Miracle-dz/UniFAR-FTUs huggingface dataset:
Pattern recognition based segmentation method of cell nuclei in tissue section analysis

A pattern recognition-based segmentation (PRS) system was developed for segmenting cell nuclei in tissue sections of urine bladder tumors and brain tumors. One hundred and thirty eight image samples from HE-stained tissue sections of urine carcinoma and brain astrocytomas were selected. Half of them were used for designing the PRS-system and the rest for evaluating its performance. The PRS-system can be designed by employing one of three classifiers: minimum distance, Bayesian, and multilayer perceptron (MLP) classifier. Classifier design was based upon two sets of training data, the nuclei set and the surrounding tissue set, which were derived from textural features. According to the pathologists' evaluation, 88% of the segmented nuclei were registered correctly and 12% incorrectly. The MLP classifier proved superior in sensitivity and discrimination of nuclei from background when compared with Bayesian and minimum distance classification performance. The PRS-system proved efficient when tested under different types of histopathological tissue samples providing an index for potential generalization of the technique.
Automatic Data Distribution for Cfd Applications on Structured Grids

Data distribution is an important step in implementation of any parallel algorithm. The data distribution determines data traffic, utilization of the interconnection network and affects the overall code efficiency. In recent years a number data distribution methods have been developed and used in real programs for improving data traffic. We use some of the methods for translating data dependence and affinity relations into data distribution directives. We describe an automatic data alignment and placement tool (ADAFT) which implements these methods and show it results for some CFD codes (NPB and ARC3D). Algorithms for program analysis and derivation of data distribution implemented in ADAFT are efficient three pass algorithms. Most algorithms have linear complexity with the exception of some graph algorithms having complexity O(n(sup 4)) in the worst case.
Modelling Typical Alphabetic Analogical Reasoning in ACT-R

In the Copycat project (Hofstadter & Mitchell, 1995), alphabetic analogical reasoning problems (see Figure 1) were studied to gain a better understanding of the fluid processes underlying analogical reasoning. Human data were gathered on 5 different types of problems (see Table 1) and modelled in the hybrid computational model Copycat. The range of responses Copycat was able to produce to this type of problems was compatible with answers given by human participants (Hofstadter & Mitchell, 1995).
A low power fast-settling frequency-presetting PLL frequency synthesizer

This work presents the design and implementation of a 2.4 GHz low power fast-settling frequency-presetting PLL frequency synthesizer in the 0.18 μm CMOS process. A low power mixed-signal LC VCO, a low power dual mode prescaler and a digital processor with non-volatile memory are developed to greatly reduce the power consumption and the setting time. The digital processor can automatically calibrate the presetting frequency and accurately preset the frequency of the VCO under process variations. The experimental results demonstrate that the power consumption of the synthesizer is about 4 mA @ 1.8 V and that the typical setting time of the synthesizer is less than 3 μs.
A high-frequency CMOS linear transconductance element

A simple four-transistor, linear, tunable, high-frequency transconductance element is described. By using a pair of composite n -channel- p -channel devices, the circuit achieves its linearity by current differencing without undue matching requirements. It is shown that linearity and frequency response can be optimized simultaneously by appropriate choice of device dimensions. The performance is verified by SPICE simulations, and an operational transconductance amplifier (OTA) is used as one example for the many applications of the proposed element.
Policy-based Information Sharing using Software-Defined Networking in Cloud Systems

Cloud Computing is rapidly becoming a ubiquitous technology. It enables an escalation in computing capacity, storage and performance without the need to invest in new infrastructure and the maintenance expenses that follow. Security is among the major concerns of organizations that are still reluctant to adopt this technology: The cloud is dynamic, and with so many different parameters involved, it is a difficult task to regulate it. With an approach that blends Usage Management and Statistical Learning, this research yielded a novel approach to mitigate some of the issues arising due to questionable security, and to regulate performance (utilization of resources). This research also explored how to enforce the policies related to the resources inside a Virtual Machine(VM), apart from providing initial access control. As well, this research compared various encryption schemes and observed their behavior in the cloud. We considerd various components in the cloud to deduce a multicost function, which in turn helps to regulate the cloud. While guarenteeing security policies in the cloud, it is esential to add security to the network becuase the virtual cloud and SDN tie together. Enforcing network-wide policies has always been a challenging task in the domain of communication networks. Software-defined networking (SDN) enables the use of a central controller to define policies, and to use each network switch to enforce policies. While this presents an
Distributed consensus filter on directed switching graphs

