Miracle-dz/UniFAR-FTUs huggingface dataset:
Three aspects of super-recursive algorithms and hypercomputation or % nding black swans

History of science and technology proves that the biggest advances come not from doing more and bigger and faster of what is already being done, but from new ideas, discoveries, and starting points. Hence this special issue concerns new ideas, discoveries, and metaphors in computer science. It is not about incremental improvements, but rather it presents the opportunities opened by these related notions: super-recursive algorithms and hypercomputation. Together these new ideas, discoveries, constructions, and metaphors form a new computer science %eld, the theory of super-recursive algorithms and hypercomputation. It is a part of such established domain as the theory of algorithms, automata, and computation. One main achievement of 20th century scienti%c thought is the theory of algorithms and computation. This theory studies abstract and real automata, computers and networks, computation and communication. In many ways, this theory is the central cornerstone for computer science. Many key accomplishments in the theory of algorithms and computation converge to the famous Church–Turing thesis, a statement determining the boundaries of algorithmic computations. The Church–Turing thesis has long been considered as the most fundamental law within computing. However, recent developments in the theory of algorithms allow overcoming limitations in the Church– Turing thesis. New mathematical models for algorithms and computation have appeared that extend prior theory in a manner similar to the way relativity theory and quantum mechanics went beyond Newtonian mechanics. These new models are more powerful than the classical recursive algorithm models, i.e., Turing machines, partial recursive functions, Lambda-calculus, and cellular automata. Algorithms and automata that are more powerful than Turing machines are called super-recursive. Computations that cannot be realized or simulated by Turing machines are called hypercomputations. The new area of computer science called the theory of super-recursive algorithms and hypercomputation consists of several directions. The most important ones are
Human Chromosome 19 and Related Regions in Mouse: Conservative and Lineage-Specific Evolution

To illuminate the function and evolutionary history of both genomes, we sequenced mouse DNA related to human chromosome 19. Comparative sequence alignments yielded confirmatory evidence for hypothetical genes and identified exons, regulatory elements, and candidate genes that were missed by other predictive methods. Chromosome-wide comparisons revealed a difference between single-copy HSA19 genes, which are overwhelmingly conserved in mouse, and genes residing in tandem familial clusters, which differ extensively in number, coding capacity, and organization between the two species. Finally, we sequenced breakpoints of all 15 evolutionary rearrangements, providing a view of the forces that drive chromosome evolution in mammals.
Improved modelling of low flows and drought impacts in Australian catchments using new rainfall-runoff model SpringSIM

Over the past few years, the modelling of streamflows has been undertaken in a number of catchments across Australia for the purposes of yield and low flow estimation. Two regular issues faced during rainfall-runoff model calibration are the difficulty in calibrating models to adequately represent the impact of the 1997-2009 drought in Victoria and the successful combination of low flow representation and total flow volumes. A new rainfall-runoff model, SpringSIM has been developed that potentially overcomes these issues across an initial sample of catchments. SpringSIM is a 12- parameter model designed to represent hydrologic processes taking place in a rural catchment. A further 2 parameters can be included for representation of impervious surfaces in urban catchments. The model provided significantly improved results across a number of catchments when compared to previous rainfall-runoff model calibrations. It also matched the best results obtained using other commonly used models for four catchments in Western Australia.
Theoretical Runtime Analyses of Search Algorithms on the Test Data Generation for the Triangle Classification Problem

Software testing plays an important role in the life cycle of software development. Because software testing is very costly and tedious, many techniques have been proposed to automate it. One technique that has achieved good results is the use of search algorithms. Because most previous work on search algorithms has been of an empirical nature, there is a need for theoretical results that confirm the feasibility of search algorithms applied to software testing. Such theoretical results might shed light on the limitations and benefits of search algorithms applied in this context. In this paper, we formally analyse the expected runtime of three different search algorithms on the problem of test data generation for an instance of the triangle classification program. The search algorithms that we analyse are random search, hill climbing and alternating variable method. We believe that this is a necessary first step that will lead and help the software engineering community to better understand the role of search based techniques applied to software testing.
Review of statistical modeling of h using very large v Airenas Vai

This paper presents state of the art language modeling (LM) of Lithuanian, which is highly inflected free word order language. Perplexities and word error rates (WER) of standard n-gram, class-based, cache-based, topic mixture and morphological LMs were estimated and compared for the vocabulary of more than 1 million words. WER estimates were obtained by solving a speaker-dependent ASR task where LMs were used to rescore acoustical hypothesis. LM perplexity appeared to be uncorrelated with WER. Cache-based language models resulted in the greatest perplexity improvement, while class-based language models achieved the greatest though insignificant WER improvement over the baseline 3-gram.
Back propagation neural network approach for channel estimation in OFDM system

