Miracle-dz/UniFAR-FTUs huggingface dataset:
Multivariate pattern connectivity

When we perform a cognitive task, multiple brain regions are engaged. Understanding how these regions interact is a fundamental step to uncover the neural bases of behavior. Most research on the interactions between brain regions has focused on the univariate responses in the regions. However, fine grained patterns of response encode important information, as shown by multivariate pattern analysis. In the present article, we introduce and apply multivariate pattern connectivity (MVPC): a technique to study the dependence between brain regions in humans in terms of the multivariate relations between their patterns of responses. MVPC characterizes the responses in each brain region as trajectories in region-specific multidimensional spaces, and models the multivariate relationship between these trajectories. Considering the fusiform face area (FFA) as a seed region, we used searchlight-based MVPC to reveal interactions between regions undetected by univariate functional connectivity analyses. MVPC (but not functional connectivity) identified significant interactions between right FFA and the right anterior temporal lobe, the right superior temporal sulcus, and the dorsal visual stream. Additionally, MVPC outperformed univariate connectivity in its ability to explain independent variance in the responses of individual voxels. In the end, MVPC uncovered different connectivity profiles associated with different representational subspaces of FFA: the first principal component of FFA shows differential connectivity with occipital and parietal regions implicated in the processing of low-level properties of faces, while the second and third components show differential connectivity with anterior temporal regions implicated in the processing of invariant representations of face identity. Author Summary Human behavior is supported by systems of brain regions that exchange infor-mation to complete a task. This exchange of information between brain regions leads to statistical relationships between their responses over time. Most likely, these relationships do not link only the mean responses in two brain regions, but also their finer spatial patterns. Analyzing finer response patterns has been a key advance in the study of responses within individual regions, and can be leveraged to study between-region interactions. To capture the overall statistical relationship between two brain regions, we need to describe each region’s responses with respect to dimensions that best account for the variation in that region over time. These dimensions can be different from region to region. We introduce an approach in which each region’s responses are characterized in terms of region-specific dimensions that best account for its responses, and the relationships between regions are modeled with multivariate linear models. We demonstrate that this approach provides a better account of the data as compared to standard functional connectivity, and we use it to discover multiple dimensions within the fusiform face area that have different connectivity profiles with the rest of the brain.
Title : Communication over the Internet using a 3 D Agent with Real-time Facial Expression Analysis , Synthesis and Text to Speech Capabilities 2

We present a system for Internet communication that enhances traditional text-based chatting with real-time analysis and synthesis of the chat parties’ facial expressions. It is composed of three main modules: a real-time facial expression analysis component, a 3D agent with facial expression synthesis and text-tospeech capabilities – a talking head, and a communication module. So far we have realized a prototype to find out attractive ways of communication on the Internet and are currently experimenting how to utilize this new type of modalities in chat communication.
Real trees in topology, geometry, and group theory

3 How do R-trees arise? 7 3.1 Convergence of based G-spaces . 7 3.2 Example: Flat tori . 8 3.3 R-trees as limits of based δ-hyperbolic G-spaces . 9 3.4 Finding approximate subtrees . 10 3.5 Selecting the basepoint and the Compactness Theorem . 12 3.6 Arc stabilizers . 13 3.7 Stable actions . 15
Uniqueness of Stationary Equilibrium Payoffs in the Baron-Ferejohn Model

An artificial substrate consisting of a 2-alkanoylamino-4-nitrophenyl phosphorylcholine hydroxide is used for determining the sphingomyelinase activity in an extract of human cells or tissues by incubating the substrate together with the extract, whereby sphingomyelinase-catalyzed hydrolysis of the substrate into the corresponding 2-alkanoylamino-4-nitrophenol and phosphorylcholine takes place in an amount proportional to the sphingomyelinase activity in the extract. Alkalinization of the 2-alkanoylamino-4-nitrophenol thereby produced to convert it into the alkali salt results in the development of a bright yellow color whose intensity is proportional to the amount of the substrate that has been hydrolyzed. Measurement of the intensity of the yellow color, for example, by means of a simple colorimeter or photometer, provides an accurate indication of the sphingomyelinase activity in the extract.
Online motion synthesis with minimal intervention control and formal safety guarantees

We present a framework for online coordinated obstacle avoidance with formal safety guarantees. Such a formally verified trajectory planner can be used in shared human-robot workspaces to guarantee safety. The obstacle avoidance is based on estimation of the human occupancy on two different time scales. A long-term plan is created based on a probabilistic task representation, learned by demonstration, and an estimate of the human occupancy to be avoided. Using an additional overapproximative, short-term prediction of human motion we guarantee that the robot can always account for sudden or reflex movements. We demonstrate our two-level obstacle avoidance in simulation. The results show that our method reduces the number of safety stops one would encounter when using only the formal safety verification, and synthesizes alternative movement plans that preserves the coordination observed in the original demonstrations.
Damaris: Addressing Performance Variability in Data Management for Post-Petascale Simulations

