Miracle-dz/UniFAR-FTUs huggingface dataset:
Ordinary Voting Behavior in the Extraordinary Election of Adolf Hitler

How did free and fair democratic elections lead to the extraordinary anti-democratic Nazi Party winning control of the Weimar Republic? The profound implications of this question have led “Who voted for Hitler? ” to be the most studied question in the history of voting behavior research. Yet, despite the overwhelming attention, scholars have treated these elections as unique events and comparison with other elections as mostly irrelevant. Indeed, the literature rarely builds on insights from voting behavior research in political science or from modern statistical methods. By adding these approaches, we find that some of the most widely accepted current theories of Nazi voting do not distinguish the Weimar elections from almost any others, in any country. Like some researchers, we adopt a restrospective voting account, but we also recognize that the voters who were most hurt by the economic depression and hence most likely to oppose the government fall into two separate groups with divergent interests. With this additional feature and improved methods, we are able to show why some of the downtrodden turned to the Nazis and others turned away. The consequences of the election of Hitler were extraordinary, but available evidence suggests that the voting behavior that led to it was not.
Percepción estudiantil sobre la calidad de un ambiente de aprendizaje mixto apoyado por Moodle.

Esta investigacion se realizo con el proposito de contribuir al conocimiento acerca de la calidad del ambiente de aprendizaje ofrecido por Moodle. En la investigacion participaron estudiantes del sistema semi-presencial de la licenciatura en Psicologia de la Universidad Autonoma de Sinaloa, Mexico. La encuesta COLLES (Constructivist On-Line Learning Environment Survey) (Taylor y Maor, 2000), fue utilizada para estimar diferencias en la percepcion del alumnado participante acerca de la calidad ofrecida por dos cursos del sistema semi-presencial: uno ofrecido sin apoyo de Moodle y otro ofrecido con apoyo de Moodle. Los resultados revelaron que la poblacion estudiantil prefiere realizar principalmente actividades colaborativas. Asi mismo, los resultados permitieron detectar fortalezas relacionadas con la Relevancia, el Apoyo del Tutor y la Interpretacion. En tanto que las areas de oportunidad estuvieron relacionadas con el Pensamiento Reflexivo, la Interactividad y el Apoyo de los companeros.
Improving context awareness in subversion through fine-grained versioning of Java code

In this paper, we present an extension of the Subversion command line to support fine-grained versioning of Java code. To this aim, for each Java file under versioning, an XML-based file representing the logical structure of the original file is automatically built by parsing the code. An additional XML-based file is also built to model collaboration constraints. This information is useful to enrich the context awareness by providing developers information about changes made by others to the same logical unit (i.e., class, method, or attribute) of the Java file. Finally, we present an extension of Subclipse, a Subversion front-end implemented as an Eclipse plug-in, aiming to support the fine-grained versioning in Subversion.
Intrinsic modulation of pulse-coupled integrate-and-fire neurons

Intrinsic neuromodulation is observed in sensory and neuromuscular circuits and in biological central pattern generators. We model a simple neuronal circuit with a system of two pulse-coupled integrate-and-fire neurons and explore the parameter regimes for periodic firing behavior. The inclusion of biologically realistic features shows that the speed and onset of neuronal response plays a crucial role in determining the firing phase for periodic rhythms. We explore the neurophysiological function of distributed delays arising from both the synaptic transmission process and dendritic structure as well as discrete delays associated with axonal communication delays. Bifurcation and stability diagrams are constructed with a mixture of simple analysis, numerical continuation and the Kuramoto phase-reduction technique. Moreover, we show that, for asynchronous behavior, the strength of electrical synapses can control the firing rate of the system.
Impossibles: A Fully Autonomous Four-Legged Robot Soccer Team

class. The first one is sense which is called every time robot gets some new information. The second virtual function is decisionMaking which is called in a specific period of time to decide about new actions. The result of this function could be any of the actions such as walking, shooting, looking, etc. Some actions such as standing back to normal position in the case the robot falls down and also Blocking Skills (which will be discussed later in detail) are done completely without interrupts or preemptions, so decisionMaking is not called during these skills. Fig. 1. Impossibles World Model base Architecture
Using Taxonomies for Product Recommendation

In this work we take advantage of valuable information encoded in taxonomies to improve the quality of recommender systems. We present three strategies that explore the use of taxonomies: (i) category descriptors, (ii) classification features and (iii) category filters. We provide a real-case study over the book domain, in which the recommendation target is a set of 100 news page from The New York Times and the items to be recommended are 1,499,792 books distributed in 1,621 category nodes from a taxonomy, both crawled from Amazon.com. In strategy (i), term descriptors of each category are combined with text descriptions of the books assigned to the category and terms that are representative of the category are added to the target page. In strategy (ii), categories that are strongly related to the target page are put together by a classifier that plays the role of a feature generator and these features are then used in the recommendation process. In strategy (iii), the output of the two strategies previously described are filtered so that only books from the same categories as the ones assigned to the target page are kept in it. We implement several methods that apply the three strategies individually and in combination. Experimental results indicate that our strategies can be successfully applied to improving traditional content-based recommender systems. In particular, when the target page is automatically assigned to a category, we obtain gains close to 13\% in average precision. On the other hand, if such an assignment is made a priori, e.g., by the author or by a content editor, the gains are close to 20% in average precision.
Refined extended preorder index with path information

The eXtensible Markup Language (XML) is becoming a new standard for information representation and exchange over the Internet. How to index XML data for efficient query processing is an important subject in the XML community. In this paper, based on the extended preorder indexing method, path information is added as part of the index. It is shown that the number of path joins can be reduced to the number of the "interested points", but not related to the length of the path expression in a query. The extra space needed is about the same as the extended preorder labeling method. Experimental results showed significant improvements by using the new indexing method over naive extended preorder labeling method.
Who Has Anticipatory Feelings

I consider a general model of decisionmaking where anticipatory feelings enter the agent's utility function in addition to physical outcomes, and allow for interactions between these two payo components. I use an equilibrium concept, personal equilibrium, that combines best response to other selves' strategies with rational expectations in the formation of feelings. A decisionmaker with feelings can exhibit informational preferences due to the insecurity she has to live with if she does not know what will happen to her, and because she might be averse to the disappointment that results if she gets a worse outcome than expected. She can also be time inconsistent because feelings pass by the time she has to `invest' in them, and she can be prone to self-ful lling expectations. The paper proves that if a decisionmaker with anticipatory feelings is distinguishable from one who only cares about physical outcomes, she has to exhibit at least one of the above phenomena. I also connect the three concepts{informational preferences, unpredictability, and time inconsistency{to the seemingly unrelated concepts of intransitivity of choice and disappointment aversion. Finally, I discuss several modeling di culties when feelings enter utility, and prove that personal equilibrium exists under general conditions. First version: May 2001. Comments are very welcome. I thank George Akerlof, Andrew Caplin, Stephen Goldman, Wei Li, M at e Matolcsi, Markus Mobius, Wolfgang Pesendorfer, Ben Polak, Attila P or, Luca Rigotti, Tanya Rosenblat, and Chris Shannon for interesting and helpful conversations.
Efficient Array Processing in the Presence of Multipath Propagation

Spatial channel modeling is critical in realizing the full potential of antenna arrays in applications such as radar, sonar, and wireless communications. The spatial channel in these applications is typically characterized by complex signal scattering leading to multiple signal components arriving at the array from a spread of angles. This multipath angle spread is well known to lead to loss of spatial signal coherence, requiring complicated combining schemes to achieve optimal performance, particularly when the signal is partially coherent across the receiving array. We show that the discrete Fourier transform serves as an efficient, robust, and asymptotically optimal spatial combiner for uniform linear arrays in multipath channels. In addition, the proposed spatial processing allows for convenient integration of conventional frequency-domain methods for angle-of-arrival searches. Simulation results show that the proposed combining scheme provides near-optimal performance at significantly less computation, even for arrays of modest size.
An adaptive backstepping design for longitudinal flight path control

The paper presents a design technique using adaptive backstepping method for the aircraft longitudinal flight path control laws. This control method constructs the Lyapunov function using backstepping loops, and designs on-line GCMAC neural networks used to study and compensate the unknown aerodynamic uncertainties in the plant model. Then, the neural network weights turn laws are designed based on Lyapunov approach, which guarantee the states tracking error of the nonlinear system and the approximation errors of neural network converge to zero. This control method is used to design the flight path angle control system and aircraft longitudinal automatic landing control system which only consider gliding and leveling phase. The simulation results show that the adaptive backstepping controller has a good tracking performance and robustness.
Success of Grizzly Bear Population Augmentation in Northwest Montana

Abstract Augmentation of large carnivore populations can be a valuable management and recovery tool, but success of many programs has not been well documented. The Cabinet–Yaak grizzly bear (Ursus arctos) population was located in northwestern Montana and northern Idaho, USA, and was estimated at 30–40 individuals. The Cabinet Mountains portion of this area may be isolated from the remainder of the zone and was the site of a test of grizzly bear population augmentation. Experimental objectives included evaluating site fidelity, reproduction, and long-term survival of the translocated bears. Four subadult females (2–6 yr old) were translocated from southeastern British Columbia, Canada, from 1990 to 1994. Three of 4 transplanted bears remained in the target area for ≥1 year and satisfied the short-term goal for site fidelity. Recent genetic evidence gathered through hair-snagging efforts has determined that at least one of the original transplanted animals has reproduced, thereby providing evidence of success for the long-term goals of survival and reproduction.
Combining Region and Edge Cues for Image Segmentation in a Probabilistic Gaussian Mixture Framework

In this paper we propose a new segmentation algorithm which combines patch-based information with edge cues under a probabilistic framework. We use a mixture of multiple Gaussians for building the statistical model with color and spatial features, and we incorporate edge information based on texture, color and brightness differences into the EM algorithm. We evaluate our results qualitatively and quantitatively on a large data-set of natural images and compare our results to other state-of-the-art methods.
Online Database of Scores in the Humdrum File Format

KernScores, an online library of musical data currently consisting of over 5 million notes, has been created to assist projects dealing with the computational analysis of musical scores. The online scores are in a format suitable for processing with the Humdrum Toolkit for Music Research, but the website also provides automatic translations into several other popular data formats for digital musical scores.
Efficiency of mutation operators and selective mutation strategies: an empirical study

This paper investigates the mutation scores achieved by individual operators of the Mothra mutation system and their associated costs in order to determine the most efficient operators. The cost of mutation analysis includes both test set generation and equivalent mutant detection. The score and cost information is then used as a heuristic for choosing a subset of the operators for use in efficient selective mutation testing. Experiments were performed using a sample of 11 programs and a number of test sets for each program. The results show that the use of efficient operators can provide significant efficiency gains for selective mutation if the acceptable mutation score is not very close to one. When mutation scores very close to one are required, a randomly selected proportion of the mutants provides a more efficient strategy than a subset of efficient operators. Copyright © 1999 John Wiley & Sons, Ltd.
Binary dirty paper coding

This paper proposes a practical scheme for implementing binary dirty paper coding (DPC) using a low density generator matrix code (LDGM) concatenated with a high rate low density parity check (LDPC) code. We also propose a new algorithm, a modified version of the belief propagation algorithm (BP), for doing lossy source coding at the encoder, with linear complexity in the block length. In contrast to the superposition coding framework, where high order alphabet codes are used, we propose to implement binary DPC using only binary codes. Through application of approximate density evolution and linear programming we optimize the degree distribution of the proposed code. Simulation results show that our scheme achieves close to state-of-the-art performance with reduced complexity.
Form of an evolutionary tradeoff affects eco-evolutionary dynamics in a predator–prey system

Significance Rapid evolution on an ecological time scale has been increasingly recognized. Ecological and evolutionary dynamics can be tightly linked and important to predict future dynamics, but there is a significant gap between theoretical predictions and empirical tests, especially on the effects of the nature of genetic variation such as the form of a fitness tradeoff. Using a predator–prey experimental system, we show for the first time to our knowledge that different forms of a fitness tradeoff produce remarkably divergent eco-evolutionary dynamics. A mathematical model supports the observed dynamics. Our results suggest that without knowing the details of genetic variation that is usually variable among wild populations, it is difficult to understand how evolution and ecology interact and what form of eco-evolutionary dynamics results. Evolution on a time scale similar to ecological dynamics has been increasingly recognized for the last three decades. Selection mediated by ecological interactions can change heritable phenotypic variation (i.e., evolution), and evolution of traits, in turn, can affect ecological interactions. Hence, ecological and evolutionary dynamics can be tightly linked and important to predict future dynamics, but our understanding of eco-evolutionary dynamics is still in its infancy and there is a significant gap between theoretical predictions and empirical tests. Empirical studies have demonstrated that the presence of genetic variation can dramatically change ecological dynamics, whereas theoretical studies predict that eco-evolutionary dynamics depend on the details of the genetic variation, such as the form of a tradeoff among genotypes, which can be more important than the presence or absence of the genetic variation. Using a predator–prey (rotifer–algal) experimental system in laboratory microcosms, we studied how different forms of a tradeoff between prey defense and growth affect eco-evolutionary dynamics. Our experimental results show for the first time to our knowledge that different forms of the tradeoff produce remarkably divergent eco-evolutionary dynamics, including near fixation, near extinction, and coexistence of algal genotypes, with quantitatively different population dynamics. A mathematical model, parameterized from completely independent experiments, explains the observed dynamics. The results suggest that knowing the details of heritable trait variation and covariation within a population is essential for understanding how evolution and ecology will interact and what form of eco-evolutionary dynamics will result.
Applying quality index criterion for flexible multi-detection of heartbeat using features of multimodal data

Introduction: In many conditions contaminated signals and noises can distort electrocardiogram (ECG) signals, so synchronously measured signals could enhance analyzing the heart rate variability. Therefore, the algorithm should be able to identify reliable and optimal segment of these multimodal signals in order to accurately locate the heart beats. Aims: This paper presents a flexible and multichannel feature extractor and classifier for the purpose of locate heartbeats by using the multi-channel recording from PhysioNet Challenge 2014 database. Methods: In this study, for feature extraction from multimodal data the common heartbeat detectors are used in other studies are employed, but the exhausted results are promising which indicates the importance and efficiency of the proposed fusion strategy and the decision of when fusion should be done as ECG itself is the best indicator of heartbeat detection. For fusion, after segmentation of waveforms, if ECG assumed to be noisy, an index is assigned to each signal based on periodicity of the extracted features and estimation of noises of detailed coefficients of discrete wavelet transform. The signal whose index is highest is processed in that segment. Results: The presented approach was evaluated by 200 recording of multimodal dataset provided for the PhysioNet/Computing in Cardiology Challenge 2014, and the average accuracy of the 95.39% was obtained.
Rotavirus NSP1 localizes in the nucleus to disrupt PML nuclear bodies during infection

The rotavirus nonstructural protein 1 (NSP1) antagonizes interferon (IFN) induction in infected host cells. The primary function of NSP1 is thought to be degradation of interferon regulatory factors (IRFs) and beta-transducin repeat-containing protein (β-TrCP) in the cytoplasm to inhibit IFN induction. Here, we report that NSP1 localizes to the cytoplasm and nucleus and disrupts promyelocytic (PML) nuclear bodies (NB) in the nucleus during infection. Nuclear localization of NSP1 did not require an intact C terminus, suggesting NSP1 has a novel function in the nucleus independent of degradation of IRFs or β-TrCP. NSP1 expression either led to a reduction in PML NB number or a change in PML NB morphology from sphere-shaped foci to oblong-shaped structures, depending on the virus strain. Additionally, infection was not affected when cells lack PML NB, suggesting that rotavirus does not require PML for replication in highly permissive cell types. PML was not essential for nuclear localization of NSP1, but PML was required for NSP1 nuclear focus formation. PML NBs play an important role in many cellular functions that include IFN induction and host stress responses. This is the first report that rotavirus, a cytoplasmically replicating virus, encodes a viral protein that localizes to the nucleus during infection, and may suggest a new function of NSP1 in the nucleus. IMPORTANCE Rotavirus causes severe gastroenteritis in young children and leads to over 200,000 deaths per year. Rotavirus is a cytoplasmically replicating virus, and must find ways to avoid or actively inhibit host antiviral responses to efficiently replicate. The nonstructural protein NSP1 is known to inhibit IFN induction by promoting degradation of host proteins in the cytoplasm of infected cells. Here, we demonstrate that NSP1 also localizes to the nucleus of infected cells, specifically to PML NB. NSP1 causes a disruption of PML NB, which may serve as an additional mechanism of IFN inhibition or interfere with other nuclear processes to promote viral replication. A detailed exploration of the manipulation of nuclear processes in cells infected with cytoplasmically replicating viruses will lead to new insights into viral evasion of host responses.
Activation of the Interventricular Septum

Multipolar recording techniques have been used to study excitation of the canine interventricular septum. Activity commences in the mid left septal endocardium and slightly later near the right anterior papillary muscle. From these sites depolarization spreads over the endocardial surfaces, probably via the Purkinje tissue, at 1 m. /sec. and through the septal thickness at one-third this speed. Near the upper septum, bilaterally and on the posterior right there is a lack of the rapid endocardial conduction. Septal contribution to the QRS is considered.
Probability density function estimation based over-sampling for imbalanced two-class problems

A novel probability density function (PDF) estimation based over-sampling approach is proposed for two-class imbalanced classification problems. The Parzen-window kernel function is applied to estimate the PDF of the positive class, from which synthetic instances are generated as additional training data to re-balance the class distribution. Utilising the re-balanced over-sampled training data, a radial basis function (RBF) classifier is constructed by applying an orthogonal forward regression, in which the classifier's structure and the parameters of RBF kernels are determined using a particle swarm optimisation algorithm based on the criterion of minimising the leave-one-out misclassification rate. The effectiveness of the proposed approach is demonstrated by an empirical study on several imbalanced data sets.
Incremental Spectral Sparsification for Large-Scale Graph-Based Semi-Supervised Learning

While the harmonic function solution performs well in many semi-supervised learning (SSL) tasks, it is known to scale poorly with the number of samples. Recent successful and scalable methods, such as the eigenfunction method focus on efficiently approximating the whole spectrum of the graph Laplacian constructed from the data. This is in contrast to various subsampling and quantization methods proposed in the past, which may fail in preserving the graph spectra. However, the impact of the approximation of the spectrum on the final generalization error is either unknown, or requires strong assumptions on the data. In this paper, we introduce Sparse-HFS, an efficient edge-sparsification algorithm for SSL. By constructing an edge-sparse and spectrally similar graph, we are able to leverage the approximation guarantees of spectral sparsification methods to bound the generalization error of Sparse-HFS. As a result, we obtain a theoretically-grounded approximation scheme for graph-based SSL that also empirically matches the performance of known large-scale methods.