Miracle-dz/UniFAR-FTUs huggingface dataset:
Tetraspanin CD9 Regulates Cell Contraction and Actin Arrangement via RhoA in Human Vascular Smooth Muscle Cells

The most prevalent cardiovascular diseases arise from alterations in vascular smooth muscle cell (VSMC) morphology and function. Tetraspanin CD9 has been previously implicated in regulating vascular pathologies; however, insight into how CD9 may regulate adverse VSMC phenotypes has not been provided. We utilized a human model of aortic smooth muscle cells to understand the consequences of CD9 deficiency on VSMC phenotypes. Upon knocking down CD9, the cells developed an abnormally small and rounded morphology. We determined that this morphological change was due to a lack of typical parallel actin arrangement. We also found similar total RhoA but decreased GTP-bound (active) RhoA levels in CD9 deficient cells. As a result, cells lacking a full complement of CD9 were less contractile than their control treated counterparts. Upon restoration of RhoA activity in the CD9 deficient cells, the phenotype was reversed and cell contraction was restored. Conversely, inhibition of RhoA activity in the control cells mimicked the CD9-deficient cell phenotype. Thus, alteration in CD9 expression was sufficient to profoundly disrupt cellular actin arrangement and endogenous cell contraction by interfering with RhoA signaling. This study provides insight into how CD9 may regulate previously described vascular smooth muscle cell pathophysiology.
Automatic Identification of Bee Movement Using Human Trainable Models of Behavior

Identifying and recording subject movements is a critical, but time-consuming step in animal behavior research. The task is especially onerous in studies involving social insects because of the number of animals that must be observed simultaneously. To address this, we present a system that can automatically analyze animal movements, and label them, on the basis of examples provided by a human expert. Further, in conjunction with identifying movements, our system also recognizes the behaviors made up of these movements. Thus, with only a small training set of hand labeled data, the system automatically completes the entire behavioral labeling process. For our experiments, activity in an observation hive is recorded on video, that video is converted into location information for each animal by a vision-based tracker, and then numerical features such as velocity and heading change are extracted. The features are used in turn to label the sequence of movements for each observed animal. Our approach uses a combination of k-nearest neighbor (KNN) classification and hidden Markov model (HMM) techniques. The system was evaluated on several hundred honey bee trajectories extracted from a 15 minute video of activity in an observation hive. Additionally, simulated data and models were used to test the validity of the behavioral recognition techniques.
Convergence Rates for Greedy Kaczmarz Algorithms, and Randomized Kaczmarz Rules Using the Orthogonality Graph

The Kaczmarz method is an iterative algorithm for solving systems of linear equalities and inequalities, that iteratively projects onto these constraints. Recently, Strohmer and Vershynin [J. Fourier Anal. Appl. , 15(2):262-278, 2009] gave a non-asymptotic convergence rate analysis for this algorithm, spurring numerous extensions and generalizations of the Kaczmarz method. Rather than the randomized selection rule analyzed in that work, in this paper we instead discuss greedy and approximate greedy selection rules. We show that in some applications the computational costs of greedy and random selection are comparable, and that in many cases greedy selection rules give faster convergence rates than random selection rules. Further, we give the first multi-step analysis of Kaczmarz methods for a particular greedy rule, and propose a provably-faster randomized selection rule for matrices with many pairwise-orthogonal rows.
Monitoring cerebral ischemia during carotid endarterectomy and stenting

Abstract Current therapy for carotid stenosis mainly includes carotid endarterectomy and endovascular stenting, which may incur procedure-related cerebral ischemia. Several methods have been employed for monitoring cerebral ischemia during surgery, such as awake neurocognitive assessment, electroencephalography, evoked potentials, transcranial Doppler, carotid stump pressure, and near infrared spectroscopy. However, there is no consensus on the gold standard or the method that is superior to others at present. Keeping patient awake for real time neurocognitive assessment is effective and essential; however, not every surgeon adopts it. In patients under general anesthesia, cerebral ischemia monitoring has to rely on non-awake technologies. The advantageous and disadvantageous properties of each monitoring method are reviewed.
Can the Site-Frequency Spectrum Distinguish Exponential Population Growth from Multiple-Merger Coalescents?

