Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimation of Cell Loss Ratio and Cell Delay for ATM Networks using Novel Genetic Algorithm Approach

In this paper, an on line genetic algorithm (GA) is proposed for estimating the cell loss probability (CLP) and the cell delay in real time. They are key parameters to many vital functions in the network such as call admission (CAC), bandwidth allocation, etc. However, the CLR and delay depend usually on many unknown and unpredictable traffic parameters such as input traffic correlations. In this paper, a novel genetic algorithm to predict the CLR and delay in different sized systems based on both a small amount of information from these systems. This algorithm is used with real time traffic measurement to maintain and design the ATM network
PAPR reduction of the power efficient asymmetrically clipped OFDM

Two methods are applied to reduce the peak-to-power ratio (PAPR) of asymmetrically clipped optical OFDM transmission systems in this paper. Bit error rate (BER) performance of different methods are compared in experiment. PAPR is reduced about 4dB by using companding transform with slightly receive sensitivity decrease.
Lock-Free Parallel Perceptron for Graph-based Dependency Parsing

Dependency parsing is an important NLP task. A popular approach for dependency parsing is structured perceptron. Still, graph-based dependency parsing has the time complexity of $O(n^3)$, and it suffers from slow training. To deal with this problem, we propose a parallel algorithm called parallel perceptron. The parallel algorithm can make full use of a multi-core computer which saves a lot of training time. Based on experiments we observe that dependency parsing with parallel perceptron can achieve 8-fold faster training speed than traditional structured perceptron methods when using 10 threads, and with no loss at all in accuracy.
Textual Entailment Recognizing by Theorem Proving Approach

In this paper we present two original methods for recognizing textual inference. First one is a modified resolution method such that some linguistic considerations are introduced in the unification of two atoms. The approach is possible due to the recent methods of transforming texts in logic formulas. Second one is based on semantic relations in text, as presented in WordNet. Some similarities between these two methods are remarked.
Incremental Volume Rendering Using Hierarchical Compression

We present a new algorithm here for efficient incremental rendering of volumetric datasets. The primary goal of this algorithm is to give average workstations the ability to efficiently render volume data received over relatively low bandwidth network links in such a way that rapid user feedback is maintained. Common limitations of workstation rendering of volume data include: large memory overheads, the requirement of expensive rendering hardware, and high speed processing ability. The rendering algorithm presented here overcomes these problems by making use of the efficient Shear‐Warp Factorisation method which does not require specialised graphics hardware. However the original Shear‐Warp algorithm suffers from a high memory overhead and does not provide for incremental rendering which is required should rapid user feedback be maintained. Our algorithm represents the volumetric data using a hierarchical data structure which provides for the incremental classification and rendering of volume data. This exploits the multiscale nature of the octree data structure. The algorithm reduces the memory footprint of the original Shear‐Warp Factorisation algorithm by a factor of more than two, while maintaining good rendering performance. These factors make our octree algorithm more suitable for implementation on average desktop workstations for the purposes of interactive exploration of volume models over a network. Results from tests using typical volume datasets will be presented which demonstrate the ability of the algorithm to achieve high rendering rates for both incremental rendering and standard rendering while reducing the runtime memory requirements.
Liquid Splash Modeling with Neural Networks

This paper proposes a new data‐driven approach to model detailed splashes for liquid simulations with neural networks. Our model learns to generate small‐scale splash detail for the fluid‐implicit‐particle method using training data acquired from physically parametrized, high resolution simulations. We use neural networks to model the regression of splash formation using a classifier together with a velocity modifier. For the velocity modification, we employ a heteroscedastic model. We evaluate our method for different spatial scales, simulation setups, and solvers. Our simulation results demonstrate that our model significantly improves visual fidelity with a large amount of realistic droplet formation and yields splash detail much more efficiently than finer discretizations.
Interference-aware Batch Memory Scheduling in heterogeneous multicore architecture