We consider distributed estimation on a directed graph with switching topologies. Motivated by a recent PI consensus filter, we modify the protocol and remove the requirement of bidirectional exchange of neighboring gains for fixed topologies. We then extend the protocol to switching topologies and propose a new hybrid consensus filter design. Convergence results under both balanced directed, and general directed graphs are given for switching graphs. Consensus error bounds are analytically derived in the case of time‐varying inputs. Satisfactory simulation results are shown. Copyright © 2014 John Wiley & Sons, Ltd.
Performance of DEMO on New Test Problems: A Comparison Study

DEMO (Differential Evolution for Multiobjective Optimization) is a new algorithm for solving multiobjective problems. This paper presents a comparison between DEMO and three state-of-the-art algorithms for multiobjective optimization using a new comparison methodology and new test problems. Results show that DEMO outperforms the other algorithms used in comparison in four out of nine test problems. On only one test problem the other algorithms achieve significantly better results than DEMO, while in the remaining four test problems DEMO is the secondbest algorithm.
Diretrizes para manutenção de múltiplos órgãos no potencial doador adulto falecido: parte II. Ventilação mecânica, controle endócrino metabólico e aspectos hematológicos e infecciosos

The role of intensive care specialists in the maintenance of deceased potential donors is not restricted to hemodynamics. Appropriate endocrine-metabolic management is fundamental to maintaining energy support and hydroelectrolytic control, which cooperate for hemodynamic stability. Hematological changes are also important, especially considering the issues caused by inappropriate transfusions. In addition, this article discusses the role of appropriate protective ventilation to prevent inflammatory responses and to provide more transplantable lungs. Finally, judicious assessment of infections and antibiotic therapy is discussed
Inclination and Anteversion Angles of the Femoral Head and Neck in the Dog Evaluation of a Standard Method of Measurement

The inclination and anteversion angles of the femoral head and neck in 30 mongrel dogs were determined using a radiographic biplanar technique. The angle of anteversion of the 30 necropsy specimens was measured directly and compared with the in vivo radiographic measurements. The average value for the angles of anteversion, inclination, and corrected real angles of inclination were 31.3°, 148.8°, and 144.7°, respectively. Graphs were established using existing trigonometric relations to facilitate the analysis. The method used was found to be simple, reliable, and accurate. The mean difference between the indirect radiographic biplanar technique and direct measurements on isolated bones was ° 1.5°. The difference between the mean values of anteversion angles determined after radiographic biplanar technique and direct bone measurements was not significant (p > 0.05).
The determinants of students' tertiary academic success

Many factors influence students’ academic performance at university, including their prior academic ability, level of wealth and demographic traits. The characteristics of the secondary school attended by students also play an important role in influencing their university outcomes. This paper considers the determinants of grades for students at a large Australian university. Using both first- and second-generation approaches to modelling the determinants of academic success, it finds that university grades are largely influenced by students’ university entrance scores. Schools also appear to affect academic performance at university.
In vitro formation of neocartilage in photocrosslinked poly(ethylene oxide) hydrogels

