Miracle-dz/UniFAR-FTUs huggingface dataset:
TomoKruithof vs Tomogravity for Backbone Networks

A traffic matrix is required to design, plan, and manage a telecommunications network. A great deal of work has been carried out to infer such a matrix from link load measurements. Of these, the Tomogravity method has shown some promise. However, in this paper it will be demonstrated that, in many realistic cases, it is possible to obtain even better results using a new method that will be referred to as TomoKruithof. This new method overcomes well-known row and column sum consistency problems with traditional gravity models published in the literature.
A novel interpolation based missing value estimation method to predict missing values in microarray gene expression data

Microarray experiments can generate data sets with multiple missing expression values, normally due to various experimental problems. Unfortunately, many algorithms for gene expression analysis require a complete matrix of gene array values as input. Thereore, effective missing value estimation methods are essential to minimize the effect of incomplete data sets on analysis, and to increase the range of data sets to which these algorithms can be applied. In this regard, a new interpolation based imputation method is proposed to predict missing values in microarray gene expression data. The proposed method selects a subset of similar genes and a subset of similar samples with respect to each missing position and then applies interpolation in a novel manner to predict that missing value. The performance of the proposed method is studied based on the normalized root mean square error with existing estimation techniques including K-nearest neighbor (KNN), Sequential K-nearest neighbor (SKNN) and Iterative K-nearest neighbor (IKNN). The effectiveness of the proposed method, along with a comparison with existing methods, is demonstrated on different microarray data sets.
Photonic Crystal Fiber for Medical Applications

Optical coherence tomography (OCT) is a new technology for noninvasive cross-sectional imaging of tissue structure in biological system by directing a focused beam of light at the tissue to be image [Bouma et al., 1995; Jiang et al., 2005; Ryu et al., 2005]. The technique measures the optical pulse time delay and intensity of backscattered light using interferometry with broadband light sources or with frequency swept lasers. It is analogous to ultrasound imaging or radar, except that it uses light rather than sound or radio waves. In addition, unlike ultrasound, OCT does not require direct contact with the tissue being imaged. OCT depends on optical ranging; in other words, distances are measured by shining a beam of light onto the object, then recording the optical pulse time delay of light. Since the velocity of light is so high, it is not possible to directly measure the optical pulse time delay of reflections; therefore, a technique known as low-coherence interferometry compares reflected light from the biological tissue to that reflected from a reference path of known length. Different internal structures produce different time delays, and crosssectional images of the structures can be generated by scanning the incident optical beam. Earlier OCT systems typically required many seconds or minutes to generate a single OCT image of tissue structure, raising the likelihood of suffering from motion artifacts and patient discomfort during in vivo imaging. To counter such problems, techniques have been developed for scanning the reference arm mirror at sufficiently high speeds to enable realtime OCT imaging [Tearnery et al., 1997]. OCT can be used where excisional biopsy would be hazardous or impossible, such as imaging the retina, coronary arteries or nervous tissue. OCT has had the largest impact in ophthalmology where it can be used to create crosssectional images of retinal pathology with higher resolution than any other noninvasive imaging technique. Now a days OCT is a prospective technology which is used not only for ophthalmology but also for dermatology, dental as well as for the early detection of cancer in digestive organs. The wavelength range of the OCT light source is spread from the 0.8 to 1.6 ┤m band. This spectral region is of particular interest for OCT because it penetrates deeply into biological tissue and permits spectrally resolved imaging of water absorption bands. In this spectral region, attenuation is minimum due to absorption and scattering. It should be noted that scattering decreases at longer wavelengths in proportion to 1/┣4, indicating that the scattering magnitude at 0.8 ~ 1.6 ┤m wavelengths is lower than at the visible wavelengths [Agrawal, 1995]. Ultrahigh-resolution OCT imaging in the spectral region from 0.8 to 1.6 ┤m requires extremely broad bandwidths because longitudinal resolution depends on the coherence length. The coherence length is inversely proportional to the bandwidth and proportional to square of the light source center wavelength. This can
Topic sensitivity based community formation in online social network

