Miracle-dz/UniFAR-FTUs huggingface dataset:
DWT based sonoelastography prostate cancer image classification using back propagation neural network

In modern days, Cancer is spreading rapidly which requires a significant attention along with its proper detection and identification, which is even more crucial. Attempt should be made to detect it an early stage so that it may be controlled and sometimes cured. But this requires proper diagnosing methods so that the demerits and pains of being diagnosed are minimized among patients. With respect to these recent day diagnosis that are leaning towards the non-invasiveness associating the Computer-aided technologies, bringing many benefits to this specific area of Medical field, we have tried to propose a methodology which will remove the manual interpretation of detecting the affected regions from an image generated from a modern and new diagnosing modality named Sonoelastography(SE). SE images marks regions in the form of color coded patches depending on the Elasticity scores in a particular Region of Interest (RoI). Here we have tried to analyze such images and classify an image that whether it is malignant or not. We have taken color SE images as our principle input to the proposed system. Discrete Wavelet Transform (DWT) is used to identify the most relevant sections of the image after the preprocessing step(s), where generation of two separate images containing the affected regions and the unaffected regions are carried out. Multilevel thresholding has been used to generate the affected and unaffected images from the original image. The images after thresholding are channeled to red and blue components and are applied to DWT from where the Low Low (LL) component is subjected to obtain features which are used finally to classify the images using Back Propagation Neural Network.
Evaluation of Expressions with Uncertainty in Databases

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BitTAKA : Anonymous BitTorrent

The highly popular BitTorrent peer-to-peer file sharing protocol does not provide users any anonymity. We present BitTAKA, a modification of the BitTorrent protocol that provide users a degree of anonymity without an overwhelming performance sacrifice. Partly inspired by onion-based routing protocols, we analyze the theoretical anonymity of BitTAKA. We also measure the performance loss in our protocol relative to the original BitTorrent protocol using a customized simulation framework.
PeliGRIFF: A Parallel DEM-DLM/FD Method for DNS of Particulate Flows with Collisions

An original Direct Numerical Simulation (DNS) method to tackle the problem of particulate sows at moderate to high concentration and Þnite Reynolds number is presented. Our method is built on the framework established by Glowin- ski and his coworkers (1) in the sense that we use their Distributed Lagrange Multiplier/Fictitious Domain (DLM/FD) formulation and their operator-splitting idea but differs in the treatment of particle collisions. The novelty of our contribution relies on replacing the simple artiÞcial repulsive force based collision model usually employed in the literature by an efÞcient Discrete Element Method (DEM) granular solver. The use of our DEM solver enables us to consider particles of arbitrary shape (at least convex) and to account for actual contacts, in the sense that particles actually touch each other, in contrast with the simple repulsive force based collision model. We recently upgraded our serial code, GRIFF 1 (2), to full MPI capabilities. Our new code, PeliGRIFF 2 , is developed under the framework of the full MPI open source platform PELI- CANS (3). The new MPI capabilities of PeliGRIFF open new perspectives in the study of particulate sows and signiÞcantly increase the number of particles that can be considered in a full DNS approach: O(100000) in 2D and O(10000) in 3D. Results on the 2D/3D sedimentation/suidization of isometric polygonal/polyedral particles with collisions are presented.
Introduction to the finite element method

1. Let Ω = (−1, 1)× (0, 1). We consider the initial boundary value problem ut − uxx = 0, in Ω, (1a) u(±1, t) = 0, for 0 ≤ t ≤ 1, (1b) u(x, 0) = sin(nπx), n ∈ N. (1c) a) Give the analytical solution u(x, t) for general n ∈ N. Hint: Use separation of variables, i. e. , u(x, t) = u1(x) · u2(t). b) Consider the problem: Find u(x, 1) for initial data u(x, 0) = sin(nπx), n ∈ N. Is this problem well posed with respect to the L-norm in (−1, 1)? c) Replace ut − uxx = 0 in (1a) by ut + uxx = 0 and redo a) and b).
GeoVisual Interface - A Visual Query Interface for Geographic Information Systems

