Miracle-dz/UniFAR-FTUs huggingface dataset:
Does International Migration Affect Political Participation? Evidence from Multiple Data Sources across Mexican Municipalities, 1990–2013

Does international migration affect political participation in origin countries? We study how different channels of international migration — migrant absence, return, circularity, and transnational engagement — affected political participation across Mexican municipalities between 1990 and 2013. Using two different data sources including panel and longitudinal survey data, we find that generally, international migration has a positive effect on formal political participation in local elections. Results, however, are mixed across specific migratory channels, and the effects are conditional on levels of civic engagement. The study shows that specifying different channels of international migration is key to understanding the conditions under which emigration enhances political and civic participation in the local democratic process in origin countries.
How Correlations Influence Lasso Prediction

We study how correlations in the design matrix influence Lasso prediction. First, we argue that the higher the correlations, the smaller the optimal tuning parameter. This implies in particular that the standard tuning parameters, that do not depend on the design matrix, are not favorable. Furthermore, we argue that Lasso prediction works well for any degree of correlations if suitable tuning parameters are chosen. We study these two subjects theoretically as well as with simulations.
NONATObase: a database for Polychaeta (Annelida) from the Southwestern Atlantic Ocean

Networks can greatly advance data sharing attitudes by providing organized and useful data sets on marine biodiversity in a friendly and shared scientific environment. NONATObase, the interactive database on polychaetes presented herein, will provide new macroecological and taxonomic insights of the Southwestern Atlantic region. The database was developed by the NONATO network, a team of South American researchers, who integrated available information on polychaetes from between 5°N and 80°S in the Atlantic Ocean and near the Antarctic. The guiding principle of the database is to keep free and open access to data based on partnerships. Its architecture consists of a relational database integrated in the MySQL and PHP framework. Its web application allows access to the data from three different directions: species (qualitative data), abundance (quantitative data) and data set (reference data). The database has built-in functionality, such as the filter of data on user-defined taxonomic levels, characteristics of site, sample, sampler, and mesh size used. Considering that there are still many taxonomic issues related to poorly known regional fauna, a scientific committee was created to work out consistent solutions to current misidentifications and equivocal taxonomy status of some species. Expertise from this committee will be incorporated by NONATObase continually. The use of quantitative data was possible by standardization of a sample unit. All data, maps of distribution and references from a data set or a specified query can be visualized and exported to a commonly used data format in statistical analysis or reference manager software. The NONATO network has initialized with NONATObase, a valuable resource for marine ecologists and taxonomists. The database is expected to grow in functionality as it comes in useful, particularly regarding the challenges of dealing with molecular genetic data and tools to assess the effects of global environment change. Database URL: http://nonatobase.ufsc. br/
On Divergence Measures and Static Index Pruning

We study the problem of static index pruning in a renowned divergence minimization framework, using a range of divergence measures such as f-divergence and Rényi divergence as the objective. We show that many well-known divergence measures are convex in pruning decisions, and therefore can be exactly minimized using an efficient algorithm. Our approach allows postings be prioritized according to the amount of information they contribute to the index, and through specifying a different divergence measure the contribution is modeled on a different returns curve. In our experiment on GOV2 data, Rényi divergence of order infinity appears the most effective. This divergence measure significantly outperforms many standard methods and achieves identical retrieval effectiveness as full data using only 50% of the postings. When top-k precision is of the only concern, 10% of the data is sufficient to achieve the accuracy that one would usually expect from a full index.
TM-RMRP: A Trust Model Based on a Recommendation Mechanism of Rewards and Punishments

As trust management can address the new security challenges in pervasive computing environments, we present our pervasive trust model TM-RMRP which is composed of input factors and trust evaluation. With the model presented, anonymous entities can collaborate with trustful parties without central security managers. To reduce or even avoid inappropriate or inaccurate recommendations, we introduce the concept of recommendation trust and propose a recommendation mechanism of rewards and punishments based on this concept to identify more reliable recommendations. From the simulation results, the mechanism is proved to be applicable in pervasive computing environments.
An Image Retrieval Method Based on Relevance Feedback and Collaborative Filtering

With image retrieval becoming increasingly important, the application of relevance feedback and content-based image retrieval technique has become a research hotspot. For the shortcomings in many of the existing image retrieval methods where there is the information of relevance feedback which is not be fully saved and used, and has poor accuracy and flexibility, an image retrieval method based on relevance feedback and collaborative filtering was proposed. It was discussed that how to improve efficiency of relevance feedback, reduce the number of interactions through analysis of historical data of relevance feedback and speed up the feedback process. Finally this method was compared with the existing retrieval methods. The experimental results showed that this method had significant improvement in retrieval effectiveness, which can effectively improve the rate of identifying all and precision rate.
Molecular Dissection of the Gibberellin/Abscisic Acid Signaling Pathways by Transiently Expressed RNA Interference in Barley Aleurone Cells Article, publication date, and citation information can be found at www.plantcell.org/cgi/doi/10.1105/tpc.003376.

