Miracle-dz/UniFAR-FTUs huggingface dataset:
Parent–Offspring Conflict in Mate Preferences

Prevailing evolutionary approaches to human mating have largely ignored the fact that mating decisions are heavily influenced by parents and other kin. This is significant because parents and children often have conflicting mate preferences. We provide a brief review of how parents have influenced their children's mating behavior across cultures and throughout history. Then, by drawing on evolutionary reasoning, we offer a hypothesis for why parents and offspring may have conflicting interests with respect to mate preferences. Specifically, parents may have a relatively stronger preference for children's mates with characteristics suggesting high parental investment and cooperation with the ingroup, whereas children may have a relatively stronger preference for mates with characteristics signaling heritable fitness. We review past research consistent with this hypothesis, and we report new results from an empirical study consisting of 768 participants from a variety of cultures that provided clear support for the hypothesis.
Are Methylenetetrahydrofolate Reductase (MTHFR) Gene Polymorphisms C677T and A1298C Associated with Higher Risk of Pediatric Migraine in Boys and Girls

Introduction: Women have been reported to have a higher risk for migraine. The pathogenesis of migraine is known to be related to genetic risk factors such as the presence of common polymorphisms C677T (rs1801133) and A1298C (rs1801131) in the MTHFR gene. The aim of the study was to examine the role of these two MTHFR polymorphisms as risk factors for pediatric migraine and examine the gender-specific differences. Methods: 56 patients (M = 26; F = 30) with migraine were randomly selected from Tartu University Hospital Children’s Clinic. 29 patients had migraine with aura (M = 13, F = 16) and 27 migraine without aura (M = 13, F = 14). The average age of the patients was 12.7 yrs (SD = 3.3). The control group consisted of 168 healthy people (M = 78, F = 90), ages 22 - 35 yrs (average 29.2 ± 3.4). Results: Among patients with aura, girls had higher frequency of A1298C and C677T mutations than boys, but not with statistical significance (minor allele frequency 0.406 and 0.231, p = 0.16; minor allele frequency 0.313 and 0.269, p = 0.72; respectively). In the migraine without aura group, girls had higher frequency in C677T (minor allele frequency 0.357 vs 0.231, p = 0.31) than boys. Comparing the migraine group with controls, higher frequencies of minor allele of A1298C and C677T in female patients were found (p = 0.63 and p = 0.32 respectively). In contrast, the C677T minor allele occurred in male controls with higher frequency (p = 0.34). Conclusion: Our results indicate a possible contribution of common MTHFR gene polymorphisms to migraine risk in children. 91% of children with migraine carried the variant alleles. Among girls, MTHFR polymorphisms occurred more frequently than among boys, but these differences were not statistically significant. We found that girls with migraine (with and without aura) showed an increased prevalence of C677T polymorphisms.
Reliable, Built-in, High-Accuracy Force Sensing for Legged Robots

An approach for achieving reliable, built-in, high-accuracy force sensing for legged robots is presented, based on direct exploitation of the properties of a robot’s mechanical structure. The proposed methodology relies on taking account of force-sensing requirements at the robot design stage, with a view to embedding force-sensing capability within the mechanical structure of the robot itself. The test case is ROBOCLIMBER, a bulky, quadruped climbing and walking machine whose weighty legs enable it to carry out heavy-duty drilling operations. The paper shows that, with finite-element analysis of ROBOCLIMBER’s mechanical configuration during the design stage, candidate positions can be selected for the placement of force transducers to measure indirectly the contact forces between the feet and the ground. Force sensors are then installed at the theoretically best positions on the mechanical structure, and several experiments are carried out to calibrate all sensors within their operational range of interest. After calibration, the built-in sensors are subjected to experimental performance evaluation, and the final best sensor option is found. The built-in force-sensing capability thus implemented is subjected to its first test of usability when it is employed to compute the actual centre of gravity of ROBOCLIMBER. The method is shown to be useful for determining variation during a gait (due to the non-negligible weight of the legs). Afterwards the force sensors are shown to be useful for controlling foot-ground interaction, and several illustrative experiments confirm the high sensitivity, reliability and accuracy of the selected approach. Lastly, the built-in sensors are used to measure ground-reaction forces and to compute the zero-moment point for ROBOCLIMBER in real time, both while standing and while executing a dynamically balanced gait.
The optical morphology of A3667 re-examined

