Miracle-dz/UniFAR-FTUs huggingface dataset:
Average-optimal single and multiple approximate string matching

We present a new algorithm for multiple approximate string matching. It is based on reading backwards enough l-grams from text windows so as to prove that no occurrence can contain the part of the window read, and then shifting the window.We show analytically that our algorithm is optimal on average. Hence our first contribution is to fill an important gap in the area, since no average-optimal algorithm existed for multiple approximate string matching.We consider several variants and practical improvements to our algorithm, and show experimentally that they are resistant to the number of patterns and the fastest for low difference ratios, displacing the long-standing best algorithms. Hence our second contribution is to give a practical algorithm for this problem, by far better than any existing alternative in many cases of interest. On real-life texts, our algorithm is especially interesting for computational biology applications.In particular, we show that our algorithm can be successfully used to search for one pattern, where many more competing algorithms exist. Our algorithm is also average-optimal in this case, being the second after that of Chang and Marr. However, our algorithm permits higher difference ratios than Chang and Marr, and this is our third contribution.In practice, our algorithm is competitive in this scenario too, being the fastest for low difference ratios and moderate alphabet sizes. This is our fourth contribution, which also answers affirmatively the question of whether a practical average-optimal approximate string-matching algorithm existed.
ALOHA IN THE DESERT: Ideologies of Ka ʻōlelo hawaiʻi a mēheuheu

My study examines ka ʻōlelo hawaiʻi (Hawaiian Language) in the cultural context of Hula. Hula is a dance with referential movements and gestures that was developed in the Hawaiian Islands by the original settlers. The goal of this study is to build on previous studies of Hawaiian Ideologies by focusing on the use of and attitudes towards the language in this key sociocultural context. The most salient concepts found in previous research are expressed in ka ʻōlelo hawaiʻi as kuleana, “authority, responsibility', malama i ka ʻāina, 'care for the land', ʻohana, 'family' and kupuna, 'the ancestors. ’ Individuals participated in interviews and their responses provide insight into the maintenance of ka ʻōlelo hawaiʻi, within the Hawaiian islands and abroad. The Kumu (teacher) of the Halau shares her knowledge of Hula by creating an inter-generational bridge, and along with hula she offers classes focusing on Hawaiian culture.
Language Modeling with Power Low Rank Ensembles

We present power low rank ensembles (PLRE), a flexible framework for n-gram language modeling where ensembles of low rank matrices and tensors are used to obtain smoothed probability estimates of words in context. Our method can be understood as a generalization of n-gram modeling to non-integer n, and includes standard techniques such as absolute discounting and Kneser-Ney smoothing as special cases. PLRE training is efficient and our approach outperforms state-of-the-art modified Kneser Ney baselines in terms of perplexity on large corpora as well as on BLEU score in a downstream machine translation task.
Role of relative humidity, temperature, and water status in dormancy alleviation of sunflower seeds during dry after-ripening

The effect of various combinations of temperature and relative humidity on dormancy alleviation of sunflower seeds during dry after-ripening was investigated. The rate of dormancy alleviation depended on both temperature and embryo moisture content (MC). Below an embryo MC of 0.1 g H2O g−1 dw, dormancy release was faster at 15 °C than at higher temperatures. This suggests that dormancy release at low MC was associated with negative activation energy, supported by Arrhenius plots, and low Q10 values. At higher MC, the rate of dormancy alleviation increased with temperature, correlating well with the temperature dependence of biochemical processes. These findings suggests the involvement of two distinct cellular mechanisms in dormancy release; non-enzymatic below 0.1 g H2O g−1 dw and associated with active metabolism above this value. The effects of temperature on seed dormancy release above the threshold MC were analysed using a population-based thermal time approach and a model predicting the rate of dormancy alleviation is provided. Sunflower embryo dormancy release was effective at temperatures above 8 °C (the base temperature for after-ripening, TbAR, was 8.17 °C), and the higher the after-ripening temperature above this threshold value, the higher was the rate of dormancy loss. Thermodynamic analyses of water sorption isotherms revealed that dormancy release was associated with less bound water and increased molecular mobility within the embryonic axes but not the cotyledons. It is proposed that the changes in water binding properties result from oxidative processes and can, in turn, allow metabolic activities.
Recent Research Trends in Neuroinflammatory and Neurodegenerative Disorders