In this paper a proposed work is based on topic sensitivity which is used to form a community by which effective influence node can be elected in online social network for information diffusion. It is effective route for performing campaign in online social network environment. In which it has to be started from the influence node selection, after that effective information diffusion pattern is identified. In general most of the cases, influence node is selected randomly. So, for the selected topic the randomly selected node may not able to predict the expected topic diffusion pattern effectively. To resolve this issue, topic sensitivity based community formation has been introduced for selecting the better influence node. By doing this, influence maximization problem is resolved and it could improve the information diffusion in online social network.
An Automat for the Semantic Processing of Structured Information

Using the database of the PuertoTerm project, an indexing system based on the cognitive model of Brigitte Enders was built. By analyzing the cognitive strategies of three abstractors, we built an automat that serves to simulate human indexing processes. The automat allows the texts integrated in the system to be assessed, evaluated and grouped by means of the Bipartite Spectral Graph Partitioning algorithm, which also permits visualization of the terms and the documents. The system features an ontology and a database to enhance its operativity. As a result of the application, we achieved better rates of exhaustivity in the indexing of documents, as well as greater precision and retrieval of information, with high levels of efficiency.
Logical Topology Design for WDM Networks Using Survivable Routing

Survivable routing of a logical topology ensures that the lightpaths are routed in such a way that a single link failure does not disconnect the network. However, even if the network remains connected after a failure, there is no guarantee that the resulting logical topology will be able to support the required traffic. In this paper, we introduce a new approach that integrates the logical topology design and survivable routing problems. When a topology is generated using our approach, it is guaranteed to have a survivable routing. We further ensure that the topology is able to handle the entire traffic demand, for any single link failure. We have formulated an ILP that optimally designs a survivable logical topology, and also proposed a fast heuristic which can be used for large networks.
Low power interconnects for SIMD computers

Driven by continued scaling of Moore's Law, the number of processing elements on a die are increasing dramatically. Recently there has been a surge of wide single instruction multiple data architectures designed to handle computationally intensive applications like 3D graphics, high definition video, image processing, and wireless communication. A limit of the SIMD width of these types of architectures is the scalability of the interconnect network between the processing elements in terms of both area and power. To mitigate this problem, we propose the use of a new interconnect topology, XRAM, which is a low power high performance matrix style crossbar. It re-uses output buses for control programming, and stores multiple swizzle configurations at the cross points using SRAM cells, significantly reducing routing congestion and control signaling. We show that compared to conventionally implemented crossbars, the area scales with the product of inputx output ports while consuming almost 50% less energy. We present an application case study, color-space conversion, utilizing XRAM and show a 1.4× gain in performance while consuming 1.5–2.5× less power.
Mesalazine Has No Effect on Mucosal Immune Biomarkers in Patients with Diarrhea-Dominant Irritable Bowel Syndrome Referred to Shariati Hospital: A Randomized Double-Blind, Placebo-Controlled Trial

BACKGROUND Intestinal mast cells may cause gastrointestinal symptoms in patients with diarrhea-dominant irritable bowel syndrome (IBS). The objective of this study was to determine the effect of mesalazine on the number of lamina propria mast cells and clinical manifestations of patients with diarrhea-dominant IBS referred to Shariati Hospital affiliated to Tehran University of Medical Sciences. METHODS This was a randomized placebo-controlled double-blind trial conducted on 49 patients with diarrhea-dominant IBS. The patients were randomly assigned to one of the experiment or control groups. The patients in experiment group took 2400 mg mesalazine daily in three divided doses for 8 weeks and the patient in control group took placebo on the same basis. Our first targeted outcome was an assigned downturn of mast cells number to the safe colonic baseline and the next one was a marked palliation of disease symptoms. Data were analyzed conforming intention-to-treat method. We used MANCOVA test to compare our both assigned outcomes in the two groups. We also compared the data with baseline values in both groups.All statistical tests were performed at the significance level of 0.05. RESULTS There was no significant difference between Mesalazine and placebo groups regarding the number of mast cells (p value=0.396), abdominal pain (p value=0.054), bloating (p value=0.365), defecation urgency (p value=0.212), and defecation frequency (p value=0.702). CONCLUSION Mesalazine had no significant effect either on the number of mast cells or on the severity of disease symptoms. This finding seems to be inconsistent with the hypothesis indicating immune mechanisms as potential therapeutic targets in IBS. The possible difference in this effect of Mesalazine should be evaluated in further studies among populations varying in race, ethnic, and geographical characteristics.
Informed mobile prefetching