The interaction between two phytohormones, gibberellins (GA) and abscisic acid (ABA), is an important factor regulating the developmental transition from seed dormancy to germination. In cereal aleurone tissue, GA induces and ABA suppresses the expression of α-amylases that are essential for the utilization of starch stored in the endosperm. In this work, the signaling pathways mediated by these hormones were investigated in the aleurone cells of barley seeds using double-stranded RNA interference (RNAi) technology. In this tissue, double-stranded RNA molecules generated from the transient expression of DNA templates caused a sequence-specific suppression of the target genes. We demonstrate that the transcription factor, GAMyb, is not only sufficient but also necessary for the GA induction of α-amylase. Another regulatory protein, SLN1, is shown to be a repressor of GA action, and the use of RNAi technology to inhibit the synthesis of SLN1 led to derepression of α-amylase even in the absence of GA. However, this effect still was suppressed by ABA. Although the ABA-induced Ser/Thr protein kinase, PKABA1, is known to suppress GA-induced α-amylase expression, PKABA1 RNAi did not hamper the inhibitory effect of ABA on the expression of α-amylase, indicating that a PKABA1-independent signaling pathway also may exist. We suggest that the generation of specific RNAi in a transient expression approach is a useful technique for elucidating the role of regulatory molecules in biological systems in which conventional mutational studies cannot be performed easily.
Nociceptin Reduces Epileptiform Events in CA3 Hippocampus via Presynaptic and Postsynaptic Mechanisms

The opiate-like peptide nociceptin/orphanin FQ (Noc) and its receptor [opiate receptor-like receptor (ORL-1)] are highly expressed in the hippocampus. Noc has inhibitory postsynaptic actions in CA1, CA3, and the dentate and seems to lack the disinhibitory, excitatory actions demonstrated for some opiate peptides in the hippocampus. The CA3 hippocampal region is important in the generation of hippocampal seizures. Therefore, we tested the action of Noc on spontaneous epileptiform activity recorded extracellularly or intracellularly in CA3 and generated by removal of Mg2+ from the bathing solution or by raising extracellular K+ from 3.5 to 7.5 mm. Superfusion of Noc robustly depressed spontaneous bursting without desensitization. The ORL-1 antagonist [Phe1Ψ(CH2-NH)Gly2]NC(1–13)NH2(1–2 μm) greatly attenuated the reduction of spontaneous bursting by Noc. To characterize the cellular mechanism of action of Noc, we recorded intracellularly from CA3 pyramidal neurons. Noc reduced EPSCs evoked by stimulating either mossy or associational/commissural fibers. Analysis of miniature EPSCs using whole-cell voltage-clamp recording suggests that Noc acts presynaptically to inhibit glutamate release. This is the first demonstration of a presynaptic effect for Noc in the hippocampus. Noc also increased K+ currents in CA3 pyramidal neurons, including the voltage-sensitive M-current. Blocking the M-current with linopirdine increased the duration of individual CA3 bursts but did not attenuate Noc-mediated inhibition of bursting. Thus, Noc acts via multiple mechanisms to reduce excitation in CA3. However, Noc inhibition of epileptiform events is not dependent on augmentation of the M-current.
HSI: A Novel Framework for Efficient Automated Singer Identification in Large Music Database

The singer’s information is essential in organising, browsing and exploring music data. As an important component of music database systems, the automated artist identification is gaining considerable momentum due to numerous potential applications including music indexing and retrieval, copy right management and music recommendation systems. Unfortunately, the most currently employed approaches are still in their infancy and the performance is by far less satisfactory. Indeed, they suffer from low effectiveness, less robustness and poor scalability to accommodate large scale of data. In this demo, we presents a novel system, called Hybrid Singer Identifier (HSI), for efficient and effective automated singer identification in large music databases.
Handling imperfect temporal relations

Ryabov, Vladimir Handling Imperfect Temporal Relations Jyväskylä: University of Jyväskylä, 2002, 75 p. (+included articles) (Jyväskylä Studies in Computing, ISSN 1456-5390; 24) ISBN 951-39-1359-7 Finnish summary
Dynamic Construction and Refinement of Utility-Based Categorization Models

