Miracle-dz/UniFAR-FTUs huggingface dataset:
Detection of anthropogenic climate change in satellite records of ocean chlorophyll and productivity

Abstract. Global climate change is predicted to alter the ocean's biological productivity. But how will we recognise the impacts of climate change on ocean productivity? The most comprehensive information available on its global distribution comes from satellite ocean colour data. Now that over ten years of satellite-derived chlorophyll and productivity data have accumulated, can we begin to detect and attribute climate change-driven trends in productivity? Here we compare recent trends in satellite ocean colour data to longer-term time series from three biogeochemical models (GFDL, IPSL and NCAR). We find that detection of climate change-driven trends in the satellite data is confounded by the relatively short time series and large interannual and decadal variability in productivity. Thus, recent observed changes in chlorophyll, primary production and the size of the oligotrophic gyres cannot be unequivocally attributed to the impact of global climate change. Instead, our analyses suggest that a time series of ~40 years length is needed to distinguish a global warming trend from natural variability. In some regions, notably equatorial regions, detection times are predicted to be shorter (~20–30 years). Analysis of modelled chlorophyll and primary production from 2001–2100 suggests that, on average, the climate change-driven trend will not be unambiguously separable from decadal variability until ~2055. Because the magnitude of natural variability in chlorophyll and primary production is larger than, or similar to, the global warming trend, a consistent, decades-long data record must be established if the impact of climate change on ocean productivity is to be definitively detected.
Spectral Clustering for Speech Separation

Spectral clustering refers to a class of recent techniques which rely on the eigenstructure of a similarity matrix to partition points into disjoint clusters, with points in the same cluster having high similarity and points in different clusters having low similarity. In this chapter, we introduce the main concepts and algorithms together with recent advances in learning the similarity matrix from data. The techniques are illustrated on the blind one-microphone speech separation problem, by casting the problem as one of segmentation of the spectrogram.
Consensus scoring criteria in structure-based virtual screening

In this paper, we demonstrated that combining multiple scoring functions improves enrichment of true positives only if (a) each of the individual scoring functions has relatively high performance, and (b) the individual scoring functions are distinctive. The major weakness, the inability to consistently identify true positives (leads), of virtual screening is likely due to the imprecise scoring algorithms. It has been demonstrated that consensus scoring improves enrichment of true positives, but they are yet to provide theoretical analysis that gives insight into real features of combinations and data fusion for virtual screening. This work thus establishes a potential theoretical basis for the probable success of data fusion approaches to improve yields in silico screening experiments. We provide initial validation of this theoretical approach using data from five scoring systems with two evolutionary docking algorithms on four targets, thymidine kinase, human dihydrofolate reductase, and estrogen receptors of antagonists and agonists. Results of the experiment show a fairly significant improvement (vs. single algorithms) in several measures of scoring quality, specifically "goodness-of-hit" scores, false positive rate, and "enrichment".
A Markov Decision Process based Flow Assignment Framework and its Performance Evaluation

We consider a scenario where devices with multiple networking capabilities access networks with heterogeneous characteristics. In such a setting, we address the problem of efficient utilization of multiple access networks (wirele ss and/or wireline) by devices via optimal assignment of traffi c flows with given utilities to different networks. We develop and analyze a device middleware functionality that monitors network characteristics and e mploys a Markov Decision Process (MDP) based control scheme that in conjunction with stochastic characterization of the available bit rate and delay of the networks generates an optimal policy for allocation of flows to different networks. The optimal policy maximizes, under available bit rate and delay constraints on the access networks, a discounted reward which is a function of the flow utilities. The flow assignment policy is periodically updated and is consulted by the flows to dynamically perform network selection during their lifetimes. We perform measurement tests to collect traces of available bit rate and delay characteristics on Ethernet an d WLAN networks on a work day in a corporate work environment. We implement our flow assignment framework in ns-2 and simulate the system performance for a set of elastic video-like flows using the collected traces. We de monstrate that the MDP based flow assignment policy
Pedigree Analysis for Genetic Counseling

We report on the design and implementation of a prototype program, GENINFER  , to assist genetic counselors in evaluating the risk of recurrence of genetic disorders based on the analysis of family pedigrees. The present version of the program integrates a convenient graphical interface that permits counselors to draw, examine and modify family pedigrees and to enter information relevant to risk analysis. It also includes a general-purpose Bayesian inference mechanism that permits the rapid calculation and display of probabilities of various genotypes, for the consultand and all other pedigree members. This is possible even in the presence of complex pedigrees with multiple consanguineous matings. The ability to support rapid calculation also enables the user to perform sensitivity analyses. Limitations of the prototype include a restriction to single-locus Mendelian disorders and an inability to make use of information from RFLP markers. Planned extensions include remedying these limitations, the incorporation of an algorithm for automated reformatting (layout) of an existing pedigree, improvements in the population genetic models used by the program, and connections to external databases for acquiring data on disease incidence, patterns of inheritance, mutation rates, penetrance, etc.
Statistical morphological disambiguation with application to disambiguation of pronunciations in Turkish