Prefetching is a double-edged sword. It can hide the latency of data transfers over poor and intermittently connected wireless networks, but the costs of prefetching in terms of increased energy and cellular data usage are potentially substantial, particularly for data prefetched incorrectly. Weighing the costs and benefits of prefetching is complex, and consequently most mobile applications employ simple but sub-optimal strategies. Rather than leave the job to applications, we argue that the underlying mobile system should provide explicit prefetching support. Our prototype, IMP, presents a simple interface that hides the complexity of the prefetching decision. IMP uses a cost-benefit analysis to decide when to prefetch data. It employs goal-directed adaptation to try to minimize application response time while meeting budgets for battery lifetime and cellular data usage. IMP opportunistically uses available networks while ensuring that prefetches do not degrade network performance for foreground activity. It tracks hit rates for past prefetches and accounts for network-specific costs in order to dynamically adapt its prefetching strategy to both the network conditions and the accuracy of application prefetch disclosures. Experiments with email and news reader applications show that IMP provides predictable usage of budgeted resources, while lowering application response time compared to the oblivious strategies used by current applications.
Distribution of cocaine‐ and amphetamine‐regulated transcript peptide in the guinea pig intrinsic cardiac nervous system and colocalization with neuropeptides or transmitter synthetic enzymes

This study was conducted to establish the presence of cocaine‐ and amphetamine‐regulated transcript peptide (CARTp) immunoreactivity in neurons and fibers within guinea pig atrial whole‐mount preparations containing the intrinsic cardiac ganglia. Many cardiac ganglia, but not all, in a given whole‐mount preparation, were innervated by CARTp‐immunoreactive (IR) fibers. Following explant culture of whole mounts for 72 hours, the CARTp‐IR fiber networks were absent, but the number of CARTp‐IR neurons was increased markedly. These observations suggested that the majority of the CARTp‐IR fibers in the intracardiac ganglia were derived from sources extrinsic to the heart. In control whole‐mount preparations, very few CARTp‐positive neurons were present. The few intrinsic CARTp‐IR neurons also exhibited choline acetyltransferase (ChAT) immunoreactivity, indicating that they make up a small subpopulation of cholinergic postganglionic neurons. Some CARTp‐IR neurons also exhibited nitric oxide synthase (NOS) immunoreactivity, indicating that they were nitrergic as well. We compared the immunohistochemical staining patterns of CARTp‐IR fibers with the staining patterns of a number of other neurotransmitters or neurotransmitter synthetic enzymes that mark specific extrinsic inputs. The CARTp‐IR fibers were not immunoreactive for ChAT, tyrosine hydroxylase, calcitonin gene‐related peptide, or substance P. However, virtually all CARTp‐IR fibers exhibited immunoreactivity to neuronal NOS (a marker for nitric oxide‐producing neurons). CARTp‐IR cells and NOS‐IR cells were present in the nodose ganglia. In addition, CARTp‐IR neurons in the nodose also were stained positively for NADPH‐diaphorase. Thus, we propose that most CARTp‐IR fibers within the guinea pig intrinsic cardiac ganglia are vagal afferent fibers that also contain NOS. J. Comp. Neurol. 439:73–86, 2001. © 2001 Wiley‐Liss, Inc.
Demonstration of SDN-based orchestration for multi-domain Segment Routing networks

