Miracle-dz/UniFAR-FTUs huggingface dataset:
Energy Efficient Clustering Algorithms in Wireless Sensor Networks-An Analytical

The most imperative ambiguity in Wireless Sensor Networks (WSNs) is energy efficiency. Various clustering algorithms have been designed on behalf of such an intention. This paper primarily focuses on the analytical view of energy efficient clustering algorithms for Wireless Sensor Networks (WSNs). A concise explanation regarding the procedure of algorithms also reviewed. Battery power is a significant factor in the algorithm to boost the life time of network. WSNs utilize restricted battery power and there are negative rechargeable batteries. Numerous researchers encompass in recent years on network characteristics like robust, energy efficiency, low energy utilization, improved network life time and low packet loss. This paper provides the foundation for researchers in clustering algorithms for wireless sensor networks in a great way.
Extracting Content from Web Pages Using the Sliding Window

Content extraction is an important technology for accessing and processing web information. In this paper, we propose a content extraction algorithm based on the sliding window. A statistical heuristic is used in the algorithm. Experiments show that our algorithm is capable of extracting most of the main content from web pages. With the simple and effective heuristic, the sliding window based algorithm shows a wide scope of application for most kinds of web pages.
Nested multigrid methods for the fast numerical computation of 3D magnetic fields

This paper deals with the numerical solution of static as well as transient 3D magnetic field problems. A finite element method (FEM) with the magnetic vector potential as field variable and a discretization with edge elements is used. For the efficient solution of the obtained matrix equation system a nested geometrical multigrid solver (MG) is presented, which reduces the solution time considerably. Numerical simulations demonstrate the superiority of the proposed method versus conventional solving strategies.
Call Graph Based Metrics to Evaluate Software Design Quality

Software defects prediction was introduced to support development and maintenance activities such as improving the software quality through finding errors or patterns of errors early in the software development process. Software defects prediction is playing the role of maintenance facilitation in terms of effort, time and more importantly the cost prediction for software maintenance and evolution activities. In this research, software call graph model is used to evaluate its ability to predict quality related attributes in developed software products. As a case study, the call graph model is generated for several applications in order to represent and reflect the degree of their complexity, especially in terms of understandability, testability and maintenance efforts. This call graph model is then used to collect some software product attributes, and formulate several call graph based metrics. The extracted metrics are investigated in relation or correlation with bugs collected from customers-bug reports for the evaluated applications. Those software related bugs are compiled into dataset files to be used as an input to a data miner for classification, prediction and association analysis. Finally, the results of the analysis are evaluated in terms of finding the correlation between call graph based metrics and software products’ bugs. In this research, we assert that call graph based metrics are appropriate to be used to detect and predict software defects so the activities of maintenance and testing stages after the delivery become easier to estimate or assess.
The reuse of uses in Smalltalk programming

Software reuse, a long-standing and refractory issue in software technology, has been specifically emphasized as an advantage of the object-oriented programming paradigm. We report an empirical study of expert Smalltalk programmers reusing user interface classes in small graphical applications. Our primary goal was to develop a qualitative characterization of expert reuse strategies that could be used to identify requirements for teaching and supporting reuse programming. A secondary interest was to demonstrate to these experts the Reuse View Matcher—a prototype reuse tool—and to collect some initial observations of this tool in use during reuse programming. We observed extensive “reuse of uses” in the programmers' work: they relied heavily on code in expample applications that provided an implicit specification for reuse of the target class. We called this implicit specification a “usage context. ” The programmers searched for relevant usage contexts early. They repeatedly evaluated the contextualized information to develop solution plans, and they borrowed and adapted it when the sample context suited their immediate reuse goals. The process of code development was highly dynamic and incremental; analysis and implementation were tightly interleaved, frequently driven by testing and debugging. These results are considered in terms of the tradeoffs that inhere in the reuse of uses and the teaching and tool support that might improve the efficiency and accuracy of this approach to reuse.
Exchangeability of power flow simulators in smart grid co-simulations with mosaik

Power flow simulators are indispensible when simulating and assessing future energy system scenarios potentially comprising vast numbers of actors, devices, markets, environmental phenomena etc. While open source power flow simulators are an appealing choice - as they come free of charge - commercially available power flow simulation and optimization suites have the clear benefit of being well established and trusted by the industry. Open source implementations often lack validation against these “trusted” outputs. In this paper we will demonstrate and discuss the integration and exchange of different (commercial as well as open source) power flow simulators with the co-simulation framework mosaik for the sake of comparing and possibly benchmarking the output of open source simulators.
Variational approach to tomographic reconstruction