In tissue engineering cartilage, photopolymerization provides many benefits including rapid polymerization times under physiological conditions with temporal and spatial control over the polymerization. In situ forming hydrogels are attractive for their high water content and tissue-like elastic properties. We are interested in using photocrosslinkable macromers based on poly(ethylene oxide) to encapsulate chondrocytes in a three-dimensional hydrogel matrix to support cell proliferation and extracellular matrix production. In this study, we demonstrated that chondrocytes (at a concentration of 100/spl times/10/sup 6/ cells/cc) in a 10% (w/w) macromer solution containing a cytocompatible photoinitiating system polymerized in the form of a /spl sim/2 mm thick disk survived the photoencapsulation process and remained healthy in the hydrogel network. The biochemical content of the hydrogels was also assessed at 4, 6, and 8 weeks in vitro to determine the cellular activity. An increase in glycosaminoglycan (GAG) and total collagen contents was observed over time, and at 8 weeks the neocartilage was comprised of 0.4/spl plusmn/0.09% total collagen and 1.34/spl plusmn/0.10% GAG per wet weight of the construct. In addition, we demonstrated that photocrosslinkable hydrogels may overcome some of the limitations of other materials in obtaining uniform cell seeding and the production of thick cartilaginous tissue. In this study, chondrocytes were photoencapsulated in a thick 8 mm hydrogel and remained healthy throughout the construct both radially and longitudinally, and the cells produced a uniform GAG content as seen histologically.
Advances in Distributed and Parallel Knowledge Discovery

foreword by Vipin Kumar Knowledge discovery and data mining (KDD) deals with the problem of extracting interesting associations, classifiers, clusters, and other patterns from data. The emergence of network-based distributed computing environments has introduced an important new dimension to this problem--distributed sources of data. Traditional centralized KDD typically requires central aggregation of distributed data, which may not always be feasible because of limited network bandwidth, security concerns, scalability problems, and other practical issues. Distributed knowledge discovery (DKD) works with the merger of communication and computation by analyzing data in a distributed fashion. This technology is particularly useful for large heterogeneous distributed environments such as the Internet, intranets, mobile computing environments, and sensor-networks.When the data sets are large, scaling up the speed of the KDD process is crucial. Parallel knowledge discovery (PKD) techniques addresses this problem by using high-performance multiprocessor machines. This book presents introductions to DKD and PKD, extensive reviews of the field, and state-of-the-art techniques.Contributors Rakesh Agrawal, Khaled AlSabti, Stuart Bailey, Philip Chan, David Cheung, Vincent Cho, Joydeep Ghosh, Robert Grossman, Yi-ke Guo, John Hale, John Hall, Daryl Hershberger, Ching-Tien Ho, Erik Johnson, Chris Jones, Chandrika Kamath, Hillol Kargupta, Charles Lo, Balinder Malhi, Ron Musick, Vincent Ng, Byung-Hoon Park, Srinivasan Parthasarathy, Andreas Prodromidis, Foster Provost, Jian Pun, Ashok Ramu, Sanjay Ranka, Mahesh Sreenivas, Salvatore Stolfo, Ramesh Subramonian, Janjao Sutiwaraphun, Kagan Tummer, Andrei Turinsky, Beat Wthrich, Mohammed Zaki, Joshua Zhang.
Practical Malware Analysis: The Hands-On Guide to Dissecting Malicious Software

Praise for Practical Malware Analysis The book every malware analyst should keep handy. --Richard Bejtlich, CSO, Mandiant & Founder of TaoSecurity An excellent crash course in malware analysis. --Dino Dai Zovi, Independent Security Consultant . the most comprehensive guide to analysis of malware, offering detailed coverage of all the essential skills required to understand the specific challenges presented by modern malware. --Chris Eagle, Senior Lecturer of Computer Science at the Naval Postgraduate School A hands-on introduction to malware analysis. I'd recommend it to anyone who wants to dissect Windows malware. --Ilfak Guilfanov, Creator of IDA Pro . a great introduction to malware analysis. All chapters contain detailed technical explanations and hands-on lab exercises to get you immediate exposure to real malware. --Sebastian Porst, Google Software Engineer . brings reverse engineering to readers of all skill levels. Technically rich and accessible, the labs will lead you to a deeper understanding of the art and science of reverse engineering. I strongly recommend this book for beginners and experts alike. --Danny Quist, PhD, Founder of Offensive Computing If you only read one malware book or are looking to break into the world of malware analysis, this is the book to get. --Patrick Engbretson, IA Professor at Dakota State University and Author of The Basics of Hacking and Pen Testing . an excellent addition to the course materials for an advanced graduate level course on Software Security or Intrusion Detection Systems. The labs are especially useful to students in teaching the methods to reverse engineer, analyze and understand malicious software. --Sal Stolfo, Professor, Columbia University
A Research Using Correlation Coefficient to Make Bayesian Classification Data Mining

