Miracle-dz/UniFAR-FTUs huggingface dataset:
HDAC6 deacetylation of tubulin modulates dynamics of cellular adhesions

Genetic or pharmacological alteration of the activity of the histone deacetylase 6 (HDAC6) induces a parallel alteration in cell migration. Using tubacin to block deacetylation of α-tubulin, and not other HDAC6 substrates, yielded a motility reduction equivalent to agents that block all NAD-independent HDACs. Accordingly, we investigated how the failure to deacetylate tubulin contributes to decreased motility in HDAC6-inhibited cells. Testing the hypothesis that motility is reduced because cellular adhesion is altered, we found that inhibiting HDAC6 activity towards tubulin rapidly increased total adhesion area. Next, we investigated the mechanism of the adhesion area increase. Formation of adhesions proceeded normally and cell spreading was more rapid in the absence of active HDAC6; however, photobleaching assays and adhesion breakdown showed that adhesion turnover was slower. To test the role of hyperacetylated tubulin in altering adhesion turnover, we measured microtubule dynamics in HDAC6-inhibited cells because dynamic microtubules are required to target adhesions for turnover. HDAC6 inhibition yielded a decrease in microtubule dynamics that was sufficient to decrease focal adhesion turnover. Thus, our results suggest a scenario in which the decreased dynamics of hyperacetylated microtubules in HDAC6-inhibited cells compromises their capacity to mediate the focal adhesion dynamics required for rapid cell migration.
Performance evaluation and optimization of embedded image sources over noisy channels

We investigate and prove the relationships among several commonly used and new performance metrics for embedded image bit streams transmitted over noisy channels. The average of the first error-free run length (FEFRL) is proposed as a simpler performance metric. On binary symmetric channels (BSC), the average FEFRL is obtained in closed form, which greatly simplifies performance optimization. Simulation results justify the merit of the proposed technique.
Notes on Linear Minimum Mean Square Error Estimators

Some connections between linear minimum mean square error estimators, maximum output SNR filters and the least square solutions are presented. The notes have been prepared to be distributed with EE 503 (METU, Electrical Engin.) lecture notes. 1 Linear Minimum Mean Square Error Estimators The following signal model is assumed: r = Hs + v (1) Here r is a N × 1 column vector denoting the observations. In this model, s is the desired signal vector, v is the interference vector on the observations. We assume that s and v are uncorrelated as in all stochastic filtering applications. It can be noted that the observations are linear combinations of desired quantities and interference. Here the matrix H is an N × M matrix (N can be less than or greater than M , hence the system can be under or over determined). H can be considered as the channel or the observation system or a linear combiner for the modes where the modes are the columns of H. The column vector s contains M entries each of which is to be estimated. In class, we have derived the optimal linear minimum mean square error estimator as the solution of the following system of equations: RrW = Rrs (2) Here Rr = E{rrH} is the auto-correlation matrix of the observations and Rrs = E{rsH} is the cross-correlation matrix of observations and the desired variables. The minimum mean square error estimate for s is given as follows: ŝ = Wr (3) The error covariance matrix for the minimum error estimator is as shown below: Re = E{(s− ŝ) } {{ } e (s− ŝ)) } {{ } eH } = E{e(sH − rHW)} (a) = E{esH} − E{erH} } {{ } 0 W = E{(s−WHr)sH} = Rs −WRrs (4) The zero matrix on the right hand side of equation shown with (a) is due to the orthogonality condition of the optimal estimator. Next, we explicitly calculate the estimator in terms of H, Rs and Rv matrices. The following can be easily verified: Rr = HRsH + Rv Rrs = HRs (5)
Assessing Antecedents of Socially Responsible Supplier Selection in Three Global Supply Chain Contexts

A number of highly publicized, controversial lapses in social responsibility within global supply chains have forced managers and scholars to reexamine long-held perspectives on supplier selection. Extending Carter and Jennings� department-level study of purchasing social responsibility, our research assesses the role of supply managers� ethical intentions and three key antecedents that drive socially responsible supplier selection. Comparing evidence from firms operating in China, the United States, and the United Arab Emirates, we identify three key drivers of supply managers� ethical intentions and examine both their direct and indirect impacts on socially responsible supplier selection. We find differential support for the predictor relationships on supply manager ethical intentions across national contexts and mediated versus nonmediated models. These observations bear important implications for firms conducting global supply management.
A Smart Fuzzing Method for Detecting Heap-Based Buffer Overflow in Executable Codes

