Miracle-dz/UniFAR-FTUs huggingface dataset:
Studies of the Activation Steps Concurrent to Ligand Binding in δOR and κOR Opioid Receptors Based on Molecular Dynamics Simulations

Although agonists and antagonists occupy the same space in opioid receptors the agonists only are able to evoke their activation and different agonists may lead to different conformational states of the receptor. Agonist binding is the first step in ligand-induced receptor activation which proceeds through series of conformational changes. To investigate the relationship between the final movements of a ligand in a receptor binding site and the first steps of the activation process in ￻ OR and ￻ OR opioid receptors we chose a set of rigid ligands with the structural motif of tyramine. On the basis of conducted molecular dynamics simulations we propose that, similarly as in the case of μOR (Kolinski and Filipek, TOSBJ 2008), agonists and antagonists bind to Y3.33 but only agonists are able to move deeper into the receptor binding site and to reach H6.52. The movement from Y3.33 to H6.52 induces breaking of the TM3-TM7 connection ("3-7 lock"). However, conversely to morphine (previous paper) butorphanol did not induce breaking of this connection in a single movement but instead a time of about 20 ns was required and this process was composed of a series of separation and joining events. We also observed a concerted motion of W6.48 and H6.52 suggesting existence of an extended "rotamer toggle switch". Simultaneous action of both switches, the "3-7 lock" and the "rotamer toggle switch", implies a temporal but also spatial (an agonist linking H6.52 and D3.32) dependence between them.
Performance Analysis Of Scalable Sql And Nosql Databases : A Quantitative Approach

PERFORMANCE ANALYSIS OF SCALABLE SQL AND NOSQL DATABASES: A QUANTITATIVE APPROACH by HARISH BALASUBRAMANIAN May 2014 Advisor: Dr.Weisong Shi Major: Computer Science Degree: Master of Science Benchmarking is a common method in evaluating and choosing a NoSQL database. There are already lots of benchmarking reports available in internet and research papers. Most of the benchmark reports measure the database performance only by overall throughput and latency. This is an adequate performance analysis but need not to be the end. We define some new perspectives which also need to be considered during NoSQL performance analysis. We have demonstrated this approach by benchmarking HBase, MongoDB and sharded MySQL using YCSB. Based on the results we observe that NoSQL databases do not consider the capability of the data nodes while assigning data to it. And these databases’ performance is seriously affected by the bottleneck nodes and the databases are not attempting to resolve this bottleneck situation automatically.
Demonstration of adaptive overcurrent protection using IEC 61850 communications

This paper contains a description of an adaptive protection scheme that has been implemented and demonstrated in a hardware in the loop simulation environment using commercially available protection hardware and IEC 61850 communications. The implementation is based on an actual 11kV system which includes distributed generation and network automation. IEC 61850 communications offers several benefits for the implementation of adaptive protection, but also presents some limitations which are discussed in the paper. An alternative approach to overcome a number of the limitations is also presented.
Robust AGC based on Fuzzy Logic Controller: Design, Comparison and Ratification

Increasing the oscillations in the affected eclectic power system caused by the growth of loading conditions and occurrence of various distributions leads to losing the synchronism in the first-swing. A maiden effort is led to attain a novel Fuzzy Logic Automatic Generation Controller (FLAGC) to provide appropriate damping low frequency power oscillations. In order to guarantee the effectiveness and robustness of the proposed FLAGC controller, it has been thoroughly compared with Proportional Integral Derivative (PID) controller considering 2% step load perturbation in thermal area of the studied power system. The Real Coded Genetic Algorithm (RCGA) optimization technique due to have high sufficiency to solve the very non-linear objective is implemented for solution of the optimization problem. The obtained results of non-linear time-domain simulation reveal the high dynamical performance of proposed FLAGC controller.
A Neo-Classical Explanation of Nominal Exchange Rate Volatility

The flexible-price two-country monetary model is extended to include a consumption externality with habit persistence. The model is simulated using the artificial economy methodology. It successfully explains the high volatility of nominal exchange rates without recourse to sticky prices and overshooting. Nominal exchange rates are volatile because of real exchange rate variability. This, in turn, arises because the marginal rate of substitution between home and foreign goods is volatile because of the habit persistence externality.
A new approach for visualizing quantified self data using avatars

In recent years, it is becoming more common for people to use applications or devices that keep track of their life and activities, such as physical fitness, places they visited, the music they listen to, or pictures they took. This generates data that are used by the service providers for a variety of (usually analytics) purposes, but commonly there are limitations on how the users themselves can also explore or interact with these data. Our position paper describes a new approach of visualizing such Quantified Self data, in a meaningful and enjoyable way that can give the users personal insights into their own data. The visualization of the information is proposed as an avatar that maps the different activities the user is engaged with, along with each such activity level, as graphical features. An initial prototype (both in terms of graphical design and software architecture) as well as possible future extensions are discussed.
A Study on the Mediation Effects of Intention to Enroll in MOOCs on its Actual Usage