In traditional Bayesian classification data mining methods, there may be defects such as predictions unreliable because the selected predictors are little or not related with the target factor. this paper analyzes the correlation between predictors and the target factor using correlation coefficient based on Bayesian classification model and combines with Hadoop distributed file system and parallel programming models to explore an improved algorithm. The experiments show that this method not only makes the prediction more reliable but also saves resources and improves the efficiency of the algorithm greatly. In addition, it is suitable for massive data processing.
Packetized Media Streaming with Comprehensive Exploitation of Feedback Information

This paper addresses the problem of streaming packetized media over a lossy packet network, with sender-driven (re)transmission using acknowledgement feedback. The different transmission scenarios associated to a group of interdependent media data units are abstracted in terms of a finite alphabet of policies, for each single data unit. A rate-distortion optimized markovian framework is proposed, which supports the use of comprehensive feedback information. Contrarily to previous works in rate-distortion optimized streaming, whose transmission policies definitions do not take into account the feedback expected for other data units, our framework considers all the acknowledgment packets in defining the streaming policy of a single data unit. More specifically, the notion of master and slave data unit is introduced, to define dependent streaming policies between media packets; the policy adopted to transmit a slave data unit becomes dependent on the acknowledgments received about its masters. One of the main contributions of our work is to propose a methodology that limits the space of dependent policies for the RD optimized streaming strategy. A number of rules are formulated to select a set of relevant master/slave relationships, defined as the dependencies that are likely to bring RD performance gain in the streaming system. These rules provide a limited complexity solution to the rate-distortion optimized streaming problem, with comprehensive use of feedback information. Based on extensive simulations, we conclude that (i) the proposed set of relevant dependent policies achieves close to optimal performance, while being computationally tractable, and (ii) the benefit of dependent policies is driven by the relative sizes and importance of interdependent data units. Our simulations demonstrate that dependent streaming policies can perform significantly better than independent streaming strategies, especially for cases where some media data units bring a relatively large gain in distortion, in comparison with other data units they depend on for correct decoding. We observe however that the benefit becomes marginal when the gain in distortion per unit of rate decreases along the media decoding dependency path. Since such a trend characterizes most conventional scalable coders, the implementation of dependent policies can reasonably be ruled out in these specific cases.
Computational Chemistry: A Practical Guide for Applying Techniques to Real World Problems

Preface. Acknowledgments. Symbols Used in This Book. Introduction. BASIC TOPICS. Fundamental Principles. Ab initio Methods. Semiempirical Methods. Density Functional Theory. Molecular Mechanics. Molecular Dynamics and Monte Carlo Simulations. Predicting Molecular Geometry. Constructing a Z-Matrix. Using Existing Basis Sets. Molecular Vibrations. Population Analysis. Other Chemical Properties. The Importance of Symmetry. Efficient Use of Computer Resources. How to Conduct a Computational Research Project. ADVANCED TOPICS. Finding Transition Structures. Reaction Coordinates. Reaction Rates. Potential Energy Surfaces. Conformation Searching. Fixing Self-Consistent Field Convergence Problems. QM/MM. Solvation. Electronic Excited States. Size Consistency. Spin Contamination. Basis Set Customization. Force Field Customization. Structure--Property Relationships. Computing NMR Chemical Shifts. Nonlinear Optical Properties. Relativistic Effects. Band Structures. Mesoscale Methods. Synthesis Route Prediction. APPLICATIONS. The Computational Chemist's View of the Periodic Table. Biomolecules. Simulating Liquids. Polymers. Solids and Surfaces. Appendix: Software Packages. Appendix 1: Integrated Packages. Appendix 2: Ab initio and DFT Software. Appendix 3: Semiempirical Software. Appendix 4: Molecular Mechanics/Molecular Dynamics/Monte Carlo Software. Appendix 5: Graphics Packages. Apendix 6: Special-purpose Programs. Glossary. Bibliography. Index.
English as a lingua franca : mutual intelligibility of Chinese, Dutch and American speakers of English