In recent years, integrating Central Processing Units (CPUs) and Graphics Processing Units (GPUs) on the same chip has become a major trend. When the number of processors increases, a large number of memory requests are generated causing memory access conflicts. Due to the heterogeneity of processors, the issue has become more serious in the heterogeneous multicore architecture. In this work, we propose a three stages memory scheduling algorithm called Interference-aware Batch Memory Scheduling Algorithm (IBM) to address this issue. We compare IBM against the First-Come First-Serve (FCFS) and Staged Memory Scheduling (SMS). Our evaluations show that IBM reduces at most 4.2% memory access latency and 24.37% CPU access latency in real-world benchmarks.
CAT: Credibility Analysis of Arabic Content on Twitter

Data generated on Twitter has become a rich source for various data mining tasks. Those data analysis tasks that are dependent on the tweet semantics, such as sentiment analysis, emotion mining, and rumor detection among others, suffer considerably if the tweet is not credible, not real, or spam. In this paper, we perform an extensive analysis on credibility of Arabic content on Twitter. We also build a classification model (CAT) to automatically predict the credibility of a given Arabic tweet. Of particular originality is the inclusion of features extracted directly or indirectly from the author’s profile and timeline. To train and test CAT, we annotated for credibility a data set of 9,000 Arabic tweets that are topic independent. CAT achieved consistent improvements in predicting the credibility of the tweets when compared to several baselines and when compared to the state-of-the-art approach with an improvement of 21% in weighted average F-measure. We also conducted experiments to highlight the importance of the user-based features as opposed to the content-based features. We conclude our work with a feature reduction experiment that highlights the best indicative features of credibility.
Brief report Generation of induced pluripotent stem cells from human blood

Human ﬁbroblasts obtained by skin biopsy can be reprogrammed di-rectly to pluripotency by the ectopic expression of deﬁned transcription factors. Here, we describe the derivation of induced pluripotent stem cells from CD34 (cid:1) mobilized human peripheral blood cells using retroviral transduction of OCT4/ SOX2/KLF4/MYC . Blood-derived human induced pluripotent stem cells are indistinguishable from human embryonic stem cells with respect to morphology, expression of surface antigens, and pluripotency-associatedtranscriptionfac-tors, DNA methylation status at pluripotent cell-speciﬁc genes, and the capacity to differentiate in vitro and in teratomas. The ability to reprogram cells from human blood will allow the generation of patient-speciﬁc stem cells for diseases in which the disease-causing somatic muta-tions are restricted to cells of the hematopoietic lineage. (Blood. 2009;113:5476-5479) (Em-eryville, CA) were collected from a 26-year-old male donor, CD34 (cid:1) cells (mPB014F) isolated. Generation of CD34 iPS cells was performed using a protocol modiﬁed from previously published methods.
Alternative measures of the monetary base: what are the differences and are they important?

‘The idea of adjusting the monetary base to reflect changes in reserve requirements was proposed initially by Karl Brunner (1961) in an effort to formulate an “empirically significant theory” of the money supply process. Brunner called this adjustment “liberated reserves. ” He was the first to compile data on the adjusted monetary base and empirically investigate the relationship between his measure and the money supply. This research agenda was pursued by both him and Allan Meltzer in a number of articles dealing with the money supply process and monetary policy. 2See Andersen and Jordan (1968).
Fitness and Phenotypic Characterization of Miltefosine-Resistant Leishmania major