The statistical morphological disambiguation of agglutinative languages suffers from data sparseness. In this study, we introduce the notion of distinguishing tag sets (DTS) to overcome the problem. The morphological analyses of words are modeled with DTS and the root major part-of-speech tags. The disambiguator based on the introduced representations performs the statistical morphological disambiguation of Turkish with a recall of as high as 95.69 percent. In text-to-speech systems and in developing transcriptions for acoustic speech data, the problem occurs in disambiguating the pronunciation of a token in context, so that the correct pronunciation can be produced or the transcription uses the correct set of phonemes. We apply the morphological disambiguator to this problem of pronunciation disambiguation and achieve 99.54 percent recall with 97.95 percent precision. Most text-to-speech systems perform phrase level accentuation based on content word/function word distinction. This approach seems easy and adequate for some right headed languages such as English but is not suitable for languages such as Turkish. We then use a a heuristic approach to mark up the phrase boundaries based on dependency parsing on a basis of phrase level accentuation for Turkish TTS synthesizers.
Judging a Movie by its Poster using Deep Learning

It is often the case that a human can determine the genre of a movie by looking at its movie poster. This task is not trivial for computers. A recent advance in machine learning called deep learning allows algorithms to learn important features from large datasets. Rather than analyzing an image pixel by pixel, for example, higher level features can be used for classification. In this project I attempted to train a neural network of stacked autoencoders to predict a movie’s genre given an image of its movie poster. My hypothesis is that a good algorithm can correctly guess the genre based on the movie poster at least half the time.
A Novel P2P Approach to S-CSCF Assignment in IMS

In IP Multimedia Subsystem (IMS), Interrogating Call Session Control Function (I-CSCF) assigns the most suitable Serving-CSCF (S-CSCF) to the user performing IMS registration, which is defined by the Third Generation Partnership Project (3GPP) as S-CSCF assignment. Current approaches to implement S-CSCF assignment, however, incur the low utilization of S-CSCF resources. In this paper, we design a hybrid Peer-to-Peer (P2P) based S-CSCF overlay network called Hyson. Based on Hyson, we propose a novel approach to implement S-CSCF assignment called HSA, which can enhance the utilization of S-CSCF resources. HSA is accomplished by collaborative work of S-CSCFs. Simulation results show that HSA enables not only IMS network to have a much greater user capacity but also S-CSCFs to produce much smaller processing delay than current approaches.
GC-Biased Segregation of Noncoding Polymorphisms in Drosophila

The study of base composition evolution in Drosophila has been achieved mostly through the analysis of coding sequences. Third codon position GC content, however, is influenced by both neutral forces (e.g., mutation bias) and natural selection for codon usage optimization. In this article, large data sets of noncoding DNA sequence polymorphism in D. melanogaster and D. simulans were gathered from public databases to try to disentangle these two factors—noncoding sequences are not affected by selection for codon usage. Allele frequency analyses revealed an asymmetric pattern of AT vs. GC noncoding polymorphisms: AT → GC mutations are less numerous, and tend to segregate at a higher frequency, than GC → AT ones, especially at GC-rich loci. This is indicative of nonstationary evolution of base composition and/or of GC-biased allele transmission. Fitting population genetics models to the allele frequency spectra confirmed this result and favored the hypothesis of a biased transmission. These results, together with previous reports, suggest that GC-biased gene conversion has influenced base composition evolution in Drosophila and explain the correlation between intron and exon GC content.
Exponential convergence of Langevin distributions and their discrete approximations

In this paper we consider a continuous-time method of approximating a given distribution using the Langevin diusion dLtdWt 1 2 r log (Lt)dt. We ®nd conditions under this diusion converges exponentially quickly to or does not: in one dimension, these are essentially that for distributions with exponential tails of the form (x)/ exp (y |x| , 0< <1, exponential convergence occurs if and only if 1. We then consider conditions under which the discrete approximations to the diusion converge. We ®rst show that even when the diusion itself converges, naive discretizations need not do so. We then consider a `Metropolis-adjusted' version of the algorithm, and ®nd conditions under which this also converges at an exponential rate: perhaps surprisingly, even the Metropolized version need not converge exponentially fast even if the diusion does. We brie y discuss a truncated form of the algorithm which, in practice, should avoid the diculties of the other forms.
How Entrepreneurship Evolves

