Miracle-dz/UniFAR-FTUs huggingface dataset:
An Algorithm for Stabilization of Fractional-Order Time Delay Systems Using Fractional-Order PID Controllers

This technical note presents a solution to the problem of stabilizing a given fractional-order system with time delay using fractional-order PllambdaDmu controllers. It is based on determining a set of global stability regions in the (kp, Ki, Kd)-space corresponding to the fractional orders lambda and mu in the range of (0, 2) and then choosing the biggest global stability region in this set. This method can be also used to find the set of stabilizing controllers that guarantees prespecified gain and phase margin requirements. The algorithm is simple and has reliable result which is illustrated by an example, and, hence, is practically useful in the analysis and design of fractional-order control systems.
BPGen: an automated breakpoint generator for debugging

During debugging processes, breakpoints are frequently used to inspect and understand runtime behaviors of programs. Although most development environments offer convenient breakpoint facilities, the use of these environments usually requires considerable human efforts in order to generate useful breakpoints. Before setting breakpoints or typing breakpoint conditions, developers usually have to make some judgements and hypotheses on the basis of their observations and experience. To reduce this kind of efforts we present a tool, named BPGen, to automatically generate breakpoints for debugging. BPGen uses three well-known dynamic fault localization techniques in tandem to identify suspicious program statements and states, through which both conditional and unconditional breakpoints are generated. BPGen is implemented as an Eclipse plugin for supplementing the existing Eclipse JDT debugger.
Clustered Compressed Sensing via Bayesian Framework

This paper provides clustered compressive sensing (CCS) based signal processing using Bayesian framework. Images like magnetic resonanse images (MRI) are usually very weak due to the presence of noise and due to the weak nature of the signal itself. Compressed sensing (CS) paradigm can be applied in order to boost such signal recoveries. We applied CS paradigm via Bayesian framework. That is incorporating the different prior information such as sparsity and the special structure that can be found in such sparse signal improves signal recovery. The method is applied on synthetic and medical images including MRI images. The results show that applying the clustered compressive sensing out performs the non clustered but only sparse counter parts when it comes to mean square error(MSE), pick signal to noise ratio (PSNR) and other performance metrics.
The opaH locus of Neisseria gonorrhoeae MS11A is involved in epithelial cell invasion

In order to produce a successful infection, Neisseria gonorrhoeae (GC) must attach to and invade mucosal epithelial cells. To identify GC gene products involved in this early interaction with host cells we constructed a gene bank derived from a clinical isolate of GC, and isolated a clone which had the capacity to adhere to the human endometrial adenocarcinoma tissue‐culture line HEC‐1‐B. The cloned sequence was identified as a member of the opa gene family whose protein products have been associated with virulence. The GC chromosome contains numerous variant opa genes which, in MS11, are designated opaA‐K. Previous work showed that expression of opaC confers a highly invasive phenotype upon strain MS11. When our cloned opa gene was mutated and returned to the GC MS11A chromosome by transformation and homologous recombination, we isolated one transformation that was significantly reduced in its invasive capacity. The locus mutated in this transformant was identified as opaH. Our resuits indicate that invasive‐ness of GC for human epithelail cells can be determined by more than one opa gene in strain MS11 A.
TreeOTU: Operational Taxonomic Unit Classification Based on Phylogenetic Trees

Our current understanding of the taxonomic and phylogenetic diversity of cellular organisms, especially the bacteria and archaea, is mostly based upon studies of sequences of the small- subunit rRNAs (ssu-rRNAs). To address the limitation of ssu-rRNA as a phylogenetic marker, such as copy number variation among organisms and complications introduced by horizontal gene transfer, convergent evolution, or evolution rate variations, we have identified protein- coding gene families as alternative Phylogenetic and Phylogenetic Ecology markers (PhyEco). Current nucleotide sequence similarity based Operational Taxonomic Unit (OTU) classification methods are not readily applicable to amino acid sequences of PhyEco markers. We report here the development of TreeOTU, a phylogenetic tree structure based OTU classification method that takes into account of differences in rates of evolution between taxa and between genes. OTU sets built by TreeOTU are more faithful to phylogenetic tree structures than sequence clustering (non phylogenetic) methods for ssu-rRNAs. OTUs built from phylogenetic trees of protein coding PhyEco markers are comparable to our current taxonomic classification at different levels. With the included OTU comparing tools, the TreeOTU is robust in phylogenetic referencing with different phylogenetic markers and trees.
The low information content of Neurospora splicing signals: implications for RNA splicing and intron origin