We formulate the tomographic reconstruction problem in a variational setting. The object to be reconstructed is considered as a continuous density function, unlike in the pixel-based approaches. The measurements are modeled as linear operators (Radon transform), integrating the density function along the ray path. The criterion that we minimize consists of a data term and a regularization term. The data term represents the inconsistency between applying the measurement model to the density function and the real measurements. The regularization term corresponds to the smoothness of the density function. We show that this leads to a solution lying in a finite dimensional vector space which can be expressed as a linear combination of generating functions. The coefficients of this linear combination are determined from a linear equation set, solvable either directly, or by using an iterative approach. Our experiments show that our new variational method gives results comparable to the classical filtered back-projection for high number of measurements (projection angles and sensor resolution). The new method performs better for medium number of measurements. Furthermore, the variational approach gives usable results even with very few measurements when the filtered back-projection fails. Our method reproduces amplitudes more faithfully and can cope with high noise levels; it can be adapted to various characteristics of the acquisition device.
THE TOPOLOGY OF T-DUALITY FOR Tn-BUNDLES

In string theory, the concept of T-duality between two principal Tn-bundles E and E over the same base space B, together with cohomology classes h ∈ H3(E,ℤ) and ĥ ∈ H3(E,ℤ), has been introduced. One of the main virtues of T-duality is that h-twisted K-theory of E is isomorphic to ĥ-twisted K-theory of E. In this paper, a new, very topological concept of T-duality is introduced. We construct a classifying space for pairs as above with additional "dualizing data", with a forgetful map to the classifying space for pairs (also constructed in the paper). On the first classifying space, we have an involution which corresponds to passage to the dual pair, i.e. to each pair with dualizing data exists a well defined dual pair (with dualizing data). We show that a pair (E, h) can be lifted to a pair with dualizing data if and only if h belongs to the second step of the Leray–Serre filtration of E (i.e. not always), and that in general many different lifts exist, with topologically different dual bundles. We establish several properties of the T-dual pairs. In particular, we prove a T-duality isomorphism of degree -n for twisted K-theory.
Association Study Indicates a Protective Role of Phosphatidylinositol-4-Phosphate-5-Kinase against Tardive Dyskinesia

Background: Tardive dyskinesia is a disorder characterized by involuntary muscle movements that occur as a complication of long-term treatment with antipsychotic drugs. It has been suggested to be related to a malfunctioning of the indirect pathway of the motor part of the cortical-striatal-thalamic-cortical circuit, which may be caused by oxidative stress-induced neurotoxicity. Methods: The purpose of our study was to investigate the possible association between phosphatidylinositol-4-phosphate-5-kinase type IIa (PIP5K2A) function and tardive dyskinesia in 491 Caucasian patients with schizophrenia from 3 different psychiatric institutes in West Siberia. The Abnormal Involuntary Movement Scale was used to assess tardive dyskinesia. Individuals were genotyped for 3 single nucleotide polymorphisms in PIP5K2A gene: rs10828317, rs746203, and rs8341. Results: A significant association was established between the functional mutation N251S-polymorphism of the PIP5K2A gene (rs10828317) and tardive dyskinesia, while the other 2 examined nonfunctional single nucleotide polymorphisms were not related. Conclusions: We conclude from this association that PIP5K2A is possibly involved in a mechanism protecting against tardive dyskinesia-inducing neurotoxicity. This corresponds to our hypothesis that tardive dyskinesia is related to neurotoxicity at striatal indirect pathway medium-sized spiny neurons.
Probe the R-parity Violating Supersymmetry Effects in the $B^0_s-\bar{B}^0_s$ Mixing

The recent measurements of the $B_s$ mass difference $\Delta M_s$ by the CDF and D{\O} collaborations are roughly consistent with the Standard Model predictions, therefore, these measurements will afford an opportunity to constrain new physics scenarios beyond the Standard Model. We consider the impact of the R-parity violating supersymmetry in the $B^0_s-\bar{B}^0_s$ mixing, and use the latest experimental results of $\Delta M_s$ to constrain the size of the R-parity violating tree level couplings in the $B^0_s-\bar{B}^0_s$ mixing. Then, using the constrained R-parity violating parameter space from $\Delta M_s$, we show the R-parity violating effects on the $B_s$ width difference $\Delta\Gamma_{s}$.
Automatic Brain MRI Classification Using Modified Ant Colony System and Neural Network Classifier

