Miracle-dz/UniFAR-FTUs huggingface dataset:
DOUBLE LINEAR DISCRIMINATE ANALYSIS FOR FACE RECOGNITION

In this paper we propose a new algorithm for face recognition named as Double Linear Discriminate Analysis (DLDA) in which we combine two linear discriminate methods: 2D-LDA and Fisher-face sequentially. Experimental results on benchmark face datasets show that DLDA not only achieves the best performance when the number of training samples is appropriate, but also its speed is the fastest among three of them. Though the proposed method can operate on both gray and colour images, the performance is significantly better while operating on colour face datasets in comparison to 2D-LDA, Fisher-face and the Discriminate Colour Space method proposed specifically for colour face recognition recently.
Fusion of Extreme Learning Machine and Graph-Based Optimization Methods for Active Classification of Remote Sensing Images

In this letter, we propose an efficient multiclass active learning (AL) method for remote sensing image classification. We fuse the capabilities of an extreme learning machine (ELM) classifier and graph-based optimization methods to boost the classification accuracy while minimizing the user interaction. First, we use the ELM to generate an initial label estimation of the unlabeled image pixels. Then, we optimize a graph-based functional energy that integrates the ELM outputs as an initial estimation of the image structure. As for the ELM, the solution to this multiclass optimization problem leads to a system of linear equations. Due to the sparse Laplacian matrix built from the lattice graph defined on the image pixels, the optimization problem is solved in a linear time. In the experiments, we report and discuss the results of the proposed AL method on two very high resolution images acquired by IKONOS-2 and GoeEye-1, as well as the well-known Pavia University hyperspectral image.
Myeloid Infection Links Epithelial and B Cell Tropisms of Murid Herpesvirus-4

Gamma-herpesviruses persist in lymphocytes and cause disease by driving their proliferation. Lymphocyte infection is therefore a key pathogenetic event. Murid Herpesvirus-4 (MuHV-4) is a rhadinovirus that like the related Kaposi's Sarcoma-associated Herpesvirus persists in B cells in vivo yet infects them poorly in vitro. Here we used MuHV-4 to understand how virion tropism sets the path to lymphocyte colonization. Virions that were highly infectious in vivo showed a severe post-binding block to B cell infection. Host entry was accordingly an epithelial infection and B cell infection a secondary event. Macrophage infection by cell-free virions was also poor, but improved markedly when virion binding improved or when macrophages were co-cultured with infected fibroblasts. Under the same conditions B cell infection remained poor; it improved only when virions came from macrophages. This reflected better cell penetration and correlated with antigenic changes in the virion fusion complex. Macrophages were seen to contact acutely infected epithelial cells, and cre/lox-based virus tagging showed that almost all the virus recovered from lymphoid tissue had passed through lysM+ and CD11c+ myeloid cells. Thus MuHV-4 reached B cells in 3 distinct stages: incoming virions infected epithelial cells; infection then passed to myeloid cells; glycoprotein changes then allowed B cell infection. These data identify new complexity in rhadinovirus infection and potentially also new vulnerability to intervention.
Prest: An Intelligent Software Metrics Extraction, Analysis and Defect Prediction Tool

Test managers use intelligent predictors to increase testing efficiency and to decide on when to stop testing. However, those predictors would be impractical to use in an industry setting, unless measurement and prediction processes are automated. Prest as an open source tool aims to address this problem. Compared to other open source prediction and analysis tools Prest is unique that it collects source code metrics and call graphs in 5 different programming languages, and performs learning based defect prediction and analysis. So far Prest in real life industry projects helped companies to achieve an average of 32% efficiency increase in testing effort.
Range adaptation of Wireless Power Transfer systems for biomedical applications

The circuit components of a Wireless Power Transfer (WPT) system are designed considering a fixed separation distance and fixed alignment between the transmitting and receiving sides of the WPT system. But, in practice, the separation distance and the alignment do not remain constant. This is especially true for a biomedical application where a moving instrumental part requires electric power. This change causes a variation in mutual coupling and consequently in the efficiency of the system. This paper describes how to maintain the efficiency more or less constant even if the separation distance and the alignment vary. First of all, the circuit elements for maximum efficiency are worked out, then simulation for various separation distances and misalignment are calculated. A means of controlling a particular parameter is worked out in order to maintain efficiency of the system more or less constant.
Ultrasonic Examination by Frequency Analysis for Creep Damage Detection of Boiler Piping