The actions taken by an automated decision-making agent can be enhanced by including mechanisms that enable the agent to categorize concepts effectively. We pose a utility-based approach to categorization based on the idea that categorization should be carried out in the service of action. The choice of concepts is critical in the effective selection of actions under resource constraints. We propose a decision-theoretic framework for categorization which involves reasoning about alternative categorization models consisting of sets of interrelated concepts at varying levels of abstraction. Categorization models that are too abstract may overlook details that are critical for selecting the most appropriate actions. Categorization models that are too detailed, however, may be too expensive to process and may contain irrelevant information. Categorization models are therefore evaluated on the basis of the expected value of their recommended action, taking into account the resource cost of their evaluation. A knowledge representation scheme, known as probabilistic conceptual networks, has been developed to support the dynamic construction of models at varying levels of abstraction. This scheme combines the formalisms of influence diagrams from decision analysis and inheritance/abstraction hierarchies from AI. We also propose an incremental approach to categorical reasoning. By applying decision-theoretic control of model refinement, a resource-constrained actor iteratively decides between continuing to improve the current level of abstraction in the model, or to act immediately. >
On removing redundant faults in synchronous sequential circuits

We describe a time-efficient procedure for removing sequentially redundant faults from synchronous sequential circuits with synchronizing sequences. We use the properties of redundant faults and propose several methods to identify subsets of redundant faults that can be removed simultaneously from the circuit. By removing several redundant faults simultaneously, the number of repetitions of the test generation procedure invoked to identify redundant faults is reduced. Experimental results presented in this work demonstrate the effectiveness of the proposed removal procedure.
Deep Lexical Segmentation and Syntactic Parsing in the Easy-First Dependency Framework

We explore the consequences of representing token segmentations as hierarchical structures (trees) for the task of Multiword Expression (MWE) recognition, in isolation or in combination with dependency parsing. We propose a novel representation of token segmentation as trees on tokens, resembling dependency trees. Given this new representation, we present and evaluate two different architectures to combine MWE recognition and dependency parsing in the easy-first framework: a pipeline and a joint system, both taking advantage of lexical and syntactic dimensions. We experimentally validate that MWE recognition significantly helps syntactic parsing.
On the Compilation of Stratified Belief Bases under Linear and Possibilistic Logic Policies

Developing efficient approaches for reasoning under inconsistency is an important issue in many applications. Several methods have been proposed to compile, possibly inconsistent, weighted or stratified bases. This paper focuses on the well-known linear order and possibilistic logic strategies. It provides a way for compiling a stratified belief base in order to be able to process inference from it in polynomial time. The resulting extra compilation cost is very low. In particular, the number of additional variables, that are added to original stratified bases, corresponds exactly to the number of priority levels existing in the base. Moreover, our compilation approach allows an efficient computation of weighted possibilistic conclusions and possibilistic conditioning.
3-D Simulations of Turbulent Compressible Convection

Three{dimensional simulations of turbulent and fully compressible thermal convection in deep atmospheres are presented and analyzed in terms of velocity power spectra, mixing{length theory, and production of vorticity. Density contrasts across these convective layers are typically around 11. The uid model is that of an ideal gas with a constant thermal conductivity. The Piecewise-Parabolic Method (PPM), with thermal conductivity added in, is used to solve the uid equations of motion. No explicit viscosity is included, and the low numerical viscosity of PPM leads to a very low e ective Prandtl number and very high e ective Rayleigh number. Mesh resolutions range as high as 512 512 256, and the corresponding e ective large{scale Rayleigh numbers range as high as 3:3 10. Compressional e ects lead to intensely turbulent down ow lanes and relatively laminar updrafts, especially near the top boundary. The enstrophy contrast between down ows and up ows increases with mesh resolution (and hence with decreasing viscosity), and ranges as high as a factor of 30 in our highest resolution model. Vorticity is everywhere preferentially aligned with the principal direction of strain associated with the large{scale circulation. Near the top boundary, the strain eld associated with the largest scale of convection dominates, which leads to a 2-D horizontal network of vortex tubes. For the same reason, both Laboratory for Computational Science and Engineering, University of Minnesota, Minneapolis, MN 55455
Edge-triggered driver circuit for ultra-wideband pulse generator with cascode impulse shaping

An ultra-wideband (UWB) pulse generator circuit is said to be the heart of any UWB system. However, the pulse generator typically requires an edge-triggered driver that is the essential signal source of any pulse generator. The design of edge-triggered driver circuit influences the amplitude, the pulse repetition, and the width of UWB pulses at the output. In this study, a low cost and low complexity edge-triggered driver circuit is proposed, which creates a square wave with very short rising and falling edges to feed a step recovery diode based pulse generator with cascode pulse shaping circuit. The proposed driver circuit is tuneable by simply changing the crystal oscillator component in the circuit, which results in different pulse repetition frequency for the UWB pulse generator. The measurement results when changing the crystal oscillator from 1MHz to 8MHz showed that the output UWB pulse was a Gaussian pulse with pulse width of 1.87 ns and pulse amplitude of 2.16 Vp.
Keeping Things that Matter : An Exploration on Delayed Feedback Online Learning