We craft a historically sensitive model of entrepreneurship linking individual actors to the evolving social structures they must navigate to acquire resources and launch new ventures. Theories of entrepreneurship and industry evolution suggest two opposing hypotheses: as an industry develops, launching a new venture may become more difficult for all but industry insiders and the socially prominent because of competition from large incumbents, or it may become easier for all people because the legitimacy accorded to the industry simplifies the entrepreneurial task. To test these two conflicting claims, we study the American magazine industry from 1741 to 1860. We find that magazine publishing was originally restricted to publishing-industry insiders, professionals, and the highly educated, but most later founders came from outside publishing and more were of middling stature. Gains by entrepreneurs from the social periphery, however, were uneven: most were doctors and clergy without college degrees in small urban areas; magazines founded by industry insiders remained predominant in the industry centers. Our analysis demonstrates the importance of grounding studies of entrepreneurship in historical context. It also shows that entrepreneurship scholars must attend to temporal shifts within the focal industry and in society at large.
Symbolic analysis of assembly traces: Lessons learned and perspectives

In this study, we have developed a software to implement a symbolic analyzer for assembly traces. The software receives as input traces of assembly instructions. It then builds a symbolic expression characterizing the possible range of values for each variable and feeds this value to the Yices STM solver. The Yices solver returns possible concrete values that respect the symbolic expressions associated with each variable. This software has several potential applications including software testing and fuzzing and vulnerability detection. To verify the validity of our approach, we have tested our software with real-life traces and investigated its potential use for malware detection. For instance, that the software automatically detects the input values that would cause a buffer overflow in some cases. To conclude, we reflect on the lessons learned during the development of this software, which can help guide the future development of symbolic analyzers.
NetSecuritas: An Integrated Attack Graph-based Security Assessment Tool for Enterprise Networks

Sophisticated cyber-attacks have become prominent with the growth of the Internet and web technology. Such attacks are multi-stage ones, and correlate vulnerabilities on intermediate hosts to compromise an otherwise well-protected critical resource. Conventional security assessment approaches can leave out some complex scenarios generated by these attacks. In the literature, these correlated attacks have been modeled using attack graphs. Although a few attack graph-based network security assessment tools are available, they are either commercial products or developed using proprietary databases. In this paper, we develop a customized tool, NetSecuritas, which implements a novel heuristic-based attack graph generation algorithm and integrates different phases of network security assessment. NetSecuritas leverages open-source libraries, tools and publicly available databases. A cost-driven mitigation strategy has also been proposed to generate network security recommendations. Experimental results establish the efficacy of both attack graph generation and mitigation approach.
The Effects of Being in a “New Relationship” on Levels of Testosterone in Men

In light of previous research showing that different types of relationships affect levels of testosterone in men, this study examined whether categorizing relationship types according to relationship length can shed further light on variations in levels of testosterone. Salivary testosterone samples were obtained from a sample of men and details about their relationship status, sociosexual orientation, extra-pair sexual interest, and their perceptions of their relationships were recorded. Using a median split analysis, participants who indicated that they had been in their relationship for less than 12 months were categorized as being in “new relationships” and those in longer relationships being categorized as in long-term relationships. Results showed that levels of testosterone of single men and men in new relationships did not differ, but both had significantly greater levels of testosterone than men in long-term relationships. Differences in levels of testosterone were unrelated to sociosexual orientation and extra-pair sexual interest. These findings support the evolutionary explanation of levels of testosterone in men varying in accordance with their internal motivation to seek new potential mates.
Reduced Photoinhibition under Low Irradiance Enhanced Kacip Fatimah (Labisia pumila Benth) Secondary Metabolites, Phenyl Alanine Lyase and Antioxidant Activity

A randomized complete block design experiment was designed to characterize the relationship between production of total flavonoids and phenolics, anthocyanin, photosynthesis, maximum efficiency of photosystem II (Fv/Fm), electron transfer rate (Fm/Fo), phenyl alanine lyase activity (PAL) and antioxidant (DPPH) in Labisia pumila var. alata, under four levels of irradiance (225, 500, 625 and 900 μmol/m2/s) for 16 weeks. As irradiance levels increased from 225 to 900 μmol/m2/s, the production of plant secondary metabolites (total flavonoids, phenolics and antocyanin) was found to decrease steadily. Production of total flavonoids and phenolics reached their peaks under 225 followed by 500, 625 and 900 μmol/m2/s irradiances. Significant positive correlation of production of total phenolics, flavonoids and antocyanin content with Fv/Fm, Fm/Fo and photosynthesis indicated up-regulation of carbon-based secondary metabolites (CBSM) under reduced photoinhibition on the under low light levels condition. At the lowest irradiance levels, Labisia pumila extracts also exhibited a significantly higher antioxidant activity (DPPH) than under high irradiance. The improved antioxidative activity under low light levels might be due to high availability of total flavonoids, phenolics and anthocyanin content in the plant extract. It was also found that an increase in the production of CBSM was due to high PAL activity under low light, probably signifying more availability of phenylalanine (Phe) under this condition.
On $r$-neighborly submanifolds in $R^N$