The paradigm shift in the usage of Massive Open Online Course in Malaysia has reached a vital landmark with the recent launch of credit transfer guidelines for MOOCs by the Malaysian Qualification Agency (MQA) on 27th September 2016. Although the use of MOOCs in Malaysian Universities are evident from the Malaysian OpenLeanring MOOCs portal, still the millions of learners who have already participated in MOOCS, do not move to the stage of obtaining a completion certificate (Kolowick, 2013). As such, in the context of Malaysia, there seems to be a serious concern for all those who wish to implement MOOCs in their pedagogical system. This research employed the bootstrapping resampling approach of Shrout and Bolger (2002) to assess the mediating effects of Intention to Enroll in MOOCs (IE). The mediation analysis was used to determine the mediation effects of Intention to Enroll in MOOCs (IE) on relationships between Facilitating Conditions (FC) and Habit (HB) as independent variables and MOOCs Actual Usage (AU) as dependent variable. From the results of mediation analysis it was found that Intention to Enroll in MOOCs (IE) partially mediates the relationship between Habit (HB) and MOOCs Actual Usage (AU).
A Theory of Order Relations in Perceptual Matching

A theory of order relations in the perceptual matching task relates order manipulations to research on retrieval processes and the representation of order information in memory. In experimental tests of the theory, presentation of a study string of letters to the subject was followed by a test string to which the subject responded same or different. The data of main interest concern the case where the test string is a permutation of the study string. When adjacent letters are switched, reaction time is long and accuracy low, suggesting that, in the comparison process, a test letter is not simply compared to the letter in the same position in the study string; rather, the comparison is distributed across positions. The memory model assumes that the representation of a letter is distributed (spread) over position and that the comparison process assesses the amount of overlap between the test string and the memory representation. The amount of overlap is transformed by a power function into the drift rate in a diffusion (random walk) comparison process. The diffusion retrieval model and overlap memory model are fitted to the data and goodness-of-fit is assessed. Shortcomings of alternative models are considered and applications of the model to related matching tasks are described.
Influence of Current Mass on the Spatially Inhomogeneous Chiral Condensate

It is known that, in the chiral limit, spatially inhomogeneous chiral condensate occurs in the Nambu-Jona-Lasinio (NJL) model at finite density within a mean-field approximation. We study here how an introduction of current quark mass affects the ground state with the spatially inhomogeneous chiral condensate. Numerical calculations show that, even if the current quark mass is introduced, the spatially inhomogeneous chiral condensate can take place. In order to obtain the ground state, the thermodynamic potential is calculated with a mean-field approximation. The influence of finite current mass on the thermodynamic potential consists of following two parts. One is a part coming from the field energy of the condensate, which favors inhomogeneous chiral condensate. The other is a part coming from the Dirac sea and the Fermi sea, which favors homogeneous chiral condensate. We also find that when the spatially inhomogeneous chiral condensate occurs, the baryon number density becomes spatially inhomogeneous.
Completeness of algebraic CPS simulations

The algebraic lambda calculus and the linear algebraic lambda calculus are two extensions of the classical lambda calculus with linear combinations of terms. They arise independently in distinct contexts: the former is a fragment of the differential lambda calculus, the latter is a candidate lambda calculus for quantum computation. They differ in the handling of application arguments and algebraic rules. The two languages can simulate each other using an algebraic extension of the well-known call-by-value and call-by-name CPS translations. These simulations are sound, in that they preserve reductions. In this paper, we prove that the simulations are actually complete, strengthening the connection between the two languages.
Sentence level opinion mining of hotel comments

With the rapid growth of Internet consumption, the various product comments' form and redundant information are not convenient for the customers to grasp the hot opinions of the historical comments. In view of this, this paper studies the hot opinions of the products' comments and takes the hotel comments data as the main research objects. We filter the comment data from the length of the comments and the feature selection aspect by analyzing the characteristics of hotel comment data. We construct the mathematical model for the processed data and then adopt the affinity propagation clustering algorithm to extract the final hot opinions. Compared with the original comments, the experiment results of the hot opinion extraction are more concise and clear expressed.
Flood water level modeling and prediction using NARX neural network: Case study at Kelang river