The galaxy cluster A3667 was observed using the Two-degree Field (2dF) multifibre spectroscopic system on the Anglo-Australian Telescope in a program designed to examine the velocity structure in the region. Specifically, we sought evidence from the optical data for the putative cluster merger believed to be responsible for the observed radio and X-ray emission. We present 184 new redshifts in the region, of which 143 correspond to member galaxies of A3667. We find the cluster velocity distribution to be well modelled by a single Gaussian in agreement with previous results. In addition, new redshift-selected isodensity plots significantly reduce the prominence of the previously reported subgroup to the north-west of the main cluster. Instead, we find the galaxy distribution to be elongated and well mixed, with a high velocity dispersion and no significant evidence for substructure. These results are consistent with the axis of the proposed merger being close to the plane of the sky.
Probabilistic self-scheduling for coverage configuration in wireless ad-hoc sensor networks

Purpose – Sensing coverage is a critical issue in sensor network deployments. The paper aims to propose a novel scheme to maintain the sensing coverage in sensor networks, which is termed coverage‐aware self‐scheduling (CASS). Design/methodology/approach – The paper describes a generic unifying framework to incorporate different connectivity and coverage maintenance schemes. Simulations are carried out under the framework by integrating CASS with an existing connectivity maintenance scheme ‐ the low‐energy adaptive clustering hierarchy.Findings – Different from the existing work on coverage maintenance, CASS probabilistically schedules sensing activities according to the sensor's contribution to the sensing coverage of the whole sensor network. CASS reduces the number of active sensors to maintain certain coverage. Besides the sensing coverage, the connectivity of the network topologies is required for the purpose of communicating among sensors to collect sensing data. Simulation results show that CASS can.
Stereo Matching Algorithm Based on a Generalized Bilateral Filter Model

Stereo matching is a kernel problem in stereo vision systems. Stereo algorithms can be roughly classified into local and global approaches. Local algorithms use Winner-Take-All strategy, simply taking disparity level that minimizes the aggregation costs. In this paper we present a local stereo matching algorithm with an adaptive cost aggregation strategy based on a generalized bilateral filter model. The range weight computation in the original bilateral filter is extended by the inner and outer weighted average processes. A pixel is assigned a high range weight to the central pixel not only if the patches of the two pixels are similar but also if the neighbouring patches around the two pixels are similar. The final range weight could more accurately reflect the similarity of considering two pixels. Different cost aggregation methods can be easily derived from the model by modifying parameters. In experimental section we compare four cost aggregation methods based on the generalized model and give conclusions which demonstrate the effectiveness of our proposed strategy.
Software with partial functions: Automating correctness proofs via nonstrict explicit domains

As our society becomes technologically more complex, computers are being used in greater and greater numbers of high consequence systems. Giving a machine control over the lives of humans can be disturbing, especially if the software that is run on such a machine has bugs. Formal reasoning is one of the most powerful techniques available to demonstrate the correctness of a piece of software. When reasoning about software and its development, one frequently encounters expressions that contain partial functions. As might be expected, the presence of partial functions introduces an additional dimension of difficulty to the reasoning framework. This difficulty produces an especially strong impact in the case of high consequence systems. An ability to use formal methods for constructing software is essential if we want to obtain greater confidence in such systems through formal reasoning. This is only reasonable under automation of software development and verification. However, the ubiquitous presence of partial functions prevents a uniform application to software of any tools not specifically accounting for partial functions. In this paper we will describe a framework for reasoning about software, based on the nonstrict explicit domain approach, that is applicable to a large class of software/hardware systems. In this framework the Hoare triples containing partial functions can be reasoned about automatically in a well-defined and uniform manner.
Large-scale Outdoor Scene Classification by Boosting a Set of Highly Discriminative and Low Redundant Graphlets