Neuroinflammatory and neurodegenerative disorders including Alzheimer’s disease (AD), Parkinson’s disease (PD), traumatic brain injury (TBI) and Amyotrophic lateral sclerosis (ALS) are chronic major health disorders. The exact mechanism of the neuroimmune dysfunctions of these disease pathogeneses is currently not clearly understood. These disorders show dysregulated neuroimmune and inflammatory responses, including activation of neurons, glial cells, and neurovascular unit damage associated with excessive release of proinflammatory cytokines, chemokines, neurotoxic mediators, and infiltration of peripheral immune cells into the brain, as well as entry of inflammatory mediators through damaged neurovascular endothelial cells, blood–brain barrier and tight junction proteins. Activation of glial cells and immune cells leads to the release of many inflammatory and neurotoxic molecules that cause neuroinflammation and neurodegeneration. Gulf War Illness (GWI) and myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) are chronic disorders that are also associated with neuroimmune dysfunctions. Currently, there are no effective disease-modifying therapeutic options available for these diseases. Human induced pluripotent stem cell (iPSC)-derived neurons, astrocytes, microglia, endothelial cells and pericytes are currently used for many disease models for drug discovery. This review highlights certain recent trends in neuroinflammatory responses and iPSC-derived brain cell applications in neuroinflammatory disorders.
A Bayesian framework for active learning

We describe a Bayesian framework for active learning for non-separable data, which incorporates a query density to explicitly model how new data is to be sampled. The model makes no assumption of independence between queried data-points; rather it updates model parameters on the basis of both observations and how those observations were sampled. A ‘hypothetical’ look-ahead is employed to evaluate expected cost in the next time-step. We show the efficacy of this algorithm on the probabilistic high-low game which is a non-separable generalisation of the separable high-low game introduced by Seung et al.
Multi-Region Active Contours with a Single Level Set Function

Segmenting an image into an arbitrary number of coherent regions is at the core of image understanding. Many formulations of the segmentation problem have been suggested over the past years. These formulations include, among others, axiomatic functionals, which are hard to implement and analyze, and graph-based alternatives, which impose a non-geometric metric on the problem. We propose a novel method for segmenting an image into an arbitrary number of regions using an axiomatic variational approach. The proposed method allows to incorporate various generic region appearance models, while avoiding metrication errors. In the suggested framework, the segmentation is performed by level set evolution. Yet, contrarily to most existing methods, here, multiple regions are represented by a single non-negative level set function. The level set function evolution is efficiently executed through the Voronoi Implicit Interface Method for multi-phase interface evolution. The proposed approach is shown to obtain accurate segmentation results for various natural 2D and 3D images, comparable to state-of-the-art image segmentation algorithms.
Paraquat regulation of hmp (flavohemoglobin) gene expression in Escherichia coli K-12 is SoxRS independent but modulated by sigma S

We report the first example of a gene, hmp, encoding a soluble flavohemoglobin in Escherichia coli K-12, which is up-regulated by paraquat in a SoxRS-independent manner. Unlike what is found for other paraquat-inducible genes, high concentrations of paraquat (200 microM) were required to increase the level of hmp expression, and maximal induction was observed only after 20 min of exposure to paraquat. Neither a mutation in soxS nor one in soxR prevented the paraquat-dependent increase in phi(hmp-lacZ) expression, but either mutant allele delayed full expression of phi(hmp-lacZ) activity after paraquat addition. Induction of hmp by paraquat was demonstrated in aerobically grown cultures during exponential growth and the stationary phase, thus revealing two Sox-independent regulatory mechanisms. Induction of hmp by paraquat in the stationary phase was dependent on the global regulator of stationary-phase gene expression, RpoS (sigma S). However, a mutation in rpoS did not prevent an increase in hmp expression by paraquat in exponentially growing cells. Induction of sigma S in the exponential phase by heat shock also induced phi(hmp-lacZ) expression in the presence of paraquat, supporting the role of sigma S in one of the regulatory mechanisms. Mutations in oxyR or rob, known regulators of several stress promoters in E. coli, had no effect on the induction of hmp by paraquat. Other known superoxide-generating agents (plumbagin, menadione, and phenazine methosulfate) were not effective in inducing hmp expression.
Advanced Conceptual Modeling of Web Applications: Embedding Operation Interfaces in Navigation Design