Flood disaster has becomes major threat around the world because it causes loss of lives and damages to property. Thus, reliable flood prediction is very much needed in order to reduce the effects of flood disaster. Hence, an accurate flood water level prediction is an important task to achieve. Since flood water level fluctuation is highly nonlinear, it is very difficult to predict the flood water level. Artificial Neural Network is well known technique is solving nonlinear cases and Nonlinear Auto Regressive with Exogenous Input (NARX) model is one class of Artificial Neural Network model. Thus, this paper proposes flood water level modeling and prediction using Nonlinear Auto Regressive with Exogenous Input (NARX) model to overcome the nonlinearity problem and come out with an advanced neural network model for the prediction of flood water level 10 hours in advance. The input and output parameters used in this model are based on real-time data obtained from Department of Irrigation and Drainage Malaysia. Results showed that NARX model successfully predicted the flood water level 10 hours ahead of time.
Study of text classification methods for data sets with huge features

Text classification has gained booming interest over the past few years. In this paper we look at the main approaches that have been taken towards text classification. The key text classification techniques including text model, feature selection methods and text classification algorithms are discussed . This work focus on the implementation of a text classification system based on Mutual Information and K-Nearest Neighbor algorithm and Support Vector Machine. The experimental results on Reuters collection are also presented. It shows that Mutual Information is a kind of efficient dimension reduction method for text data sets with huge features.
Analytical Model for the Eeects of Learning on Spike Count Distributions Spike Counts and the S+f Model an S+f Model Reened by Learning Result and Parameters

The spike count distribution observed when recording from a variety of neurons in many diierent conditions has a fairly stereotypical shape, with a single mode at zero or close to a low average count, and a long quasi-exponential tail to high counts. Such a distribution has been suggested to be the direct result of three simple facts: the ring frequency of a typical cortical neuron is close to linear in the summed input current entering the soma, above a threshold; the input current varies on several time scales, both faster and slower than the window used to count spikes; and the input distribution at any time scale can be taken to be approximately normal. The third assumption is violated , for example, by associative learning, which generates correlations between the synaptic weight vector on the dendritic tree of a neuron, and the input activity vectors it is repeteadly subject to. We show analytically that, for a simple feedforward model, the normal distribution of the slow components of the input current becomes the sum of two quasi-normal terms. The term important below threshold shifts with learning, while the term important above threshold does not shift, but grows in width. These deviations from the standard distribution may be observable in appropriate recording experiments. The variability in the emission of action potentials by nerve cells may be characterized by several measures, one of which is the distribution of spike counts in a time window of xed length. While other measures, such as the distribution of consecutive interspike intervals, are more direct descriptions of the irregularity in the ring, spike count distributions provide estimates of the entropy of neural codes, inasmuch as the code used by a particular neuron is thought to be expressed simply by its ring frequency. Because of this interpretation, the observation that spike count distributions often have quasi-exponential tails (Abeles et al., 1990; Barnes et al., 1990) has been linked to an optimal coding principle (Levy and Baxter, 1996). If the spike count is taken to be the symbol coding for the message represented by the input arriving at the cell at any given time, and the mean count is constrained by a xed metabolic budget, then optimal usage of symbols occurs, in the noiseless case, when their probabilities are exponentially distributed, that is have maximal entropy under the constraint (Shannon, 1948). It is not clear, however, how this coding …
Initial soil moisture retrievals from the METOP‐A Advanced Scatterometer (ASCAT)

This article presents first results of deriving relative surface soil moisture from the METOP‐A Advanced Scatterometer. Retrieval is based on a change detection approach which has originally been developed for the Active Microwave Instrument flown onboard the European satellites ERS‐1 and ERS‐2. Using model parameters derived from eight years of ERS scatterometer data, first global soil moisture maps have been produced from ASCAT data. The ASCAT data were distributed by EUMETSAT for validation purposes during the ASCAT product commissioning activities. Several recent cases of drought and excessive rainfall are clearly visible in the soil moisture data. The results confirm that seamless soil moisture time series can be expected from the series of two ERS and three METOP scatterometers, providing global coverage on decadal time scales (from 1991 to about 2021). Thereby, operational, near‐real‐time ASCAT soil moisture products will become available for weather prediction and hydrometeorological applications.
Identification of microRNAs and mRNAs associated with multidrug resistance of human laryngeal cancer Hep-2 cells

