Miracle-dz/UniFAR-FTUs huggingface dataset:
On error control codes for random network coding

The random network coding approach is an effective technique for linear network coding, however it is highly susceptible to errors and adversarial attacks. Recently Kötter and Kschischang [14] introduced the operator channel, where the inputs and outputs are subspaces of a given vector space, showing that this is a natural transmission model in noncoherent random network coding. A suitable metric, defined for subspaces: dS(U; V ) = dimU + dim V - 2 dim(U ∩ V), gives rise to the notion of codes capable of correcting different kinds of errors (like packet errors, erasures etc.) in noncoherent random network coding. In this paper we continue the study of coding for operator channels started in [14]. We consider codes correcting insertions/deletions (dimension enlargement and dimension reduction respectively). Bounds and constructions for those codes are presented.
BIOTECHNOLOGY IN AGRICULTURE : AGRONOMIC AND ENVIRONMENTAL CONSIDERATIONS & REFLECTIONS BASED ON 15 YEARS OF GM CROPS

Genetically-modified (GM) varieties of crops, notably soybean, maize, rape (canola) and cotton were first grown commercially in 1996. In 2010 they occupied 148 million ha in 29 countries, mostly in the Americas and Asia but with an obvious absence in Europe where their introduction has been controversial due to concerns about environmental impairment and adverse impacts on human health. This paper reviews the published literature on the agronomic and environmental impact of GM crops in the last 15 years (a related paper by Morse and Mannion reviews the socioeconomic impact). Overall, the impact of GM crops has largely been agronomically and environmentally positive in both developed and developing world contexts. The often claimed negative impacts of GM crops have yet to materialise on large scales in the field. Agronomically, there have been yield increases per unit area, mainly due to reduced losses as a result of improved pest i.e. insect and weed control; in the case of conventional crops grown near GM varieties with insect resistance 2 there have been benefits due to the so-called 'halo' effect. Environmentally, the decrease in insecticide use has benefitted non-target and beneficial organisms while surface and groundwater contamination is less significant; human-health problems related to pesticide use have also declined. Equally important is the reduced carbon footprint as energy inputs are reduced. Of particular note, however, is the recognition that the success or longevity of GM crops is reliant on the speed with which resistance develops in target weeds and insects. However, resistance to GM-based plant resistance is already being detected in some pest populations and this suggests that scientists and farmers cannot be complacent. Current GM approaches are relatively transitory as a means of combating pests, as are conventional pesticides, and good management will determine how long this strategy proves positive. However, GM is a comparatively new science and the possibilities are considerable.
ON MAX CUT IN CUBIC GRAPHS

Abstract This paper is concerned with the maximum cut problem in parallel on cubic graphs. New theoretical results characterizing the cardinality of the cut are presented. These results make it possible to design a simple combinatorial O(log n) time parallel algorithm, running on a CRCW P-RAM with O(n) processors. The approximation ratio achieved by the algorithm is 1·3 and improves the best known parallel approximation ratio, i.e. 2, in the special class of cubic graphs. The algorithm also guarantees that the size of the returned cut is at least ((9g −3)/8 g)n, where g is the odd girth of the input graph. Experimental results round off the paper, showing that the solutions obtained in practice are likely to be much better than the theoretical lower bound.
Marijuana Withdrawal Syndrome: Should Cannabis Withdrawal Disorder Be Included in DSM-5?

DSM-IV, published in 1994, did not include a cannabis withdrawal disorder diagnosis. DSM-IV-TR clearly stated the reason for the omission: “Symptoms of possible cannabis withdrawal . have been described in association with the use of very high doses, but their clinical significance is uncertain. ”1
Rely-Guarantee Based Reasoning for Message-Passing Programs