English has become the language of international communication. As a result of this development, we are now confronted with a bewildering variety of ‘Englishes’, spoken with non-native accents. Research determining how intelligible non-native speakers of varying native-language backgrounds are to each other and to native speakers of English has only just started to receive attention. This thesis investigated to what extent Chinese, Dutch and American speakers of English are mutually intelligible. Intelligibility of vowels, simplex consonants and consonant clusters was tested in meaningless sound sequences, as well as in words in meaningless and meaningful short sentences. Speakers (one male, one female per language background) were selected so as to be optimally representative of their peer groups, which were made up of young academic users of English. Intelligibility was tested for all nine combinations of speaker and listener backgrounds. Results show that Chinese-accented English is less intelligible overall than Dutch-accented English, which is less intelligible than American English. Generally, the native-language background of the speaker was less important for the intelligibility than the background of the listener. Also, the results reveal a clear and consistent so-called interlanguage speech intelligibility benefit: speakers of English – whether foreign or native – are more intelligible to listeners with whom they share the native-language background than to listeners with a different native language.
Impact of cerebral blood flow changes due to arterial bypass surgery on cognitive function in adult patients with symptomatic ischemic moyamoya disease.

OBJECTIVESome adult patients with moyamoya disease (MMD) undergoing revascularization surgery show an improvement or decline in cognition postoperatively. Revascularization surgery for ischemic MMD augments cerebral blood flow (CBF) and improves cerebral oxygen metabolism. However, cerebral hyperperfusion, which is a short-term, major increase in ipsilateral CBF that is much greater than the metabolic needs of the brain, sometimes occurs as a complication. Cerebral hyperperfusion produces widespread, minimal injury to the ipsilateral white matter and cortical regions. The aim of the present prospective study was to determine how changes in CBF due to arterial bypass surgery affect cognitive function in adult patients with symptomatic ischemic MMD and misery perfusion.METHODSThirty-two patients with cerebral misery perfusion, as determined on the basis of 15O gas positron emission tomography, underwent single superficial temporal artery-middle cerebral artery (M4 in the precentral region) anastomosis. Brain perfusion single-photon emission computed tomography (SPECT) studies were performed preoperatively, on the 1st postoperative day, and 2 months after surgery. Neuropsychological tests were also performed preoperatively and 2 months after surgery.RESULTSPostoperative neuropsychological assessments demonstrated cognitive improvement in 10 cases (31%), no change in 8 cases (25%), and decline in 14 cases (44%). Based on brain perfusion SPECT and symptoms, 10 patients were considered to have cerebral hyperperfusion syndrome, and all of these patients exhibited a postoperative decline in cognition. Relative precentral CBF on the 1st postoperative day was significantly greater in patients with postoperative cognitive decline (167.3% ± 15.3%) than in those with improved (105.3% ± 18.2%; p < 0.0001) or unchanged (131.4% ± 32.1%; p = 0.0029) cognition. The difference between relative precentral CBF 2 months after surgery and that before surgery was significantly greater in patients with postoperative cognitive improvement (17.2% ± 3.8%) than in those with no postoperative change (10.1% ± 2.4%; p = 0.0003) or with postoperative decline (11.5% ± 3.2%; p = 0.0009) in cognition.CONCLUSIONSCerebral hyperperfusion in the acute stage after arterial bypass surgery impairs cognitive function. An increase in CBF in the chronic stage without acute-stage cerebral hyperperfusion improves cognitive function in adult patients with symptomatic ischemic MMD and misery perfusion.