Miracle-dz/UniFAR-FTUs huggingface dataset:
Functional Implications of Glycogen Synthase Kinase-3-Mediated Tau Phosphorylation

Tau is primarily a neuronal microtubule-associated protein that has functions related to the stabilisation of microtubules. Phosphorylation of tau is an important dynamic and regulatory element involved in the binding of tau to tubulin. Thus, highly phosphorylated tau is more likely to be present in the cytosolic compartment of neurons, whereas reduced phosphate burden allows tau to bind to and stabilise the microtubule cytoskeleton. Highly phosphorylated forms of tau are deposited in the brain in a range of neurodegenerative disorders including Alzheimer's disease, progressive supranuclear palsy, and frontotemporal lobar degeneration associated with Pick bodies. A key candidate kinase for both physiological and pathological tau phosphorylation is glycogen synthase kinase-3 (GSK-3). Multiple phosphorylation sites have been identified on tau exposed to GSK-3 in vitro and in cells. In this review, we highlight recent data suggesting a role for GSK-3 activity on physiological tau function and on tau dysfunction in neurodegenerative disease.
Application of data mining in traffic management: Case of city of Isfahan

This paper describes the work investigating the application of data mining tools to aid in the development of traffic signal timing plans. A case study was conducted to illustrate that the use of hierarchical cluster analysis. This approach can be used for designing of a TOD signal control system, since it automatically identifies time-of-day (TOD) intervals using the historical collected data. The cluster analysis approach is able to utilize a high-resolution system state definition that takes full advantage of the extensive set of sensors deployed in a traffic signal system and cluster validation supports the hypotheses presented. The results of this research indicate that advanced data mining techniques hold high potential to provide automated signal control techniques.
These go to eleven : Cranking up the knobs on IDS scaling performance

Signature-based intrusion detection system (IDS) approaches represent the brunt of modern threat detection methods. This is primarily due to their specificity and low false-positive rates and in spite of scalability issues. The inherent scaling issues have meant that measurements of these systems generally ignore the scaling of systems beyond conventional parameter spaces. In particular, while signature-based systems are conventionally measured for total packet processing throughput and false alarm rates, performance is highly dependent on ruleset size. While IDS packet processing performance may appear to be well understood, IDS scaling performance has not been adequately characterized beyond available rulesets. In this paper I present my measurement methods, describe a straightforward method for generating large random rulesets, and present an analysis of the scaling performance of the Snort IDS system.
Japanese Firms in Asia

This book reports findings of a research project conducted by ten social scientists over a three-year period, on a grant given by the Ministry of Education to the Center for Southeast Asian Studies, Kyoto University, for a large project entitled "Cultural Friction in Asia. " A questionnaire was prepared by the research team on "tensions and frictions involved in the overseas advance of Japanese firms to Southeast Asia. "
On the design and performance evaluation of notification support for P2P-based network management

In this paper we present the design of a notification service used in a peer-to-peer (P2P) based network management solution called ManP2P. The notification service is based on the publish/subscribe paradigm, and implemented over a P2P overlay that carry the notification messages using SOAP, the Web services basic protocol. The performance of the notification service is evaluated considering the network traffic, the delivery delay, and the processing power required to forward notifications from physical devices (e.g., router, switches) to interested network managers. The results show that the notification service performs better if an increasing number of intermediate elements called mid-level managers is used between a notification source and destination.
Selecting neural network architectures via the prediction risk: application to corporate bond rating prediction

The notion of generalization can be defined precisely as the prediction risk, the expected performance of an estimator on new observations. The authors propose the prediction risk as a measure of the generalization ability of multi-layer perceptron networks and use it to select the optimal network architecture. The prediction risk must be estimated from the available data. The authors approximate the prediction risk by v-fold cross-validation and asymptotic estimates of generalized cross-validation or H. Akaike's (1970) final prediction error. They apply the technique to the problem of predicting corporate bond ratings. This problem is very attractive as a case study, since it is characterized by the limited availability of the data and by the lack of complete a priori information that could be used to impose a structure to the network architecture. <<ETX>>
The Endtimes of Human Rights

Our goal is always to offer you an assortment of cost-free ebooks too as aid resolve your troubles. We have got a considerable collection of totally free of expense Book for people from every single stroll of life. We have got tried our finest to gather a sizable library of preferred cost-free as well as paid files. Searching for a lot of sold publication or reading resource on the planet? We supply them all in format type as word, txt, kindle, pdf, zip, rar and also ppt. one of them is this certified the endtimes of human rights that has actually been written by Still confused how to get it? Well, simply check out online or download by signing up in our website here. Click them. This is really going to save you time and your money in something should think about. If you're seeking then search around for online. Without a doubt there are several these available and a lot of them have the freedom. However no doubt you receive what you spend on. An alternate way to get ideas would be to check another the endtimes of human rights. GO TO THE TECHNICAL WRITING FOR AN EXPANDED TYPE OF THIS THE ENDTIMES OF HUMAN RIGHTS, ALONG WITH A CORRECTLY FORMATTED VERSION OF THE INSTANCE MANUAL PAGE ABOVE.
The Entrepreneur's Guide to Finance and Business: Wealth Creation Techniques for Growing a Business