This work presents GeoVisual Interface an interface for visual queries in Geographic Information Systems (GIS). GeoVisual Interface is the upper level module of a complete visual query system architecture. It allows users to formulate queries using visual elements (i.e. spatial objects from a visualization area, from the database schemas or from a geographic entity metaphor) and spatial operators via direct manipulation. Cognitive, ergonomic and technical aspects are also considered to improve the spatial query formulation capability and enhance the humancomputer interaction, making the system more attractive to users.
CARBON NANOTUBE AS A DRUG DELIVERY SYSTEM: A REVEIW

Carbon nanotubes (CNTs) are allotropes of carbon with a nanostructure that can have a length-to-diameter ratio greater than 1,000,000. These cylindrical carbon molecules have novel properties that make them potentially useful in many applications in nanotechnology. Their unique surface area, stiffness, strength and resilience have led to much excitement in the field of pharmacy. Nanotubes are categorized as single-walled nanotubes and multiple walled nanotubes. Techniques have been developed to produce nanotubes in sizeable quantities, including arc discharge, laser ablation, chemical vapor deposition, silane solution method and flame synthesis method. The properties and characteristics of CNTs are still being researched heavily and scientists have barely begun to tap the potential of these structures. They can pass through membranes, carrying therapeutic drugs, vaccines and nucleic acids deep into the cell to targets previously unreachable. Overall, recent studies regarding CNTs have shown a very promising glimpse of what lies ahead in the future of medicines.
Full-diversity, high-rate space-time block codes from division algebras

We present some general techniques for constructing full-rank, minimal-delay, rate at least one space-time block codes (STBCs) over a variety of signal sets for arbitrary number of transmit antennas using commutative division algebras (field extensions) as well as using noncommutative division algebras of the rational field /spl Qopf/ embedded in matrix rings. The first half of the paper deals with constructions using field extensions of /spl Qopf/. Working with cyclotomic field extensions, we construct several families of STBCs over a wide range of signal sets that are of full rank, minimal delay, and rate at least one appropriate for any number of transmit antennas. We study the coding gain and capacity of these codes. Using transcendental extensions we construct arbitrary rate codes that are full rank for arbitrary number of antennas. We also present a method of constructing STBCs using noncyclotomic field extensions. In the later half of the paper, we discuss two ways of embedding noncommutative division algebras into matrices: left regular representation, and representation over maximal cyclic subfields. The 4/spl times/4 real orthogonal design is obtained by the left regular representation of quaternions. Alamouti's (1998) code is just a special case of the construction using representation over maximal cyclic subfields and we observe certain algebraic uniqueness characteristics of it. Also, we discuss a general principle for constructing cyclic division algebras using the nth root of a transcendental element and study the capacity of the STBCs obtained from this construction. Another family of cyclic division algebras discovered by Brauer (1933) is discussed and several examples of STBCs derived from each of these constructions are presented.
A Sufficient Condition for Rate Monotonic Schedulability Based on Response Time Analysis

Rate Monotonic scheduling algorithm is widely used in real-time systems and embedded systems for its implementation simplicity and optimality in fixed priority scheduling. Since determining the schedulability of a task set in Rate Monotonic scheduling is an important step in designing a real-time system, schedulability analysis has been extensively studied. Although there is an exact schedulability test method for Rate Monotonic scheduling, the exact test is too complex to be executed on-line for large number of tasks because it is based on the response time calculation which is NP-hard. Thus many researchers studied on polynomial time sufficient conditions on Rate Monotonic schedulability. The existing polynomial time sufficient conditions are in the form of a utilization bound; that is, if a task set's total utilization is not larger than a utilization bound, the task set is accepted as schedulable one. Though the utilization bound tests are simple, they suffer from poor processor utilization. In this paper, we propose a new sufficient schedulability condition based on the response time test, different from the previous approaches. The new test provides a condition that is both necessary and sufficient for task sets with period ratio less than 2, while the previous tests provide only sufficient conditions. Extensive simulations show that our test significantly outperforms the existing tests with utilization bound.
Answering definitional questions before they are asked