When we expressed a small (0.9 kb) nonprotein-coding transcript derived from the mitochondrial VS plasmid in the nucleus of Neurospora we found that it was efficiently spliced at one or more of eight 5′ splice sites and ten 3′ splice sites, which are present apparently by chance in the sequence. Further experimental and bioinformatic analyses of other mitochondrial plasmids, random sequences, and natural nuclear genes in Neurospora and other fungi indicate that fungal spliceosomes recognize a wide range of 5′ splice site and branchpoint sequences and predict introns to be present at high frequency in random sequence. In contrast, analysis of intronless fungal nuclear genes indicates that branchpoint, 5′ splice site and 3′ splice site consensus sequences are underrepresented compared with random sequences. This underrepresentation of splicing signals is sufficient to deplete the nuclear genome of splice sites at locations that do not comprise biologically relevant introns. Thus, the splicing machinery can recognize a wide range of splicing signal sequences, but splicing still occurs with great accuracy, not because the splicing machinery distinguishes correct from incorrect introns, but because incorrect introns are substantially depleted from the genome.
Effective caching of Web objects using Zipf's law

Web accesses follow Zipf's law with a good approximation, as measurements and observations indicate. This property provides an important tool in the design of Web caching architectures, because it allows designers to calculate appropriate cache sizes to achieve the desired hit ratios. The appropriate cache size combined with an LFU replacement policy achieves high cache hit rates. However, LFU replaces objects based on frequency measurements of past accesses. Thus, the system achieves high hit rates only after these measurements are reliable and converge to the final Zipf distribution. In this paper, we provide an analysis using Chernoff's bound and a calculation of an upper bound of the number of initial requests that need to be processed in order to obtain measurements of popularity with high confidence and a measured Zipf distribution which converges to the correct one.
Interest Rate – Private Capital Formation Nexus in South Africa: Bounds Test Approach

The study uses the annual time series data for the period 1975 – 2012 to empirically examine the impact of monetary policy on private capital formation in South Africa with the view to establish any long-run relationship. The unit root test was conducted prior to regression using the Dickey-Fuller Generalised Least Squares (DF-GLS) and the Ng – Perron tests. The Granger causality test was also conducted to establish the direction of causation between variables included in the model. The unit root test results reveal that inflation, gross private capital formation and real exchange rate are stationary at first difference while real interest rate is stationary at levels.The bounds test approach asserts that variables could still drift together even when they are integrated of different orders (Pesaran, 2001). Cointegration test indicates an existence of long-run relationship among the variables included using Autoregressive Distributed Lag(ARDL)-ECM cointegration procedure advanced by Pesaran et al.(2001). The results of this study indicate that the cost of capital (interest rate) exerts a significant and negative impact on South Africa’s private capital formation. DOI: 10.5901/mjss.2014. v5n1p181
Experience Report: An Empirical Study of API Failures in OpenStack Cloud Environments

Stories about service outages in cloud environments have been making the headlines recently. In many cases, the reliability of cloud infrastructure Application Programming Interfaces (APIs) were at fault. Hence, understanding the factors affecting the reliability of these APIs is important to improve the availability of cloud services. In this study, we mined bugs of 25 modules within the 5 most important OpenStack APIs to understand API failures and characteristics. Our results show that in OpenStack, only one third of all API-related changes are due to fixing failures, with 7% of all fixes even changing the API interface, potentially breaking clients. Through qualitative analysis of 230 sampled API failures we observed that the majority of API related failures are due to small programming faults. Fortunately, the subject, message and stack trace as well as reply lag between comments included in these failures' bug reports provide a good indication of the cause of the failure.
Hashing based feature aggregating for fast image copy retrieval

Recently the methods based on visual words have become very popular in near- duplicate retrieval and content identification. However, obtaining the visual vocabulary by quantization is very time-consuming and unscalable to large databases. In this paper, we propose a fast feature aggregating method for image representation which uses machine learning based hashing to achieve fast feature aggregation. Since the machine learning based hashing effectively preserves neighborhood structure of data, it yields visual words with strong discriminability. Furthermore, the generated binary codes leads image representation building to be of low-complexity, making it efficient and scalable to large scale databases. The evaluation shows that our approach significantly outperforms state-of-the-art methods.
Learning to Rank from Noisy Data

