Miracle-dz/UniFAR-FTUs huggingface dataset:
Computers and Mathematics with Applications a Semi-analytical Fourier Spectral Method for the Allen–cahn Equation

Keywords: Phase-field method Stabilized semi-implicit Fourier spectral method Unconditional stability First and second order convergence a b s t r a c t In recent years, Fourier spectral methods have been widely used as a powerful tool for solving phase-field equations. To improve its effectiveness, many researchers have employed stabilized semi-implicit Fourier spectral (SIFS) methods which allow a much larger time step than a usual explicit scheme. Our mathematical analysis and numerical experiments, however, suggest that an effective time step is smaller than a time step specified in the SIFS schemes. In consequence, the SIFS scheme is inaccurate for a considerably large time step and may lead to incorrect morphologies in phase separation processes. In order to remove the time step constraint and guarantee the accuracy in time for a sufficiently large time step, we present a first and a second order semi-analytical Fourier spectral (SAFS) methods for solving the Allen–Cahn equation. The core idea of the methods is to decompose the original equation into linear and nonlinear subequations, which have closed-form solutions in the Fourier and physical spaces, respectively. Both the first and the second order methods are unconditionally stable and numerical experiments demonstrate that our proposed methods are more accurate than the stabilized semi-implicit Fourier spectral method.
A Study of Modeling Crowd Dynamics

The following paper considers the field of Crowd Dynamics, and also the modeling thereof. This discipline has been becoming increasingly important in recent years and concerns itself with the propagation of pedestrian traffic in dense quarters. By considering such traffic flow a number of unique features and patterns can be discerned. Studied will be the current 'state of the art', and a modeling project based on one of the cited papers will be constructed and considered in further depth.
An energy aware QoS routing protocol for wireless sensors network

Actually, wireless sensor networks (WSNs) are used for various applications, such as habitat monitoring, automation, agriculture, and security. Since numerous sensors are usually deployed on remote and inaccessible places, and these nodes are battery powered, the deployment and maintenance should be easy and scalable. Energy effectiveness is a crucial factor in these networks. So, its software and hardware design must be energy aware. In this paper, we consider the lifetime maximization problem; we propose a energy effecient clustering routing protocol called Energy Aware Routing Protocol (EARP)1. This protocol divides the network into dynamic clusters. The method to choose the cluster-head and to belong to them is done to improve the network lifetime. Simulation results show that the network lifetime is increased largely comparable to existing schemes.
The Personal Is Political: School Counselors' Use of Self in Social Justice Advocacy Work.

This qualitative study explored the aspects of “self” school counselors (N = 16) described as central to advocating for social justice in their school systems. Using grounded theory, this study explored racial, feminist, and advocacy identity development in relation to the personhood of the counselor, and how these elements coalesced around action for social change. Implications for school counselor advocacy, training, and research are discussed.
Insider Threat Identification System Model Based on Rough Set Dimensionality Reduction

Insider threat makes great damage to the security of information system, traditional security methods are extremely difficult to work. Insider attack identification plays an important role in insider threat detection. Monitoring user’s abnormal behavior is an effective method to detect impersonation, this method is applied to insider threat identification, to built user’s behavior attribute information database based on weights changeable feedback tree augmented Bayes network, but data is massive, using the dimensionality reduction based on rough set, to establish the process information model of user’s behavior attribute. Using the minimum risk Bayes decision can effectively identify the real identity of the user when user’s behavior departs from the characteristic model.
The Securitization of Gold and Its Potential Impact on Gold Stocks

We study the market impact of a very successful financial innovation – the SPDR Gold Trust exchange-traded fund (GLD). GLD holds physical gold, and provides traders with a convenient and cost-effective way to gain exposure to gold. We find that after the introduction of GLD, the liquidity of gold company stocks declined, and their adverse-selection risk increased. Over the two-month period after GLD’s introduction, the stocks’ relative effective bid-ask spreads increased by over 15%, while their adverse-selection cost, as measured by the price impact of trades, went up by more than 30%. Gold stocks also experienced significant negative abnormal returns (−12% on average) in the month after GLD started trading. Our findings suggest that GLD attracted traders, especially uninformed traders, away from gold company stocks, and that the resulting negative demand shocks and decrease in the stocks’ liquidity caused their prices to decline. Our results show that existing securities can be seriously adversely affected when a new security enters the market.
Energy-aware routing protocol in fading channel for Mobile Ad Hoc networks

Analyze classical MANET routing protocol, and combining with the idea of min cost max flow in networks, put forward a routing optimization model based on energy aware. Study the channel propagation property on physical layer and analyze the basic principles of large-scale path loss and multipath fading. Results of simulations in real radio propagation environment show that this model can prolong network lifetime efficiently.
Is Willingness to Communicate a Reliable Predictor of Learner Autonomy in an EFL Context?