In the last years the integration of operation calls in the context of dynamic, personalized interfaces has become a ’must’ for Web Applications. However, existing conceptual modeling proposals have just tangentially tackled the conceptual definition of its corresponding operation interfaces, which many times involve concepts far more complex than those related to text field input. This paper presents the OO-H modeling proposal for the seamless integration of both single and multi-step operation interfaces in the context of its interface navigational model. The notation and semantics provided by OO-H allow the reuse of context information and/or navigation paths for the assignment of values to operation parameters and the visualization of operation results.
Non-Linear Segmentation of Touched Roman Characters Based on Genetic Algorithm

The segmentation accuracy of Roman cursive characters, especially touched characters, is essential for the high performance of Optical Character Recognition Systems. This paper presents a new approach for non-linear segmentation of multiple touched Roman cursive characters based on genetic algorithm. Initially, a possible segmentation zone is detected and then best segmentation path is evolved by genetic algorithm. The initial population is composed of each point column in possible segmentation zone. The individual coding, fitness function, crossover operator and mutation operator are also defined for this task. Experimental results on a test set extracted on the IAM benchmark database exhibit high segmentation accuracy up to 89.76%. Proposed approach can handle some complex types of touched cursive characters without special heuristic rules and recognition.
Interfacing Indigolog and OAA: A Toolkit for Advanced Multiagent Applications

In this paper, we describe an interface library IG-OAAlib that supports the development of Open Agent Architecture (OAA) agents using the INDIGOLOG agent programming language. OAA is a multi-agent infrastructure that supports facilitated communication. INDIGOLOG is a high-level agent programming language based on logic that supports planning and allows complex agent behaviors to be specified. Full-fledged INDIGOLOG agents written using our interface library can be both reactive and proactive, thus overcoming one of the limitations of PROLOG-based agents in the OAA framework. The interface hides all of the low-level procedures that are used to communicate with the OAA system, as well as OAA initialization, thereby leaving the INDIGOLOG programmer free to concentrate on the functionality of the agent. A multi-robot mail delivery application developed using the library is presented.
Refining Outdoor Photometric Stereo Based on Sky Model

: We propose an outdoor photometric stereo method, which considers environmental lighting for improving the performance of surface normal estimation. In the previous methods, the sky illumination e ﬀ ect has been either assumed to be constant throughout the scene, or to be removed by pre-processing. This paper exploits a sky model which can derive the entire sky luminance from a sky zenith image; then, sky illumination e ﬀ ect on a surface can be correctly calculated by iteratively reﬁning its normal direction. This paper also extends a two-source photometric stereo method by introducing RANSAC, so that the input images of this method can be taken in a day. Experimental results with real outdoor objects show the e ﬀ ectiveness of the method.
Future Generation Computer Systems Deadline-driven Provisioning of Resources for Scientific Applications in Hybrid Clouds with Aneka

Scientific applications require large computing power, traditionally exceeding the amount that is available within the premises of a single institution. Therefore, clouds can be used to provide extra resources whenever required. For this vision to be achieved, however, requires both policies defining when and how cloud resources are allocated to applications and a platform implementing not only these policies but also the whole software stack supporting management of applications and resources. Aneka is a cloud application platform capable of provisioning resources obtained from a variety of sources, including private and public clouds, clusters, grids, and desktops grids. In this paper, we present Aneka's deadline-driven provisioning mechanism, which is responsible for supporting quality of service (QoS)-aware execution of scientific applications in hybrid clouds composed of resources obtained from a variety of sources. Experimental results evaluating such a mechanism show that Aneka is able to efficiently allocate resources from different sources in order to reduce application execution times.
Big Data Analytics Using Cloud and Crowd

The increasing application of social and human-enabled systems in people's daily life from one side and from the other side the fast growth of mobile and smart phones technologies have resulted in generating tremendous amount of data, also referred to as big data, and a need for analyzing these data, i.e., big data analytics. Recently a trend has emerged to incorporate human computing power into big data analytics to solve some shortcomings of existing big data analytics such as dealing with semi or unstructured data. Including crowd into big data analytics creates some new challenges such as security, privacy and availability issues. In this paper study hybrid human-machine big data analytics and propose a framework to study these systems from crowd involvement point of view. We identify some open issues in the area and propose a set of research directions for the future of big data analytics area.
Human Activity Recognition using Smartphone