The ability of the site-frequency spectrum (SFS) to reflect the particularities of gene genealogies exhibiting multiple mergers of ancestral lines as opposed to those obtained in the presence of population growth is our focus. An excess of singletons is a well-known characteristic of both population growth and multiple mergers. Other aspects of the SFS, in particular, the weight of the right tail, are, however, affected in specific ways by the two model classes. Using an approximate likelihood method and minimum-distance statistics, our estimates of statistical power indicate that exponential and algebraic growth can indeed be distinguished from multiple-merger coalescents, even for moderate sample sizes, if the number of segregating sites is high enough. A normalized version of the SFS (nSFS) is also used as a summary statistic in an approximate Bayesian computation (ABC) approach. The results give further positive evidence as to the general eligibility of the SFS to distinguish between the different histories.
Private Processing of Outsourced Network Functions: Feasibility and Constructions

Aiming to reduce the cost and complexity of maintaining networking infrastructures, organizations are increasingly outsourcing their network functions (e.g., firewalls, traffic shapers and intrusion detection systems) to the cloud, and a number of industrial players have started to offer network function virtualization (NFV)-based solutions. Alas, outsourcing network functions in its current setting implies that sensitive network policies, such as firewall rules, are revealed to the cloud provider. In this paper, we investigate the use of cryptographic primitives for processing outsourced network functions, so that the provider does not learn any sensitive information. More specifically, we present a cryptographic treatment of privacy-preserving outsourcing of network functions, introducing security definitions as well as an abstract model of generic network functions, and then propose a few instantiations using partial homomorphic encryption and public-key encryption with keyword search. We include a proof-of-concept implementation of our constructions and show that network functions can be privately processed by an untrusted cloud provider in a few milliseconds.
Variation in the interaction between familial and reproductive factors on the risk of breast cancer according to age, menopausal status, and degree of familiality.

BACKGROUND Studies have found that reproductive factors might have a variable effect on the occurrence of breast cancer (BC) according to the existence or not of a family history of BC. The effect of a family history of BC on the risk of BC may also vary according to the age at diagnosis and the degree of kinship. This may confound the relation between familial risk and reproductive factors. A combined analysis was performed to study the interaction between familial risk and reproductive factors according to degree of familiality, age at interview and menopausal status. METHODS The present analysis included 2948 cases and 4170 controls in seven case-control studies from four countries. The combined relative risks were estimated using a Bayesian random-effects logistic regression model. RESULTS The main effects of reproductive life factors on the risk of BC are in agreement with previous studies. Two-way interactions between subject's age or menopausal status and a family history of BC were not significant. Although the three-way interaction between age, familial risk and parity was not significant, familial risk seemed to be increased slightly for women with high parity compared with women with low parity in the older age group, and seemed to be slightly decreased for women with high parity compared with women with low parity in younger women. The subject's age also appeared to have an effect on the interaction between familial risk and the age at first childbirth (P = 0.1). CONCLUSIONS A possible influence of reproductive and menstrual factors on familial risk of BC has been suggested previously and was also evident in the present study. Three-way interactions between age, family history and parity or age at first childbirth might exist and they merit further investigation.
Scheduling using Superposition Coding: Design and Software Radio implementation

Cellular base stations typically orthogonalize downlink transmissions, although this approach is not always throughput-optimal. Indeed, it can be shown that removing the orthogonality constraint (as in Superposition Coding) can provide significant benefits in some scenarios. Based on this principle, we propose a scheduling algorithm for a two-user downlink that leverages the disparity in their respective channel qualities. By a judicious reallocation of the transmit power and bandwidth, this algorithm improves the throughput to both users vis-à-vis an orthogonal scheme. We design a software-defined radio platform to implement the scheduler and experimentally verify the gains promised by theory.
Gut Microbiota: A Potential Regulator of Neurodevelopment

During childhood, our brain is exposed to a variety of environmental inputs that can sculpt synaptic connections and neuronal circuits, with subsequent influence on behavior and learning processes. Critical periods of neurodevelopment are windows of opportunity in which the neuronal circuits are extremely plastic and can be easily subjected to remodeling in response to experience. However, the brain is also more susceptible to aberrant stimuli that might lead to altered developmental trajectories. Intriguingly, postnatal brain development is paralleled by the maturation of the gut microbiota: the ecosystem of symbionts populating our gastro-intestinal tract. Recent discoveries have started to unveil an unexpected link between the gut microbiome and neurophysiological processes. Indeed, the commensal bacteria seem to be able to influence host behavioral outcome and neurochemistry through mechanisms which remain poorly understood. Remarkably, the efficacy of the gut flora action appears to be dependent on the timing during postnatal life at which the host gut microbes’ signals reaches the brain, suggesting the fascinating possibility of critical periods for this microbiota-driven shaping of host neuronal functions and behavior. Therefore, to understand the importance of the intestinal ecosystem’s impact on neuronal circuits functions and plasticity during development and the discovery of the involved molecular mechanisms, will pave the way to identify new and, hopefully, powerful microbiota-based therapeutic interventions for the treatment of neurodevelopmental and psychiatric diseases.
Time series clustering of weather observations in predicting climb phase of aircraft trajectories