Online learning algorithms operate on a single instance at a time, allowing for updates that are fast, simple and perform well in a wide range of practical settings [1]. In this paper, we focus on online learning algorithms under delayed feedback, where the true labels arrive minutes, hours or even days later. Current solutions to this problem assume that all the instances are kept until the corresponding labels arrive [2]. In this project, we aim to examine this problem in a scenario where limited resources are available, so that it becomes infeasible to keep all of the instances in memory until the correct labels arrive. Herein we analyze different strategies for keeping only the data that matters: either by dropping certain training instances, features or both. We borrow techniques from the Active Learning literature, modified to fit this scenario. We also propose new techniques and baselines.
Cost-Sensitive Support Vector Ranking for Information Retrieval

In recent years, the algorithms of learning to rank have been proposed by researchers. However, in information retrieval, instances of ranks are imbalanced. After the instances of ranks are composed to pairs, the pairs of ranks are imbalanced too. In this paper, a cost-sensitive risk minimum model of pairwise learning to rank imbalanced data sets is proposed. Following this model, the algorithm of cost-sensitive supported vector learning to rank is investigated. In experiment, the standard Ranking SVM is used as baseline. The document retrieval data set is used in experiment. The experiment results show that the performance of cost-sensitive support vector learning to rank is better than Ranking SVM on two rank imbalanced data sets.
Blind Packet Forwarding in a hierarchical architecture with Locator/Identifier Split

In our previous work, we discussed the issue that with existing techniques we can either provide end-to-end information security or establish smart in-network services. Using the example of Blind Packet Forwarding (BPF), we demonstrated the feasibility to simultaneously realise both goals. However, the basic BPF design is not sufficiently scalable and lacks support for mobility and multihoming. This paper focuses on the issues of the basic BPF design and resolves them by applying the Locator/Identifier Split approach. We extend our BPF design and especially adopt the features of the Hierarchical Architecture for Internet Routing (HAIR). Additionally, we present our prototype implementation of the improved BPF, which benefits from the flow-based forwarding in OpenFlow, and evaluate it.
Assisting versus repelling force-feedback for human learning of a line following task

Previous work has shown that training with `assist-as-needed' method using force-feedback joystick can improve driving performance of children and adults. This work is the first study to evaluate training with a repelling force versus an assisting force for learning of a line following task. We designed a robotic training wheelchair that can accurately localize itself in the training environment, and implemented assisting and repelling force fields on the force-feedback joystick. The training protocol included three groups. The control (CT) group received no force feedback. The assisting force (AF) group was trained using the `assist-as-needed' paradigm. The repelling force (RF) group was trained with the repelling force field. We observed that both the AF and RF group improved their driving skills, however, the RF group had the greatest trajectory error reduction. We believe that this pilot study could provide a promising foundation regarding robotic wheelchair algorithm effects on adult learning.
An Extensible AOP Framework for Runtime Monitoring

We present a design and initial prototype for Teams, a new aspect oriented programming framework designed specifically for usage as an abstract instrumentation capability for runtime monitoring and dynamic analysis. Our goals for this framework are simplicity, extensibility, portability, and monitoring concept coverage. If successful, Teams will provide us and other researchers an easy-to-use platform for building instrumentation that will support their monitoring and analysis research, and will provide practioners an ability to craft their own analyses without needing to understand low-level instrumentation.
TI-QSSVM: Two Independent Quarter Sphere Support Vector Machine for binary classification

One of the important extensions of SVM is TWSVM which uses two hyperplanes to classify two classes of data. Since one hyperplane cannot efficiently model one class of data so the better choice is employing one hypersphere which covers as many data points in the corresponding class as possible and can better depict the characteristics of that class. Quarter sphere SVM uses a minimum radius centered hypersphere to describe data points such that it covers the majority of data and makes the outliers lied out of this hypersphere. In this paper inspired by the merit of QSSVM algorithm, we proposed a new two independent quarter sphere SVM (TI-QSSVM) to classify two classes of data. TI-QSSVM generates two quarter sphere with the minimum radiuses for two classes such that each one centered at the mean point of the corresponding class and covers as many data points in that class as possible. TI-QSSVM obtains these two quarter sphere by solving two linear programming problems. As can be seen in the experiment section, TI-QSSVM has significant advantages in terms of the learning speed and generalization performance compared with the other algorithms.