Most question answering systems narrow down their search space by issuing a boolean IR query on a keyword indexed corpus. This technique often proves futile for defini-tional questions, because they only contain one keyword or name. Thus, an IR search for only that term is likely to produce many spurious results; documents that contain mentions of the keyword, but not in a definitional context. An alternative approach is to glean the corpus in pre-processing for syntactic constructs in which entities are defined. In this thesis, I describe a regular expression language for detecting such constructs, with the help of a part-of-speech tagger and a named-entity recognizer. My system, named CoL. ForBIN, extracts entities and their definitions, and stores them in a database. This reduces the task of definitional question answering to a simple database lookup. "Col. Forbin, I know why you've come here And I'll help you with the quest to gain the knowledge that you lack. " Acknowledgments I'd like to thank the following people for making this thesis possible: * Boris Katz, for giving me this opportunity, and for all his guidance and patience throughout the years. * Gregory Marton for providing the initial spark (and being my personal sysad-min), and Jimmy Lin for reigniting it. * Sue Felshin, for her thoughtful editing remarks. * Prof. Winston, who taught me all the rules for writing a thesis (so I could go ahead and break them). * Jerome MacFarland, for showing me the Infolab way; Matthew Bilotti, for keeping me company in 822; Federico, Daniel, and Roni, for entertaining me all summer; and all my other fellow Infolabers. * Jason Chiu, "the human annotator". * All my friends beyond the Infolab, including, but not limited to the DU boys, the Husky Club, and the Phans. Thanks for keeping me sane. the rest of my family, for all their love and support. Without you, none of this is possible. 1Oh yeah, I'd also like to thank the band that provided these words of wisdom, and a world of enlightenment. This has all been wonderful, and you will be missed.
Exploiting Social Trust Assisted Reciprocity (STAR) Toward Utility-Optimal Socially-Aware Crowdsensing

Mobile crowdsensing takes advantage of pervasive mobile devices to collect and process data for a variety of applications (e.g., traffic monitoring and spectrum sensing). In this study, a socially-aware crowdsensing system is advocated in which a cloud-based platform incentivizes mobile users to participate in sensing tasks by leveraging social trust among users, upon receiving sensing requests. For this system, social trust assisted reciprocity (STAR), a synergistic marriage of social trust and reciprocity, is exploited to design an incentive mechanism that stimulates users' participation. Given the social trust structure among users, the efficacy of STAR for satisfying users' sensing requests is thoroughly investigated. Specifically, it is first shown that all requests can be satisfied if and only if sufficient social credit can be “transferred” from users who request more sensing service than they can provide to users who can provide more than they request. Then utility maximization for sensing services under STAR is investigated, and it is shown that it reduces to maximizing the utility of a circulation flow in the combined social graph and request graph. Accordingly, an algorithm that iteratively cancels a cycle of positive weight in the residual graph is developed, which computes the optimal solution efficiently, for both cases of divisible and indivisible sensing service. Extensive simulation results corroborate that STAR can significantly outperform the mechanisms using social trust only or reciprocity only.
Electrical Network Frequency (ENF) Maximum-Likelihood Estimation Via a Multitone Harmonic Model

Estimation of the parameters of the electric network signal, usually present in many audio and video recordings, is known to have several important forensic applications. In this paper, we consider the problem of estimating the base frequency and signal to noise ratio (SNR). Although the electric network signal is present via its base frequency and its integer multiplies, recent estimators in the literature focus on single-tone models. In this work, we offer a multitone harmonic model for the electric network signal. We use the Cramer-Rao bound for the frequency estimation problem and show that this approach can lead to a theoretical O(M3) factor improvement in the estimation accuracy, where M is the number of harmonics. We then derive the computationally efficient form of the maximum-likelihood estimator, applicable in the limit of large number of measurements. The problem of estimating the SNR of the signal is also discussed. Through extensive tests on real data and data sets reported in the current literature, the performance of the new estimators is evaluated. Results indeed show a significant gain compared to the single-tone model, and are better than previously reported estimators in the literature for moderate and high SNR values.
Impact of TCP ACK Losses on TCP Fairness in Wireless Mesh Networks