The present quantitative study investigated the relationship between willingness to communicate (WTC) and learner autonomy in an EFL context. In addition, it explored the probable effect of gender on the relationship between learner autonomy and WTC. Furthermore, it investigated whether WTC could predict learner autonomy. The data were collected from 142 upper-intermediate and advanced EFL learners (72 males and 70 females) by means of two questionnaires, the Learner Autonomy Questionnaire and the WTC Scale. The findings indicated that WTC correlated significantly and positively with learner autonomy. Moreover, correlational analysis revealed that gender modified this relationship in that the relationship between WTC and learner autonomy was stronger and more positive among female learners. The results revealed that WTC was a partial predictor of learner autonomy in this EFL context.
Robust but Robust to What ?

Generalization is at the core of evaluation, we estimate the performance of a model on data we have never seen but expect to encounter later on. Our current evaluation procedures assume that the data already seen is a random sample of the domain from which all future data will be drawn. Unfortunately, in practical situations this is rarely the case. Changes in the underlying probabilities will occur and we must evaluate how robust our models to such differences. This paper takes the position that models should be robust in two senses. Firstly, that any small changes in the joint probabilities should not cause large changes in performance. Secondly, that when the dependencies between attributes and the class are constant and only the marginals change, simple adjustments should be sufficient to restore a model’s performance. This paper is intended to generate debate on how measures of robustness might become part of our normal evaluation procedures. Certainly some clear demonstrations of robustness would improve our confidence in our models’ practical merits.
A MATRIX MODEL FOR MINING FREQUENT PATTERNS IN LARGE DATABASES

This paper proposes a model for mining frequent patterns in large databases by implementing a matrix approach. The whole database is scanned only once and the data is compressed in the form of a matrix. The frequent patterns are then mined from this compressed database which brings efficiency in data mining, as the number of database scans is effectively less than two. The computation time is reduced as some of the patterns are mined simultaneously and searching is minimized. Appropriate mathematical operations are designed and performed on matrices to achieve this efficiency.
A new generalization of the proportional conflict redistribution rule stable in terms of decision

In this chapter, we present and discuss a new generalized proportional conflict redistribution rule. The Dezert-Smarandache extension of the Demster-Shafer theory has relaunched the studies on the combination rules especially for the management of the conflict. Many combination rules have been proposed in the last few years. We study here different combination rules and compare them in terms of decision on didactic example and on generated data. Indeed, in real applications, we need a reliable decision and it is the final results that matter. This chapter shows that a fine proportional conflict redistribution rule must be preferred for the combination in the belief function theory.
A Sequential Monte Carlo Algorithm for Adaptation to Intersession Variability in On-line Signature Verification

Personal authentication is becoming increasingly important, and on-line signature verification is one of the most promising approaches to the authentication problem. A factor known as intersession variability in signatures causes deterioration of authentication performance. This paper proposes a new algorithm for overcoming this problem. We propose an algorithm that integrates a model parameter updating scheme in order to suppress deterioration in the authentication system. A model is provided for each user to calculate the score using fused multiple distance measures with respect to previous work. The algorithm consists of an updating phase in addition to a training phase and a testing phase. In the training phase, a model is generated via Markov Chain Monte Carlo for each individual. In the testing phase, the generated model determines whether a test signature is genuine. Finally, in the updating phase, the parameters are updated with test data by using a Sequential Monte Carlo algorithm. Several experiments were performed on a public database. The proposed algorithm achieved an EER of 6.39%, using random forgery for training and skilled forgery for testing.
Ins and Outs of News: Twitter as a Real-Time News Analysis Service

Both time and user popularity play a crucial role within the domain of news search the fundamental problem lies in integrating these two onto a single platform for news retrieval systems. In this paper we propose techniques for enhancement of these systems by identifying the news topics that are popular over a certain period of time. The notion of “popularity over time” is studied with the help of the well-known real-time microblogging service “Twitter” – our method performs linguistic analysis of the news data published daily on news sites for extraction and detection of news topics that are in high demand using Twitter. We also present a prototype of our framework which detects popular news in real-time. The results obtained suggest the need of taking into account users’ interest for effective news services and strongly imply that harnessing of microblogging data for this purpose can lead to surprising outcomes.
Limits of Transaction Cost Analysis

Nevertheless, there are many unanswered questions of a conceptual, theoretical and empirical nature. Even the term ‘transaction cost’ awaits an adequately precise definition. As Williamson (1995, p. 33) himself notes: ‘There is nonetheless a grave problem with broad, elastic and plausible concepts – of which “transaction costs” is one and “power” is another – in that they lend themselves to ex post rationalization. Concepts that explain everything explain nothing. ’
The Evolution of Patent Functions: New Trends, Main Challenges and Implications for Firm Strategy