In high data rate communication systems which use orthogonal frequency division multiplexing as a modulation scheme, at receiver channel impulse responses must be estimated for coherent demodulation. In this paper, multilayered perceptrons (MLP) neural network with back propagation (BP) learning algorithm is proposed as a channel estimator for OFDM systems. Our proposed MLP neural channel estimator is compared to least square (LS) algorithm, minimum mean square error (MMSE) algorithm and radial basis function neural network (RBF) in respect to bit error rate (BER) and mean square error (MSE) criteria in order to evaluate the performances. MLP neural network has better performance than LS algorithm and RBF neural network and its performance is close to MMSE algorithm and the perfect channel impulse responses. Moreover, there is unnecessary of channel statistics, matrix computation and noise information when our proposed neural network is used for channel estimation.
Prognostic significance of CLPTM1L expression and its effects on migration and invasion of human lung cancer cells.

BACKGROUND CLPTM1L (Cleft lip and palate transmembrane protein 1-like) has been previously shown to be overexpressed in lung cancer and is involved in regulating cisplatin sensitivity. In this study, we assessed the relationship between CLPTM1L expression and prognosis of lung cancer in patients and explored its role in regulating cell migration and invasion. METHODS Immunohistochemistry was used to examine CLPTM1L expression levels on a tissue microarray containing 73 sets of human lung cancer specimens with adjacent normal tissue. The correlation between CLPTM1L expression and patient survival was analysed by the Kaplan-Meier method. In addition, CLPTM1L-knockdown lung cells were used to investigate the effect of CLPTM1L on cell migration and invasion in vitro. RESULTS The results of immunohistochemical analysis showed that CLPTM1L was overexpressed in lung cancer tissues compared with adjacent normal tissues. Kaplan-Meier analyses revealed that patients with high CLPTM1L expression showed poorer survival than those with low CLPTM1L expression. In addition, in vitro studies revealed that the knockdown of CLPTM1L expression in 95-D lung cancer cells suppressed cell migration and invasion. Further, the loss of CLPTM1L resulted in decreased matrix metalloproteinase-2 (MMP-2) expression in these cells. CONCLUSION Our data demonstrate that CLPTM1L overexpression can predict poor prognosis in patients with lung cancer and suggest that CLPTM1L might be associated with the regulation of cell migration and invasion.
Endoplasmic Reticulum Stress and the Making of a Professional Secretory Cell

ABSTRACT Homeostasis of the protein folding machinery in the endoplasmic reticulum (ER) is maintained via several parallel unfolded protein response pathways that are remarkably conserved from yeast to man. Together, these pathways are integrated into a complex circuitry that can be modulated in various ways, not only to cope with various stress conditions, but also to fine-tune the capacity of the ER folding machinery when precursor cells differentiate into professional secretory cells.
Stochasticity in Algorithmic Statistics for Polynomial Time

A fundamental notion in Algorithmic Statistics is that of a stochastic object, i.e., an object having a simple plausible explanation. Informally, a probability distribution is a plausible explanation for x if it looks likely that x was drawn at random with respect to that distribution. In this paper, we suggest three definitions of a plausible statistical hypothesis for Algorithmic Statistics with polynomial time bounds, which are called acceptability, plausibility and optimality. Roughly speaking, a probability distribution m is called an acceptable explanation for x, if x possesses all properties decidable by short programs in a short time and shared by almost all objects (with respect to m). Plausibility is a similar notion, however this time we require x to possess all properties T decidable even by long programs in a short time and shared by almost all objects. To compensate the increase in program length, we strengthen the notion of `almost all' - the longer the program recognizing the property is, the more objects must share the property. Finally, a probability distribution m is called an optimal explanation for x if m(x) is large. Almost all our results hold under some plausible complexity theoretic assumptions. Our main result states that for acceptability and plausibility there are infinitely many non-stochastic objects, i.e. objects that do not have simple plausible (acceptable) explanations. Using the same techniques, we show that the distinguishing complexity of a string x can be super-logarithmically less than the conditional complexity of x with condition r for almost all r (for polynomial time bounded programs). Finally, we study relationships between the introduced notions.
Distributed coordination-by-constraint strategies for multi-agent networked systems

In this paper we present a novel distributed supervision strategy for networks of linear locally regulated and possibly dynamically coupled systems connected via data links and subject to coordination constraints on the evolutions of relevant variables of them. Such a coordination-by-constraint paradigm is accomplished by resorting to a Distributed Command Governor approach where each agent of the strategy is in charge to locally modify, whenever necessary and on the basis of a reduced amount of data exchanged with the other agents, the prescribed set-points to the regulated subsystems so as that the global pointwise-in-time coordination constraints are satisfied along the overall network evolutions. A sequential strategy, where only one agent per time is allowed to manipulate its own reference signal, is fully described and analyzed. The constrained coordination of a network of dynamically coupled eight water tanks is presented as an example in order to show the effectiveness of the proposed method.
Accuracy characterization of cell tower localization

