Miracle-dz/UniFAR-FTUs huggingface dataset:
The Emerging Democratic Majority

Авторы книги утверждают, что при сохранении и продолжении текущей демографической и политической ситуации в США, власть неизбежно перейдет к демократам. В доказательство они приводят ряд аргументов: возрастающую размытость электората; увеличение доли азиатского, латиноамериканского и афро-американского населения, склонного голосовать за демократические силы, в то время как количество белых американцев, склонных голосовать за республиканцев,остается неизменным; постиндустриализация американской экономики, порождающая новые социальные группы избирателей, склонных поддерживать демократические силы. Рассматриваются также и многие другие значимые элементы. Анализируя ситуацию, Джудис и Тиксейра предсказывают, что все необходимые предпосылки сойдутся вместе к 2008 г. , и в США появится новое демократическое большинство.
Chaetopteryx bucari sp. n., a new species from the Chaetopteryx rugulosa group from Croatia (Insecta, Trichoptera, Limnephilidae) with molecular, taxonomic and ecological notes on the group

Abstract We describe a new autumnal caddisfly species Chaetopteryx bucari sp. n. from 8 localities in the Banovina region of Croatia. We also present molecular, taxonomic and ecological notes (emergence, sex ratio and seasonal dynamics) on the new species and discuss the distribution of Chaetopteryx species in general and the Chaetopteryx rugulosa group in particular. Based on Bayesian phylogenetic analysis Chaetopteryx rugulosa schmidi was separated from the clade containing the other subspecies of Chaetopteryx rugulosa. Thus the subspecies Chaetopteryx rugulosa schmidi is here raised to species level, Chaetopteryx schmidi, as it was described originally. We further present distribution data on rare species in the genus Chaetopteryx in Croatia.
Optimized Low-Complexity Implementation of Least Squares Based Model Extraction for Digital Predistortion of RF Power Amplifiers

Least squares (LS) estimation is widely used in model extraction of digital predistortion for RF power amplifiers. In order to reduce computational complexity and implementation cost, it is desirable to use a small number of training samples in the model parameter estimation. However, due to strong correlations between data samples in a real transmit signal, the ill-conditioning problem becomes severe in standard LS, which often leads to large errors occurring in model extraction. Using a short training sequence can also cause mismatch between the statistical properties of the training data and the actual signal that the amplifier transmits, which could degrade the linearization performance of the digital predistorter. In this paper, we propose first to use a 1-bit ridge regression algorithm to eliminate the ill-conditioning problem in the LS estimation and then use root-mean-squares based coefficients weighting and averaging approach to reduce the errors caused by the statistical mismatch. Experimental results show that the proposed approach can produce excellent model extraction accuracy with only a very small number of training samples, which dramatically reduces the computational complexity and the system implementation cost.
A Hierarchical Semi-Markov Model for Detecting Enrichment with Application to ChIP-Seq Experiments

Chromatin immunoprecipitation followed by direct sequencing (ChIP-Seq) has revolutionalized the experiments in profiling DNA-protein interactions and chromatin remodeling patterns. However, limited statistical tools are available for modeling and analyzing the ChIP-Seq data thoroughly. We carefully study the data generating mechanism of ChIP-Seq data and propose a new model-based approach for detecting enriched regions. Our model is based on a hierarchical mixture model which gives rise to a zero-inflated negative binomial (ZINB), coupled with a hidden semi-Markov model (HSMM) to address the sequencing depth and biases, the inherent spatial data structure and allows for detection of multiple non-overlapping variable size peaks. In particular, we demonstrate that the proposed ZINB accounts for the excess zeroes and over-dispersion in the observed data relative to a Poisson distribution, and this model provides a better fit as the background distribution. We also propose a new meta false discovery rate (FDR) control at peak level as an alternative to the usual heuristic postprocessing of enriched bins identified via bin level FDR control. We show with simulations and case studies that this new procedure allows for the boundaries of peak regions to be declared probabilistically and provides accurate FDR control.
Multi-scale analysis and fractal signature (MSFS): A new approach to invariant corner extraction for image registration

Corner that is invariant to geometry transform and illumination change is a very important feature for image registration and target recognition etc. Corners extracted using intensities directly are robust, but with poor property in localization. Corners extracted by edge information are invariant to geometry transform and outstanding in localization, but not robust. Therefore in this paper, an algorithm is designed for corner extraction, which combines fractal signature, edge information and multi-scale analysis. The proposed algorithm is divided into four steps: firstly, extract edges from the image; secondly, compute the properties and prominent values of every point on the edge; thirdly, extract satisfactory corners using non-maximal suppress; finally, compute the descriptor of extracted points. The experiment results show that the proposed algorithm is robust to scale, rotation and illumination, and has good property in localization.
A 0.18μm 1V 5GHz LC VCO designed for WSN applications

