Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimating Landholders’ Probability of Participating in a Stewardship Program, and the Implications for Spatial Conservation Priorities

The need to integrate social and economic factors into conservation planning has become a focus of academic discussions and has important practical implications for the implementation of conservation areas, both private and public. We conducted a survey in the Daly Catchment, Northern Territory, to inform the design and implementation of a stewardship payment program. We used a choice model to estimate the likely level of participation in two legal arrangements - conservation covenants and management agreements - based on payment level and proportion of properties required to be managed. We then spatially predicted landholders’ probability of participating at the resolution of individual properties and incorporated these predictions into conservation planning software to examine the potential for the stewardship program to meet conservation objectives. We found that the properties that were least costly, per unit area, to manage were also the least likely to participate. This highlights a tension between planning for a cost-effective program and planning for a program that targets properties with the highest probability of participation.
Non-Linear Feedback Control for Energy Efficient On-Chip Streaming Computation

In this work we take a control theoretic approach to the dynamic voltage/frequency scaling (DVS) in a MPSoC architecture with mixed pipelined/parallel processing. The aim is that of minimizing energy consumption with throughput guarantees. Theoretical analysis and experiments, carried out on a cycle-accurate, energy-aware, multiprocessor simulation platform, are provided. We give a dynamic model of the system behavior on the basis of which we synthesize a non-linear feedback controller for the run-time adjustment the frequencies of the processing stages. We compare, from an energy consumption viewpoint, the proposed feedback approaches with local DVS policies.
Maximum likelihood decoding analysis of LT codes over AWGN channels

Luby-Transform (LT) codes are a class of Fountain codes which can approach the capacity of the erasure channels. In this paper, we investigate the performance of the LT codes over AWGN channels with BPSK modulation. First, the ensemble weight distribution of an LT code is derived. Secondly, we use a refined union bound to analyze the performance of LT codes under the maximum likelihood decoding assumption. Finally, simulation results using the iterative sum-product decoding algorithm are given for the comparison with the computational bounds.
Parameter Bounds on Estimation Accuracy Under Model Misspecification

When the assumed data distribution differs from the true distribution, the model is said to be misspecified or mismatched. Model misspecification at some level is an inevitability of engineering practice. While Huber's celebrated work assesses maximum-likelihood (ML) performance under misspecification, no simple theory for bounding parameter estimation exists. The class of parameter bounds emerging from the covariance inequality, or equivalently the minimum norm theorem is revisited. The expectation operator is well-known to form an inner product space. Flexibility in the choice of expectation integrand and measure for integration exists, however, to establish a class of parameter bounds under a general form of model misspecification, i.e., distribution mismatch. The Cramér-Rao bound (CRB) primarily, and secondarily the Barankin/Hammersley-Chapman-Robbins, Bhattacharyya, and Bobrovsky-Mayer-Wolf-Zakai bounds under misspecification are considered. Huber's sandwich covariance is readily established as a special case of the misspecified CRB subject to ML constraints, and generalizations of the Slepian and Bangs formulae under misspecification are obtained.
Stereo-inertial pose estimation and online sensors extrinsic calibration

The fusion of visual and inertial measurement has been popular in mobile robotics community for decades due to the complementary properties of two sensors. The combination of these two sensors offers rich texture of environment and accurate short-time motion prediction, making it particularly suitable for pose estimation, especially in GPS-denied unknown environment. In this paper, we propose a method which fuses stereo visual and inertial cues based on Multi-State Constraint Kalman Filter (MSCKF), to estimate 6DOF pose of mobile robot. Stereo vision offers real-metric perception of surroundings, giving a better initial scale estimation of the visual-inertial system. Compared with another class of methods based on batch nonlinear optimization, this filter-based method is more suitable for resource-constrained mobile platforms. In addition, the finite precision of sensors extrinsic calibration often makes estimator inconsistent. The proposed method includes extrinsic parameters in state vector to do online calibration. Experimental results on real-world datasets demonstrate that proposed method is consistent and substantially improves the accuracy of pose estimation, as well as the calibration between sensors.
Fast Synchronization of Mirrored Game Servers : Outcomes from a Testbed Evaluation

The deployment of online games over the Internet encompasses the use of novel, smart strategies able to guarantee, on one side, a high level of responsiveness in the game system and, one the other side, the consistency of the computed game state. The use of mirrored game servers has been recognized as a scalable, fault tolerant architectural solution for the support of Multiplayer Online Games (MOGs). We developed a new optimistic synchronization scheme devised for MOGs. The intelligence behind our scheme is based on the idea of exploiting two new notions of obsolescence and correlation among game events. These notions allow one to speed up the synchronization among replicated servers, while maintaining the consistency of the game state. In this work, we report on an experimental assessment based on a real implementation of a mirrored game server architecture, deployed over the Internet, that exploits our synchronization algorithm. Specific attention is devoted to the experimental assessment. Results show the viability and the efficacy of our approach.
Npas4: A Neuronal Transcription Factor with a Key Role in Social and Cognitive Functions Relevant to Developmental Disorders