In this paper, a hybrid intelligent machine learning technique for automatic classification of brain magnetic resonance images is presented. The proposed multistage technique involves the following computational methods, Otsu's method for skull removal, Fuzzy Inference System for image enhancement, Modified Fuzzy C Means with the Optimized Ant Colony System for image segmentation, Second Order Statistical Analysis and Wavelet Transform Method for feature extraction and the Feed Forward back-propagation neural network to classify inputs into normal or abnormal. The experiments were carried out on 200 images consisting of 100 normal and 100 abnormal (malignant and benign tumors) from a real human brain MRI data set. Experimental results indicate that the proposed algorithm achieves high classification rate and outperforms recently introduced methods while it needs a least number of features for classification.
Swift: Using Distributed Disk Striping to Provide High I/O Data Rates

We present an I/O architecture, called Swift, that addresses the problem of data rate mismatches between the requirements of an application, storage devices, and the interconnection medium. The goal of Swift is to support high data rates in general purpose distributed systems. Swift uses a high-speed interconnection medium to provide high data rate transfers by using multiple slower storage devices in parallel. It scales well when using multiple storage devices and interconnections, and can use any appropriate storage technology, including high-performance devices such as disk arrays. To address the problem of partial failures, Swift stores data redundantly. Using the UNIX operating system, we have constructed a simplified prototype of the Swift architecture. The prototype provides data rates that are significantly faster than access to the local SCSI disk, limited by the capacity of a single Ethernet segment, or in the case of multiple Ethernet segments by the ability of the client to drive them. We have constructed a simulation model to demonstrate how the Swift architecture can exploit advances in processor, communication and storage technology. We consider the effects of processor speed, interconnection capacity, and multiple storage agents on the utilization of the components and the data rate of the system. We show that the data rates scale well in the number of storage devices, and that by replacing the most highly stressed components by more powerful ones the data rates of the entire system increase significantly.
Nondegenerate four-wave mixing in quantum dot distributed feedback lasers

We present wavelength conversion using nondegenerate four-wave mixing in loss-coupled distributed feedback lasers based on InAs quantum dots (QDs) grown on a GaAs substrate. The conversion efficiency is measured to be -15 to -30 dB for a signal-pump detuning range from 0.33 to 8 nm. The third-order optical susceptibility (/spl chi//sup (3)/) normalized to the optical linear gain in the QDs is estimated to be 3/spl times/10/sup -20/ m/sup 3//V/sup 2/ to 9/spl times/10/sup -20/ m/sup 3//V/sup 2/ for the signal-pump detuning range.
Meaning Acquisition by Semiotic AgentsSemiotic Cognitive Information Processing in a language environment

Anything we know or believe about the world can (more or less precisely) be communicated verbally. We do so by using words, forming sentences and producing texts whose meanings are understood to stand for, represent, or deal with the topics and subjects, the domains and structures in the real world they are meant to refer to. Natural language texts (still) are the most exible and as that a highly eecient form to represent knowledge for and convey learning to others. Traditional approaches to the study of language understanding in CL and AI employ rule based formats of linguistic knowledge and symbol representations of world knowledge structures to model language processing by machine. Providing these initial knowledge bases and allowing them to be modiied by system designers (external change), or dynamically as a function of processing (internal learning) proved to be everything from enormously laborious to error prone, from extremely diicult to virtually impossible. Computational Semiotics (CS) neither depends on rule-based or symbolic formats for (linguistic) knowledge representations, nor does it subscribe to the notion of (world) knowledge as some static structures that may be abstracted from and represented independently of the way they are processed. Consequently, knowledge structures and the processes operating on them are to be modelled procedurally and have to be implemented as algorithms which determine SCIP systems. As a collection of cognitive information processing devices these systems' semiotic character consists in their multi-level representational performance of (working) structures emerging from and being modiied by such processing. The emergence of semantic structure as a self-organizing process ist studied on the basis of word usage regularities in natural language discourse, whose linearly agglomerative (or syntagmatic) and whose selectively interchangeable (or paradigmatic) constraints are exploited by text analysing algorithms. They accept natural language discourse as input and end up to produce a vector space structure as output. This may be interpreted as an (internal) representation of the semiotic system's states of adaptation to the (external) structures of its environment as mediated by the natural language discourse processed. In order to evaluate the internal picture which the system computes from the natural language texts according to its processing capabilities against the external reality whose structure and properties are described by natural language discourse only, a corpus of texts { composed of correct and true sentences with well-deened referential meanings { was generated according to a (very simple) phrase structure grammar and …
Robust airfoil optimization to achieve consistent drag reduction over a mach range