With exascale computing on the horizon, reducing performance variability in data management tasks (storage, visualization, analysis, etc.) is becoming a key challenge in sustaining high performance. This variability significantly impacts the overall application performance at scale and its predictability over time. In this article, we present Damaris, a system that leverages dedicated cores in multicore nodes to offload data management tasks, including I/O, data compression, scheduling of data movements, in situ analysis, and visualization. We evaluate Damaris with the CM1 atmospheric simulation and the Nek5000 computational fluid dynamic simulation on four platforms, including NICS’s Kraken and NCSA’s Blue Waters. Our results show that (1) Damaris fully hides the I/O variability as well as all I/O-related costs, thus making simulation performance predictable; (2) it increases the sustained write throughput by a factor of up to 15 compared with standard I/O approaches; (3) it allows almost perfect scalability of the simulation up to over 9,000 cores, as opposed to state-of-the-art approaches that fail to scale; and (4) it enables a seamless connection to the VisIt visualization software to perform in situ analysis and visualization in a way that impacts neither the performance of the simulation nor its variability. In addition, we extended our implementation of Damaris to also support the use of dedicated nodes and conducted a thorough comparison of the two approaches—dedicated cores and dedicated nodes—for I/O tasks with the aforementioned applications.
Stability Analysis of Discrete-time Lure Systems with Slope-restricted Odd Monotonic Nonlinearities

Many nonlinear dynamical systems can be written as Lure systems, which are described by a linear time-invariant system interconnected with a diagonal static sector-bounded nonlinearity. Sufficient conditions are derived for the global asymptotic stability analysis of discrete-time Lure systems in which the nonlinearities have restricted slope and/or are odd, which is the usual case in real applications. A Lure-Postnikov-type Lyapunov function is proposed that is used to derive sufficient analysis conditions in terms of linear matrix inequalities (LMIs). The derived stability critera are provably less conservative than criteria published in the literature, with numerical examples indicating that conservatism can be reduced by orders of magnitude.
Folic acid-tethered Pep-1 peptide-conjugated liposomal nanocarrier for enhanced intracellular drug delivery to cancer cells: conformational characterization and in vitro cellular uptake evaluation

Background A novel dual ligand–modified liposome, folic acid-tethered Pep-1 peptide-conjugated liposomal nanocarrier (FP-Lipo), was designed to overcome the nonselectivity of conventional penetrating peptide-tagged nanoparticulates and to provide the advantage of selective targeting of the folic acid receptor, which is frequently overexpressed on epithelial cancer cells. Methods FP-Lipo was prepared by a sequential process of formation of a maleimide-derivatized small unilamellar vesicle, postinsertion of distearoyl phosphatidyl ethanolamine-polyethylene glycol 2000–folate to the vesicle, and Pep-1 peptide conjugation via thiol-maleimide linkage. Conformational and physical characteristics of the FP-Lipo nanocarriers were investigated for the extent of Pep-1 peptide and folic acid on the surface, vesicle size, and zeta potential. In vitro cellular uptake behaviors of the novel carrier containing a fluorescein dextran isothiocyanate probe were examined by spectrophotometry or by confocal laser scanning microscopy. Results A novel nanocarrier bearing approximately 750 folate ligands and 100 penetrating peptides per vesicle was successfully prepared. The physical properties were as follows: 140 nm in size; 5 mV in zeta potential; less than 0.3 in polydispersity index. An in vitro cellular uptake study revealed that the FP-Lipo nanocarrier system exhibited more than twofold enhanced translocation into the folic acid receptor–positive HeLa cells compared with the single Pep-1 peptide–modified liposome. Meanwhile, its cellular association and internalization into the folic acid receptor–negative normal HaCaT cells was comparable with that of Pep-1 peptide–modified liposome. Conclusion An advanced dual ligand-modified liposome is potentially useful for the treatment of folic acid receptor–positive tumors with high translocation capability of the penetrating peptide–modified liposome.
A Combined Approach for Feature Subset Selection and Size Reduction for High Dimensional Data