Cell tower triangulation is a popular technique for determining the location of a mobile device. However, cell tower triangulation methods require the knowledge of the actual locations of cell towers. Because the locations of cell towers are not publicly available, these methods often need to use estimated tower locations obtained through wardriving. This paper provides the first large scale study of the accuracy of two existing methods for cell tower localization using wardriving data. The results show that naively applying these methods results in very large localization errors. We analyze the causes for these errors and conclude that one can localize a cell accurately only if it falls within the area covered by the wardriving trace. We further propose a bounding technique to select the cells that fall within the area covered by the wardriving trace and identify a cell combining optimization that can further reduce the localization error by half.
Interactive feature learning from SAR image patches

Feature learning algorithms aim to provide a compact and discriminative representation of complex datasets in order to increase the speed and accuracy of clustering or classification. In this paper, we propose a novel interactive feature learning approach which is mainly based on 3D interactive data visualization and Non-negative Matrix Factorization (NMF). Here, the data is visualized in a 3D interface to support human-data interaction. The user interactions are exploited in an NMF framework to learn a compact representation of the data. The conducted experiments on Synthetic Aperture Radar (SAR) images confirm the efficiency of the proposed approach.
High Precision Relative Localization Using a Single Camera

In this paper a method for high precision relative localization using a single camera is proposed. The method provides pose estimates comparable in resolution to wheel odometry, but being independent of the kinematics and based on an exteroceptive sensor, is resistant to wheel slippage. The concept is based on extracting the planar transformations between frames from a sequence of ground images, which correspond to the change in robot pose. Results on a plain colored carpeted surface provide the proof of concept of the method as an alternative to wheel odometry. The contributions in this paper include a method to estimate the planar transformations between images to a high degree of precision (e.g., 0.01 degrees), and a method to calibrate the system using a ID calibration object and known motions of the robot.
Application of Feature Selection Methods and Ensembles on Network Security Dataset

Generally intrusion detection systems (IDS) use all the data features to classify normal and anomaly packet. It has been observed in the studies that some of the data features may be redundant or are less important in this classification process. Authors have studied NSL KDD dataset with different feature selected from Gain Ratio and ChiSquare feature selection methods and carried out the experiments with single Decision Tree and then applied ensemble with Random Forests and Decision Tree with Bagging. Results show that significant feature selection is very important in the design of a lightweight and efficient intrusion detection system. Random Forests are better than Single Decision Tree and Decision Tree with Bagging for the current dataset. Performance of Gain Ratio is better than Chi square feature selection method for this dataset.
"Living high-training low": effect of moderate-altitude acclimatization with low-altitude training on performance.

The principal objective of this study was to test the hypothesis that acclimatization to moderate altitude (2,500 m) plus training at low altitude (1,250 m), "living high-training low," improves sea-level performance in well-trained runners more than an equivalent sea-level or altitude control. Thirty-nine competitive runners (27 men, 12 women) completed 1) a 2-wk lead-in phase, followed by 2) 4 wk of supervised training at sea level; and 3) 4 wk of field training camp randomized to three groups: "high-low" (n = 13), living at moderate altitude (2,500 m) and training at low altitude (1,250 m); "high-high" (n = 13), living and training at moderate altitude (2,500 m); or "low-low" (n = 13), living and training in a mountain environment at sea level (150 m). A 5,000-m time trial was the primary measure of performance; laboratory outcomes included maximal O2 uptake (VO2 max), anaerobic capacity (accumulated O2 deficit), maximal steady state (MSS; ventilatory threshold), running economy, velocity at VO2 max, and blood compartment volumes. Both altitude groups significantly increased VO2 max (5%) in direct proportion to an increase in red cell mass volume (9%; r = 0.37, P < 0.05), neither of which changed in the control. Five-kilometer time was improved by the field training camp only in the high-low group (13.4 +/- 10 s), in direct proportion to the increase in VO2 max (r = 0.65, P < 0.01). Velocity at VO2 max and MSS also improved only in the high-low group. Four weeks of living high-training low improves sea-level running performance in trained runners due to altitude acclimatization (increase in red cell mass volume and VO2 max) and maintenance of sea-level training velocities, most likely accounting for the increase in velocity at VO2 max and MSS.
Routing memento requests using binary classifiers