This paper analyzes TCP fairness in IEEE 802.11- based Wireless Mesh Networks (WMNs). Fairness of bandwidth sharing is important for equitable network access by various users. With our Indoor WMN testbed, we observe that even though TCP flows have similar round trip time and loss probability, they encounter an unfairness problem. Especially, when TCP flows experience high TCP ACK losses (around 20%), unfairness happens in a relatively long period (1∼100 seconds). We denote this phenomenon as a Semi-Long-Term Unfairness in the Same Path (SLU-SP) problem because it happens even when TCP flows share the same routing path. Additionally, we find out that the extended hidden terminal problem boosts up high TCP ACK losses and leads to a serious SLU-SP problem. Also, by carefully observing TCP dynamics through an active monitoring tool, PaPMo (Packet-accurate Protocol Monitor), we find out that the cause of SLU-SP problem comes from the cumulative ACK mechanism of TCP. Cumulative ACK makes TCP flows tolerable to few ACK losses, but when the TCP flows have different sizes of congestion window, the TCP flows have unfair bandwidth sharing in the relatively long period.
Algorithm for Reducing Overhead of Message Purging in Large-Scale Sender-Based Logging

When attempting to apply sender-based message logging with checkpointing into large-scale and geographically distributed systems, two important things should be reconsidered: reducing the number of messages passing on core networks during its fully message logging and recovery procedures and purging effectively logged messages from their senders' volatile memories. This paper presents a novel message purging algorithm to solve the second problem based on our previous work having alleviated the first one. Its first step results in no extra message and forced checkpoint by piggybacking a vector on each sent message. If additional empty buffer space for logging is needed even after the first step has executed, its second step is performed to remove the useful log information each sender maintains while satisfying the consistency condition by using a vector recording its size for every other process.
ANALYSIS OF MACHINE LEARNING TECHNIQUES FOR GENE SELECTION AND CLASSIFICATION OF MICROARRAY DATA

The rapid developments in the genetics field have generated a huge amount of biological data. Micro array gene expression data is an important instance of biological data. It has high dimensionality with a small number of samples accompanied with large number of genes.Therefore, using machine learning techniques for knowledge discovery in such data become a rich area for researchers. Classification taskdragged a high attention during the analysis ofsuch data, which objectiveclassifyingnewunseen gene expression data sets into predefined classes. Therefore, implementing an effective gene selection technique and then adjusting a powerful classifier to achieve accurate classification accuracy is a challenge. This paper discusses some of recent research on machine learning approaches for gene expression selection and classification tasks.The paper also, presented a comparative study of these approaches as a first phase of our research motivated by building a newly, promising system for gene expression analysis. Theresults of the analysis showed that the support vector machine is the most often used classifier of higher classification accuracy.
Differential Regulation of the Transcriptional Activities of Hypoxia-Inducible Factor 1 Alpha (HIF-1α) and HIF-2α in Stem Cells

ABSTRACT Transcriptional responses to hypoxia are primarily mediated by hypoxia-inducible factors (HIFs), HIF-1α and HIF-2α. The HIF-1α and HIF-2α subunits are structurally similar in their DNA binding and dimerization domains but differ in their transactivation domains, implying they may have unique target genes and require distinct transcriptional cofactors. Our previous results demonstrated that HIF-1α and HIF-2α regulate distinct target genes. Here, we report that HIF-2α is not transcriptionally active in embryonic stem (ES) cells, as well as possible inhibition by a HIF-2α-specific transcriptional repressor. Using DNA microarray analysis of hypoxia-inducible genes in wild-type (WT), Hif-1α−/−, and Hif-2α−/− ES cells, we show that HIF-1α induces a large number of both confirmed and novel hypoxia-inducible genes, while HIF-2α does not activate any of its previously described targets. We further demonstrate that inhibition of HIF-2α function occurs at the level of transcription cofactor recruitment to endogenous target gene promoters. Overexpression of WT and, notably, a DNA-binding-defective HIF-2α mutant restores endogenous HIF-2α protein activity, suggesting that ES cells express a HIF-2α-specific corepressor that can be titrated by overexpressed HIF-2α protein. HIF-2α repression may explain why patients with mutations in the VHL tumor suppressor gene display cancerous lesions in specific tissue types.
Pharmacological Basis for Traditional Use of the Lippia thymoides