This work demonstrates a hierarchical control plane architecture for Software Defined Networking (SDN)-based Segment Routing (SR) in multi-domain networks. An orchestrator application, on top of multiple open source SDN controllers, creates a hierarchical control plane architecture using northbound RESTFul APIs of controllers. The orchestrator has control, visibility and traffic engineering capabilities to manage multi-domain SR service creation. Standard southbound interfaces with proper SR extensions are exploited to manage SR tunnels in the MPLS data plane.
Simulating droplet motion on virtual leaf surfaces

A curvilinear thin film model is used to simulate the motion of droplets on a virtual leaf surface, with a view to better understand the retention of agricultural sprays on plants. The governing model, adapted from Roy et al. (2002 J. Fluid Mech. 454, 235–261 (doi:10.1017/S0022112001007133)) with the addition of a disjoining pressure term, describes the gravity- and curvature-driven flow of a small droplet on a complex substrate: a cotton leaf reconstructed from digitized scan data. Coalescence is the key mechanism behind spray coating of foliage, and our simulations demonstrate that various experimentally observed coalescence behaviours can be reproduced qualitatively. By varying the contact angle over the domain, we also demonstrate that the presence of a chemical defect can act as an obstacle to the droplet's path, causing break-up. In simulations on the virtual leaf, it is found that the movement of a typical spray size droplet is driven almost exclusively by substrate curvature gradients. It is not until droplet mass is sufficiently increased via coalescence that gravity becomes the dominating force.
Special Relativistic Visualization by Local Ray Tracing

Special relativistic visualization offers the possibility of experiencing the optical effects of traveling near the speed of light, including apparent geometric distortions as well as Doppler and searchlight effects. Early high-quality computer graphics images of relativistic scenes were created using offline, computationally expensive CPU-side 4D ray tracing. Alternate approaches such as image-based rendering and polygon-distortion methods are able to achieve interactivity, but exhibit inferior visual quality due to sampling artifacts. In this paper, we introduce a hybrid rendering technique based on polygon distortion and local ray tracing that facilitates interactive high-quality visualization of multiple objects moving at relativistic speeds in arbitrary directions. The method starts by calculating tight image-space footprints for the apparent triangles of the 3D scene objects. The final image is generated using a single image-space ray tracing step incorporating Doppler and searchlight effects. Our implementation uses GPU shader programming and hardware texture filtering to achieve high rendering speed.
Improving Kalign via Reconstruction of Phylogenetic Tree and Iteration

The multiple sequence alignment of DNA or protein sequences is one of the fundamental research topics in bioinformatics. Kalign is an widely used multiple sequence alignment method employing the Wu-Manber approximate string matching algorithm, which improves both the accuracy and speed of multiple sequence alignment, and it is especially well suited for the task of aligning large numbers of sequences or divergent sequences. However, the alignment quality is poor because of the inaccurate estimate of the distances between sequences. In this paper, a novel similarity measure based on matching protein subsequences is presented. Then an iterative algorithm, which combines re-estimation of distance and reconstruction of phylogenetic tree, is introduced to refine the alignment created by Kalign. As the result of experiment, we use the BAliBASE 3.0 alignment benchmark set for the assessment of our method. The result shows that our algorithm achieves more accurate alignment than Kalign does.
Globalization and the (Mis)Governance of Nations

We analyze whether or not the globalization of capital, ‘disciplines’ governments and improves governance. We demonstrate that globalization affects governance, by increasing a country’s vulnerability to sudden capital flight. This increased threat of capital flight can discipline governments and improve governance and welfare by placing countries in a ‘golden straitjacket’. However, globalization may also ‘overdiscipline’ governments - resulting in a perverse impact on governmental incentives that catalyzes (mis)governance. Accordingly, the paper suggests a novel (and qualified) role for capital controls. Finally, we provide some suggestive evidence consistent with the predictions from our theoretical framework.
Time Delayed Causal Gene Regulatory Network Inference with Hidden Common Causes