The difficulties of verifying concurrent programs lie in their inherent non-determinism and interferences. Rely-Guarantee reasoning is one useful approach to solve this problem for its capability in formally specifying interthread interferences. However, modern verification requires better locality and modularity. It is still a great challenge to verify a message-passing program in a modular and composable way. In this paper, we propose a new reasoning system for message-passing programs. It is a novel logic that supports Hoare style triples to specify and verify distributed programs modularly. We concretize the concept of event traces to represent interactions among distributed agents, and specify behaviors of agents by their local traces with regard to environmental assumptions — an idea inspired by Rely-Guarantee reasoning. Based on trace semantics, the verification is compositional in both temporal and spatial dimensions. To show validity, we apply our logic to modularly prove several examples.
Development of Time Triggered hybrid data bus System for small aircraft digital avionic system

Digital avionics using line replaceable unit (LRU) and integrated module avionics (IMA) has been promoted into very simplified and flexible design supports in modern system. Digital data bus system using controller area network (CAN) for small aircrafts has improved the accidental event by adding time trigger (Lin et al., 2006). However, the predictability on data transmission requires enforced. The arbitration mechanism in CAN has awarded higher priority to those high frequency data or important real time data. Some deficiency may cause unpredictable delays on lower priority message and data lose when high bus load situation. To integrate time trigger into time triggered CAN (TTCAN) chip can improve the time scheduling in periodic data transmission (Shaheen et al., 2002). Consequently, the complexity in time sequence synchronization has brought in significant load in development. This research method classifies the data frequency and assigning the concept of time to integrate hybrid data bus system. The research goal uses concept of TTCAN on dual bus CAN system and improve disadvantage of CAN.
Optimality of payoffs in Lévy models Ernst August

In this paper we determine the lowest cost strategy for a given payoff in Lévy markets where the pricing is based on the Esscher martingale measure. In particular, we consider Lévy models where the price process is driven by an NIGand a VG-process. Explicit solutions for cost-efficient strategies are derived for a variety of vanilla options, spreads, and forwards. Determination of efficient put prices based on estimated parameters from German stock prices reveals that the potential savings that optimal payoffs provide can be quite substantial. The empirical findings are supplemented by a result that relates the magnitude of these savings to the strength of the market trend. Moreover, we consider the problem of hedging efficient claims, derive explicit formulas for the deltas of efficient calls and puts and apply the results to German stock market data. As a main result we find that cost-efficient options also show an improved behaviour concerning delta hedging compared to their classical counterparts. AMS subject : 60G51; 60E15
SOPHIA: an interactive cluster-based retrieval system for the OHSUMED collection

The ability to perform an exploratory search and retrieval of relevant documents from a large collection of domain-specific documents is an important requirement both in the field of medicine and other areas. In this paper, we present a unsupervised distributional clustering technique called SOPHIA. SOPHIA provides a semantically meaningful visual clustering of the document corpus in conjunction with an intuitive interactive search facility. We assess the effectiveness of SOPHIA's cluster-based information retrieval for the MEDLINE testset collection known as OHSUMED.
Characterization of Key Molecular Mechanisms and Biological Pathways Involved in Wortmannin Induced Breast Cancer MCF-7 Programmed Cell

The present study aimed to explore the molecular mechanisms and biological pathways involved in Wortmannin induced breast cancer MCF-7 programmed cell death. The direct cellular and molecular effect of Wtmn was investigated selectively on the MCF-7 cancer cell line. To study morphological effects phase contrast microscopy was used. The mitochondrial membrane potential and detection of caspase activity were investigated using fluorescent microscopy. Our morphological, molecular, and caspases expression indicated the intrinsic apoptosis pathways involved, and confirmed the role of Wtmn in these pathways. Our experimental results demonstrated that Wortmannin (Wtmn), an irreversible and selective PI3-K inhibitor, inhibits the proliferation of MCF-7 breast cancer cells, and facilitates their entry into apoptosis. Our data established the anti-cancer properties of Wtmn on MCF-7 breast cancer cells. These studies directed us towards elucidating the molecular mechanisms and biological pathways mainly involved in this process. This findings offer potential drug screening to search for novel inducers of apoptosis on MCF-7 cells, and could be used to design better drugs.
Separating cosmic shear from intrinsic galaxy alignments: Correlation function tomography