Boiler piping of fossil-fuel combustion power generation plants are exposed to high-temperature and high-pressure environments and failure of piping due to creep damage has been a concern. Therefore, a precise creep damage assessment method has been needed. This paper proposes a nondestructive method for creep damage detection of piping in fossil-fuel combustion power generation plants by ultrasonic testing. Ultrasonic signals are transformed to signals in frequency domain by Fourier transform and a specific frequency band is chosen. To determine the creep damage, the spectrum intensities are calculated. Calculated intensities have good correlation to life consumption of the weld joints and this method is able to predict the remaining life of high-temperature piping, which have been already installed.
CINEMATIC-BASED MODEL FOR SCENE BOUNDARY DETECTION

Most current video retrieval systems use shot as the basic unit for information organization and access. A shot, however, models only a visually contiguous sequence of video frames with no coherent semantic meanings. On the other hand, viewers tend to “view” video contents in terms of episodes or scenes. In cinematography, scene is the basic story unit that the directors use to compose and convey their ideas. This paper proposes a cinematic-based model to analyze video contents and extract scene boundaries. Using the list of shots as the base, we first locate rough scene boundaries based on visual similarity. Next, we employ the cinematic rules computationally to refine and cluster the rough scenes. We use a combination of camera focal length and similar shot sequence information to derive the final set of scenes. We test our system on two videos of about 44 minutes in duration. The system has been found to be effective.
Disruption of Dnmt1/PCNA/UHRF1 Interactions Promotes Tumorigenesis from Human and Mice Glial Cells

Global DNA hypomethylation is a hallmark of cancer cells, but its molecular mechanisms have not been elucidated. Here, we show that the disruption of Dnmt1/PCNA/UHRF1 interactions promotes a global DNA hypomethylation in human gliomas. We then demonstrate that the Dnmt1 phosphorylations by Akt and/or PKC abrogate the interactions of Dnmt1 with PCNA and UHRF1 in cellular and acelluar studies including mass spectrometric analyses and the use of primary cultured patient-derived glioma. By using methylated DNA immunoprecipitation, methylation and CGH arrays, we show that global DNA hypomethylation is associated with genes hypomethylation, hypomethylation of DNA repeat element and chromosomal instability. Our results reveal that the disruption of Dnmt1/PCNA/UHRF1 interactions acts as an oncogenic event and that one of its signatures (i.e. the low level of mMTase activity) is a molecular biomarker associated with a poor prognosis in GBM patients. We identify the genetic and epigenetic alterations which collectively promote the acquisition of tumor/glioma traits by human astrocytes and glial progenitor cells as that promoting high proliferation and apoptosis evasion.
Fixing Popular Participation in Constitution-Making

This article addresses the double-standard of popular participation at the moment of creating or reforming constitutions. While constitutions require in most cases qualified majorities in order to be approved at the constitutional assembly, they normally require only simple majorities to be ratified at the referendum. The article shows that the normative reasons to use either qualified majorities or simple majorities are applicable to both cases. It then argues that in order to fix popular participation, thereby increasing the legitimacy and stability of constitutions, the same majority rule should be used at the assembly stage and the referendum stage unless specific circumstances of the case dictate otherwise. In order to identify those, it presents a two-stage evaluation mechanism to identify the cases in which each majority rule should be used in order to increase the stability and the legitimacy of the constitutional draft.
Optimal bit allocation for maximum absolute error distortion in the application of JPEG2000 part 2

This paper proposes a strategy to deal with bit rate allocation for the optimization of maximum absolute error (MAE), or 1-infinity, distortion metric in 3D data compression using JPEG2000. Part 2 of this standard has the capability to compress 3D data by treating data as separate 2D slices; these slices could be taken directly from the data or from the data after it has undergone a decorrelation transform (KLT) in one direction. To perform bit rate allocation we use a mixed model approximation to the MAE rate-distortion curve, that is used in an optimization algorithm. To solve the problem of MAE-related bit rate allocation in the KLT domain, we theoretically derive an upper bound for MAE based on the basis vectors of the KLT; we also develop an algorithm for optimizing this upper bound, and we illustrate how the minimization of this upper bound can decrease the actual MAE.
Contracting with a Time-Inconsistent Agent