1. Whole Numbers: How to Dissect and Solve Word Problems 2. Fractions 3. Decimals 4. Banking 5. Solving for the Unknown: A How-To Approach for Solving Equations 6. Percents and Their Applications 7. Discounts: Trade and Cash 8. Markups and Markdowns 9. Payroll 10. Simple Interest 11. Promissory Notes, Simple Discount Notes, and the Discount Process 12. Compound Interest and Present Value Appendix A: Additional Homework by Learning Unit Appendix B: Check Figures Appendix C: Glossary
Towards Fast Safe Mission Planning

Robotic and cyber-physical systems are proliferating at a breakneck pace. A key technological hurdle is to ensure safety of such systems especially within proximity of humans. While there has been a push in identifying obstacles and unsafe situations via sensors and machine learned predictors, the task of embedding such information to determine safe course while obeying rules-of-the-road is non-trivial. Further, the uncertainty and noise in prediction together with near real-time requirements under bounded computation resources makes this problem very challenging. This work proposes an architecture for fast, safe planning of autonomous missions. We build upon the recent work in Probabilistic Signal Temporal Logic (PrSTL) that synthesizes provably safe controllers that take into account noisy sensors and associated uncertainty in learned classifier/regressor predictions. Currently, solution for PrSTL requires solving Mixed Integer Semi-Definite Programs (MISDPs), which quickly become infeasible to solve in reasonable time as the number of constraints grow. Further, PrSTL needs the description of the mission goal and the required safety invariants as logical formulations and often expressing such objectives and constraints for long horizons and complicated rules-of-the-road remain non-trivial at best. We alleviate these problems by combining PrSTL with random sampling based planners. We propose using Rapidly-exploring Random Trees (RRT) and associated variants like RRT* to simplify computation by first efficiently sampling feasible points in the robot’s configuration space and then generating trajectories by connecting them via safe control. Such fast sampling of the feasible trajectories effectively reduces the optimization from a MISDP to a sequence of Second Order Cone Programs (SOCP), which being convex, can be solved much more efficiently.
Cyclical and Structural Deficits on the Road to Accession: Fiscal Rules for an Enlarged European Union

The Paper is one of the first attempts to compute cyclical and structural deficits for a set of countries candidate to accession to the EU. Three main results are derived: first, the high deficits observed in candidate countries in recent years have a structural nature. Second, the fiscal stance has been pro-cyclical in candidate countries. Finally, because of higher volatility of output in these countries, the risks of surpassing the 3% budget limit that applies to all members of the EU are much higher for them. Without changing existing rules in the European Union, it is likely that candidate countries will have to undertake costly and inefficient adjustments of the last minute. Recent proposals by the European Commission would improve matters for candidate countries, without however solving some of their major potential difficulties. The Paper suggests a new fiscal rule for an enlarged European Union, which is consistent with the philosophy of the Stability and growth Pact but focuses on ex ante limits to expenditure. Such a rule would imply that in periods of growth above trend, countries are forced to run budget surplus, while in periods of economic slack they will run deficits. The rule is fully consistent with the theory of optimal tax smoothing and would, at the same time, strengthen fiscal discipline.
Constructing university timetable using constraint satisfaction programming approach

The timetabling problem consists of a set of subjects to be scheduled in different timeslots, a set of rooms in which the subjects can take place, a set of students who attend the subjects, and a set of subjects satisfied by rooms and required by timeslots. The heart of the problem is the constraints that exist as regulations within each resource and between resources. There are various solution approaches to solve the timetabling problem. This paper focuses on developing a constraint satisfaction problem model for a university timetabling problem. A solution of a constraint satisfaction problem is a consistent assignment of all variables to values in such a way that all constraints are satisfied. A sample case study problem is investigated and a constraint satisfaction programming approach is implemented using ILOG scheduler and ILOG solver. We use various goals in ILOG to investigate the performance of the CSP approach
Utilization Behavior: What Is Known and What Has to Be Known?