This paper presents a new concolic execution-based smart fuzzer for detecting heap-based buffer overflow in the executable codes. The proposed fuzzer executes the target program with concrete input data and calculates the constraints of the executed path symbolically. The path constraints are used to generate test data that traverse new execution paths in the target program. For each executed path, the fuzzer also calculates heap-based buffer overflow constraints. These constraints determine what input data may cause heap-based buffer overflow in the executed path. By combining the path and vulnerability constraints new test data are generated, if possible, that traverse a specific execution path and activate specific vulnerability in that path. We implemented the proposed smart fuzzer as a plug-in for Valgrind framework. The implemented fuzzer is tested on different groups of test programs. The experiments demonstrate that the fuzzer can detect the vulnerabilities in these programs accurately.
Methods of Optimal Shape Design in Electrical Machines: a Comparative Study

Optimal shape design has been developed rapidly during the last decade. The present paper focuses on the presentation and comparison of the three most popular approaches for optimal design: the classical deterministic optimization, the genetic algorithm and application of the artiicial neural networks. Advantages and disadvantages of these methods from an inexperienced user's point of view are discussed.
Matrix Characterization and Development for the Vacuum Assisted Resin Transfer Molding Process

The curing kinetics and viscosity of an epoxy resin system, SI-ZG-5A, have been characterized for application in the vacuum assisted resin transfer molding (VARTM) process. Impregnation of a typical carbon fiber perform provided the test bed for the characterization. Process simulations were carried out using the process model, COMPRO, to examine heat transfer and curing kinetics for a fully impregnated panel, neglecting resin flow. The predicted viscosity profile and final degree of cure were found to be in good agreement with experimental observations.
Symmetry hybrids

How we perceive the world is strongly governed by symmetry. Symmetry presents itself in both natural and man-made structures giving aesthetic appeal to the world. This paper presents an approach to form intuitive tree based representations that minimally describe input patterns. We explore how new hybrid patterns can be generated by grafting different symmetry trees together. A new algorithm is proposed to generate new hybrid patterns that maintain the overall appearance of the inputs while allowing control over the amount of variation generated.
Power and Partnership in Education: Parents, Children and Special Educational Needs

Recent legislation - the 1981 and 1993 Education Acts - have emphasized the need for parents to work as partners with professionals in the assessment of children's special educational needs. This book explores that notion of partnership and subjects it to critical scrutiny. It describes the assessment process from both the parental and professional standpoints, looking in particular at the parent-professional relationship and the barriers that might inhibit effective partnerships between parents and professionals. The child's viewpoint is equally important, and later chapters examine children's own accounts of the assessment process.
A Comparison of Global Timber Models

This study identifies differences of global timber models in its three different versions by comparing marginal abatement cost. The forest-only model has a relatively linear marginal cost curve; the other two models show concave cost curves, indicating that the marginal cost for carbon sequestration increases faster than in the forest-only model. Such basic differences among the models may be caused by the characteristics of the CET(Constant Elasticity of Transformation) model. The differences between the forest-only model and CET model are more narrowed when CET results are converted to physical units. This study contributes to the enhancement of the understanding of GTM development and provides foundation for future studies to improve global timber modeling.
A memory efficient graph kernel

In this paper, we show how learning models generated by a recently introduced state-of-the-art kernel for graphs can be optimized from the point of view of memory occupancy. After a brief description of the kernel, we introduce a novel representation of the explicit feature space of the kernel based on an hash function which allows to reduce the amount of memory needed both during the training phase and to represent the final learned model. Subsequently, we study the application of a feature selection strategy based on the F-score to further reduce the number of features in the final model. On two representative datasets involving binary classification of chemical graphs, we show that it is actually possible to sensibly reduce memory occupancy (up to one order of magnitude) for the final model with a moderate loss in classification performance.
Exploration of Structure and Dynamics of Large Phone and SMS Networks

The effect of the network structure on the dynamics of social and communication networks has been of interest in recent years. It has been observed that network properties such as neighborhood overlap, clustering coefficient, etc. influence the tie strengths and link persistence between individuals. In this paper we study the communication records (both phonecall and SMS) of 2 million anonymized customers of a large mobile phone network over a period of 6 months. We analyze different network parameters in these call/SMS networks and the correlations between them. We also formulate a learning problem to determine whether existing links between users will persist in the future. Experimental results show that our method performs better than existing rule based techniques. We also detect change points in the collective behavior of users using eigenvalue analysis of various behavioral features extracted over time. Our analysis shows that there exists correlation between detected behavior change-points and social events and festivals during those points in time.
A stochastic model for the deficient order Affine Projection algorithm

This paper presents a statistical analysis of the Affine Projection (AP) adaptive algorithm for autoregressive (AR) input signals when the order of the AP algorithm is smaller than the order of the AR process. Deterministic recursive equations are derived for the mean weight and mean-square error behavior. Monte Carlo simulations show excellent agreement with the theoretical predictions in steady-state and, under certain conditions, during transient. These results are of special interest in practical applications where the computational complexity prevents implementation of the sufficient order AP algorithm for high order AR inputs.
Impact of Artificial Noise on Cellular Networks: A Stochastic Geometry Approach

This paper studies the impact of artificial noise (AN) on the secrecy performance of a target cell in multi-cell cellular networks. Although AN turns out to be an efficient approach for securing a point-to-point/single-cell confidential transmission, it would increase the inter-cell interference in a multi-cell cellular network, which may degrade the network reliability and secrecy performance. For analyzing the average secrecy performance of the target cell which is of significant interest, we employ a hybrid cellular deployment model, where the target cell is a circle of fixed size, and the base stations outside the target cell are modeled as a homogeneous Poisson point process. We investigate the impact of AN on the reliability and security of users in the target cell in the presence of pilot contamination using a stochastic geometry approach. The analytical results of the average connection outage and the secrecy outage of its cellular user (CU) in the target cell are given, which facilitates the evaluation of the average secrecy throughput of a randomly chosen CU in the target cell. It shows that with an optimized power allocation between the desired signals and AN, the AN scheme is an efficient solution for securing the communications in a multi-cell cellular network.
Energy-efficiency maximization mechanism for IEEE 802.11e contention-based MAC protocols

This paper proposes a new mechanism to improve the energy-efficiency of the IEEE 802.11e Enhanced Distributed Channel Access (EDCA) MAC protocol. The proposed mechanism tries to reduce the number of collisions, which represents a major source of energy waste, especially in high traffic load. This mechanism tries to adapt the value of AIFSN MAC parameter of EDCA for the different access categories, according to the collision rate in the network. This dynamic adaptation mechanism is based on a fuzzy logic controller. The simulation results show that the proposed mechanism significantly improves the energy-efficiency of IEEE 802.11e EDCA, and in addition, contributes in improving the traffic performance (delay, throughput, etc.).
Climate-Driven Synchronized Growth of Alpine Trees in the Southeast Tibetan Plateau

Knowledge about the spatiotemporal tree growth variability and its associations with climate provides key insights into forest dynamics under future scenarios of climate change. We synthesized 17 tree-ring width chronologies from four tree species at the high-elevation sites in the southeast Tibetan Plateau (SETP) to study the regional tree growth variability and climate-growth relationships. Despite of diverse habitats and different physiological characteristics of these species, these tree-ring chronologies shared a significant common variance in SETP. An unprecedented increase in the shared variance is found along the latter half of the 20th century, coinciding with the enhancement of the frequency of extreme rings among chronologies. It is found that minimum winter temperature tends to be the dominant climate for trees in this region. The site-specific responses in cold (1965–1980) and warm (1990–2005) intervals by means of Fuzzy Cmeans (FCM) clustering reveal that the remarkable enhancement of growth synchrony among trees mainly occur in warm conditions. This is different from previous findings indicating that increased consistence among temperature sensitive tree rings in cold periods. This may be related to the reduced temperature sensitivity of regional tree growth as winter minimum temperature is lower than a certain threshold, which is in agreement with the “principle of ecological amplitude”. In addition, it is worth noting that precipitation in June have started to restrain the tree growth since the beginning of the 1980s, which is possibly an important contributor for synchronized growth among trees in SETP.
Machine Transliteration of Names from Different Language Origins into Chinese

Machine transliteration is the process of automatically transforming the script of a word from a source language to a target language based on the pronunciation. It is a useful complement for a machine translation system, and important in cross-language information retrieval. In this thesis, focusing on the problem of transliterating names from different source languages into Chinese, we build a HMM based machine transliteration system. We use four different approaches to modify the baseline system. The modified system achieves ACC of 0.370 and Mean F-score of 0.714, which is close to the precision of the state-of-art machine transliteration systems. We apply the constructed systems on 14 different language sets for further experiments. The results show that the language specific systems are more accurate and efficient than the generic system. They are particularly effective on certain language sets. On the other hand, the language specific systems have some limitations, as each system is only oriented to a certain language. Our future work will include improving the accuracy of our general transliteration model as well as expanding and modifying the language specific transliteration systems.
Surveillance Mining System for LowResolution Face Image Recognition UsingKernel Coupling

Video surveillance systems for face recognition are confronted with low-resolution face images. Low resolution face images coming from real time video does not give discriminant information to identify similar images in a dataset. Traditional method solved this problem through employing super- resolution (SR). But these are time-consuming, sophisticated SR algorithms. These algorithm are not suitable for real-time applications. To avoid the limitations, in this work, new feature extraction method for LR faces called coupled kernel distance metric learning (KCDML) is proposed without any SR pre-processing. By using a kernel trick and a specialized locality preserving criterion, we formulated the problem of coupled kernel embedding as an optimization problem whose aims are to search for the pair-wise sample staying as close as possible and to preserve the local structure intrinsic data geometry. Instead of an iterative solution, one single generalized Eigen- decomposition can be leveraged to compute the two transformation matrices for two classifications of data sets.
Overfitting at SemEval-2016 Task 3: Detecting Semantically Similar Questions in Community Question Answering Forums with Word Embeddings

This paper presents an approach for estimating the question-question similarity of an English dataset specified in Shared Task 3, subtask B of SemEval-2016. Given a new question and a set of the first 10 related questions retrieved by a search engine, participants are asked to produce a binary relevant/irrelevant judgement and rerank the related questions according to their similarity with respect to the original question. Our submitted system uses a 2-layer feed-forward neural network with the averages of word embedding vectors to predict the semantic similarity score of two questions. We also evaluate the results of Random Forests and Support Vector Machine in comparison to the Neural Network. Results on the test dataset show that the model achieves a Mean Average Precision 69.681.
Tumorigenesis and Neoplastic Progression Galectin-3 Expression Correlates with Apoptosis of Tumor-Associated Lymphocytes in Human Melanoma Biopsies

The immune system recognizes diverse melanoma antigens. However , tumors can evade the immune response, therefore growing and progressing. It has been reported that galectin-3 and galectin-1 can induce apoptosis of activated lymphocytes. However , there is strong evidence indicating that the regulation of galectins function in the human tumor microenvironment is a complex process that is influenced by diverse biological circumstances. Here , we have investigated 33 biopsies (eight primary and 25 metastases) from 24 melanoma patients (15–72 years old) and describe the correlation between the expression of galectin-3 or galectin-1 and the level of apoptosis of tumor-associated lymphocytes using immunohistochemistry and an in situ nick translation assay. The range of galectin-3-positive tumor cells varied between 0% and 93% and that of galectin-1-positive tumor cells varied between 5% and 97%. In addition, 23 27% of tumor-associated lymphocytes were apoptotic. Although our results show a correlation between galectin-3 expression and apoptosis of tumorassociated lymphocytes, we could not find such correlation with galectin-1. Considering the complex process of cancer immunoediting, various interacting factors must be considered. (Am J Pathol 2006, 168:1666–1675; DOI: 10.2353/ajpath.2006.050971)
Energy-Aware Programming Utilizing the SEEP Framework and Symbolic Execution

Resource-constrained devices, such as wireless sensor nodes, smart phones, tablet computers, and laptops most notably suffer from the limited amount of energy they have available. Yet, emerging battery technologies addressing this issue were unable to improve the situation significantly. This is well documented by rates of growth in the various technology areas. While clock speed, data storage, and data transfer rates have seen growth rates from factor 10 to 10 during the last three decades, battery life could merely be improved by a factor of 10. At the same time, research efforts led to energyaware system software exploiting the available energy resources in the most efficient way. Static and dynamic optimizations for energy-aware execution have been widely explored. Though, writing energyefficient programs in the first place has only received limited attention. To address this, we present SEEP, a framework which exploits symbolic execution and platform-specific energy profiles to provide the basis for energy-aware programming [1]. SEEP equips developers with the necessary knowledge to take energy demand into account during the task of writing programs.
Reducing Computational Complexity in Markov Decision Processes Using Abstract Actions

In this paper we present a new approach for the solution of Markov decision processes based on the use of an abstraction technique over the action space, which results in a set of abstract actions. Markovian processes have successfully solved many probabilistic problems such as: process control, decision analysis and economy. But for problems with continuous or high dimensionality domains, high computational complexity arises because the search space grows exponentially with the number of variables. In order to reduce computational complexity, our approach avoids the use of the whole domain actions during value iteration, calculating instead over the abstract actions that really operate on each state, as a state function. Our experimental results on a robot path planning task show an important reduction of computational complexity.
Analyzing Bilateral Trade Barriers Under Global Trade Context: A Gravity Model Adjusted Trade Intensity Index Approach

There are two strands of approaches gauging bilateral trade barriers: the gravity models and the trade intensity index. This paper integrates these two approaches by developing a new trade intensity index. This so called “gravity model adjusted trade intensity” (GMATI) index can reflect the short run trade barriers while controlling for the long run ones under global trade context. Based on 182 countries' export data during 1988–2005, we first estimate the expected bilateral trade level using a gravity model with comparative advantage effects. Then we apply the GMATI index for China. While the conventional trade intensity index shows that China trades less than its expected level, our GMATI index nevertheless suggests the opposite in most cases. It indicates that after considering the effect of country characteristics such as distance, economic size and comparative advantage, China's world trade face less trade barriers than the world average level.