A 1V 5GHz LC voltage controlled oscillator (VCO) fabricated in 0. 18μm RF/MS CMOS process is proposed. To conquer the problems brought by low voltage, VCO's structure is carefully selected. Also, switched capacitor array and 2 stages buffer are introduced. Against different phase noise sources, corresponding phase noise and power performance optimization is realized. The die area of the VCO is 0.52×0.86 mm2. The measured phase noise of the VCO is -113.4dBc/Hz @1MHz and -124.5dBc/Hz @ 3.5MHz respectively while the tuning range is 4.56-5.32GHz. The figure of merits (FOM) of the VCO is -185dBc/Hz and the power consumption is 2.3mW.
An Input-Representing Interneuron Regulates Spike Timing and Thereby Phase Switching in a Motor Network

Despite the importance of spike-timing regulation in network functioning, little is known about this regulation at the cellular level. In the Aplysia feeding network, we show that interneuron B65 regulates the timing of the spike initiation of phase-switch neurons B64 and cerebral-buccal interneuron-5/6 (CBI-5/6), and thereby determines the identity of the neuron that acts as a protraction terminator. Previous work showed that B64 begins to fire before the end of protraction phase and terminates protraction in CBI-2-elicited ingestive, but not in CBI-2-elicited egestive programs, thus indicating that the spike timing and phase-switching function of B64 depend on the type of the central pattern generator (CPG)-elicited response rather than on the input used to activate the CPG. Here, we find that CBI-5/6 is a protraction terminator in egestive programs elicited by the esophageal nerve (EN), but not by CBI-2, thus indicating that, in contrast to B64, the spike timing and protraction-terminating function of CBI-5/6 depends on the input to the CPG rather than the response type. Interestingly, B65 activity also depends on the input in that B65 is highly active in EN-elicited programs, but not in CBI-2-elicited programs independent of whether the programs are ingestive or egestive. Notably, during EN-elicited egestive programs, hyperpolarization of B65 delays the onset of CBI-5/6 firing, whereas in CBI-2-elicited ingestive programs, B65 stimulation simultaneously advances CBI-5/6 firing and delays B64 firing, thereby substituting CBI-5/6 for B64 as the protraction terminator. Thus, we identified a neural mechanism that, in an input-dependent manner, regulates spike timing and thereby the functional role of specific neurons.
A SystemC Transaction Level Model for the MIPS R3000 Processor

Processor cores in embedded applications build today the cornerstone of System-on-Chip designs. Among the most successful RISC (Reduced Instruction Set Computer) cores are the MIPS processors used in applications such as DVD, automotive, broadband access, networking, etc. In this paper, we design and verify a Transaction Level Modeling (TLM) architecture of the MIPS R3000 in SystemC. The TLM in SystemC is adopted so that abstract data types can be used for higher (abstract) level modeling and faster simulation. The processor is implemented such that the instruction and data caches contain all the necessary instructions and data to eliminate complex memory access management, respectively. To simulate the processor, we provide a script that automatically generates the machine instruction code from assembly language.
Expressing cross-loop dependencies through hyperplane data dependence analysis

Traditional dependence analysis techniques usually attempt to recognize the existence of dependencies between iterations of a loop and, in some cases, characterize these dependencies by finding direction vectors or distance vectors. A more general form of data dependence called hyperplane dependence is introduced. It is a dependence whose source and destination are subspaces of the iteration space. This dependence form can be useful mainly for expressing dependencies across loop-nests, and consequently better understand the interaction between the loops. In order to be able to express across loop dependencies and analyze all loops in the code simultaneously, a global iteration space for all loops in the code is formed. Hyperplane dependence analysis is used to improve automatic generation of communication statements across loops and index alignment for n-dimensional grid target machines. <<ETX>>
Measuring confidence intervals for the machine translation evaluation metrics

Automatic evaluation metrics for Machine Translation (MT) systems, such as BLEU and the related NIST metric, are becoming increasingly important in MT. This paper reports a novel method of calculating the confidence intervals for BLEU/NIST scores using bootstrapping. With this method, we can determine whether two MT systems are significantly different from each other. We study the effect of test set size and number of reference translations on the confidence intervals for these MT evaluation metrics.
Cytosolic Ras Supports Eye Development in Drosophila