Npas4 is a transcription factor, which is highly expressed in the brain and regulates the formation and maintenance of inhibitory synapses in response to excitatory synaptic activity. A deregulation of the inhibitory-excitatory balance has been associated with a variety of human developmental disorders such as schizophrenia and autism. However, not much is known about the role played by inhibitory synapses and inhibitory pathways in the development of nervous system disorders. We hypothesized that alterations in the inhibitory pathways induced by the absence of Npas4 play a major role in the expression of the symptoms observed in psychiatric disorders. To test this hypothesis we tested mice lacking the transcription factor (Npas4 knock-out mice (Npas4-KO)) in a battery of behavioral assays focusing on general activity, social behaviors, and cognitive functions. Npas4-KO mice are hyperactive in a novel environment, spend less time exploring an unfamiliar ovariectomized female, spend more time avoiding an unfamiliar male during a first encounter, show higher social dominance than their WT littermates, and display pre-pulse inhibition, working memory, long-term memory, and cognitive flexibility deficits. These behavioral deficits may replicate schizophrenia-related symptomatology such as social anxiety, hyperactivity, and cognitive and sensorimotor gating deficits. Immunohistochemistry analyses revealed that Npas4 expression is induced in the hippocampus after a social encounter and that Npas4 regulates the expression of c-Fos in the CA1 and CA3 regions of the hippocampus after a cognitive task. Our results suggest that Npas4 may play a major role in the regulation of cognitive and social functions in the brain with possible implications for developmental disorders such as schizophrenia and autism.
A new multiobjective evolutionary algorithm for environmental/economic power dispatch

In this paper, a new multiobjective evolutionary algorithm for environmental/economic power dispatch (EED) optimization problem is presented. The EED problem is formulated as a nonlinear constrained multiobjective optimization problem with both equality and inequality constraints. A new nondominated sorting genetic algorithm (NSGA) based approach is proposed to handle the problem as a true multiobjective optimization problem with competing and noncommensurable objectives. The proposed approach employs a diversity-preserving technique to overcome the premature convergence and search bias problems and produce a well-distributed Pareto-optimal set of nondominated solutions. A hierarchical clustering technique is also imposed to provide the decision maker with a representative and manageable Pareto-optimal set. Several optimization runs of the proposed approach are carried out on a standard IEEE test system. The results demonstrate the capabilities of the proposed NSGA based approach to generate the true Pareto-optimal set of nondominated solutions of the multiobjective EED problem in one single run. Simulation results with the proposed approach have been compared to those reported in the literature. The comparison shows the superiority of the proposed NSGA based approach and confirms its potential to solve the multiobjective EED problem.
BIOLOGY TEACHING THROUGH SELF-REGULATED LEARNING AND COGNITIVE STRUCTURE: AN ANALYSIS OF THE EFFECT OF LEARNING STRATEGIES FOR COGNITIVE DEVELOPMENT VIA LATENT GROWTH MODEL

Studies performed by cognitive psychologists in relation to memory provided the models of memory and fundamental knowledge on the functioning of memory. In parallel with the studies on cognition, the research conducted by Ausubel (1960) was a pioneering in using cognitive approaches in the process of science learning with such ideas as meaningful learning, prior knowledge, and advance organizer. Meaningful learning is considered as structuring the new meaning—as the integration of existing information with what is newly learnt (cognitive structure or mental model; Novak, 2002). In this context, such concepts as conceptual understanding, misconceptions, and cognitive structure have frequently been researched in studies concerning science learning. These mental structures are considered learners’ understanding of a topic and the reflection of their experiences. According to Tsai and Huang (2002), cognitive structure considered to be related to processing of information, is the construction of the knowledge representation along with the concepts and their relations in long-term memory. These structures, which are handled as related to information processing, are also thought to be related with self-regulation and problem-solving processes (Lee, Goh, Chia, & Chin, 1996; Tsai & Huang, 2002).
Patterns of East Asian pig domestication, migration, and turnover revealed by modern and ancient DNA