selection of relevant feature from a given set of feature is one of the important issues in the field of data mining as well as classification. In general the dataset may contain a number of features however it is not necessary that the whole set features are important for particular analysis of decision making because the features may share the common information‟s and can also be completely irrelevant to the undergoing processing. This generally happen because of improper selection of features during the dataset formation or because of improper information availability about the observed system. However in both cases the data will contain the features that will just increase the processing burden which may ultimately cause the improper outcome when used for analysis. Because of these reasons some kind of methods are required to detect and remove these features hence in this paper we are presenting an efficient approach for not just removing the unimportant features but also the size of complete dataset size. The proposed algorithm utilizes the information theory to detect the information gain from each feature and minimum span tree to group the similar features with that the fuzzy c-means clustering is used to remove the similar entries from the dataset. Finally the algorithm is tested with SVM classifier using 35 publicly available real-world high-dimensional dataset and the results shows that the presented algorithm not only reduces the feature set and data lengths but also improves the performances of the classifier.
JBSP: A BSP Programming Library in Java

Abstract In this paper, we introduce a Java implementation of the Bulk Synchronous Parallel (BSP) model. JBSP (a Java-based BSP system) uses a two-daemon architecture which makes a clear separation of the computation and communication involved in parallel programs. Java threads are used in the implementation of the JBSP system to realize user defined JBSP tasks as well as to carry out system activities. The JBSP library provides programmers with both explicit message-passing and remote memory access communication methods. In addition, a set of benchmark experiments was also carried out to compare the performance of JBSP with that of BSPlib. Results show that there is considerable overhead in communications in the current implementation of JBSP due to the use of Java's Object Serialization. It was also found that although BSPlib outperforms JBSP in all the cases, JBSP is more scalable in terms of the BSP machine-parameters.
Biological mechanisms underlying inter- and intra- individual variability of face cognition

1 Abstract Given the importance of correctly perceiving and remembering faces for successful social interaction, face processing is one of the most widely studied cognitive domain in behavioral, neurophysiological and neuroimaging research, particularly, based on a group-mean approach. However, above mean differences, interand intra-individual variability in face processing provide valuable information for investigating the underlying mechanisms and binding the behavioral and neural substrates for better understanding of face processing. In my dissertation I investigated the biological mechanisms underlying face cognition from an interand intra-individual variability perspective at the genetic, neural, and behavioral levels. The neural activities related to face processing are measured by event-related potentials (ERPs) and their trial-by-trial latency variability are estimated using a novel and well-established method, Residue Iteration Decomposition (RIDE). Study 1 demonstrates the reliability of RIDE in extracting single-trial parameters of the P3b component, which is used in the investigation of the neural basis of intra-subject variability (ISV) in face processing speed in Study 2. In the Study 2, individual differences in ISV of face processing speed, measured at both behavioral and neural levels during a face processing task, are studied in their genetic variation. The results suggest that individual differences in ISV are related not only to the COMT Val158Met polymorphism, but also to the type of cognitive processing (e.g., memory domain). Moreover, we showed that ISV in reaction time can be partially explained by ISV in the speed of central cognitive processes. Furthermore, the individual differences approach in Study 3, provided valuable and novel information beyond the common group-mean approach applied in the N1/N170-related research. Based on this approach, not only we could replicate previous findings that the N170 predicts individual differences in face cognition abilities, but also we could decompose individual differences in the N170 into a domain-general and a face-specific part with different predictive powers. Moreover, we showed that top-down modulations on the N170 have separable and qualitatively different relationships to face cognition abilities. In summary, the integrated results from different studies in my dissertation demonstrate the psychological importance of the information provided by interand intra-individual variability in face processing in the investigation of its underlying biological mechanisms.
Pushing back the limit of ab-initio quantum transport simulations on hybrid supercomputers

The capabilities of CP2K, a density-functional theory package and OMEN, a nano-device simulator, are combined to study transport phenomena from first-principles in unprecedentedly large nanostructures. Based on the Hamiltonian and overlap matrices generated by CP2K for a given system, OMEN solves the Schrödinger equation with open boundary conditions (OBCs) for all possible electron momenta and energies. To accelerate this core operation a robust algorithm called SplitSolve has been developed. It allows to simultaneously treat the OBCs on CPUs and the Schrödinger equation on GPUs, taking advantage of hybrid nodes. Our key achievements on the Cray-XK7 Titan are (i) a reduction in time-to-solution by more than one order of magnitude as compared to standard methods, enabling the simulation of structures with more than 50000 atoms, (ii) a parallel efficiency of 97% when scaling from 756 up to 18564 nodes, and (iii) a sustained performance of 15 DP-PFlop/s.
A Reference Architecture for Generation Systems