ABSTRACT Ras proteins associate with cellular membranes as a consequence of a series of posttranslational modifications of a C-terminal CAAX sequence that include prenylation and are thought to be required for biological activity. In Drosophila melanogaster, Ras1 is required for eye development. We found that Drosophila Ras1 is inefficiently prenylated as a consequence of a lysine in the A1 position of its CAAX sequence such that a significant pool remains soluble in the cytosol. We used mosaic analysis with a repressible cell marker (MARCM) to assess if various Ras1 transgenes could restore photoreceptor fate to eye disc cells that are null for Ras1. Surprisingly, we found that whereas Ras1 with an enhanced efficiency of membrane targeting could not rescue the Ras1 null phenotype, Ras1 that was not at all membrane targeted by virtue of a mutation of the CAAX cysteine was able to fully rescue eye development. In addition, constitutively active Ras112V,C186S not targeted to membranes produced a hypermorphic phenotype and stimulated mitogen-activated protein kinase (MAPK) signaling in S2 cells. We conclude that the membrane association of Drosophila Ras1 is not required for eye development.
Mean-Risk and Stochastic Dominance Efficient Sets Under Comovements

In this paper we exploit the relationship between mean-risk and stochastic dominance efficient sets in order to derive optimal portfolio strategies that can be superior to the meanvariance benchmark under comovements episodes of the market. To do this we decompose the risk in a portfolio in risk due to the likelihood of comovements and risk due to the allocation of weights. In this way we are able to discern whether a portfolio defined by an optimal allocation of weights is optimal under comovements episodes, or there are other allocations better suited to minimize risk in this regime. To implement this we develop a novel test statistic for testing stochastic dominance that, in contrast to most test statistics in the literature, makes allowance for testing orders of dominance higher than zero and for some types of dependence between portfolios. This test is extended to conditional stochastic dominance by using a simple reformulation of the test statistic and the asymptotic theory. These results are illustrated in the empirical application where we stress the importance of the marginal distribution of the assets in the portfolio and the likelihood of comovements for the mean-variance efficient portfolio to be stochastically superior to alternative mean-risk efficient sets.
Mental Rotation Ability and Computer Game Experience

Computer games, which are currently very popular among students, can affect different cognitive abilities. The purpose of the present study is to examine undergraduate students' experiences and preferences in playing computer games as well as their mental rotation abilities. A total of 163 undergraduate students participated. The results showed a significant difference between students' mental rotation abilities in terms of their experiences and preferences in playing computer games. Moreover, 2D or 3D computer game preference was shown to be dependent on gender. This study also explores the quantity of time spent by undergraduate students playing computer games.
The most attentive person selection using HMM with multiple sources

We presented a novel HMM framework, generative state model-based HMM (GHMM), treating the multiple sources whose outputs are simultaneously emitted while the conventional HMM is equipped for a single source. GHMM is designed for particular problems in which there is competition among sources (i.e., any GHMM state is a particular event when any source is more distinctive than others). The generative state model not only has ability to deal with the changes of the number of sources in runtimes but also forms the group relation in the sense of competition among sources, unlike the conventional HMM which is a predefined or fixed state model. We also applied the proposed method to the most attentive person selection. We have confirmed by preliminary experiments that the proposed method works well in the selection of the most attentive person to communicate with the robot.
Local Texton Dissimilarity with applications on biomass classification

Texture classification, texture synthesis, or similar tasks are an active topic in computer vision and pattern recognition. This paper aims to present a novel texture dissimilarity measure based on textons, namely the Local Texton Dissimilarity (LTD), inspired from (Dinu et al., 2012). Textons are represented as a set of features extracted from image patches. The proposed dissimilarity measure shows its application on biomass type identification. A new data set of biomass texture images is provided by this work, which is available at http://biomass.herokuapp. com. Images are separated into three classes, each one representing a type of biomass. The biomass type identification and quality assessment is of great importance when one in the biomass industry needs to produce another energy product, such as biofuel, for example. Two more experiments are conducted on popular texture classification data sets, namely Brodatz and UIUCTex. The proposed method benefits from a faster computational time compared to (Dinu et al., 2012) and a better accuracy when used for texture classification. The performance level of the machine learning methods based on LTD is comparable to the state of the art methods.
The Binding Problem in Population Neurodynamics : A Network Model for Stimulus-Specific Coherent Oscillations

Sensoiy recognition systems obviously take advantage both of senal and parallel computing mechanisms m the form of distributed hierarchical processing Serial processing requires hierarchically organized neural networks A stimulus o\oked neuional activity entering the biain via parallel pathways with divergent projections integrates at higher levels by convergence on common neuronal pools Such moiphological and functional concepts create "intelligent" neuron (grandinothei cell object specific neuion, cardinal cell, pontifical cell, gnostic cell, key nem on) capable of discriminating and identifying very complex and specific str muh Parallel processing m diveigent systems is much moie difficult to define In this case the nemal image of a stimulus is not represented by an "intelligent" neuion but by simultaneous neuronal activity in several specialized brain areas at different levels These frequently remote regions must be functionally coupled (the binding pioblem) to provide global percept and memory formation (Edelman 1987) In addition to binding built in by genes (anatomical ai chitecture) or built
Optical Music Recognition and Playback on Mobile Devices

