management respondents of public-sector organizations in the UAE. Findings The findings suggest that change-oriented leadership has a positive and significant direct effect on planned change (s = 0.20, p < 0.01) and a positive and significant but indirect effect on planned change (s = 0.279, p < 0.01) and emergent change (s = 0.262, p < 0.01) change. Furthermore, hierarchical culture was found to positively and significantly impact directly on both planned (s = 0.480, p < 0.001) and emergent (s = 0.245, p < 0.01) change management in the UAE public-sector service organizations. Practical implications Based on the study’s findings, the role of the hierarchical culture in effecting change in the UAE public-sector organizations provides new and significant insights into the research literature on organizational culture as regards change management issues and the challenges facing these organizations. Originality/value The study makes a significant original contribution toward knowledge on the management of organizational change in UAE public-sector service organizations. It has practical implications for managers and leaders confronting organizational change management in the UAE.
A demand‐driven approach to slicing legacy COBOL systems

Maintenance of aging legacy COBOL systems is labor‐intensive and is becoming a more and more difficult problem. Program slicing is a potentially useful analysis for aiding different maintenance activities, including program comprehension, reverse engineering, debugging, and testing. Numerous techniques have been proposed in the past decades; however, in interactive contexts, we found none of them suitable for slicing industrial‐scale COBOL systems due to their large space or preprocessing requirements. This paper proposes a novel static program slicing approach, which is based on context‐sensitive token propagation over control flow graphs (CFGs). CFGs require less space compared with program dependence graphs (PDGs) used by other techniques, and the token propagation method computes the necessary information only, on demand. Algorithms are presented for data flow and full slicing to calculate precise program slices. Preliminary application experiences on industrial‐scale COBOL systems are also summarized. Copyright © 2011 John Wiley & Sons, Ltd.
Emission from dust in galaxies: Metallicity dependence

Infrared (IR) dust emission from galaxies is frequently used as an indicator of star formation rate (SFR). However, the effect of the dust-to-gas ratio (i.e., amount of the dust) on the conversion law from IR luminosity to SFR has not so far been considered. Then, in this paper, we present a convenient analytical formula including this effect. In order to obtain the dependence on the dust-to-gas ratio, we extend the formula derived in our previous paper, in which a theoretical formula converting IR luminosity to SFR was derived. That formula was expressed as ${\rm SFR}/(M_\odot~{\rm yr}^{-1})=\{3.3\times 10^{-10}(1- \eta)/(0.4-0.2f+0.6\epsilon)\} (L_{\rm IR}/L_\odot)$, where f is the fraction of ionizing photons absorbed by hydrogen, $\epsilon$ is the efficiency of dust absorption for nonionizing photons, $\eta$ is the cirrus fraction of observed dust luminosity, and $L_{\rm IR}$ is the observed luminosity of dust emission in the 8-1000-$\mu$m range. Our formula explains the IR excess of the Galaxy and the Large Magellanic Cloud. In the current paper, especially, we present the metallicity dependence of our conversion law between SFR and $L_{\rm IR}$. This is possible since both f and $\epsilon$ can be estimated via the dust-to-gas ratio, which is related to metallicity. We have confirmed that the relation between the metallicity and the dust-to-gas ratio is applied to both giant and dwarf galaxies. Finally, we apply the result to the cosmic star formation history. We find that the comoving SFR at z=3 calculated from previous empirical formulae is underestimated by a factor of 4-5.
Parent-Implemented Trial-Based Functional Analysis for Young Children With ASD

There is growing support for the use of trial-based functional analysis (TBFA) in applied settings. The purpose of the present study was to assess the accuracy and social validity of parent-implemented TBFAs. The participants were three young children (ages 3–7 years old) with autism spectrum disorder (ASD) and their mothers. Each mother implemented the TBFA with coaching from a behavior consultant. Following the TBFA, parent-implemented function-based interventions were evaluated using a multiple-probe across parent–child dyads design. Child challenging behavior decreased and communication increased following the implementation of the function-based intervention. TBFAs required approximately 5 to 7 hr of consultant time, and each mother rated the TBFA as socially valid. The results of this study indicate parent-implemented TBFAs may be an accurate and socially valid method to evaluate the function of challenging behavior in homes. The discussion includes implications for practice and directions for future research.
Low Cost Eye Tracking: The Current Panorama

Despite the availability of accurate, commercial gaze tracker devices working with infrared (IR) technology, visible light gaze tracking constitutes an interesting alternative by allowing scalability and removing hardware requirements. Over the last years, this field has seen examples of research showing performance comparable to the IR alternatives. In this work, we survey the previous work on remote, visible light gaze trackers and analyze the explored techniques from various perspectives such as calibration strategies, head pose invariance, and gaze estimation techniques. We also provide information on related aspects of research such as public datasets to test against, open source projects to build upon, and gaze tracking services to directly use in applications. With all this information, we aim to provide the contemporary and future researchers with a map detailing previously explored ideas and the required tools.
Security and privacy enhancement in VANETs using mobility pattern

Securing the Vehicular Adhoc Networks (VANETs) along with appropriate protection of the privacy of drivers or vehicle owners is a very challenging task. Drivers' behavioral tendencies can be reflected in the mobility patterns of the vehicles (rational behavior). Misbehaving vehicles are always threat to safety, VANETs security and privacy. Detection of misbehavior can enhance security and privacy in VANETs. This paper is presenting a mobility pattern based misbehavior detection approach in VANETs. Simulation results show that the Location Anonymous Message based proposed approach has the potential to improve security and maintain privacy in VANETs.
Spectral Probability Models for IR Images with Applications to IR Face Recognition

| Seeking statistical models for IR images, we decompose observed images into their spectral components and characterize the lower-order statistics of these components. Speci cally, we lter IR images using Gabor lters and model the marginal density functions of the ltered features via a parametric family. A two-parameter density, called a Bessel form, is shown to model well the histograms of the spectral components. For K lters, an image is represented by 2K Bessel parameters and any two IR images can be compared using a closed form expression for the L2-metric between their corresponding Bessel forms. Results from experiments involving IR face-recognition, and comparisons with simple versions of PCA and ICA are pre-