Recent publications in the field of Intellectual Property (IP) have shown that the previous literature did not grasp how complex patents are. The goal of this paper is to present an overview of all identified functions of patents and of the main strategic implications of such a complex picture. We first survey the main patent functions : innovation protection, functions related to trade and finance, defensive roles, and patent as an input in the innovation process. We then define each function and analyse their main evolution trends in relation with the current environment. We finally identify the strategic implications of each function. We focus on the implications of the newly identified functions and on the interaction between the different functions.
Semantic Web Service Discovery Approach Using Service Clustering

Discovery of suitable web services for a given user requirement is one of the key technology. A mechanism is proposed to support QoS-based semantic web service discovery in the paper. Firstly, to reduce the overhead and enhance the discovery efficiency, genetic algorithm is adopted as a method of service clustering. Furthermore, based on user’s QoS requirements, the QoS-based service matchmaker was realized. In the end, simulation results demonstrated the effectiveness of the proposed method. Keywords-semantic Web service; genetic algorithm; QoS; service discovery
Detect Polymorphic Worms Based on Semantic Signature and Data-Mining

In recent years, Internet worms increasingly threaten the Internet hosts and service and polymorphic worms can evade signature-based intrusion detection systems. In this paper, we propose new methods to detect polymorphic worms based on semantic signature and data-mining. Our main contributions of this work are as follows: (1) we propose a worm attack model - the OSJUMP model. (2) Based on the attack model, we analyse the feature of polymorphic worms and the feature of perfect ones. (3) We propose methods to detect worms through recognizing JUMP address based on data-mining such as Bayes and ANN. We evaluate some famous worm and polymorphic ones generated from them. The results show that the false negative and performance improved a lot compared to signature-based IDSs.
miR-155 mediates drug resistance in osteosarcoma cells via inducing autophagy

Frequent acquisition of drug resistance is often associated with the chemotherapy of malignant tumors, including osteosarcoma. A number of studies have demonstrated a critical role for autophagy in osteosarcoma development, therapy and drug resistance. However, the molecular mechanisms underlying the autophagy-mediated chemotherapy resistance of osteosarcoma cells remain largely unknown. In the present study, we determined the autophagy and microRNA-155 (miR-155) expression induced by chemotherapeutic drugs in osteosarcoma cells. Then we determined the promotory role of miR-155 to the chemotherapy-induced autophagy. Our results demonstrated that microRNA-155 (miR-155) expression was highly induced during chemotherapy of osteosarcoma cells, and this was accompanied by upregulated autophagy. The increased miR-155 expression levels upregulated anticancer drug-induced autophagy in osteosarcoma cells and ameliorated the anticancer drug-induced cell proliferation and viability decrease. Therefore, the results of the present study demonstrated that miR-155 mediated drug-resistance in osteosarcoma cells by inducing autophagy. The present study recognized a novel mechanism of chemoresistance in osteosarcoma cancers.
Racial Prejudice, Intergroup Hate, and Blatant and Subtle Bias of Whites toward Blacks in Legal Decision Making in the United States

The present study examined the multidimensional nature of intergroup hate and the potential roles of hate and prejudice in expressions of White Americans' treatment of Blacks within the context of the U.S. legal system. White participants in the U.S. read about a provoked or unprovoked violent assault perpetrated by a Black assailant on a White victim. Emotional reactions and recommendations for punishment (prescribed sentencing and support for the death penalty) were assessed. Supportive of Sternberg's (2003) duplex model of hate, we found that explicit (self-reported) hate reflected separate components of negation of intimacy (e.g., disgust and repulsion), passion (anger and fear), and devaluation/commitment (e.g., attributions of evil and inhuman); these components, in turn, differentially mediated punitiveness toward the assailant. The results also revealed that although the direct effect of prejudice on retribution was mediated by self-reported hate, more subtle and indirect effects occurred independently of hate or its affective components. Practical and theoretical implications of these findings for biased decision making in legal contexts are considered.
Spirituality and Entrepreneurship: The Role of Magical Thinking in Future-Oriented Sensemaking

Drawing from a qualitative empirical study of Canadian entrepreneurs, we seek to understand the nature of entrepreneurial thinking. More specifically, we analyse entrepreneurs’ cognitive capacity to mitigate the risk inherent in an uncertain future and overcome low community expectations of entrepreneurial success. We introduce the notion of ‘magical thinking’, an emergent construct that refers to a cluster of beliefs that maintain the motivation and focus of entrepreneurs by transmuting agency from a rational-scientific context in which the entrepreneur imposes his or her will on the environment, to a spiritual context in which the entrepreneur perseveres by remaining true to trust in a wider cosmological belief system. We identify three key elements of magical thinking – finding one’s path, obtaining the answers and being at peace.
Real-Time Instant Event Detection in Egocentric Videos by Leveraging Sensor-Based Motion Context