Human activity recognition has wide applications in medical research and human survey system. In this project, we design a robust activity recognition system based on a smartphone. The system uses a 3-dimentional smartphone accelerometer as the only sensor to collect time series signals, from which 31 features are generated in both time and frequency domain. Activities are classified using 4 different passive learning methods, i.e., quadratic classifier, k-nearest neighbor algorithm, support vector machine, and artificial neural networks. Dimensionality reduction is performed through both feature extraction and subset selection. Besides passive learning, we also apply active learning algorithms to reduce data labeling expense. Experiment results show that the classification rate of passive learning reaches 84.4% and it is robust to common positions and poses of cellphone. The results of active learning on real data demonstrate a reduction of labeling labor to achieve comparable performance with passive learning.
Genevestigator V3: A Reference Expression Database for the Meta-Analysis of Transcriptomes

The Web-based software tool Genevestigator provides powerful tools for biologists to explore gene expression across a wide variety of biological contexts. Its first releases, however, were limited by the scaling ability of the system architecture, multiorganism data storage and analysis capability, and availability of computationally intensive analysis methods. Genevestigator V3 is a novel meta-analysis system resulting from new algorithmic and software development using a client/server architecture, large-scale manual curation and quality control of microarray data for several organisms, and curation of pathway data for mouse and Arabidopsis. In addition to improved querying features, Genevestigator V3 provides new tools to analyze the expression of genes in many different contexts, to identify biomarker genes, to cluster genes into expression modules, and to model expression responses in the context of metabolic and regulatory networks. Being a reference expression database with user-friendly tools, Genevestigator V3 facilitates discovery research and hypothesis validation.
Enabling on-chip diversity through architectural communication design

In this paper, we explore a new concept, called on-chip dive+, and introduce a design methodology for such emerging system. Simply speaking, on-chip diversity means mixing ditTemt architectures and/or technologies in a multiple voltagdfrequency island setup in order to achieve the highest levels of performance, fault-tolerance and the needed flexibility in SoC design. As the main contribution, we present the challenges in implementing an efticient communication architecture for on-chip diversity and outline a unified framework which addresses some of these issues. We then provide comparative experimental results and make a qualitative analysis of different architectural choices in the design of the on-chip communication. Having the acoustic beamforming as driver application, we show that an efticient communication infrastructure can be constructed by carefully analyzing the characteristics of the application and the required levels of power, performance and fault-tolerance.
Decompositions Sensor-based Coverage of Unknown Environments : Incremental Construction of Morse

The goal of coverage path planning is to determine a path that passes a detector over all points in an environment. This work prescribes a provably complete coverage path planner for robots in unknown spaces. We achieve coverage using Morse decompositions which are exact cellular decompositions whose cells are defined in terms of critical points of Morse functions. Generically, two critical points define a cell. We encode the topology of the Morse decomposition using a graph that has nodes corresponding to the critical points and edges representing the cells defined by pairs of critical points. The robot simultaneously covers the space while incrementally constructing this graph. To achieve this, the robot must sense all the critical points. Therefore, we first introduce a critical point sensing method that uses range sensors. Then we present a provably complete algorithm which guarantees that the robot will encounter all the critical points, thereby constructing the full graph, i.e., achieving complete coverage. We also validate our approach by performing experiments on a mobile robot equipped with a sonar ring. KEY WORDS—sensor-based coverage, Morse decompositions, Reeb graph
Picky, hungry eaters in the cold: persistent substrate selectivity among polar pelagic microbial communities

Polar pelagic microbial communities access a narrower range of polysaccharide substrates than communities at lower latitudes. For example, the glucose-containing polysaccharide pullulan is typically not hydrolyzed in fjord waters of Svalbard, even though pullulan is rapidly hydrolyzed in sediments from Svalbard fjords, other polysaccharides are hydrolyzed rapidly in Svalbard waters, and pullulan is hydrolyzed rapidly in temperate waters. The purpose of this study was to investigate potential factors preventing hydrolysis of pullulan in Svalbard fjord waters. To this end, in two separate years, water from Isfjorden, Svalbard, was amended with different carbon sources and/or additional nutrients in order to determine whether increasing the concentration of these potentially-limiting factors would lead to measurable enzymatic activity. Addition of nitrate, phosphate, glucose, or amino acids did not yield detectable pullulan hydrolysis. The only treatment that led to detectable pullulan hydrolysis was extended incubation after the addition of maltotriose (a subunit of pullulan, and potential inducer of pullulanase). In these fjords, the ability to enzymatically access pullulan is likely confined to numerically minor members of the pelagic microbial community. These results are consistent with the hypothesis that pelagic microbial communities at high latitudes exhibit streamlined functionality, focused on a narrower range of substrates, than their temperate counterparts.