Since the first description by Lhermitte (1983), the utilization behavior (UB) still represents an enigma for behavioral neurology and neuropsychology. Recent findings shed some light on new frameworks for interpreting this interesting phenomenon. Functional neuroanatomical basis is still unclear, although recent advances in neuroimaging techniques have contributed to a better understanding of the syndrome. An important and promising step is given by shifting researcher's attention from frontoparietal to intrafrontal mechanisms. From a cognitive standpoint, three models have been proposed. However, a comprehensive account for the UB neurobehavioral complexity is still lacking. Aims of this paper are to briefly review the reported cases of utilization behavior (UB) and to describe the putative neurological mechanisms underlying UB. Furthermore, the cognitive models proposed to interpret UB will be summarized. For clinical purposes, features suitable for distinguishing UB from other neurobehavioral symptoms will be briefly described.
Analysis of Structural Strand Asymmetry in Non-coding RNAs

Many RNA functions are determined by their specific secondary and tertiary structures. These structures are folded by the canonical G::C and A::U base pairings as well as by the non-canonical G::U complementary bases. G::U base pairings in RNA secondary structures may induce structural asymmetries between the transcribed and non-transcribed strands in their corresponding DNA sequences. This is likely so because the corresponding C::A nucleotides of the complementary strand do not pair. As a consequence, the secondary structures that form from a genomic sequence depend on the strand transcribed. We explore this idea to investigate the size and significance of both global and local secondary structure formation differentials in several non-coding RNA families and mRNAs. We show that both thermodynamic stability of global RNA structures in the transcribed strand and RNA structure strand asymmetry are statistically stronger than that in randomized versions preserving the same di-nucleotide base composition and length, and is especially pronounced in microRNA precursors. We further show that a measure of local structural strand asymmetry within a fixed window size, as could be used in detecting and characterizing transcribed regions in a full genome scan, can be used to predict the transcribed strand across ncRNA families.
The rate of secular evolution in elliptical galaxies with central masses

We study a series of N−body simulations representing elliptical galaxies with ce ntral masses. Starting from two different systems with smooth centres, which have initially a triaxial configuration and are in equilibrium, we insert to them central masses of various values. Immediately after such an insertion a system presents a high fraction of p articles moving in chaotic orbits, a fact causing a secular evolution towards a new equilibrium state. The chaotic orbits responsible for the secular evolution are identified. Their typica l Lypaunov exponents are found to scale with the central mass as a power law L ∝ m s with s close to 1/2. The requirements for an effective secular evolution within a Hubble time are examined. These requirements are quantified by introducing a quantity called effective chaotic momentumL . This quantity is found to correlate well with the rate of the systems’ secular evolution. In particular, we find that when L falls below a threshold value (0.004 in our N−body units) a system does no longer exhibit significant secular evolution.
Complete theory for E47 and 94LVI algorithms solving inequality-and-bound constrained quadratic program efficiently

This paper presents the relatively complete theory for the two new numerical algorithms, i.e., E47 algorithm and 94LVI algorithm, for solving the quadratic program (QP) subject to inequality and bound constraints efficiently. Specifically, via the important “Bridge” theorems and with their proofs provided, the QP problem is converted equivalently into a piecewise-linear projection equation (PLPE). The E47 and 94LVI algorithms are thus developed to solve the PLPE and also the QP. Note that, compared with the previous achievements with only the sufficiency proved, strict proofs (i.e., both the sufficiency proofs and the necessity proofs) of the “Bridge” theorems are firstly presented. Besides, the convergence property of the two algorithms is analyzed in the paper, and theoretical proofs ensure the global linear convergence of the two algorithms about the final decision variable vector x, instead of the primal-dual decision variable vector y.
Novel Susceptibility Locus at 22q11 for Diabetic Nephropathy in Type 1 Diabetes

Background Diabetic nephropathy (DN) affects about 30% of patients with type 1 diabetes (T1D) and contributes to serious morbidity and mortality. So far only the 3q21–q25 region has repeatedly been indicated as a susceptibility region for DN. The aim of this study was to search for new DN susceptibility loci in Finnish, Danish and French T1D families. Methods and Results We performed a genome-wide linkage study using 384 microsatellite markers. A total of 175 T1D families were studied, of which 94 originated from Finland, 46 from Denmark and 35 from France. The whole sample set consisted of 556 individuals including 42 sib-pairs concordant and 84 sib-pairs discordant for DN. Two-point and multi-point non-parametric linkage analyses were performed using the Analyze package and the MERLIN software. A novel DN locus on 22q11 was identified in the joint analysis of the Finnish, Danish and French families by genome-wide multipoint non-parametric linkage analysis using the Kong and Cox linear model (NPLpairs LOD score 3.58). Nominal or suggestive evidence of linkage to this locus was also detected when the three populations were analyzed separately. Suggestive evidence of linkage was found to six additional loci in the Finnish and French sample sets. Conclusions This study identified a novel DN locus at chromosome 22q11 with significant evidence of linkage to DN. Our results suggest that this locus may be of importance in European populations. In addition, this study supports previously indicated DN loci on 3q21–q25 and 19q13.
An RFID-enabled inkjet-printed soil moisture sensor on paper for “smart” agricultural applications