With rapid growth of egocentric videos from wearable devices, the need for instant video event detection is emerging. Different from conventional video event detection, it requires more considerations on real-time event detection and immediate video recording due to the computational cost on wearable devices (e.g., Google Glass). Conventional work of video event detection analyzed video content in an offline process and it is time-consuming for visual analysis. Observing that wearable devices are usually along with sensors, we propose a novel approach for instant event detection in egocentric videos by leveraging sensor-based motion context. We compute statistics of sensor data as features. Next, we predict the user's current motion context by a hierarchical model, and then choose the corresponding ranking model to rate the importance score of the timestamp. With importance score provided in real-time, camera on the wearable device can dynamically record micro-videos without wasting power and storage. In addition, we collected a challenging daily-life dataset called EDS (Egocentric Daily-life Videos with Sensor Data), which contains both egocentric videos and sensor data recorded by Google Glass of different subjects. We evaluate the performance of our system on the EDS dataset, and the result shows that our method outperforms other baselines.
Annexin 1 regulates DC efferocytosis and cross-presentation during Mycobacterium tuberculosis infection

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Annexin1 regulates DC efferocytosis and cross-presentation during Mycobacterium tuberculosis infection Fanny Tzelepis, Mark Verway, Jamal Daoud, Joshua Gillard, Kimya Hassani-Ardakani, Jonathan Dunn, Jeffrey Downey, Marilena Elena Gentile, Joanna Jaworska, Anthony Michel Jean Sanchez, et al.
Prediction of distortion of transmitted video in IP networks

This paper presents an implementation to try to predict the distortion of a video transmitted in WLAN networks. So was developed a model of video distortion prediction, which not requires access to the original video. The model is intended to be used under read conditions of video transmission in WLAN networks. The results obtained with this implementation can demonstrate the influence of packet loss in the final quality of the received video, presenting a new strategy that can be used as a selection networks criteria, in order to ensure to the user a good experience in the reception of videos during their movement between WLAN networks.
Assessing the Influence of Lecture/Laboratory Instructor Pairings on Student Perception and Learning Outcomes

Dr. J. Michael Grayson is a former National Science Foundation Graduate Research Fellow at Clemson University where he received the Bachelor of Science in Civil Engineering (BSCE) with a minor in Environmental Engineering (summa cum laude) followed by the Master of Science in Civil Engineering (MSCE) and PhD both with an emphasis in structural engineering. Dr. Grayson has extensive experience in the structural framing and finishing of light-frame commercial and residential buildings, as well as experience in bridge and highway construction and inspection practices with the South Carolina Department of Transportation (SCDOT). A native of Okatie, SC, firsthand experience of the devastation left in the wake of Hurricane Hugo (1989) continues to have a profound influence on Dr. Grayson’s teaching and research accomplishments and goals. Dr. Grayson continuously strives to improve his teaching in the classroom in order to produce principled civil and environmental engineering leaders that are capable of thinking critically about topics while fostering a lifelong love and capacity for independent learning.
Classification model of arousal and valence mental states by EEG signals analysis and Brodmann correlations

This paper proposes a methodology to perform emotional states classification by the analysis of EEG signals, wavelet decomposition and an electrode discrimination process, that associates electrodes of a 10/20 model to Brodmann regions and reduce computational burden. The classification process were performed by a Support Vector Machines Classification process, achieving a 81.46 percent of classification rate for a multi-class problem and the emotions modeling are based in an adjusted space from the Russell Arousal Valence Space and the Geneva model.
Flexible Paxos: Quorum Intersection Revisited

Distributed consensus is integral to modern distributed systems. The widely adopted Paxos algorithm uses two phases, each requiring majority agreement, to reliably reach consensus. In this paper, we demonstrate that Paxos, which lies at the foundation of many production systems, is conservative. Specifically, we observe that each of the phases of Paxos may use non-intersecting quorums. Majority quorums are not necessary as intersection is required only across phases. Using this weakening of the requirements made in the original formulation, we propose Flexible Paxos, which generalizes over the Paxos algorithm to provide flexible quorums. We show that Flexible Paxos is safe, efficient and easy to utilize in existing distributed systems. We conclude by discussing the wide reaching implications of this result. Examples include improved availability from reducing the size of second phase quorums by one when the number of acceptors is even and utilizing small disjoint phase-2 quorums to speed up the steady-state.