The establishment of agricultural economies based upon domestic animals began independently in many parts of the world and led to both increases in human population size and the migration of people carrying domestic plants and animals. The precise circumstances of the earliest phases of these events remain mysterious given their antiquity and the fact that subsequent waves of migrants have often replaced the first. Through the use of more than 1,500 modern (including 151 previously uncharacterized specimens) and 18 ancient (representing six East Asian archeological sites) pig (Sus scrofa) DNA sequences sampled across East Asia, we provide evidence for the long-term genetic continuity between modern and ancient Chinese domestic pigs. Although the Chinese case for independent pig domestication is supported by both genetic and archaeological evidence, we discuss five additional (and possibly) independent domestications of indigenous wild boar populations: one in India, three in peninsular Southeast Asia, and one off the coast of Taiwan. Collectively, we refer to these instances as “cryptic domestication,” given the current lack of corroborating archaeological evidence. In addition, we demonstrate the existence of numerous populations of genetically distinct and widespread wild boar populations that have not contributed maternal genetic material to modern domestic stocks. The overall findings provide the most complete picture yet of pig evolution and domestication in East Asia, and generate testable hypotheses regarding the development and spread of early farmers in the Far East.
Frequency dependent statistical model for the suppression of late reverberations

Suppression of late reverberations is a challenging problem in reverberant speech enhancement. A promising recent approach to this problem is to apply a spectral subtraction mask to the spectrum of the reverberant speech, where the spectral variance of the late reverberations was estimated based on a frequency independent statistical model of the decay rate of the late reverberations. In this paper, we develop a dereverberation algorithm by following a similar process. Instead of using the frequency independent model, however, we estimate the frequency dependent reverberation time and decay rate, and use them for the estimation of the spectral subtraction mask. In order to remove the processing artifacts, the mask is further filtered by a smoothing function, and then applied to reduce the late reverberations from the reverberant speech. The performance of the proposed algorithm, measured by the segmental signal to reverberation ratio (SegSRR) and the signal to distortion ratio (SDR), is evaluated for both simulated and real data. As compared with the related frequency indepenent algorithm, the proposed algorithm offers considerable performance improvement.
Life-logs Aggregation for Quality of Life Monitoring

We investigate how life-logs from commercial wearable trackers and smartphone sensors can be leveraged to automatically provide patients with knowledge for self-reflection on their disease. We present here: (1) An architecture for the acquisition of life-logs, their fusion, and their storage; and (2) A prototype GUI for the visualization of quality of life indicators.
Attitudes, Identity, and Self-Esteem: Implications for Lesbian, Bisexual, and Questioning Women

The relations among implicit and explicit measures of sexual orientation attitudes, identity, and self-esteem were explored, as well as how these measures related to sexual-orientation related behaviors. On an explicit level, this study found that self-esteem increased as participants reported more outness, more positive attitudes toward their own sexual orientations, and less time trying to pass as straight. This study also found differential predictive validity of implicit and explicit measures for predicting sexual-orientation related behaviors in lesbians, bisexual, queer, and questioning women. As people reported a larger amount of time being out of the closet, they revealed more positive attitudes toward homosexuality (as assessed by implicit and explicit measures), greater homosexual identity (as assessed by implicit and explicit measures), and greater self-esteem (as assessed by the explicit measure). Implications for understanding the use of implicit and explicit measures in general and for research on sexual orientation attitudes and related behaviors in particular are discussed.
Role of Kv1 Potassium Channels in Regulating Dopamine Release and Presynaptic D2 Receptor Function

Dopamine (DA) release in the CNS is critical for motor control and motivated behaviors. Dysfunction of its regulation is thought to be implicated in drug abuse and in diseases such as schizophrenia and Parkinson's. Although various potassium channels located in the somatodendritic compartment of DA neurons such as G-protein-gated inward rectifying potassium channels (GIRK) have been shown to regulate cell firing and DA release, little is presently known about the role of potassium channels localized in the axon terminals of these neurons. Here we used fast-scan cyclic voltammetry to study electrically-evoked DA release in rat dorsal striatal brain slices. We find that although G-protein-gated inward rectifying (GIRK) and ATP-gated (KATP) potassium channels play only a minor role, voltage-gated potassium channels of the Kv1 family play a major role in regulating DA release. The use of Kv subtype-selective blockers confirmed a role for Kv1.2, 1.3 and 1.6, but not Kv1.1, 3.1, 3.2, 3.4 and 4.2. Interestingly, Kv1 blockers also reduced the ability of quinpirole, a D2 receptor agonist, to inhibit evoked DA overflow, thus suggesting that Kv1 channels also regulate presynaptic D2 receptor function. Our work identifies Kv1 potassium channels as key regulators of DA release in the striatum.
A new Meta Machine Learning (MML) method based on combining non-significant different neural networks

Model combination provides an alternative to model selection. With a little additional effort we can obtain MML models that improve the generalization capabilities of their individual members. However, it has been recognized that the individual members must be as accurate and diverse as possible. In this paper we present a novel method for building MML models by combining neural networks which are not significantly different from the network selected by some model selection method.
Resolution and reproducibility of BOLD and perfusion functional MRI at 3.0 Tesla