Learning to rank, which learns the ranking function from training data, has become an emerging research area in information retrieval and machine learning. Most existing work on learning to rank assumes that the training data is clean, which is not always true, however. The ambiguity of query intent, the lack of domain knowledge, and the vague definition of relevance levels all make it difficult for common annotators to give reliable relevance labels to some documents. As a result, the relevance labels in the training data of learning to rank usually contain noise. If we ignore this fact, the performance of learning-to-rank algorithms will be damaged. In this article, we propose considering the labeling noise in the process of learning to rank and using a two-step approach to extend existing algorithms to handle noisy training data. In the first step, we estimate the degree of labeling noise for a training document. To this end, we assume that the majority of the relevance labels in the training data are reliable and we use a graphical model to describe the generative process of a training query, the feature vectors of its associated documents, and the relevance labels of these documents. The parameters in the graphical model are learned by means of maximum likelihood estimation. Then the conditional probability of the relevance label given the feature vector of a document is computed. If the probability is large, we regard the degree of labeling noise for this document as small; otherwise, we regard the degree as large. In the second step, we extend existing learning-to-rank algorithms by incorporating the estimated degree of labeling noise into their loss functions. Specifically, we give larger weights to those training documents with smaller degrees of labeling noise and smaller weights to those with larger degrees of labeling noise. As examples, we demonstrate the extensions for McRank, RankSVM, RankBoost, and RankNet. Empirical results on benchmark datasets show that the proposed approach can effectively distinguish noisy documents from clean ones, and the extended learning-to-rank algorithms can achieve better performances than baselines.
Transmission of correlated sources over a MAC: A Gaussian approximation-based analysis

The problem of separately encoding and subsequently transmitting correlated sources over a discrete memoryless multiple-access channel is revisited. In particular, we examine the sufficient conditions on the source and the channel under which there exists a n-length block code satisfying the condition that the error probability of reconstructing the discrete memoryless multiple source is no larger than some fixed constant ϵ > 0, or in short, ϵ-lossless transmission. We modify the decoding rule of Cover-El Gamal-Salehi and analyze the error probability using Gaussian approximations to derive a second-order generalization their sufficient condition for the ϵ-lossless transmission of the correlated sources.
FWNS: A Framework for Web-based Network Simulation

Many of today's communication network systems are built using oo-the-shelf components ooered by third party manufacturers. The diversity in the available components has made the task of design and analysis of diierent network topologies and various conngurations of network components, complicated. To alleviate these problems and to addresses the associated issues, a web-based framework for modeling and simulating computer networks was developed. Using the framework, a designer can explore design alternatives by constructing and analyzing conng-urations of the design using components ooered by different researchers and manufacturers. The motivation for this design is to extend the framework to active network simulations. The framework should provide a exible and robust environment for selecting the optimal solution set from a large and complex solution space. This paper presents the issues involved in the design and development of a framework for web-based network simulation (fwns).
Budgeted Online Collective Inference

Updating inference in response to new evidence is a fundamental challenge in artificial intelligence. Many real problems require large probabilistic graphical models, containing millions of interdependent variables. For such large models, jointly updating the most likely (i.e., MAP) configuration of the variables each time new evidence is encountered can be infeasible, even if inference is tractable. In this paper, we introduce budgeted online collective inference, in which the MAP configuration of a graphical model is updated efficiently by revising the assignments to a subset of the variables while holding others fixed. The goal is to selectively update certain variables without sacrificing quality with respect to full inference. To formalize the consequences of partially updating inference, we introduce the concept of inference regret. We derive inference regret bounds for a class of graphical models with strongly-convex free energies. These theoretical insights, combined with a thorough analysis of the optimization solver, motivate new approximate methods for efficiently updating the variable assignments under a budget constraint. In experiments, we demonstrate that our algorithms can reduce inference time by 65% with accuracy comparable to full inference.
Parametric reshaping of human bodies in images

We present an easy-to-use image retouching technique for realistic reshaping of human bodies in a single image. A model-based approach is taken by integrating a 3D whole-body morphable model into the reshaping process to achieve globally consistent editing effects. A novel body-aware image warping approach is introduced to reliably transfer the reshaping effects from the model to the image, even under moderate fitting errors. Thanks to the parametric nature of the model, our technique parameterizes the degree of reshaping by a small set of semantic attributes, such as weight and height. It allows easy creation of desired reshaping effects by changing the full-body attributes, while producing visually pleasing results even for loosely-dressed humans in casual photographs with a variety of poses and shapes.
Qualitative aspects of memory performance in depressed versus demented elderly