During the past few years, secure detections of cosmic shear have been obtained, manifest in the correlation of the observed ellipticities of galaxies. Constraints have already been placed on cosmological parameters, such as the normalisation of the matter power spectrum σ8. One possible systematic contaminant of the lensing correlation signal arises from intrinsic galaxy alignment, which is still poorly constrained. Unlike lensing, intrinsic correlations only pertain to galaxies with small physical separations, the correlation length being a few Mpc. We present a new method that harnesses this property, and isolates the lensing and intrinsic components of the galaxy ellipticitycorrelation function using measurements between different redshift slices. The observed signal is approximated by a set of template functions, making no strong assumptions about the amplitude or correlation length of any intrinsic alignment. We also show that the near-degeneracy between the matter density parameter Ωm and σ8 can be lifted using correlation function tomography, even in the presence of an intrinsic alignment signal.
Antibiotics and the Human Gut Microbiome: Dysbioses and Accumulation of Resistances

The human microbiome is overly exposed to antibiotics, due, not only to their medical use, but also to their utilization in farm animals and crops. Microbiome composition can be rapidly altered by exposure to antibiotics, with potential immediate effects on health, for instance through the selection of resistant opportunistic pathogens that can cause acute disease. Microbiome alterations induced by antibiotics can also indirectly affect health in the long-term. The mutualistic microbes in the human body interact with many physiological processes, and participate in the regulation of immune and metabolic homeostasis. Therefore, antibiotic exposure can alter many basic physiological equilibria, promoting long-term disease. In addition, excessive antibiotic use fosters bacterial resistance, and the overly exposed human microbiome has become a significant reservoir of resistance genes, contributing to the increasing difficulty in controlling bacterial infections. Here, the complex relationships between antibiotics and the human microbiome are reviewed, with focus on the intestinal microbiota, addressing (1) the effects of antibiotic use on the composition and function of the gut microbiota, (2) the impact of antibiotic-induced microbiota alterations on immunity, metabolism, and health, and (3) the role of the gut microbiota as a reservoir of antibiotic resistances.
Multimodal medical image fusion based on Integer Wavelet Transform and Neuro-Fuzzy

Medical image fusion is used to derive useful information from multimodality medical image data. The idea is to improve the image content by fusing images like computer tomography (CT) and magnetic resonance imaging (MRI) images, so as to provide precise information to the doctor and clinical treatment planning system. This paper proposes image fusion based on Integer Wavelet Transform (IWT) and Neuro-Fuzzy. The anatomical and functional images are decomposed using Integer Wavelet Transform. The wavelet coefficients are then fused using neuro-fuzzy algorithm. Then Inverse Integer Wavelet Transform (IIWT) is applied to the fused coefficients to get the fused Image. The performance of this algorithm is compared with image fusion based on Discrete Wavelet Transform (DWT) and neuro-fuzzy using entropy metric. Fusion Symmetry (FS) which quantifies the relative distance in terms of mutual information of the fused image with respect to input images is measured. Fusion Factor (FF) the criterion of maximizing the joint mutual information is also quantified.
B–SPLINE–BASED MONOTONE MULTIGRID METHODS — EXTENDED VERSION

Abstract. For the efficient numerical solution of elliptic variational inequalities on closed convex sets, multigrid methods based on piecewise linear finite elements have been investigated over the past decades. Essential for their success is the appropriate approximation of the constraint set on coarser grids which is based on function values for piecewise linear finite elements. On the other hand, there are a number of problems which profit from higher order approximations. Among these are the problem of prizing American options, formulated as a parabolic boundary value problem involving Black–Scholes’ equation with a free boundary. In addition to computing the free boundary, the optimal exercise prize of the option, of particular importance are accurate pointwise derivatives of the value of the stock option up to order two, the so–called Greek letters. In this paper, we propose a monotone multigrid method for discretizations in terms of B–splines of arbitrary order to solve elliptic variational inequalities on a closed convex set. In order to maintain monotonicity (upper bound) and quasi–optimality (lower bound) of the coarse grid corrections, we propose an optimized coarse grid correction (OCGC) algorithm which is based on B–spline expansion coefficients. We prove that the OCGC algorithm is of optimal complexity of the degrees of freedom of the coarse grid and, therefore, the resulting monotone multigrid method is of asymptotically optimal multigrid complexity. Finally, the method is applied to a standard model for the valuation of American options. In particular, it is shown that a discretization based on B–splines of order four enables us to compute the second derivative of the value of the stock option to high precision.
Texture Analysis for Nematode Genera Classification