Inferring the gene regulatory network (GRN) is crucial to understanding the working of the cell. Many computational methods attempt to infer the GRN from time series expression data, instead of through expensive and time-consuming experiments. However, existing methods make the convenient but unrealistic assumption of causal sufficiency, i.e. all the relevant factors in the causal network have been observed and there are no unobserved common cause. In principle, in the real world, it is impossible to be certain that all relevant factors or common causes have been observed, because some factors may not have been conceived of, and therefore are impossible to measure. In view of this, we have developed a novel algorithm named HCC-CLINDE to infer an GRN from time series data allowing the presence of hidden common cause(s). We assume there is a sparse causal graph (possibly with cycles) of interest, where the variables are continuous and each causal link has a delay (possibly more than one time step). A small but unknown number of variables are not observed. Each unobserved variable has only observed variables as children and parents, with at least two children, and the children are not linked to each other. Since it is difficult to obtain very long time series, our algorithm is also capable of utilizing multiple short time series, which is more realistic. To our knowledge, our algorithm is far less restrictive than previous works. We have performed extensive experiments using synthetic data on GRNs of size up to 100, with up to 10 hidden nodes. The results show that our algorithm can adequately recover the true causal GRN and is robust to slight deviation from Gaussian distribution in the error terms. We have also demonstrated the potential of our algorithm on small YEASTRACT subnetworks using limited real data.
Learning to Paraphrase : Using Paraphrase Detection of Spoken Utterances to Predict Learner Expertise

Predicting student knowledge from text has become an increasingly common for automatic grading and assessment. Much of this work, however, hinges on natural language processing techniques that tend to neglect the relative locations of individual words by using a bag of words model. Instead of using this technique we segment text into a bag of utterances. We then use those utterances to examine the extent to which individuals of different levels of expertise paraphrase one another. Results indicate clear distinctions among paraphrase frequencies of the different levels of expertise. Furthermore, results suggest that experts and novices have many of the same intuitions, but that the expert’s knowledge is more accurately applied.
Driven Approach to Software Evolution

From a maintenance perspective, only software that is well understood can evolve in a controlled and high quality manner. Software evolution itself is a knowledgedriven process that requires the use and integration of different knowledge resources. In this paper, we present a formal representation of an existing process model to support the evolution of software systems by representing knowledge resources and the process model using a common representation based on ontologies and description logics. This formal representation supports the use of reasoning services across different knowledge resources, allowing for the inference of explicit and implicit relations among them. Furthermore, we introduce an interactive story metaphor to guide maintainers during their software evolution activities and to model the interactions between users, knowledge resources, and the process model.
Twist2 Is Upregulated in Early Stages of Repair Following Acute Kidney Injury

The aging kidney is a marked by a number of structural and functional changes, including an increased susceptibility to acute kidney injury (AKI). Previous studies from our laboratory have shown that aging male Fischer 344 rats (24 month) are more susceptible to apoptosis-mediated injury than young counterparts. In the current studies, we examined the initial injury and early recovery phases of mercuric chloride-induced AKI. Interestingly, the aging kidney had decreased serum creatinine compared to young controls 1 day following mercuric chloride injury, but by day 4, serum creatinine was significantly elevated, suggesting that the aging kidney did not recover from injury. This conclusion is supported by the findings that serum creatinine and kidney injury molecule-1 (Kim-1) gene expression remain elevated compared to young controls at 10 days post-injury. To begin to elucidate mechanism(s) underlying dysrepair in the aging kidney, we examined the expression of Twist2, a helix-loop-helix transcription factor that may mediate renal fibrosis. Interestingly, Twist2 gene expression was elevated following injury in both young and aged rats, and Twist2 protein expression is elevated by mercuric chloride in vitro.