We prove mathematically that in order to avoid point-optimization at the sampled design points for multipoint airfoil optimization, the number of design points must be greater than the number of free-design variables. To overcome point-optimization at the sampled design points, a robust airfoil optimization method (called the profile optimization method) is developed and analyzed. This optimization method aims at a consistent drag reduction over a given Mach range and has three advantages: (a) it prevents severe degradation in the off-design performance by using a smart descent direction in each optimization iteration, (b) there is no random airfoil shape distortion for any iterate it generates, and (c) it allows a designer to make a trade-off between a truly optimized airfoil and the amount of computing time consumed. For illustration purposes, we use the profile optimization method to solve a lift-constrained drag minimization problem for 2-D airfoil in Euler flow with 20 free-design variables. A comparison with other airfoil optimization methods is also included.
Weibel-Palade bodies recruit Rab27 by a content-driven, maturation-dependent mechanism that is independent of cell type

The identification of organelles is crucial for efficient cellular function, yet the basic underlying mechanisms by which this might occur have not been established. One group of proteins likely to be central to organelle identity is the Rab family of small GTPases. We have thus investigated Rab recruitment to membranes using endothelial cells as a model system. We report that Weibel-Palade bodies, the Von Willebrand Factor storage compartment of human umbilical vein endothelial cells, contain Rab27a. We have also found that Weibel-Palade body-like structures induced in HEK-293 cells by the expression of von Willebrand factor can recruit endogenous Rab27a. In the absence of von Willebrand Factor, Rab27a is not lysosome associated, indicating that it can distinguish between the Weibel-Palade-body-like organelle and a classical lysosome. Finally, a time course of Weibel-Palade-body formation was established using a green-fluorescent version of von Willebrand factor. Newly formed Weibel-Palade bodies lack Rab27a, which is acquired some hours after initial appearance of the cigar-shaped organelle. We conclude that a lumenal cargo protein drives the recruitment of Rab27a to the organelle membrane by a novel mechanism that is indirect, maturation-dependent and cell-type independent.
Learning to manage combined energy supply systems

The operability of a portable embedded system is severely constrained by its supply's duration. We propose a novel energy management strategy for a combined (hybrid) supply consisting of a battery and a set of supercapacitors to extend the system's lifetime. Batteries are not sufficient for handling high load fluctuations and demands in modern complex systems. Supercapacitors hold promise for complementing battery supplies because they possess higher power density, a larger number of charge/recharge cycles, and less sensitivity to operational conditions. However, supercapacitors are not efficient as a standalone supply because of their comparatively higher leakage and lower energy density. Due to the nonlinearity of the hybrid supply elements, multiplicity of the possible supply states, and the stochastic nature of the workloads, deriving an optimal management policy is a challenge. We pose this problem as a stochastic Markov Decision Process (MDP) and develop a reinforcement learning method, called Q-learning, to derive an efficient approximation for the optimal management strategy. This method studies a diverse set of workload profiles for a mobile platform and learns the best policy in form of an adaptive approximation approach. Evaluations on measurements collected from mobile phone users show the effectiveness of our proposed method in maximizing the combined energy system's lifetime.
K-target coverage & connectivity in wireless sensor network considering the angle coverage

One of the challenges of the wireless sensor networks is problem of having coverage while keeping connectivity. In this paper we have proposed a method in which we can cover a set of discrete targets in a field considering the k-angle coverage. In k-angled coverage, it is guaranteed that any target in the area should at least be covered by k sensor in a way that the peripheral area of such targets is covered by the sensors. Using this method, in each round, a set of sensors is selected in a way that they can cover all targets while keeping the connectivity. Then each sensor of this group reduces its communication range as less as the network in not disconnected. This approach may also use the sensors that are not capable of separating data of their targets. The results show that in comparison with similar methods, the proposed method consumes less energy and it can also increase the accuracy of the submitted data. The proposed method is a distributed algorithm which is executed in the nodes locally and is able to be implemented in the wide area networks having plenty of nodes.
On the degrees of freedom of MIMO X channel with delayed CSIT

The multiple-input multiple-output (MIMO) Gaussian X channel in i.i. d. fading environment and with delayed channel state information at transmitters (delayed CSIT) is considered. It is assumed that each transmitter has M antennas and each receiver has N antennas. New achievable results on the sum degrees of freedom (DoF) of this channel are provided and shown to be tight for all possible values of M and N except for 1/2 <; N/M <; 4/3. It is noteworthy that for certain values of M and N, the channel DoF coincides with the DoF of the broadcast channel obtained by assuming perfect transmitter cooperation.