Nematodes are the most diverse animals and the most numerous multi-cellular organisms on earth. They exist in almost every ecological niche and vary in size from microscopic round worms to large classes such as Cestoda, which can grow to over 30 meters. Not only are nematodes greatly abundant, but they have significant impacts on human life. Nearly two billion people worldwide are infected by a single nematode genus and billions of dollars of yearly agricultural damage can be attributed to just a handful of nematode species. Because these affects originate from a small subset of the phylum, classification of nematodes is necessary to assist disease prevention and eradication. Genetically based pesticides rely on accurate identification and monitoring of nematode populations. Classification of a single nematode by field experts requires several days, an automated system would improve the accuracy, throughput, and efficiency of this process. This paper focuses on classifying microscopic nematodes. This presents additional challenges in classification due to the size, lack of visual features, and great abundance of the subjects. Microscopic nematodes have simple, transparent bodies. A texture based analysis is conducted towards classification of a sample's genus due to their structure. Nematode images are attained using video capture and editing microscopy (VCE) techniques, resulting in a series of multifocal images per nematode sample. By applying texture based methods on multifocal image stacks of nematodes, an accurate image based classifier is constructed.
LTP after Stress: Up or Down?

When an organism is exposed to a stressful situation, corticosteroid levels in the brain rise. This rise has consequences for behavioral performance, including memory formation. Over the past decades, it has become clear that a rise in corticosteroid level is also accompanied by a reduction in hippocampal long-term potentiation (LTP). Recent studies, however, indicate that stress does not lead to a universal suppression of LTP. Many factors, including the type of stress, the phase of the stress response, the area of investigation, type of LTP, and the life history of the organism determine in which direction LTP will be changed.
Evaluation of Protein-Protein Interaction Management Systems

Protein-protein interactions (PPIs) are very important for observing the behavior of known proteins in biological processes and in the study of many diseases. Currently, there is a number of PPI databases publicly available in the Web. In most cases, these datasets are managed by traditional relational database management systems (RDBMS) and they are shared as plain or XML files. A very useful approach would be the the unification of these separated data sources following the Semantic Web linked open data (LOD) paradigm, in order to complement and extend the existing knowledge of each data source. Semantic representation and storage of linked open datasets can be performed by many off-the-shelf systems modeling them as Resource Description Framework (RDF) models. RDF modeling, provides great flexibility for the linking, querying and mining of various PPI data sources. In this paper, we evaluate experimentally the interconnection and storage of various PPI data sources with off-the-shelf RDF storages. We examine the performance of such storages against traditional RDBMS in the context of PPI dataset management. Our main findings show that each one of the alternative storage methods, has its own advantages and disadvantages (in processing time and memory utilization) according to various types of queries.
A Kalman Filtering Perspective for Multiatlas Segmentation

In multiatlas segmentation, one typically registers several atlases to the novel image, and their respective segmented label images are transformed and fused to form the final segmentation. In this work, we provide a new dynamical system perspective for multiatlas segmentation, inspired by the following fact: The transformation that aligns the current atlas to the novel image can be not only computed by direct registration but also inferred from the transformation that aligns the previous atlas to the image together with the transformation between the two atlases. This process is similar to the global positioning system on a vehicle, which gets position by inquiring from the satellite and by employing the previous location and velocity-neither answer in isolation being perfect. To solve this problem, a dynamical system scheme is crucial to combine the two pieces of information; for example, a Kalman filtering scheme is used. Accordingly, in this work, a Kalman multiatlas segmentation is proposed to stabilize the global/affine registration step. The contributions of this work are twofold. First, it provides a new dynamical systematic perspective for standard independent multiatlas registrations, and it is solved by Kalman filtering. Second, with very little extra computation, it can be combined with most existing multiatlas segmentation schemes for better registration/segmentation accuracy.
The effect of acetazolamide on cerebral blood flow and oxygen utilization in the rhesus monkey.