In this paper, an RFID-enabled inkjet-printed passive soil moisture sensor on paper for agricultural applications is presented and the design procedure, the operation principle and benchmarking experimental results are discussed in detail. A prototype made of a passive RFID tag integrated with a capacitive sensor, which consists of a printed interdigitated capacitor (IDC), has been printed on a low-cost paper substrate. The capacitance variation of the IDC shifts the resonant frequency of the RFID tag taking advantage of the soil-moisture based load matching variations since a matching condition changes due to the capacitive sensor. The proposed sensor can be easily integrated with conventional RFID systems for practical large-scale agricultural applications.
Latent OLAP: data cubes over latent variables

We introduce a novel class of data cube, called latent-variable cube. For many data analysis tasks, data in a database can be represented as points in a multi-dimensional space. Ordinary data cubes compute aggregate functions over these "observed" data points for each cell (i.e., region) in the space, where the cells have different granularities defined by hierarchies. While useful, data cubes do not provide sufficient capability for analyzing "latent variables" that are often of interest but not directly observed in data. For example, when analyzing users' interaction with online advertisements, observed data informs whether a user clicked an ad or not. However, the real interest is often in knowing the click probabilities of ads for different user populations. In this example, click probabilities are latent variables that are not observed but have to be estimated from data. We argue that latent variables are a useful construct for a number of OLAP application scenarios. To facilitate such analyses, we propose cubes that compute aggregate functions over latent variables. Specifically, we discuss the pitfalls of common practice in scenarios where latent variables should, but are not considered; we rigorously define latent-variable cube based on Bayesian hierarchical models and provide efficient algorithms. Through extensive experiments on both simulated and real data, we show that our method is accurate and runs orders of magnitude faster than the baseline.
Alzheimer's disease detection using a Self-adaptive Resource Allocation Network classifier

This paper presents a new approach using Voxel-Based Morphometry (VBM) detected features with a Self-adaptive Resource Allocation Network (SRAN) classifier for the detection of Alzheimer's Disease (AD) from Magnetic Resonance Imaging (MRI) scans. For feature reduction, Principal Component Analysis (PCA) has been performed on the morphometric features obtained from the VBM analysis and these reduced features are then used as input to the SRAN classifier. In our study, the MRI volumes of 30 ‘mild AD to moderate AD’ patients and 30 normal persons from the well-known Open Access Series of Imaging Studies (OASIS) data set have been used. The results indicate that the SRAN classifier produces a mean testing efficiency of 91.18% with only 20 PCA reduced features whereas, the Support Vector Machine (SVM) produces a mean testing efficiency of 90.57% using 45 PCA reduced features. Also, the results show that the SRAN classifier avoids over-training by minimizing the number of samples used for training and provides a better generalization performance compared to the SVM classifier. The study clearly indicates that our proposed approach of PCA-SRAN classifier performs accurate classification of AD subjects using reduced morphometric features.
Fusion of local and global features for face recognition

In recent years, there have been lots of research developments done in face recognition systems. Face recognition systems are widely used for access control, border control, surveillance and in law enforcement. Among other biometrics, it is the most natural and acceptable way of identifying an individual. Face recognition system does not require physical interaction with the user. Research is still being done intensively to produce systems that can cater for several challenges such as changes in pose, illumination, occlusion and low resolution images. Algorithms reported in literature use either global feature extraction or local feature extraction. In this work, a different technique is proposed that combines both global and local approaches for face recognition. The Principal Components Analysis (PCA) and Local Binary Patterns (LBP) have been employed. Face recognition yields a recognition rate of 90 % with PCA and 92 % with LBP. However, results show an improvement in recognition rate to 95 % when both approaches are fused.
Towards Enhanced Presence Filtering

We live in a world of multiple communication channels which, on the face of it, should make persons more easily contactable. However, it can in fact be a time consuming process to establish contact due to these many avenues of communication. It is also true that many communication channels can equate to an increase in unwanted and disruptive communication attempts. The use of presence information is viewed as a possible solution to both the foregoing problems. This is achievable by persons making their presence information available for subscription by others, thus improving the communication experience of all concerned. However, there are many attributes denoting presence which are of a sensitive nature, and the release of such information needs to be tightly controlled to ensure it is released only to authorized persons. Current standards provide for the use of presence authorization rules to filter presence information and in so doing, provide the required access control. However the authors believe that shortcomings exist in this current approach. The intent of this paper is to investigate an alternative method of addressing availability through the use of, but not disclosure of sensitive information. Such a method could improve communication efficacy for both caller and callee, while reducing the release of privacy sensitive information.