The Memento protocol provides a uniform approach to query individual web archives. Soon after its emergence, Memento Aggregator infrastructure was introduced that supports querying across multiple archives simultaneously. An Aggregator generates a response by issuing the respective Memento request against each of the distributed archives it covers. As the number of archives grows, it becomes increasingly challenging to deliver aggregate responses while keeping response times and computational costs under control. Ad-hoc heuristic approaches have been introduced to address this challenge and research has been conducted aimed at optimizing query routing based on archive profiles. In this paper, we explore the use of binary, archive-specific classifiers generated on the basis of the content cached by an Aggregator, to determine whether or not to query an archive for a given URI. Our results turn out to be readily applicable and can help to significantly decrease both the number of requests and the overall response times without compromising on recall. We find, among others, that classifiers can reduce the average number of requests by 77% compared to a brute force approach on all archives, and the overall response time by 42% while maintaining a recall of 0.847.
Acetylcholine receptors in regenerating muscle accumulate at original synaptic sites in the absence of the nerve

We examined the role of nerve terminals in organizing acetylcholine receptors on regenerating skeletal-muscle fibers. When muscle fibers are damaged, they degenerate and are phagocytized, but their basal lamina sheaths survive. New myofibers form within the original basal lamina sheaths, and they become innervated precisely at the original synaptic sites on the sheaths. After denervating and damaging muscle, we allowed myofibers to regenerate but deliberately prevented reinnervation. The distribution of acetylcholine receptors on regenerating myofibers was determined by histological methods, using [125I] alpha-bungarotoxin or horseradish peroxidase-alpha-bungarotoxin; original synaptic sites on the basal lamina sheaths were marked by cholinesterase stain. By one month after damage to the muscle, the new myofibers have accumulations of acetylcholine receptors that are selectively localized to the original synaptic sites. The density of the receptors at these sites is the same as at normal neuromuscular junctions. Folds in the myofiber surface resembling junctional folds at normal neuromuscular junctions also occur at original synaptic sites in the absence of nerve terminals. Our results demonstrate that the biochemical and structural organization of the subsynaptic membrane in regenerating muscle is directed by structures that remain at synaptic sites after removal of the nerve.
PADMA-28, a Tibetan herbal preparation is an inhibitor of inflammatory cytokine production.

BACKGROUND Previous studies have shown that PADMA-28, a multicomponent, traditional Tibetan herbal plant preparation possesses a variety of beneficial effects on several experimental models of inflammatory and immune processes, including autoimmune diabetes and autoimmune encephalomyelitis. In humans, PADMA-28 attenuated the symptoms associated with intermittent claudications in atherosclerotic patients. OBJECTIVE To assess the effect of PADMA 28 on the immune system, e.g. cytokine (interleukins) production. DESIGN Cytokine production by human blood monocytes (derived from 12 healthy donors) stimulated in vitro, either by endotoxin (LPS) from Salmonella typhi or by lipoteichoic acid (LTA) from group A Streptococci was modulated by PADMA-28. RESULTS The present study showed that an aqueous extract of PADMA-28 strongly decreased the production of the inflammatory cytokines IL-1beta, IL-6, IL-8 and TNF-alpha, and more moderately, also decreased the anti-inflammatory cytokine IL-10 induced by LPS. However, the LTA - induced IL-10 production was [not significantly] increased by the low dose PADMA-28, while not effected at all by the higher dose of PADMA-28. CONCLUSIONS The data from these finding suggest a possible clinical efficacy of PADMA-28 either in autoimmune and in inflammatory conditions or in post-inflammatory sequelae, as previously shown in in vivo and human studies, probably by decreasing inflammatory cytokines.
Clustering and conservation patterns of human microRNAs

MicroRNAs (miRNAs) are ∼22 nt-long non-coding RNA molecules, believed to play important roles in gene regulation. We present a comprehensive analysis of the conservation and clustering patterns of known miRNAs in human. We show that human miRNA gene clustering is significantly higher than expected at random. A total of 37% of the known human miRNA genes analyzed in this study appear in clusters of two or more with pairwise chromosomal distances of at most 3000 nt. Comparison of the miRNA sequences with their homologs in four other organisms reveals a typical conservation pattern, persistent throughout the clusters. Furthermore, we show enrichment in the typical conservation patterns and other miRNA-like properties in the vicinity of known miRNA genes, compared with random genomic regions. This may imply that additional, yet unknown, miRNAs reside in these regions, consistent with the current recognition that there are overlooked miRNAs. Indeed, by comparing our predictions with cloning results and with identified miRNA genes in other mammals, we corroborate the predictions of 18 additional human miRNA genes in the vicinity of the previously known ones. Our study raises the proportion of clustered human miRNAs that are <3000 nt apart to 42%. This suggests that the clustering of miRNA genes is higher than currently acknowledged, alluding to its evolutionary and functional implications.