The brain is critically dependent for its moment to moment function and survival on an adequate supply of oxygen. The enzyme carbonic anhydrase (EC 4.2.1.1) may play an important role in oxygen delivery to brain tissue by facilitating the hydration of metabolically produced carbon dioxide in erythrocytes in brain capillaries, thus permitting the Bohr effect to occur. We examined the effect of 30 mg/kg i.v. acetazolamide, a potent inhibitor of carbonic anhydrase, upon cerebral blood flow and oxygen consumption in lightly anesthetized, passively ventilated rhesus monkeys. Cerebral blood flow and oxygen consumption were measured with oxygen-15-labeled water and oxygen-15-labeled oxyhemoglobin, respectively, injected into the internal carotid artery and monitored externally. Acetazolamide produced an immediate and significant increase in cerebral blood flow (from a mean of 64.7 to 83.8 ml/100 g per min), an increase in arterial carbon dioxide tension (from a mean of 40.7 to 47.5 torr), and a decrease in cerebral oxygen consumption (from a mean of 4.16 to 2.82 ml/100 g per min). Because the change in cerebral oxygen consumption occurred within minutes of the administration of acetazolamide, we believe that this effect probably was not due to a direct action on brain cells but was achieved by an interference with oxygen unloading in brain capillaries. A resultant tissue hypoxia might well explain part of the observed increase in cerebral blood flow.
Information Extraction - A User Guide

This technical memo describes Information Extraction from the point-of-view of a potential user of the technology. No knowledge of language processing is assumed. Information Extraction is a process which takes unseen texts as input and produces fixed-format, unambiguous data as output. This data may be used directly for display to users, or may be stored in a database or spreadsheet for later analysis, or may be used for indexing purposes in Information Retrieval applications. See also this http URL
Insulin Internalizes GLUT2 in the Enterocytes of Healthy but Not Insulin-Resistant Mice

OBJECTIVES—A physiological adaptation to a sugar-rich meal is achieved by increased sugar uptake to match dietary load, resulting from a rapid transient translocation of the fructose/glucose GLUT2 transporter to the brush border membrane (BBM) of enterocytes. The aim of this study was to define the contributors and physiological mechanisms controlling intestinal sugar absorption, focusing on the action of insulin and the contribution of GLUT2-mediated transport. RESEARCH DESIGN AND METHODS—The studies were performed in the human enterocytic colon carcinoma TC7 subclone (Caco-2/TC7) cells and in vivo during hyperinsulinemic-euglycemic clamp experiments in conscious mice. Chronic high-fructose or high-fat diets were used to induce glucose intolerance and insulin resistance in mice. RESULTS AND CONCLUSIONS—In Caco-2/TC7 cells, insulin action diminished the transepithelial transfer of sugar and reduced BBM and basolateral membrane (BLM) GLUT2 levels, demonstrating that insulin can target sugar absorption by controlling the membrane localization of GLUT2 in enterocytes. Similarly, in hyperinsulinemic-euglycemic clamp experiments in sensitive mice, insulin abolished GLUT2 (i.e., the cytochalasin B-sensitive component of fructose absorption), decreased BBM GLUT2, and concomitantly increased intracellular GLUT2. Acute insulin treatment before sugar intake prevented the insertion of GLUT2 into the BBM. Insulin resistance in mice provoked a loss of GLUT2 trafficking, and GLUT2 levels remained permanently high in the BBM and low in the BLM. We propose that, in addition to its peripheral effects, insulin inhibits intestinal sugar absorption to prevent excessive blood glucose excursion after a sugar meal. This protective mechanism is lost in the insulin-resistant state induced by high-fat or high-fructose feeding.