Multidrug resistance (MDR) poses a serious impediment to the success of chemotherapy for laryngeal cancer. To identify microRNAs and mRNAs associated with MDR of human laryngeal cancer Hep-2 cells, we developed a multidrug-resistant human laryngeal cancer subline, designated Hep-2/v, by exposing Hep-2 cells to stepwise increasing concentrations of vincristine (0.02-0.96'µM). Microarray assays were performed to compare the microRNA and mRNA expression profiles of Hep-2 and Hep-2/v cells. Compared to Hep-2 cells, Hep-2/v cells were more resistant to chemotherapy drugs (∼45-fold more resistant to vincristine, 5.1-fold more resistant to cisplatin, and 5.6-fold more resistant to 5-fluorouracil) and had a longer doubling time (42.33±1.76 vs 28.75±1.12'h, P<0.05), higher percentage of cells in G0/G1 phase (80.98±0.52 vs 69.14±0.89, P<0.05), increased efflux of rhodamine 123 (95.97±0.56 vs 12.40±0.44%, P<0.01), and up-regulated MDR1 expression. A total of 7 microRNAs and 605 mRNAs were differentially expressed between the two cell types. Of the differentially expressed mRNAs identified, regulator of G-protein signaling 10, high-temperature requirement protein A1, and nuclear protein 1 were found to be the putative targets of the differentially expressed microRNAs identified. These findings may open a new avenue for clarifying the mechanisms responsible for MDR in laryngeal cancer.
Correlation between the Distribution of Software Bugs and Network Motifs

With the increase of scale and complexity of software systems as well as the long existing threats of software accidents such as Therac-25 radiation exposure and the Toyota Prius braking system failure, improving the reliability and quality has become the major pressure in developing safety-or security-critical software systems. Since more and more large-scale software systems exhibit the properties of complex network, how can we employ the concepts and metrics of complex networks to the area of software engineering? What structural patterns can be used as indications for software quality and reliability? Though applying the concept of complex network to the field of software engineering has attracted attentions from both the academia and industry, these questions have not been researched thoroughly with satisfactory unanimity. In this study, we analyzed the bug distribution in 1,047 versions of four open source software projects downloaded from GitHub with complex network theory and focused on the correlation between software bugs and a specific type of software network structure -- network motifs. Our results indicate that the functions containing bugs are more likely to be involved in feedforward loop motifs, one type of network motifs with highest degree of uniqueness. This paper could serve as a guide to further investigate the nature of software failures and a powerful tool for software fault prediction and quality evaluation.
RAPTOR : Routing Attacks on Privacy in

The Tor network is a widely used system for anonymous communication. However, Tor is known to be vulnerable to attackers who can observe traffic at both ends of the communication path. In this paper, we show that prior attacks are just the tip of the iceberg. We present a suite of new attacks, called Raptor, that can be launched by Autonomous Systems (ASes) to compromise user anonymity. First, AS-level adversaries can exploit the asymmetric nature of Internet routing to increase the chance of observing at least one direction of user traffic at both ends of the communication. Second, AS-level adversaries can exploit natural churn in Internet routing to lie on the BGP paths for more users over time. Third, strategic adversaries can manipulate Internet routing via BGP hijacks (to discover the users using specific Tor guard nodes) and interceptions (to perform traffic analysis). We demonstrate the feasibility of Raptor attacks by analyzing historical BGP data and Traceroute data as well as performing real-world attacks on the live Tor network, while ensuring that we do not harm real users. In addition, we outline the design of two monitoring frameworks to counter these attacks: BGP monitoring to detect control-plane attacks, and Traceroute monitoring to detect data-plane anomalies. Overall, our work motivates the design of anonymity systems that are aware of the dynamics of Internet routing.
On reverse-engineering the KUKA Robot Language

Most commercial manufacturers of industrial robots require their robots to be programmed in a proprietary language tailored to the domain - a typical domain-specific language (DSL). However, these languages oftentimes suffer from shortcomings such as controller-specific design, limited expressiveness and a lack of extensibility. For that reason, we developed the extensible Robotics API for programming industrial robots on top of a general-purpose language. Although being a very flexible approach to programming industrial robots, a fully-fledged language can be too complex for simple tasks. Additionally, legacy support for code written in the original DSL has to be maintained. For these reasons, we present a lightweight implementation of a typical robotic DSL, the KUKA Robot Language (KRL), on top of our Robotics API. This work deals with the challenges in reverse-engineering the language and mapping its specifics to the Robotics API. We introduce two different approaches of interpreting and executing KRL programs: tree-based and bytecode-based interpretation.
Phrase Break Prediction Using a Finite State Transducer

This paper presents a method for phrase break prediction using a finite state transducer. In the literature, several algorithms have been proposed using statistical techniques for predicting phrase breaks. Some of these methods rely on linguistic information, such as syllables, words, part-of-speech, accents, etc. Our proposal is a probabilistic finite state transducer to convert part-ofspeech tags into phrase break boundaries. The results show that even using only part-of-speech tags the accuracy is high, which is advantageous because of its simplicity. This method can be extended to include more linguistic features.