I consider a repeated principal-agent model with moral hazard, in which the agent has βδ-preferences, which are widely used to capture time-inconsistency. I first analyze the case where the agent is sophisticated in the sense that he is fully aware of his inconsistent discounting. I characterize the optimal wage scheme for such an agent and compare it to time-consistent benchmarks. The marginal cost of rewarding the agent for high output today exceeds the marginal benefit of delaying these rewards until tomorrow. In this sense, the principal does not smooth the agent’s rewards over time. When facing a sophisticated agent, it is optimal for the principal to reward the good performance more and punish the bad performance more in the early period, relative to the optimal wage scheme for a time-consistent agent. I also consider the case where the agent is naive in the sense that he is not aware of his time-inconsistency. I show that the principal’s maximum utility is the same from a sophisticated agent and a naive agent.
Scaling up Natural Gradient by Sparsely Factorizing the Inverse Fisher Matrix

Second-order optimization methods, such as natural gradient, are difficult to apply to highdimensional problems, because they require approximately solving large linear systems. We present FActorized Natural Gradient (FANG), an approximation to natural gradient descent where the Fisher matrix is approximated with a Gaussian graphical model whose precision matrix can be computed efficiently. We analyze the Fisher matrix for a small RBM and derive an extremely sparse graphical model which is a good match to the covariance of the sufficient statistics. Our experiments indicate that FANG allows RBMs to be trained more efficiently compared with stochastic gradient descent. Additionally, our analysis yields insight into the surprisingly good performance of the "centering trick" for training RBMs.
The Effects of Multinational Production on Domestic Performance: Evidence from Japanese Firms

In the present paper we investigate the causal effect of becoming a multinational on home performance for a large panel of Japanese firms for the period 1995-2002. We adopt matching techniques in combination with a difference-in-difference estimator to evaluate the causal effect of establishing a foreign affiliate on productivity, output and employment. We find that Japanese outward FDI tends to strengthen the economic activities of Japanese firms in Japan in terms of both output and employment. This finding is in line with the stylized fact in the literature that FDI and exports are complements. However, we do not find a significant positive effect on productivity.
Vajra: Distributed Fault Injection for Dependability Evaluation

Distributed computer systems are able to provide many capabilities that single machines cannot. These include larger storage and processing capacities, increased scalability, and the ability to tolerate many types of faults. Through the use of specialized communication protocols, distributed systems can detect and mask faults ranging from simple crash faults to arbitrary Byzantine faults. Several Byzantine fault-tolerant distributed systems have been developed over the past decade, including Fleet, Immune and Castro-Liskov BFT. For the most part, the empirical studies of these systems have tended to focus on the systems’ performance overheads, i.e., the costs of achieving Byzantine fault-tolerance. None of the empirical assessments of these systems have attempted to quantify the survivability of these systems, i.e., how successful they are at tolerating Byzantine faults. Thus, there does not currently exist, in the literature, any known way of comparing one Byzantine fault-tolerant protocol with another, in terms of their perceived benefits to the end-users or applications. Because these systems are built using different underlying mechanisms (e.g., RMI vs. CORBA) and different programming languages (Java vs. C++), it becomes harder to compare the various systems against each other. In this paper we present Vajra, a new distributed survivability benchmark. Vajra aims for the objective and quantitative comparison of the runtime implementations of various Byzantine fault-tolerant distributed systems. The Vajra framework intercepts APIs that are common across various Byzantine fault-tolerant systems, and uses these APIs as the point of injection for a variety of accidental and malicious faults at various rates, and more importantly, across components in a distributed system. Vajra operates transparently and requires no modification to the operating system or the system under test. We show the results of using Vajra to evaluate the survivability of one specific system, namely, Castro-Liskov BFT.
A Layout-Level Logic Restructuring Framework for LUT-Based FPGAs

It is well known that optimizations made by traditional logic synthesis tools often correlate poorly with post-layout performance; this is largely a result of interconnect effects only being visible after layout. In this paper, a corrective methodology is proposed for timing-driven logic restructuring at the placement level. The approach focuses on a lookup table (LUT)-based field programmable gate arrays. The approach is iterative in nature. In each iteration, using current placement information, the method induces a timing-critical fan-in tree via (temporary) replication. Such trees are then reimplemented where the degrees of freedom include functional decomposition of LUTs, ldquomini-LUTrdquo tree mapping, and physical embedding. A dynamic programming algorithm optimizes over all of these freedoms simultaneously. All simple disjoint LUT decompositions (i.e., Ashenhurst style) are encoded in a ldquomini-LUTrdquo tree using choice nodes similar to those in the paper by Lehman At the same time, because embedding is done simultaneously, interconnect delay is directly taken into account. Over multiple iterations, the design is progressively improved. The framework has been implemented, and promising experimental results are reported.
Discrete Preference Games in Heterogeneous Social Networks: Subverted Majorities and the Swing Player