Reliable trajectory prediction is paramount in Air Traffic Management (ATM) as it can increase safety, capacity, and efficiency, and lead to commensurate fuel savings and emission reductions. Inherent inaccuracies in forecasting winds and temperatures often result in large prediction errors when a deterministic approach is used. A stochastic approach can address the trajectory prediction problem by taking environmental uncertainties into account and training a model using historical trajectory data along with weather observations. With this approach, weather observations are assumed to be realizations of hidden aircraft positions and the transitions between the hidden segments follow a Markov model. However, this approach requires input observations, which are unknown, although the weather parameters overall are known for the pertinent airspace. We address this problem by performing time series clustering on the current weather observations for the relevant sections of the airspace. In this paper, we present a novel time series clustering algorithm that generates an optimal sequence of weather observations used for accurate trajectory prediction in the climb phase of the flight. Our experiments use a real trajectory dataset with pertinent weather observations and demonstrate the effectiveness of our algorithm over time series clustering with a k-Nearest Neighbors (k-NN) algorithm that uses Dynamic Time Warping (DTW) Euclidean distance.
Internal statistics of a single natural image

Statistics of ‘natural images’ provides useful priors for solving under-constrained problems in Computer Vision. Such statistics is usually obtained from large collections of natural images. We claim that the substantial internal data redundancy within a single natural image (e.g., recurrence of small image patches), gives rise to powerful internal statistics, obtained directly from the image itself. While internal patch recurrence has been used in various applications, we provide a parametric quantification of this property. We show that the likelihood of an image patch to recur at another image location can be expressed parametricly as a function of the spatial distance from the patch, and its gradient content. This “internal parametric prior” is used to improve existing algorithms that rely on patch recurrence. Moreover, we show that internal image-specific statistics is often more powerful than general external statistics, giving rise to more powerful image-specific priors. In particular: (i) Patches tend to recur much more frequently (densely) inside the same image, than in any random external collection of natural images. (ii) To find an equally good external representative patch for all the patches of an image, requires an external database of hundreds of natural images. (iii) Internal statistics often has stronger predictive power than external statistics, indicating that it may potentially give rise to more powerful image-specific priors.
Cloud path selection using Fuzzy Analytic Hierarchy Process for offloading in Mobile Cloud Computing

Mobile Cloud Computing (MCC) gains a significant attention in research domain due to the enhancement of capability and popularity of mobile devices in recent years. Due to the development of a variety of clouds which provides similar services to mobile devices, it becomes quite difficult to choose an optimum cloud for offloading the computationally intensive applications. The aim of selecting the cloud path is to select an optimum cloud from a set of clouds which provides the same cloud services. The selection of optimum cloud saves the execution time and availability of resources during the migration of computational tasks. For making final decision about selection of cloud path, many criteria need to be considered such as cost, speed, capacity, privacy, availability. In this paper, Fuzzy Analytic Hierarchy Process (FAHP) based multiple criteria decision making approach is proposed which allows the user to select an optimal choice from a set of available choices. This paper uses FAHP technique, a fuzzy extension of Analytic hierarchy Process (AHP) to compare a set of clouds.
Hands-on the process control: users preferences and associations on hand movements

Design of future smart environments is challenging especially when we are talking about smart control room environments for advanced process control activity. One key element of our concept of a future smart control room is a movable multi-touch table called "Affordance table". In this paper we present the results of a user test in which we explored what kind of hand gestures are intuitive and comfortable when using multi-touch sensitive displays. Overall, simple gestures were preferred over more complex ones, and dimensions of gestures such as duration, force and direction have an impact on participants' evaluations. Some gestures also evoked similar associations among them. Our results suggest that user preferences and associations should be carefully considered when mapping gestures to system commands.
Probabilistic classification between foreground objects and background

Tracking of deformable objects like humans is a basic operation in many surveillance applications. Objects are detected as they enter the field of view of the camera and they are then tracked during the time they are visible. A problem with tracking deformable objects is that the shape of the object should be re-estimated for each frame. We propose a probabilistic framework combining object detection, tracking and shape deformation. We make use of the probabilities that a pixel belongs to the background, a new object or any of the known objects. Instead of using arbitrary thresholds for deciding to which class the pixel should be assigned we assign the pixel based on the Bayes criterion. Preliminary experiments show the classification error drops to about half the error of traditional approaches.
Sequential Monte Carlo methods for Discharge Time Prognosis in Lithium-Ion Batteries

This paper presents the implementation of a particlefiltering-based prognostic framework that allows estimating the state-of-charge (SOC) and predicting the discharge time of energy storage devices (more specifically lithium-ion batteries). The proposed approach uses an empirical statespace model inspired in the battery phenomenology and particle-filtering to study the evolution of the SOC in time; adapting the value of unknown model parameters during the filtering stage and enabling fast convergence for the state estimates that define the initial condition for the prognosis stage. SOC prognosis is implemented using a particlefiltering-based framework that considers a statistical characterization of uncertainty for future discharge profiles.
Identifying Threats in Diplomatic Correspondence CS 229 Final Project