Large-scale outdoor scene classification is an important issue in multimedia information retrieval. In this paper, we propose an efficient scene classification model by integrating outdoor scene image's local features into a set of highly discriminative and less redundant graph lets (i.e., small connected sub graph). Firstly, each outdoor scene image is segmented into a number of regions in terms of its color intensity distribution. And a region adjacency graph (RAG) is defined to encode the geometric property and color intensity distribution of outdoor scene image. Then, the frequent sub-structures are mined statistically from the RAGs corresponding to the training outdoor scene images. And a selecting process is carried out to obtain a set of sub-structures from the frequent ones towards being highly discriminative and low redundant. And these selected sub-structures are used as templates to extract the corresponding graph lets. Finally, we integrate these extracted graph lets by a multi-class boosting strategy for outdoor scene classification. The experimental results on the challenging SUN~\cite{sun} data set and the LHI~\cite{lotus} data set validate the effectiveness of our approach.
Analysis of coupled phase-locked loops with independent oscillators for beam control active phased arrays

Phase dynamics and phase noise of N coupled phase-locked loops (PLLs) with N independent oscillators are analyzed. The phase noise of coupled PLLs with independent oscillators is analyzed by using the signal flow graph and Mason's gain rules. The near-carrier phase noise of PLL elements in the array can be reduced to 1/N that of a single independent (without coupling) VCO of the PLL element, and return to its free-running phase noise far from the carrier frequency, provided the coupling network is reciprocal, the feedback loop gain of the PLL elements is large, and the phase detector and amplifier noise are neglected. The phase dynamics and noise improvement of coupled PLL arrays are verified by using two coupled PLLs with independent GaAs MESFET oscillators operating at X-band.
Immunochromatographic Assay for Ultrasensitive Detection of Aflatoxin B1 in Maize by Highly Luminescent Quantum Dot Beads

Highly luminescent quantum dot beads (QBs) were synthesized by encapsulating CdSe/ZnS and used for the first time as immunochromatographic assay (ICA) signal amplification probe for ultrasensitive detection of aflatoxin B1 (AFB1) in maize. The challenges to using high brightness QBs as probes for ICA are smooth flow of QBs and nonspecific binding on nitrocellulose (NC) membrane, which are overcome by unique polymer encapsulation of quantum dots (QDs) and surface blocking method. Under optimal conditions, the QB-based ICA (QB-ICA) sensor exhibited dynamic linear detection of AFB1 in maize extract from 5 to 60 pg mL–1, with a median inhibitory concentration (IC50) of 13.87 ± 0.16 pg mL–1, that is significantly (39-fold) lower than those of the QD as a signal probe (IC50 = 0.54 ± 0.06 ng mL–1). The limit of detection (LOD) for AFB1 using QB-ICA sensor was 0.42 pg mL–1 in maize extract, which is approximately 2 orders of magnitude better than those of previously reported gold nanoparticle based immunochromatographic assay (AuNP-ICA) and is even comparable with or better than the conventional enzyme-linked immunosorbent assay (ELISA) method. The performance and practicability of our QB-ICA sensor were validated with a commercial ELISA kit and further confirmed with liquid chromatography tandem mass spectrometry (LC–MS/MS). Given its efficient signal amplification performance, the proposed QB-ICA offers great potential for rapid, sensitive, and cost-effective quantitative detection of analytes in food safety monitoring.
What's Wrong with Government Interventions? Wrong Banks, Inadequate Strategies or Ineffective Measures?

The current crisis has promoted the regulatory authorities to implement the directives regulating the future authorities’ actions in resolving the systemic banking crises. The recent results however document that only a small portion of banks gets recovered, as a result of government interventions. With our study we look for the reason and investigate how effective are the regulatory intervention instruments. Consistent with the theory, our results show that weaker banks are more likely to get the government support during the crisis. We also document that the type of the support addresses the banks’ problems. Our regression results however indicate that at the end the intervened banks are more likely to go bankrupt than their non-intervened counterparties. We argue that government interventions must be sufficiently large, and an optimal banking recovery program should include a deep restructuring process. Our results are consistent with the literature opting for the bail-in before the implementation of the bail-out instruments.
A Tableau Algorithm for SROIQ under Infinitely Valued Gödel Semantics

Fuzzy description logics (FDLs) are knowledge representation formalisms capable of dealing with imprecise knowledge by allowing intermediate membership degrees in the interpretation of concepts and roles. One option for dealing with these intermediate degrees is to use the so-called Gödel semantics. Despite its apparent simplicity, developing reasoning techniques for expressive FDLs under this semantics is a hard task. We present a tableau algorithm for deciding consistency of a SROIQ ontology under Gödel semantics. This is the first algorithm that can handle the full expressivity of SROIQ as well as the full Gödel semantics.
Reshaped Window Iterative Super-Resolution Imaging Algorithm

To overcome the competition between the side lobe suppression and the main lobe spread in radar imaging, we introduce a super-resolution imaging algorithm based on the iterative processes of the reshaped window function. The side lobes of the target imaging are sensitively dependent on the structures of reshaped window function, and the main lobes, however, are not changed. One could obtain a super-resolution image by using the iterative process, which minimizes the energy between the radar images constructed from the different reshaped window functions. The experimental results of two cylinders agree with the analysis of electromagnetic theory, which verifies the new algorithm.
Effect of crude plant extracts from some Oaxacan flora on two deleterious fungal phytopathogens and extract compatibility with a biofertilizer strain

The antimicrobial activity of 12 plant extracts was tested against the phytopathogens Alternaria alternata and Fusarium solani. In addition, the compatibility of the extracts toward Bacillus liqueniformis, a biofertilizer and a non-target microorganism, was assessed. Plants tested belong to the Euphorbiaceae, Asteraceae, Crassulaceae, Rubiaceae, Convolvulaceae, Verbenaceae, Orchidaceae, Nyctaginaceae, Boraginaceae, and Tiliaceae families and were collected in the State of Oaxaca. The antifungal activity of the plant extracts (50–100 mg/mL) against A. alternata and F. solani, was determined by measuring the mycelium radial growth and obtaining the minimum inhibitory concentration (MIC) of fungal growth. In addition, with the aim of finding plant extracts which are compatible with a B. licheniformis biofertilizer strain and to test the non-toxic nature of the treatments, the toxicity of the extracts toward this strain was evaluated using the agar diffusion method. Azoxystrobin (12 μg) and chloramphenicol (30 μg) were used as positive controls for the pathogens and for the non-target bacteria, respectively. Plant extracts inhibited fungal growth in the ranges of 0.76–56.17% against F. solani and 2.02–69.07% against A. alternata. The extracts of Acalypha subviscida, Ipomoea murucoides, Tournefortia densiflora and Lantana achyranthifolia showed MIC values between 5.77–12.5 mg/mL for at least one of the fungal species. The best treatment, Adenophyllum aurantium, exhibited a maximum inhibition for both F. solani (56.17%, MIC = 7.78 mg/mL) and A. alternata (68.64% MIC = 7.78 mg/mL), and resulted innocuous toward B. licheniformis. Therefore, this plant has an outstanding potential for the agroecological control of fungal phytopathogens in industrial crops.
Inventory Reduction and Productivity Growth: Evidence from the Japanese Automotive Sector

The literature on JIT production suggests a causal link between work-in-process inventory and manufacturing productivity. Such a connection has been described in numerous case studies but never tested statistically. This paper uses historical data for 52 Japanese automotive companies to evaluate the inventory-productivity relationship. We find that inventory reductions stimulated gains in productivity, rather than vice versa. On average, each 10% reduction in inventory led to about a 1% gain in labor productivity, with a lag of about one year. Significant differences are found among company groups: Toyota affiliates had a shorter lag; while Nissan affiliates demonstrated no productivity effect. Firms that made inventory reductions typically saw an increase in their productivity rank.
M-Best-Diverse Labelings for Submodular Energies and Beyond

We consider the problem of finding M best diverse solutions of energy minimization problems for graphical models. Contrary to the sequential method of Batra et al., which greedily finds one solution after another, we infer all M solutions jointly. It was shown recently that such jointly inferred labelings not only have smaller total energy but also qualitatively outperform the sequentially obtained ones. The only obstacle for using this new technique is the complexity of the corresponding inference problem, since it is considerably slower algorithm than the method of Batra et al. In this work we show that the joint inference of M best diverse solutions can be formulated as a submodular energy minimization if the original MAP-inference problem is submodular, hence fast inference techniques can be used. In addition to the theoretical results we provide practical algorithms that outperform the current state-of-the-art and can be used in both submodular and non-submodular case.
Just-intime Acceleration of JavaScript

JavaScript has seen tremendous growth in popularity driven by increasingly interactive web sites and sophisticated web interfaces. However, the performance of JavaScript continues to be a hurdle in using it for tasks that are computationally intensive, such as gaming, simulations, and visualization. JavaScript has also been slow to exploit the available parallelism on modern computers. Specifically, it is not currently easy to exploit GPGPUs within JavaScript. A part of the reason is that the low-level interface demanded for GPGPU programming is often not approachable by JavaScript programmers. In this paper, we present a novel approach that provides a mechanism to accelerate portions of JavaScript programs without requiring the programmers to learn new syntax or low-level APIs. We achieve that through an embedded DSL used to specify GPGPU computations. We have designed a JavaScript library, and an accompanying Firefox extension, that work together to compile the embedded DSL just-in-time using the LLVM backend for generating PTX. The compiled code is cached to minimize the compilation overhead. Our evaluation of the system using a micro-benchmark, two applications kernels, and an application benchmark demonstrates that our approach imposes minimal performance overhead, while providing an easy GPGPU programming interface to JavaScript programmers.
Applying Text Categorization to Vocabulary Enhancement for Japanese-English Cross-Language Retrieval

In this paper we explore a new method for vocabulary enhancement in cross-language retrieval. The focus is on whether we can improve upon dictionary-based retrieval, machine translation of queries, or the use of a bilingual lexicon derived from parallel corpus alignment. All experiments are done with the NACSIS collection of Japanese scientific abstracts with titles and author-assigned keywords available also in English. Our results show that use of text categorization methods to mine documents for words associated with subject keywords will provide almost equivalent performance to direct vocabulary search, but not better than bilingual lexicon-based retrieval.
Chisel: A Resource Savvy Approach for Handling Skew in MapReduce Applications

Skew mitigation has been a major concern in distributed programming frameworks like MapReduce. It is becoming more prominent with the increasing complexity in user requirements and computation involved. We present Chisel, a self-regulating skew detection and mitigation policy for MapReduce applications. The novelty of the approach is that it involves no scanning or sampling of input data to detect skew and hence incurs low overhead, provides better resource utilization and maintains output order and file structure. It is also transparent to the users and can be used as a plugin whenever required. We use Hadoop to implement our skew handling policies. Chisel implements two skew handling policies for mitigating skew. It does late skew detection for map operators i.e at the last wave of map execution, where skewed maps are selected on the basis of remaining time to complete. More maps are created dynamically over remaining data per block. An early skew detection i.e before starting shuffle phase, is done for reduce operator. This prevents the expensive shuffle and sort phases from delaying skew detection and job completion time. Multiple reducers are created per skewed partition, each shuffling data from a subset of total maps and starts processing it when their portion of maps are over. They need not wait for the completion of all the maps. Therefore, the barrier between map and reduce phase no longer remains a constraint for effective resource utilization. Chisel additionally implements an online job profiler to determine the start point of reduce tasks and also modifies the capacity scheduler to distribute reduce tasks evenly in the cluster. Chisel significantly decreases the overall execution time of jobs and increases resource utilization. Improvement depends directly upon the availability of resources in the cluster and skewness in the job.