We study discrete preference games in heterogeneous social networks. These games model the interplay between a player's private belief and his/her publicly stated opinion (which could be different from the player's belief) as a strategic game in which the players' strategies are the opinions and the cost of an opinion in a state is a convex combination through a parameter $\alpha\in[0,1]$ of two factors: the disagreement between the player's opinion and his/her internal belief and the number of neighbors whose opinions differ from the one of the player. The parameter $\alpha$ models how stubborn a player is: players with large $\alpha$ change their opinion only if many neighbors disagree with his/her belief. We consider social networks that are heterogeneous in the sense that the parameter $\alpha$ can vary from player to player. We ask if it is possible that the belief shared by the majority of the players does not coincide with the opinion that is publicly announced by the majority of the players in an equilibrium state. Our main result is a characterization of the social networks that admit an initial belief assignment for which there exists a sequence of best response moves that reach an equilibrium in which the initial majority is subverted. Our characterization is effective in the sense that can be tested efficiently and the initial belief assignment that can be subverted can be computed in time polynomial in the number of players. Our result is actually stronger as we show that in each initial belief assignment that can be subverted, subversion is actually obtained in a very strong way: it only takes one move of a single player, the swing player, to lead the social network to a point of no return in which any rational move from any player leads to a subverted majority.
An Open Distributed Real-time Environment Based on Variable Utilization Server

This paper extends the environment for open distributed real-time systems, where hard or soft real-time applications may run concurrently with non-real-time applications. We replace the top-level improved constant utilization server scheduler of the open system architecture in with variable utilization server scheduler. The proposed open real-time environment in this paper allows tasks in each application to share local and global nonpreemptive resources, which accommodates a much broader spectrum of real-time applications. Additionally, the extended environment assigns CPU bandwidth to each server in a more flexible and more adaptive way. The evaluation results show the performance of the open system is also improved considerably
Shifting demand or just moving price?: A multi-level analysis of student price demand for college education and state policy preferences

College affordability and rising tuition and fees are a topic of national interest. While a number of recent studies examine this subject separately from state policy and individual student perspectives, the body of scholarship that reviews the intersection of two components of supply and demand for higher education is relatively small. My focus for this dissertation is to amplify the understanding of how individual characteristics of students explain differences in their price demand for college education in their first year at four-year public and private not-for-profit colleges and universities. I am also interested in analyzing how state policy preferences, namely state postsecondary education efforts and proportional shares of need-based state student grant aid, mitigate such individual differentials. Drawing from previous studies, my study is guided by a combination of the human capital investment perspective, student price responsiveness to college education, and college choice framework. I contend that the price tag of college education--either sticker or net--is roughly where a student's price demand meets a supply of college education. I also argue that a student's price demand for college education is shaped by different forms of capital (e.g., cultural, monetary, and social capital) that each student possesses; thus, some students have more willingness and ability to pay for college education than others. Further, public policy has a role in offsetting such differences in individual capital, thereby making college-going options more equitable regardless of socioeconomic status. I ran several multi-level analyses using the National Postsecondary Student Aid Survey (NPSAS) datasets of the 1999-00, 2003-04, and 2007-08 cohorts as well as institutional- and state-level data drawn from various sources. The result confirms the presence of sticker shocks, or significant differences in student price demand for college, based on socioeconomic characteristics. The result also suggests that state postsecondary education efforts negatively correlate with sticker and net tuition and fees for students at public colleges and universities; however, this was not the case for states offering more need-based aid. Therefore, while state postsecondary education efforts offset individual differences in price demand for college education, the same was not true for direct student aid awards.
Passive DNS Replication

The domain name system (abbreviated ‘DNS’) provides a distr ibuted database that maps domain names to record sets (for example, IP addresses). DNS is one of the core protocol suites of the Internet. Yet DNS data is often volatile, and there are many unwanted records present in the domain name system. This paper presents a technology, calledpassive DNS replication , to obtain domain name system data from production networks, and store it in a database for later reference. The present paper is structured as follows: