Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effect of the Guide Tree on Multiple Sequence Alignments and Subsequent Phylogenetic Analysis

Many multiple sequence alignment methods (MSAs) use guide trees in conjunction with a progressive alignment technique to generate a multiple sequence alignment but use differing techniques to produce the guide tree and to perform the progressive alignment. In this paper we explore the consequences of changing the guide tree used for the alignment routine. We evaluate four leading MSA methods (ProbCons, MAFFT, Muscle, and ClustalW) as well as a new MSA method (FTA, for "Fixed Tree Alignment") which we have developed, on a wide range of simulated datasets. Although improvements in alignment accuracy can be obtained by providing better guide trees, in general there is little effect on the "accuracy" (measured using the SP-score) of the alignment by improving the guide tree. However, RAxML-based phylogenetic analyses of alignments based upon better guide trees tend to be much more accurate. This impact is particularly significant for ProbCons, one of the best MSA methods currently available, and our method, FTA. Finally, for very good guide trees, phylogenies based upon FTA alignments are more accurate than phylogenies based upon ProbCons alignments, suggesting that further improvements in phylogenetic accuracy may be obtained through algorithms of this type.
Complexity of Counting Output Patterns of Logic Circuits

Let C be a logic circuit consisting of s gates g1, g2,. , gs, then the output pattern of C for an input x e {0, 1}n is defined to be a vector (g1(x), g2(x),. , gs(x)) ∈ {0, 1}s of the outputs of g1, g2,. , gs for x. For each f: {0, 1}2 → {0, 1}, we define an f-circuit as a logic circuit where every gate computes f, and investigate computational complexity of the following counting problem: Given an f-circuit C, how many output patterns arise in C? We then provide a dichotomy result on the counting problem: We prove that the problem is solvable in polynomial time if f is PARITY or any degenerate function, while the problem is #P-complete even for constant-depth f-circuits if f is one of the other functions, such as AND, OR, NAND and NOR.
Trade, Aid, Remittances and Migration

I investigated whether migration is interrelated with trade, aid and remittances so that any policies that consider trade, aid and remittances also affect the decision to migrate. We developed and estimated an empirical model of Turkish migration to Germany and tested the model for the 1969-2004, using the cointegration technique. A single cointegrating vector is found among the gross migration inflows and the following explanatory variables: the relative income ratio between Germany and Turkey, the unemployment rates in Germany and Turkey, aid, the trade intensity variable and the ratio of manufacturing exports with Germany to total exports with Germany and remittances as a ratio of Turkish GDP. The results of this study show that migration, trade, aid and remittances are interrelated, however, migration will be better managed when the dynamic gains from trade and aid are considered. Hence, the broad-based and rapid economic development with increase in income is the only effective means of reducing migration pressures in a labour-surplus country. This is mainly because the income differential is the most significant factor in determining migration flows.
Tumor-Conditioned Macrophages Secrete Migration-Stimulating Factor: A New Marker for M2-Polarization, Influencing Tumor Cell Motility

Tumor-associated macrophages (TAMs) are key orchestrators of the tumor microenvironment directly affecting neoplastic cell growth, neoangiogenesis, and extracellular matrix remodeling. In turn, the tumor milieu strongly influences maturation of TAMs and shapes several of their features. To address the early macrophage (Mϕ) differentiation phase in a malignant context, we mimicked a tumor microenvironment by in vitro coculturing human blood monocytes with conditioned media from different cancer cell lines. Only 2 out of 16 tumor cell lines induced Mϕ differentiation due to secreted M-CSF isoforms, including high molecular mass species. A global gene profiling of tumor-conditioned Mϕ was performed. Comparison with other datasets (polarized M1-Mϕ, M2-Mϕ, and TAMs isolated from human tumors) highlighted the upregulation of several genes also shared by TAM and M2-polarized Mϕ. The most expressed genes were selenoprotein 1, osteoactivin, osteopontin, and, interestingly, migration-stimulating factor (MSF), a poorly studied oncofoetal isoform of fibronectin. MSF (present in fetal/cancer epithelial and stromal cells but not in healthy tissues) was never identified in Mϕ. MSF production was confirmed by immunohistochemistry in human TAMs. MSF was induced by M-CSF, IL-4, and TGFβ but not by proinflammatory stimuli. RNA and protein analysis clearly demonstrated that it is specifically associated with the M2 polarization of Mϕ. Tumor-conditioned Mϕ-derived MSFs strongly stimulated tumor cell migration, thus contributing to the motile phenotype of neoplastic cells. In conclusion, MSF is a new molecule associated with the M2 polarization of Mϕ and expressed by TAMs. Its biological function may contribute to Mϕ-mediated promotion of cancer cell invasion and metastasis.
ffects of mindfulness meditation training on anticipatory alpha modulation in rimary somatosensory cortex

During selective attention, ∼7–14 Hz alpha rhythms are modulated in early sensory cortices, suggesting a mechanistic role for these dynamics in perception. Here, we investigated whether alpha modulation can be enhanced by “mindfulness” meditation (MM), a program training practitioners in sustained attention to body and breath-related sensations. We hypothesized that participants in the MM group would exhibit enhanced alpha power modulation in a localized representation in the primary somatosensory neocortex in response to a cue, as compared to participants in the control group. Healthy subjects were randomized to 8-weeks of MM training or a control group. Using magnetoencephalographic (MEG) recording of the SI editation lpha rhythm modulation ttention EG lpha rhythm omatosensory cortex finger representation, we found meditators demonstrated enhanced alpha power modulation in response to a cue. This finding is the first to show enhanced local alpha modulation following sustained attentional training, and implicates this form of enhanced dynamic neural regulation in the behavioral effects of meditative practice. © 2011 Elsevier Inc. All rights reserved.
Anomaly-based detection using synergetic neural network

Network traffic anomaly detection has become a popular research tendency, as it can detect new type attacks in real time. However, the network traffic appears as a complex dynamic system, causing by the collaboration of many network factors. Although various methods have been proposed to detect anomalies, they are mostly based on the traditional statistical physics. In these methods, all factors are integrated to analyze the variation of the network traffic. But in fact, the changing trend of the network traffic at some moment is only determined by a few primary factors. This paper presents a non-statistical network traffic anomaly detection method based on the synergetic neural networks. In our method, a synergetic dynamic equation based on the order parameters is used to describe the complex behavior of the network traffic system. When the synergetic dynamic equation is evolved, only the order parameter determined by the primary factors can converge to 1. Therefore, the network traffic anomaly can be detected by referring to the primary factors. We evaluate our approach using the intrusion evaluation data set of the network traffic provided by the defense advanced research projects agency (DARPA). Experiment results show that our approach can effectively detect the network anomaly and achieve high detection probability and low false alarms rate.
Human Emotion Classification From Videos

In 2014, YouTube users uploaded 72 hours of new video content every one minute. Because of this, we wanted to extend the research made in emotion detection for text, to videos. We carried out this task by analyzing video input that we collect from our peers. The new dataset, created for this project, composed of more than 140 third-person videos consists of six emotions expressed by more than 25 different people. In this work we extract optical flow features to describe the movement of the facial expressions from the videos and classify them with a support vector machine classifier.
Common Currencies and FDI Flows

The paper investigates the impact of EMU on foreign direct investment flows. Using the option value approach to investment decisions, it is possible to show that exchange rate uncertainty hinders cross-border investment flows. By permanently fixing bilateral exchange rates, a currency union can then be expected to spur international investment. Results from a gravity model on a sample of OECD countries confirm the hypothesis that currency unions have a positive impact on FDI; moreover, adopting the same currency appears to do more than merely eliminating exchange rate volatility. These findings closely resemble those recently obtained in the trade literature. Copyright 2007 , Oxford University Press.
Assist disabled to control electronic devices and access computer functions by voice commands

To assist people with certain physical disabilities, this paper presents an assistive system based on speech recognition. Through the proposed system, the disabled can control electronic devices such as TV, fan, etc., through simple voice commands. The proposed system includes a mouse emulator and a keyboard emulator to assist the handicapped in accessing computer functions, such as browsing the Internet, sending emails, editing a document, etc.
Fast controller for a unity-power-factor PWM rectifier

This paper presents an analog implementation of a fast controller for a unity-power-factor (UPF) PWM rectifier. The best settling times of many popular controllers for this type of power converter are on the order of a few line cycles, corresponding to bandwidths under 20 Hz. The fast controller demonstrated in this paper can exercise control action at a rate comparable to the switching frequency rather than the line frequency. In order to accomplish this while maintaining unity power factor during steady-state operation, the fast controller employs a ripple-feedback cancellation scheme.
Optimization of LDO voltage regulators by NSGA-II

Two different low-dropout (LDO) voltage regulators are optimized by applying the Non-Dominated Sorting Genetic Algorithm II (NSGA-II). First, from a sensitivity analysis a set of design variables are selected to establish a reduced chromosome for performing multi-objective optimization by NSGA-II. The computed sensitivities are used to reduce the search spaces for the design variables included into the chromosome, so that the optimization process is accelerated. Second, a comparison between traditional and optimization-based design approaches is shown by considering 2 figures of merit (FoM). Finally, we list the results for optimizing 2 LDO voltage regulators for the 2 FoMs, and provide optimized sizes that are compared to traditional design.
Quantitative Evaluation of Behavioral Synthesis A pproaches for Reconfigurable Devices

State-of-the-art behavioral synthesis tools for reconfigurable archi- tectures barely have high-level transformations in order to achieve highly par- allelized implementations. If any, they apply loop unrolling to obtain a higher throughput. In this paper, we use the PARO behavioral synthesis tool which has the unique ability to perform both loop unrolling or loop partitioning. Loop un- rolling replicates the loop kernel and exposes the parallelism for hardware im- plementation. Whereas, partitioning tiles the loop program onto a regular array consisting of tightly coupled processing elements. The usage of the same design tool for both the variants enables for the first time, a quantitative evaluation of the two approaches with help of selected computationally intensive algorithms. The processor array approach achieves in all experiments a far better throughput, up to 61% more compared with loop unrolling because of its regularity and the clustering of resources into several processing elements.
Visual-similarity-based phishing detection

Phishing is a form of online fraud that aims to steal a user's sensitive information, such as online banking passwords or credit card numbers. The victim is tricked into entering such information on a web page that is crafted by the attacker so that it mimics a legitimate page. Recent statistics about the increasing number of phishing attacks suggest that this security problem still deserves significant attention. In this paper, we present a novel technique to visually compare a suspected phishing page with the legitimate one. The goal is to determine whether the two pages are suspiciously similar. We identify and consider three page features that play a key role in making a phishing page look similar to a legitimate one. These features are text pieces and their style, images embedded in the page, and the overall visual appearance of the page as rendered by the browser. To verify the feasibility of our approach, we performed an experimental evaluation using a dataset composed of 41 real-world phishing pages, along with their corresponding legitimate targets. Our experimental results are satisfactory in terms of false positives and false negatives.
6.962 Week 6 Summary: Approximate Inference Techniques for Graphs with Cycles

Graphical models are a tool for compactly specifying the probabilistic relationships between a set of random variables. They are used in application areas as diverse as coding, image processing, and arti cial intelligence. For many practical graphs, exact statistical inference is too complex to be tractable. This has motivated the development of a wide variety of approximate inference techniques. Recently, there has been a huge amount of interest in iterative estimation algorithms. This has been primarily motivated by the amazing empirical performance of a class of error{ correcting codes known as turbo codes. The turbo decoding algorithm has been shown to be equivalent to Pearl's belief propagation (BP) algorithm [4, 6] when applied to a graph with cycles. However, BP was only \supposed" to work for acyclic graphs. This has led to a variety of studies which attempt to understand the behavior of BP on graphs with cycles [7{9]. This summary reviews some recent ideas which provide further insight into the speci c approximation employed by loopy BP, and make the connections between BP and other approximate inference techniques like mean eld theory more apparent. The material is primarily drawn from two tutorial papers by Yedidia [11] and Jordan et. al. [2]. The focus will be on the structural similarities which connect the various algorithms.
Learning with Cross-Kernels and Ideal PCA

We describe how cross-kernel matrices, that is, kernel matrices between the data and a custom chosen set of `feature spanning points' can be used for learning. The main potential of cross-kernels lies in the fact that (a) only one side of the matrix scales with the number of data points, and (b) cross-kernels, as opposed to the usual kernel matrices, can be used to certify for the data manifold. Our theoretical framework, which is based on a duality involving the feature space and vanishing ideals, indicates that cross-kernels have the potential to be used for any kind of kernel learning. We present a novel algorithm, Ideal PCA (IPCA), which cross-kernelizes PCA. We demonstrate on real and synthetic data that IPCA allows to (a) obtain PCA-like features faster and (b) to extract novel and empirically validated features certifying for the data manifold.
Automatic image annotation based on fuzzy association rule and decision tree

Automatic image annotation based on traditional association rules exists the problem of "sharp boundary", which makes classification more fuzzy and inaccurate. Moreover, with the development of multimedia technology, the storage of image information is expanded quickly, massive image data will produce many redundant association rules, which will greatly decrease the accuracy and efficiency of image classification. In order to solve these two problems, this paper proposed an automatic image annotation model based on fuzzy association rules and decision tree. This approach firstly obtains association rules which represent the correlations between image features and high-level semantic concepts of training images. Then, the decision tree is added to reduce the unnecessary rules. As a result, algorithm complexity and time cost are reduced to a large degree. Experiments are based on two datasets Corel5k and IAPR-TC12. Experiments show the proposed method performs well in comparison with other automatic annotation method.
eCOMPASS – TR – 059 Customizable Contraction Hierarchies

Technology We consider the problem of quickly computing shortest paths in weighted graphs. Often, this is achieved in two phases: 1) derive auxiliary data in an expensive preprocessing phase, 2) use this auxiliary data to speedup the query phase. Instead, by adding a fast weight-customization phase, we extend Contraction Hierarchies to support a three-phase workflow: The expensive preprocessing is split into a phase exploiting solely the unweighted topology of the graph, as well as a lightweight phase that adapts the auxiliary data to a specific weight. We achieve this by basing our Customizable Contraction Hierarchies on nested dissection orders. We provide an in-depth experimental analysis on large road and game maps that shows that Customizable Contraction Hierarchies are a very practicable solution in scenarios where edge weights often change.
System Identification: Theory for the User, 2nd Edition (Ljung, L.; 1999) [On the Shelf]

Roboticists are increasingly dealing with challenging complex problems in system identification for model-based control, and this book lays a foundation of knowledge for the reader to absorb, which can help address the said challenges. That being said, the reader should prepare for a challenge when reading this book. Though it is an excellent text, it is not for the casual reader. A background in Fourier series and Laplace transforms helps as well,though Ljung does a good job with the early chapters, providing an overview of the concepts that will be used throughout the book. This book covers parametric and nonparametric methods, parameter estimation methods in the prediction error framework, frequency domain data and interpretations, various ways to compute estimates, recursive estimation techniques, model validation,and case studies. Each chapter ends with an extensive bibliography for further reading and, often, has an appendix for proofs and derivations. The author presents a thorough and complete picture of system identification as a methodology, set of tools,and practical approach to generating models from data. The author's work has been influential for decades, and anyone interested in buildingmodels from data would be well served to put the effort into mastering thismaterial.
The Guilded Classroom: Using Gamification to Engage and Motivate Undergraduates.

There is a breadth of psychological research that points to potential cognitive benefits of game play. Games engage and motivate learners while promoting mastery of skills and content knowledge. Further, thoughtfully applying gaming elements and structures to classroom environments, an approach called gamification, has the potential to optimize learning. This paper discusses theorydriven classroom gamification innovations implemented in an undergraduate educational psychology course and uses a case study approach to understand how these changes impacted students’ in-class learning experiences in positive ways. We will discuss specific interventions, students’ perceptions of these interventions, instructor reflection of the effects on student learning outcomes, and implications for classroom practice.
Basolateral amygdala bidirectionally modulates stress-induced hippocampal learning and memory deficits through a p25/Cdk5-dependent pathway

Significance Chronic stress has emerged in the epidemiologic literature as a risk factor for both psychiatric and neurodegenerative diseases. Thus, neurologic maladaptation to chronic stress is highly relevant to the pathogenesis of human diseases such as depression and Alzheimer's disease, yet it remains poorly understood. Here we report a study of the neural circuits and molecular pathways that govern the relationship between stress and cognition. We present data demonstrating that behavioral stress impairs cognitive function via activation of a specific direct neural circuit from the basolateral amygdala to the dorsal hippocampus. Moreover, we delineate a molecular mechanism by which behavioral stress is translated to hippocampal dysfunction via a p25/Cdk5 (cyclin-dependent kinase 5)-dependent pathway and epigenetic alterations of neuroplasticity-related gene expression. Repeated stress has been suggested to underlie learning and memory deficits via the basolateral amygdala (BLA) and the hippocampus; however, the functional contribution of BLA inputs to the hippocampus and their molecular repercussions are not well understood. Here we show that repeated stress is accompanied by generation of the Cdk5 (cyclin-dependent kinase 5)-activator p25, up-regulation and phosphorylation of glucocorticoid receptors, increased HDAC2 expression, and reduced expression of memory-related genes in the hippocampus. A combination of optogenetic and pharmacosynthetic approaches shows that BLA activation is both necessary and sufficient for stress-associated molecular changes and memory impairments. Furthermore, we show that this effect relies on direct glutamatergic projections from the BLA to the dorsal hippocampus. Finally, we show that p25 generation is necessary for the stress-induced memory dysfunction. Taken together, our data provide a neural circuit model for stress-induced hippocampal memory deficits through BLA activity-dependent p25 generation.
Finite state machine decomposition for low power

Clock-gating techniques have been shown to be very effective in the reduction of the switching activity in sequential logic circuits. The authors describe a new clock-gating technique based on finite state machine (FSM) decomposition. They compute two sub-FSMs that together have the same functionality as the original FSM. For all the transitions within one sub-FSM, the clock for the other sub-FSM is disabled. To minimize the average switching activity, they search for a small cluster of states with high stationary state probability and use it to create the small sub-FSM. This way one will have a small amount of logic that is active most of the time, during which is disabling a much larger circuit, the other sub-FSM. They provide a set of experimental results that show that power consumption can be substantially reduced, in some cases up to 80%.
Point: How Family Involvement Influences Organizational Change

ABSTRACT Family firms tend to exhibit substantial differences in their goals, governance, and resources in comparison to those of non-family firms. In this article, we briefly discuss these differences and their implications for organizational change and change management. We then summarize key findings in the literature pertaining to innovation and succession, two topics that have been extensively investigated and are closely allied to the topics of change and change management in family firms.