The recognition of printed sheet music has been studied since the late 1960s. However, current applications often require careful scanning of musical scores, making it inconvenient for average music enthusiasts. With the advances in mobile devices and network connectivity, we develop an optical music recognition system to remotely interpret printed sheet music captured by an Android phone and present the music through MIDI playback on the device. By applying various image segmentation, filtering, morphological processing, and feature detection techniques, the application is able to identify the pitch of the notes by spatial position, form chords based on spatial alignment, and generate corresponding music file through MIDI table lookup. This application can be used by music learners in practicing unfamiliar pieces of music. It can also be deployed as a software extension to the control unit of player pianos currently in the market. Keywords-optical music recognition; mobile image processing;
Spectral Surface Deformation with Dual Mesh

In this paper, we introduce a new surface deformation algorithm by combining the spectral method with the dual Laplacian editing algorithm. The dual Laplacian editing algorithm tries to maintain the Laplacian coordinates of all the vertices before and after deformation in dual space as much as possible. By using an iteration process, the local rotational effects can be achieved to get very natural results. In our new algorithm, we exploit manifold harmonics to perform the dual Laplacian editing in frequency domain. By using a small number of frequencies, the size of the linear system in each iteration is greatly decreased, and thus the speed is increased accordingly. Our experiments shows that our algorithm is particularly suitable for large meshes, and the speed of our algorithm is much faster than the spatial counterpart.
Improved Speech Summarization and Spoken Term Detection with Graphical Analysis of Utterance Similarities

We present summarization and spoken term detection (STD) approaches that take into account similarities between utterances to be scored for summary extraction or ranking in STD. A graph is constructed in which each utterance is a node. Similar utterances are connected by edges, with the edge weights representing the degree of similarity. The similarity for summarization is topical similarity; that for STD is featurespace similarity. The score of each utterance for extraction in summarization and ranking in STD is not solely decided by the individual utterance but is influenced by similar utterances on the graph. Experimental results show significant improvements compared with two baselines in terms of the ROUGE evaluation for summarization and mean average precision for STD.
PRECISE 3D MEASUREMENT WITH STANDARD MEANS AND MINIMIAL USER INTERACTION - EXTENDED SINGLE-VIEW RECONSTRUCTION

The paper proposes a new method for general 3D measurement and 3D point reconstruction. Looking at its features, the method explicitly aims at practical applications. These features especially cover low technical expenses and minimal user interaction, a clear problem separation into steps that are solved by simple mathematical methods (direct, stable and optimal with respect to least error squares), and scalability. The method expects the internal and radial distortion parameters of the used camera(s) as inputs, and a plane quadrangle with known geometry within the scene. At first, for each single picture the 3D position of the reference quadrangle (with respect to each camera coordinate frame) is calculated. These 3D reconstructions of the reference quadrangle are then used to yield the relative external parameters of each camera regarding the first one. With known external parameters, triangulation is finally possible. The differences from other known procedures are outlined, paying attention to the stable mathematical methods (no usage of nonlinear optimization) and the low user interaction with good results at the same time.
A stereoscopic imaging model and its calibration of micro stereovision for 3D measurement

Micro stereovision based on a stereo light microscope (SLM) is used in micro-domains such as MEMS, micromanipulation, microassembly et al. SLM is an important device obtaining images of micro objects and play an important role in auto micro-navigation, auto micro-position, micro-measurement et al. Definition of the imaging model for a SLM differs from the imaging model of a normal optical microscope in the following aspects: (1) The vision principle of SLM uses the same principle with binocular stereovision. For obtaining depth information of the object space, the stereoscopic imaging model of SLM needs image data from matching of stereo images. (2) There is correlation between the two optical paths of SLM. This indicates that a stereoscopic imaging model of SLM is nonlinear. (3) A stereoscopic imaging model of SLM has its unique calibration parameters differing from parameters of other optical systems. In this paper, a stereoscopic imaging model of SLM is constructed. The symmetry and the differences between the two optical paths of SLM are used to calibrate the main parameters and the rectification parameters of an imaging model. Through experiments, we analyze the robustness and dependency of parameters under different magnification of the objective lens.
The algebra of q‐pseudodifferential symbols and the q‐WKP(n) algebra

In this paper we continue with the program to explore the topography of the space of W‐type algebras. In the present case, the starting point is the work of Khesin, Lyubashenko, and Roger on the algebra of q‐deformed pseudodifferential symbols and their associated integrable hierarchies. The analysis goes on by studying the associated Hamiltonian structures for which compact expressions are found. The fundamental Poisson brackets yield q‐deformations of WKP and related W‐type algebras which, in specific cases, coincide with the ones constructed by Frenkel and Reshetikhin. The construction underlies a continuous correspondence between the Hamiltonian structures of the Toda lattice and the KP hierarchies.