Since the inception of the field, International Relations (hereafter IR) scholars have placed substantial emphasis on the importance of threats and credibility in explaining interstate behavior— especially in the midst of crises that may precipitate armed conflict. Countless studies of theoretical, historical, and formal flavors have been devoted to the study of threat perception (Jervis 1976; Sartori 2002; Stein 2013). Empirical studies that actually measure and evaluate perceived threat, on the other hand, have been lacking. The paucity of micro-level data has hampered analysis on the use and effectiveness of threats. IR academics have long recognized this shortcoming but been unable to address it. Some recent studies have used laboratory experiments to assess threat perception within the general public (Rousseau and Garcia-Retamero 2007), but much of this research fails to explain actual policy decisions by governmental elites. To better understand threat perception, the IR field requires new empirical approaches that capture this elusive but crucial concept in a large-scale and systematic way. Machine learning may resolve this.
Draft from Graphical Models and Image Processing , vol . 58 , no . 5 , September 1996 Reflectance Analysis for 3 D Computer Graphics Model Generation Running head : Reflectance Analysis for 3 D CG Model Generation

For synthesizing realistic images of a real three dimensional object, reflectance properties of the object surface, as well as the object shape, need to be measured. This paper describes one approach to create a three dimensional object model with physically correct reflectance properties by observing a real object. The approach consists of three steps. First, a sequence of range images and color images is measured by rotating a real object on a rotary table with fixed viewing and illumination directions. Then, the object shape is obtained as a collection of triangular patches by merging multiple range images. Secondly, by using the recovered object shape, color pixel intensities of the color image sequence are mapped to the object surface and separated into the diffuse and specular reflection components. Finally, the separated reflection components are used to estimate parameters of the Lambertian reflection model and a simplified Torrance-Sparrow reflection model. We have successfully tested our approach using images of a real object. Synthesized images of the object under arbitrary illumination conditions are shown in this paper.
Path Integral Monte Carlo and Density Functional Molecular Dynamics Simulations of Hot, Dense Helium

Two first-principles simulation techniques, path integral Monte Carlo (PIMC) and density functional molecular dynamics (DFT-MD), are applied to study hot, dense helium in the density-temperature range of 0.387 - 5.35 g/cc and 500 K - 1.28x10^8 K. One coherent equation of state (EOS) is derived by combining DFT-MD data at lower temperatures with PIMC results at higher temperatures. Good agreement between both techniques is found in an intermediate temperature range. For the highest temperatures, the PIMC results converge to the Debye-Hueckel limiting law. In order derive the entropy, a thermodynamically consistent free energy fit is introduced that reproduces the internal energies and pressure derived from the first-principles simulations. The equation of state is presented in form of a table as well as a fit and is compared with chemical models. In addition, the structure of the fluid is analyzed using pair correlation functions. Shock Hugoniot curves are compared with recent laser shock wave experiments.
Sentiment Analysis on Commodity Forecasts using Random Forest with Bayesian Optimization

6 Commodity market can have significant impact on many aspects of 7 economics and the price of commodities concern investors and market 8 analyst. Price forecasts reports are produced at daily-basis on Internet. 9 This paper presents a novel approach to classify sentiment of a corpus of 10 financial report by using Random Forest. Optimized with Bayesian Optimization 11 technique, Random Forest Model produces better result than conventional Naïve 12 Bayes algorithm. 13
Scalable Frequent Itemset Mining Using Heterogeneous Computing : Parapriori Algorithm

Association Rule mining is one of the dominant tasks of data mining, which concerns in finding frequent itemsets in large volumes of data in order to produce summarized models of mined rules. These models are extended to generate association rules in various applications such as e-commerce, bio-informatics, associations between image contents and non image features, analysis of effectiveness of sales and retail industry, etc. In the vast increasing databases, the major challenge is the frequent itemsets mining in a very short period of time. In the case of increasing data, the time taken to process the data should be almost constant. Since high performance computing has many processors, and many cores, consistent run-time performance for such very large databases on association rules mining is achieved. We, therefore, must rely on high performance parallel and/or distributed computing. In literature survey, we have studied the sequential Apriori algorithms and identified the fundamental problems in sequential environment and parallel environment. In our proposed ParApriori, we have proposed parallel algorithm for GPGPU, and we have also done the results analysis of our GPU parallel algorithm. We find that proposed algorithm improved the computing time, consistency in performance over the increasing load. The empirical analysis of the algorithm also shows that efficiency and scalability is verified over the series of datasets experimented on many core GPU platform.