A submanifold $M \subset R^N$ is $r$-neighborly if for any $r$ points in $M$ there is a hyperplane, supporting $M$ and touching it at exactly these $r$ points. We prove that the minimal dimension $\Delta(k,r)$ of the Euclidean space, containing a stably $r$-neighborly submanifold, is asymptotically not smaller than $2kr-k$.
The correlation of sunspot data

This paper employ the detrended fluctuation analysis method to investigate the properties of the sunspot time series. The data is downloaded from SIDC's website observed from January, 1749 to March, 2009 through about 260 years. The different scaling exponents estimated by R/S method and DFA method indicate the different correlation in the sunspot data. The appearance of crossovers show the periodic or other characteristic exists in the signals.
caWaC – A web corpus of Catalan and its application to language modeling and machine translation

In this paper we present the construction process of a web corpus of Catalan built from the content of the . cat top-level domain. For collecting and processing data we use the Brno pipeline with the spiderling crawler and its accompanying tools. To the best of our knowledge the corpus represents the largest existing corpus of Catalan containing 687 million words, which is a significant increase given that until now the biggest corpus of Catalan, CuCWeb, counts 166 million words. We evaluate the resulting resource on the tasks of language modeling and statistical machine translation (SMT) by calculating LM perplexity and incorporating the LM in the SMT pipeline. We compare language models trained on different subsets of the resource with those trained on the Catalan Wikipedia and the target side of the parallel data used to train the SMT system.
“ Infinite LDA ” – Implementing the HDP with minimum code complexity

Shows how the hierarchical Dirichlet process (HDP) may be implemented in a simple way, following the idea that the HDP is an extension to its parametric counterpart, latent Dirichlet allocation (LDA). Document version: draft, version 0.92, 20 Feb. 2011 (version 0.1: May 2008).
Les jeunes alévis du quartier de Gazi (Istanbul) et les associations de hemşehri : identifications croisées

This article aims to explore the impact of the hemsehri identity on the second generation of Alevi youth living in Gazi district of Istanbul. It advances the following hypothesis: the second generation of gecekondu (shanty town) inhabitants consider the district as the basis of their spatial identity. Most of them are committed to left-wing political parties, and not to hometown associations where social control remains strong. In this context, it seems that the impact of hemsehri identity on second-generation youth is related to the ability of the hometown associations to provide activities tailored for young people, as well as the opportunities it provides for their inclusion within their administrative structure.
Partial discharge characteristics of piezoelectric ceramics under bipolar and unipolar applied voltages

Electrical partial discharge measurement has been investigated as a nondestructive test method to evaluate the mechanical reliability of piezoelectric ceramics. In order to understand the factors affecting partial discharge in piezoelectric ceramics, the effect of electric loading conditions is investigated. In this work, electrical partial discharge tests have been carried out on piezoelectric bulk ceramics under electric loading in both bipolar and unipolar modes. Partial discharge inception fields are measured for piezoelectric ceramic samples. The results demonstrate that under bipolar sinusoidal applied voltage, electrical partial discharges tend to occur at relatively low electric fields as compared to the case of unipolar field application. This suggests that the partial discharge recurrence rate may be lower under the unipolar applied voltage. Besides, the polarization change during domain switching under bipolar waveform in piezoelectric ceramics may facilitate partial discharges.
Spectrum estimation, notch filters, and MUSIC

A novel extension of the Multiple Signal Classification (MUSIC) algorithm for the frequency estimation problem is proposed in this work. It is shown that the MUSIC algorithm is a data-dependent optimum notch filter design technique. When the input data are passed through this optimum notch filter, the signal-to-noise ratio (SNR) of the output is minimized. The zeros of the optimum notch filter are then used to estimate the frequencies. In the classical MUSIC algorithm, this optimization is carried out over the set of all finite impulse response (FIR) filters having a fixed order. The framework of this paper allows the use of rational notch filters. The fact that the rational filters have sharper spectral characteristics is used to obtain improved estimates, particularly at low SNR. Some interesting properties of the proposed algorithm are derived, and further perspectives on the implementation aspects are provided. The analytical predictions are substantiated using numerical simulation results.