The aim of this study was to evaluate crude extracts and fractions from leaves and stems of Lippia thymoides and to validate their use in folk medicine. In vitro antioxidant and antimicrobial activities and in vivo wound healing in rats, baker yeast-induced fever in young rats, and acute oral toxicity in mice assays were realized. The crude extracts and their dichloromethane and ethyl acetate fractions had potent radical-scavenging activity against the DPPH but were not effective in the β-carotene bleaching method. The dichloromethane fraction from the leaves extract showed the broadest spectrum of activity against S. aureus, B. cereus, and C. parapsilosis. The animals treated with crude extracts showed no difference in wound healing when compared with the negative control group. The crude extract from leaves (1200 mg/kg) has equal efficacy in reducing temperature in rats with hyperpyrexia compared to dipyrone (240 mg/kg) and is better than paracetamol (150 mg/kg). In acute toxicity test, crude extract of leaves from Lippia thymoides exhibited no mortality and behavioral changes and no adverse effects in male and female mice. This work validates the popular use of Lippia thymoides for treating the wound and fever, providing a source for biologically active substances.
Self-assembled microactuated XYZ stages for optical scanning and alignment

A novel self-assembled, surface-micromachined micro-XYZ stage with large displacements and fine positioning accuracy has been demonstrated on a Si microoptical bench for optical scanning/alignment applications. Continuous lateral scanning up to 120 /spl mu/m and vertical scanning up to 250 /spl mu/m have been achieved with integrated scratch drive actuators (SDA), which have step resolutions of 27 nm. The XYZ stage can be fully assembled by applying an electric bias only. Focus adjustment of the micro-Fresnel lens integrated on the micro-XYZ stage has also been successfully demonstrated. This is useful for two-dimensional scanning, dynamic focusing/tracking, and fine optical alignment.
Identification of GRP75 as an Independent Favorable Prognostic Marker of Neuroblastoma by a Proteomics Analysis

Purpose: Neuroblastoma (NB) is a heterogeneous neoplasm. Detailed biological discrimination is critical for the effective treatment of this disease. Because the tumor behavior of NB is closely associated with the histologic state of differentiation, we thus aimed to identify novel differentiation-associated markers of NB with prognostic implication. Experimental Design: A human NB cell line SH-SY5Y was used as a model system to explore potential biomarkers for the differentiation of NB by proteomic analyses. Seventy-two NB tumor tissues were subsequently investigated by immunohistochemistry to validate the correlations between the expression of a novel prognostic marker, various clinicopathologic and biological factors, and patient survival. Results: Using two-dimensional differential gel electrophoresis, we found a total of 24 spots of proteins in SH-SY5Y cells whose expression was enhanced following differentiation. Glucose-regulated protein 75 (GRP75) was unambiguously identified as one of the five proteins that were dramatically up-regulated following differentiation. Immunohistochemical analyses of 72 NB tumor tissues further revealed that positive GRP75 immunostaining is strongly correlated with differentiated histologies (P < 0.001), mass-screened tumors (P = 0.016), and early clinical stages (P < 0.001) but inversely correlated with MYCN amplification (P = 0.010). Univariate and multivariate survival analyses showed that GRP75 expression is an independent favorable prognostic factor. Conclusions: The present findings clearly showed that our proteomics-based novel experimental paradigm could be a powerful tool to uncover novel biomarkers associated with the differentiation of NB. Our data also substantiate an essential role of GRP75 in the differentiation of NB.