Previous research concerning the differences in memory of normal and impaired elderly has focused primarily on standard quantitative psychometric aspects. Quantitative psychometric measures alone, however, are not always sufficient to differentiate diagnostic groups. Hie present study investigated quantitative and qualitative aspects of memory in three age-and-education-matched groups (1) 38 normal elderly, (2) 15 patients with dementia of the Alzheimer's type (DAT), and (3) 26 depressed elderly. Four clusters of dependent variables were used to examine possible differences between the groups: (1) demographic, (2) standard psychometric (Wechsler Memory Scale logical memory and visual reproduction subtests), (3) experimental verbal recall measures (free verbal recall measures of primary memory, secondary memory, prior item intrusions and extra list intrusions), and (4) experimental verbal recognition memory measures (true positive, false positive, true negative, and false negative responses). Dementia patients were expected to be significantly lower on all tasks compared to both the depressed and normal elderly. Depressed patients were expected to perform similarly to normal older adults but were predicted to make more prior item intrusion and false negative errors than normal elderly. Analyses of variance, with specified contrasts, found the DAT patients to differ from normal elderly on all variables but prior item
Discordant Evaluations of Blacks Affect Nonverbal Behavior

Previous research suggests that automatic prejudice directly manifests in nonverbal behavior. The authors offer a more complex picture of the relation between automatic processes and nonverbal behavior by suggesting that any discomfort that appears in nonverbal behavior stems not from negative attitudes per se but from discordance between automatically activated attitudes toward Blacks and the specific evaluations being expressed. White participants for whom estimates of automatic prejudice were available provided videotaped evaluations of several individuals, including two matched Black and White males. Discordance between general racial attitudes and evaluations of specific targets manifested in discomfort-related nonverbal behavior. Moreover, naïve Black judges, but not White judges, doubted the sincerity of individuals characterized by discordance. The nature of the nonverbal “leakage” that automatic prejudice produces is discussed.
To Match or Not to Match

Modern online advertising increasingly relies on the ability to follow the same user across the Internet using technology called cookie matching to increase efficiency in ad allocation. Web publishers today use this technology to share information about the websites a user has visited, making it possible to target advertisements to users based on their prior history. This begs the question: do publishers (who are competitors for advertising money) always have the incentive to share online information? Intuitive arguments as well as anecdotal evidence suggest that sometimes a premium publisher might suffer information sharing through an effect called information leakage: by sharing user information with the advertiser, the advertiser will be able to target the same user elsewhere on cheaper publishers, leading to a dilution of the value of the supply on the premium publishers. The goal of this article is to explore this aspect of online information sharing. We show that, when advertisers are homogeneous in the sense that their relative valuations of users are consistent, publishers always agree about the benefits of cookie matching in equilibrium: either all publishers’ revenues benefit, or all suffer, from cookie matching. We also show using a simple model that, when advertisers are not homogeneous, the information leakage indeed can occur, with cookie matching helping one publisher’s revenues while harming the other.
Scleral Ossicles of Teleostei: Evolutionary and Developmental Trends

Scleral ossicles are bones within the sclera of the eye. A total of 547 teleost species (744 specimens) from 36 orders and 163 families were investigated with respect to scleral ossicle presence/absence and number. This is the first extensive investigation into the distribution of scleral ossicles in living teleosts. Derived orders were found to have the most variable scleral ossicle numbers (zero, one, or two per eye), while more basal groups tend to have no ossicles. Whereas more data on the activity level of individual families and on family‐level interrelationships is needed, significant findings were nevertheless made. Ninety‐four percent of the families investigated have a consistent ossicle number, indicating that family level is a reliable predictor of scleral ossicle presence/absence. In a subgroup analysis of 28 families, additional trends were observed with regard to activity level, namely that 80% of the families that are described as sluggish have no scleral ossicles while 100% of those that are very active have two ossicles per eye. In addition, fish that inhabit deep sea environments are the most likely ones to lack scleral ossicles. The analysis also supports the hypothesis that scleral ossicle number declined from the basal condition of four elements per eye seen in Cheirolepis to no ossicles in the eye, early in teleost evolution. At least 24 evolutionary steps are needed to account for the scleral ossicle distribution seen in teleost orders today. This study describes the variation of scleral ossicles in the most diverse group of vertebrates, Teleostei, and provides the first step in understanding the evolvability of these elements in bony fishes. Anat Rec, 291:161–168, 2008. © 2008 Wiley‐Liss, Inc.