Trypanosomatid parasites of the genus Leishmania are the causative agents of leishmaniasis, a neglected tropical disease with several clinical manifestations. Leishmania major is the causative agent of cutaneous leishmaniasis (CL), which is largely characterized by ulcerative lesions appearing on the skin. Current treatments of leishmaniasis include pentavalent antimonials and amphotericin B, however, the toxic side effects of these drugs and difficulty with distribution makes these options less than ideal. Miltefosine (MIL) is the first oral treatment available for leishmaniasis. Originally developed for cancer chemotherapy, the mechanism of action of MIL in Leishmania spp. is largely unknown. While treatment with MIL has proven effective, higher tolerance to the drug has been observed, and resistance is easily developed in an in vitro environment. Utilizing stepwise selection we generated MIL-resistant cultures of L. major and characterized the fitness of MIL-resistant L. major. Resistant parasites proliferate at a comparable rate to the wild-type (WT) and exhibit similar apoptotic responses. As expected, MIL-resistant parasites demonstrate decreased susceptibility to MIL, which reduces after the drug is withdrawn from culture. Our data demonstrate metacyclogenesis is elevated in MIL-resistant L. major, albeit these parasites display attenuated in vitro and in vivo virulence and standard survival rates in the natural sandfly vector, indicating that development of experimental resistance to miltefosine does not lead to an increased competitive fitness in L. major.
A linearized representation for the analysis and synthesis of animate motion

Efficient detection of the presence of another animate creature has an immense evolutionary significance. Animate motion, however, not only signals the presence of another creature but also carries information about its individual characteristics, actions and intentions. Humans are well able to recognize a good friend by the way he or she moves and an unknown person can easily be classified according to attributes such as gender and age based only on his or her gait patterns. How is this kind of information encoded in animate motion patterns and how can it be retrieved from them? We present a computational model for the analysis of human gait. This model is used as a tool to analyze animate motion patterns in order to extract diagnostic invariants. It also serves as a model for animate motion perception in the visual system. Animate motion can be described as a set of feature points (e. g. joints of an articulated body) that change their position in time. The core of the method described here is an algorithm that maps animate motion defined as a set of feature trajectories into a linear space such that linear operations become meaningful. Analogous to methods used to linearize image information (Troje and Vetter, 1998; Vetter and Troje, 1997) linearization of motion data is achieved by dissociating the information into a range-specific part and a domain-specific part (Ramsay and Silverman, 1997). For motion data, the domain-specific part contains information about the timing of the motion, whereas the range-specific part contains information about the position of the features. The resulting linearized representation allows access to methods from linear statistics and classical pattern recognition that can be used to decode information from the motion patterns. Since the representation is lossless and thus invertible it can also be used to synthesize new, artificial motion patterns with well defined and parameterized features. This not only allows us to generate well-controlled animate motion stimuli for psychophysical and neuroethological investigations but also has applications in computer animation industry.
MaPU: A novel mathematical computing architecture

As the feature size of the semiconductor process is scaling down to 10nm and below, it is possible to assemble systems with high performance processors that can theoretically provide computational power of up to tens of PLOPS. However, the power consumption of these systems is also rocketing up to tens of millions watts, and the actual performance is only around 60% of the theoretical performance. Today, power efficiency and sustained performance have become the main foci of processor designers. Traditional computing architecture such as superscalar and GPGPU are proven to be power inefficient, and there is a big gap between the actual and peak performance. In this paper, we present the MaPU architecture, a novel architecture which is suitable for data-intensive computing with great power efficiency and sustained computation throughput. To achieve this goal, MaPU attempts to optimize the application from a system perspective, including the hardware, algorithm and corresponding program model. It uses an innovative multi-granularity parallel memory system with intrinsic shuffle ability, cascading pipelines with wide SIMD data paths and a state-machine-based program model. When executing typical signal processing algorithms, a single MaPU core implemented with a 40nm process exhibits a sustained performance of 134 GLOPS while consuming only 2.8 W in power, which increases the actual power efficiency by an order of magnitude comparable with the traditional CPU and GPGPU.
Digital Communication Receivers Using Gaussian Processes for Machine Learning

We propose Gaussian processes (GPs) as a novel nonlinear receiver for digital communication systems. The GPs framework can be used to solve both classification (GPC) and regression (GPR) problems. The minimum mean squared error solution is the expectation of the transmitted symbol given the information at the receiver, which is a nonlinear function of the received symbols for discrete inputs. GPR can be presented as a nonlinear MMSE estimator and thus capable of achieving optimal performance from MMSE viewpoint. Also, the design of digital communication receivers can be viewed as a detection problem, for which GPC is specially suited as it assigns posterior probabilities to each transmitted symbol. We explore the suitability of GPs as nonlinear digital communication receivers. GPs are Bayesian machine learning tools that formulates a likelihood function for its hyperparameters, which can then be set optimally. GPs outperform state-of-the-art nonlinear machine learning approaches that prespecify their hyperparameters or rely on cross validation. We illustrate the advantages of GPs as digital communication receivers for linear and nonlinear channel models for short training sequences and compare them to state-of-the-art nonlinear machine learning tools, such as support vector machines.
An immunoaffinity purified Schizosaccharomyces pombe TBP-containing complex directs correct initiation of the S.pombe rRNA gene promoter.

The multi-protein complex SL1, containing TBP, which is essential for RNA polymerase I catalyzed transcription, has been analyzed in fission yeast. It was immunopurified based on association of component subunits with epitope-tagged TBP. To enable this analysis, a strain of Schizosaccharomyces pombe was created where the only functional TBP coding sequences were those of FLAG-TBP. RNA polymerase I transcription components were fractionated from this strain and the TBP-associated polypeptides were subsequently immunopurified together with the epitope- tagged TBP. An assessment of the activity of this candidate SL1 complex was undertaken cross-species. This fission yeast TBP-containing complex displays two activities in redirecting transcriptional initiation of an S. pombe rDNA gene promoter cross-species in Saccharomyces cerevisiae transcription reactions: it both blocks an incorrect transcriptional start site at +7 and directs initiation at the correct site for S. pombe rRNA synthesis. This complex is essential for accurate initiation of the S.pombe rRNA gene: rRNA synthesis is reconstituted when this S.pombe TBP-containing complex is combined with a S.pombe fraction immunodepleted of TBP.
Informal environmental regulation of industrial air pollution: Does neighborhood inequality matter?

This paper analyzes if neighborhood income inequality has an effect on informal regulation of environmental quality, using census tract-level data on industrial air pollution exposure from EPA´s Risk Screening Environmental Indicators and income and demographic variables from the American Community Survey and EPA´s Smart Location Database. Estimating a spatial lag model and controlling for formal regulation at the states level, we find evidence that overall neighborhood inequality - as measured by the ratio between the fourth and the second income quintile or the neighborhood Gini coefficient - increases local air pollution exposure, whereas a concentration of top incomes reduces local exposure. The positive coefficient of the general inequality measure is driven by urban neighborhoods, whereas the negative coefficient of top incomes is stronger in rural areas. We explain these findings by two contradicting effects of inequality: On the one hand, overall inequality reduces collective action and thus the organizing capacities for environmental improvements. On the other hand, a concentration of income at the top enhances the ability of rich residents to negotiate with regulators or polluting plants in their vicinity. (authors' abstract)
High-Efficiency SiGe-BiCMOS $E$ -Band Power Amplifiers Exploiting Current Clamping in the Common-Base Stage

This paper proposes high-efficiency <inline-formula> <tex-math notation="LaTeX">$E$ </tex-math></inline-formula>-band power amplifiers (PAs) in SiGe-BiCMOS based on a common-base output stage. A comparison between common-emitter and common-base configurations proves the latter yields higher output power, enhanced linear range, and robustness against self-heating issues. Furthermore, the base–emitter junction of the BJT in common base can be exploited to implement the current-mode version of the well-known diode voltage clamper, so that the dc current tracks the signal current. This yields an improvement of efficiency, particularly in power back-off. Two PA designs are described: a single-path, two-stage amplifier and a second version delivering higher output power with transformer-based power combining at minimal efficiency degradation. At 80 GHz, the two PAs deliver 18- and 20.5-dBm <inline-formula> <tex-math notation="LaTeX">${\mathrm {OP}}_{1\,\mathrm {dB}}$ </tex-math></inline-formula> and 19- and 21.5-dBm <inline-formula> <tex-math notation="LaTeX">$P_{\mathrm {sat}}$ </tex-math></inline-formula>. The PAE at <inline-formula> <tex-math notation="LaTeX">${\mathrm {OP}}_{1\,\mathrm {dB}}$ </tex-math></inline-formula> is 22% and 20%; at 6-dB back-off, it is still 8.5% and 7.2%, respectively. The prototypes demonstrate two—three times higher PAE compared to previously reported silicon PAs with similar output power at the <inline-formula> <tex-math notation="LaTeX">$E$ </tex-math></inline-formula>-band.
Experiencing vicarious rejection in the wake of the 2016 presidential election

Presidential elections in the United States pit two (or more) candidates against each other. Voters elect one and reject the others. This work tested the hypothesis that supporters of a losing presidential candidate may experience that defeat as a personal rejection. Before and after the 2016 U.S. presidential election between Donald Trump and Hillary Clinton, voters reported their current feelings of rejection and social pain, along with potential predictors of these feelings. Relative to Trump supporters, Clinton (losing candidate) supporters reported greater feelings of rejection, lower mood, and reduced fundamental needs post-election, while controlling for pre-election levels of these variables. Moreover, as self–candidate closeness and liberal political orientation increased, so too did feelings of rejection and social pain among Clinton supporters. We discuss the implications of these results for understanding human sensitivity to belonging threats and for the vicarious rejection literature.
Natural evolution and collective optimum-seeking

On the one hand many people admire the often strikingly eecient results of organic evolution, on the other hand, however, they presuppose mutation and selection to be a rather prodigal and uneecient trial-and-error strategy like Monte-Carlo sampling. Taking into account the parallel processing of a heterogeneous population and sexual propagation with recombination as well as the endogenous adaptation of strategy characteristics, simulated evolution reveals a couple of interesting, sometimes surprising, properties of nature's learning-by-doing algorithm. `Survival of the ttest', often taken as Darwin's view, turns out to be a bad advice. Forgetting, i.e. individual death, and even regression show up to be necessary ingredients of the life game. Whether the process should be termed gradualistic or punctualistic, is a matter of the observer's point of view. He even might observèlong waves'.
Adaptive Robust Control of Robot Manipulators : Theory and Comparative Experiments

Adaptive robust control schemes are developed for the trajectory tracking control of robot manipulators. The schemes utilize a dynamic sliding mode and achieve a guaranteed transient performance and nal tracking accuracy in the presence of both parametric uncertainties and uncertain nonlinearities. In the absence of uncertain nonlinearities, the schemes also achieve asymptotic tracking. In addition, three conceptually di erent adaptive and robust control schemes | a very simple nonlinear PID type robust control, a gain-based nonlinear PID type adaptive control, and a combined parameter and gain based adaptive robust control | are derived for comparison. All algorithms, as well as two benchmark adaptive schemes, are implemented and compared on a two-link direct-drive robot. Comparative experimental results show the importance of using both proper controller structure and parameter adaptation in designing high performance controllers. It is observed that in these experiments, the proposed scheme improves tracking performance without increasing control e ort.
Casting a broader net: Using microfluidic metagenomics to capture aquatic biodiversity data from diverse taxonomic targets

Environmental DNA (eDNA) assays for single‐ and multi‐species detection show promise for providing standardized assessment methods for diverse taxa, but tech‐ niques for evaluating multiple taxonomically divergent assemblages are in their in‐ fancy. We evaluated whether microfluidic multiplex metabarcoding on the Fluidigm Access Array™ platform and high‐throughput sequencing could identify diverse stream and riparian assemblages from 48 taxon‐general and taxon‐specific meta‐ barcode primers. eDNA screening was paired with electrofishing along a stream continuum to evaluate congruence between methods. A fish hatchery located mid‐ way along the stream continuum provided a dispersal barrier, and a point source for non‐native White Sturgeon ( Acipencer transmontanus ). Microfluidic metabarcoding had 87% accuracy with respect to