We present the RAGS (Reference Architecture for Generation Systems) framework, a specification of an abstract Natural Language Generation (NLG) system architecture to support sharing, re-use, comparison and evaluation of NLG technologies. We argue that the evidence from a survey of actual NLG systems calls for a different emphasis in a reference proposal from that seen in similar initiatives in information extraction and multimedia interfaces. We introduce the framework itself, in particular the two-level data model that allows us to support the complex data requirements of NLG systems in a flexible and coherent fashion, and describe our efforts to validate the framework through a range of implementations.
Developing the New Web-based Training Environments: towards an Instructional-based Learning Object Strategy

Learning objects have changed the way computer-based instruction is viewed and used and have provided new opportunities for delivering customized and personalized e-learning. Since so far there are a lot of research and development efforts concerning the technical implementation of the learning object concept and the interest for the subsequent development of standards is very high. Nevertheless the learning object concept, as used in design and development of e-learning, has also instructional – pedagogical value to offer. This paper discusses a research effort aiming to develop a web-based training service by stressing the instructional benefits of the learning object-based implementation. During this effort a certain strategy guided the whole design and development process while certain learning theories were associated to the learning strategy followed. The idea behind this association was to highlight the advantages from applying the learning object idea to create appealing e-learning courses for adult learners; advantages that are not directly related to any technological or standard implementation but contribute a lot to the pedagogical quality of an e-learning service which is usually underestimated or neglected. The main purpose of this effort was to prepare, structure and develop web-based courses and training materials in a way that would provide sound instructional benefits to the main recipients of such services: adult learners and trainees.
Analyzing Kernel Matrices for the Identification of Differentially Expressed Genes

One of the most important applications of microarray data is the class prediction of biological samples. For this purpose, statistical tests have often been applied to identify the differentially expressed genes (DEGs), followed by the employment of the state-of-the-art learning machines including the Support Vector Machines (SVM) in particular. The SVM is a typical sample-based classifier whose performance comes down to how discriminant samples are. However, DEGs identified by statistical tests are not guaranteed to result in a training dataset composed of discriminant samples. To tackle this problem, a novel gene ranking method namely the Kernel Matrix Gene Selection (KMGS) is proposed. The rationale of the method, which roots in the fundamental ideas of the SVM algorithm, is described. The notion of ''the separability of a sample'' which is estimated by performing -like statistics on each column of the kernel matrix, is first introduced. The separability of a classification problem is then measured, from which the significance of a specific gene is deduced. Also described is a method of Kernel Matrix Sequential Forward Selection (KMSFS) which shares the KMGS method's essential ideas but proceeds in a greedy manner. On three public microarray datasets, our proposed algorithms achieved noticeably competitive performance in terms of the B.632+ error rate.
Implementation of Multiwavelet Transform coding for lossless image compression

Today the performance of the Multiwavelets plays a vital role in the field of image processing which is well known from Wavelet theory. Now a day's Multiwavelets take wavelets a step ahead in its performance. In this paper the performance of the Integer Multiwavelet Transform (IMWT) for Lossless has been studied. The IMWT provides good result with the image reconstructed. In this paper the performance of the IMWT for lossless compression of images with Magnitude set coding have been obtained. The Transform coefficients are coded with Magnitude set coding and run length Encoding techniques. The Transform coefficient matrix is coded without considering the sign, which make uses of Magnitude Set-Variable Length Integer Representation. The sign information of the coefficients is coded inform of bit plane with zero thresholds. The Bit plane obtained can be used as it is or it can be coded to reduce the bits per pixel (bpp). The Simulation was done using Mat lab.
Physical-layer network coding via low density lattice codes

We present a new PLNC scheme based on the recently developed compute-and-forward (C&F) paradigm and low density lattice codes (LDLC). LDLC possesses high coding gain and good algebraic structure which is inherently suitable for C&F. We also show that the ring-based constellation can be used to improve the average rate per dimension.
Characterizing web application performance for maximizing service providers' profits in clouds

A number of challenges in implementing cloud technique related to further improving Web application performance and decreasing the cost. In order to achieve high profits, cloud-based web application providers must carefully balance cloud resources and dynamic workloads. However, this task is usually difficulty because of the complex nature of most web application. In this paper, we presented a predictive performance model to analyze such applications and to determine when and how much resource to allocate to each tier of an application. In addition, we proposed a new profit model to describe revenues specified by the Service Level Agreement (SLA) and costs generated by leased resources. Furthermore, we employed profit driven model to guide our resource management algorithms to maximize the profits earned to the service providers. We also designed and implemented a simulation experiment on CloudSim that adopts our proposed methodology. Experimental results indicated that our model faithfully captures the performance and resources are allocated properly in response to the changing workload, thus the goal of maximizing the profit has been achieved.