Visual and somatosensory activation studies were performed on normal subjects to compare the spatial discrimination and reproducibility between functional MRI (fMRI) methods based on blood oxygen level‐dependent (BOLD) and perfusion contrast. To allow simultaneous measurement of BOLD and perfusion contrast, a dedicated MRI acquisition technique was developed. Repeated experiments of sensory stimulation of single digits of the right hand showed an average variability of activation amplitude of 25% for BOLD data, and a significantly lower variability of 21% for perfusion data. No significant difference in the variability of the locus of activity was observed between the BOLD and perfusion data. In somatotopy experiments, digits II and V were subjected to passive sensory stimulation. Both the BOLD and perfusion data showed substantial overlap in the activation patterns from the two digits. In a retinotopy study, two stimuli were alternated to excite different patches of V1. Again there was substantial overlap between the activation patterns from both stimuli, although the perfusion performed somewhat better than the BOLD method. Particularly for the visual studies, the overlap in activation patterns was more than expected based on the fine‐scale retinotopic mapping of cortical activity, suggesting that both BOLD and perfusion contrast mechanisms contribute substantially to the point‐spread function (PSF). Magn Reson Med, 2005. Published 2005 Wiley‐Liss, Inc.
Hierarchical peer-to-peer networks using lightweight superpeer topologies

The use of superpeers has been proposed to improve the performance of both structured and unstructured peer-to-peer (P2P) networks. In this paper, we study the performance of Yao-graph based superpeer topologies for hierarchical P2P networks. Since a Yao-graph is defined as a geometric structure, we are using the "highways" proximity clustering and placement scheme to assign geometric co-ordinates to superpeers and peers with respect to the underlying network conditions. Because of the lightweight structure of Yao-graphs, the resulting hierarchical P2P networks have promising properties with regard to scalability and performance, while still offering the benefits of the P2P approach with regard to resiliency.
A system to filter unsolicited texts from social learning networks

In the present day scenario online social networks (OSN) are very trendy and one of the most interactive medium to share, communicate and exchange numerous types of information like text, image, audio, video etc. All these publicly shared information are explicitly viewed by connected people in the blog or networks and having an enormous social impact in human mind. Posting or commenting on particular public/private areas called wall, may include superfluous messages or sensitive data. Information filtering can therefore have a solid influence in online social networks and it can be used to give users the facility to organize the messages written on public areas by filtering out unwanted wordings. In this paper, we have proposed a system which may allow OSN users to have a direct control on posting or commenting on their walls with the help of information filtering. This is achieved through text pattern matching system, that allows users to filter their open space and a privilege to add new words treated as unwanted. For experimental analysis a test social learning website is designed and some unwanted words/texts are kept as blacklisted vocabulary. To provide control to the user, pattern matching of texts are done with the blacklisted vocabulary. If it passes then only text can be posted on someone's wall, otherwise text will be blurred or encoded with special symbols. Analysis of experimental results shows high accuracy of the proposed system.
Epigenetic alterations in folate transport genes in placental tissue from fetuses with neural tube defects and in leukocytes from subjects with hyperhomocysteinemia

The objectives of this study were to identify tissue-specific differentially methylated regions (T-DMR’s) in the folate transport genes in placental tissue compared with leukocytes, and from placental tissues obtained from normal infants or with neural tube defects (NTDs). Using pyrosequencing, we developed methylation assays for the CpG islands (CGIs) and the CGI shore regions of the folate receptor α (FOLR1), proton-coupled folate transporter (PCFT) and reduced folate carrier 1 (RFC1) genes. The T-DMRs differed in location for each gene and the difference in methylation ranged between 2 and 54%. A higher T-DMR methylated fraction was associated with a lower mRNA level of the FOLR1 and RFC1 genes. Methylation fractions differed according to RFC1 80G > A genotype in the NTD cases and in leukocytes from subjects with high total plasma homocysteine (tHcy). There were no differences in methylated fraction of folate transporter genes between NTD cases and controls. We suggest that T-DMRs participate in the regulation of expression of the FOLR1 and RFC1 genes, that the RFC1 80G > A polymorphism exerts a gene-nutrition interaction on DNA methylation in the RFC1 gene, and that this interaction appears to be most prominent in NTD-affected births and in subjects with high tHcy concentrations.
Solving Feature Subset Selection Problem by a Hybrid Metaheuristic

The aim of this paper is to develop a hybrid metaheuristic based on Variable Neighbourhood Search and Tabu Search for solving the Feature Subset Selection Problem in classification. Given a set of instances characterized by several features, the classification problem consists of assigning a class to each instance. Feature Subset Selection Problem selects a relevant subset of features from the initial set in order to classify future instances. The proposed hybrid metaheuristic is compared with a Genetic Algorithm proposed in the literature. Although he hybrid metaheuristic and the genetic algorithm had a similar performance according to the accuracy percentages, the hybrid metaheuristic provided a higher reduction in the set of features.