Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantitative EEG and Live Z-Score Neurofeedback—Current Clinical and Scientific Context

This article discusses the relevance of quantitative EEG (QEEG) and live z-score training (LZT) to the field of mental health in general, and to neurofeedback in particular. We examine what practitioners might learn about clients when QEEG is used for assessment, and the relevance of LZT as a treatment modality. Clinicians can benefit from viewing the brain as a dynamic system, and this point of view can provide a foundation for QEEG and LZT. This approach emphasizes understanding the value of brain activation as a basis for observed symptoms and behaviors. Of paramount importance are localization and frequency information, as well as connectivity metrics. The brain can be viewed as a complex self-controlled system operating with various identifiable networks and frequencies that, when dysregulated, produce what we commonly refer to as “disorders. ”
Robust random dot markers: towards augmented unprepared maps with pure geographic features

Augmented maps have many important applications. However, no mature registration method exists to associate unprepared maps with a Geographical Information System (GIS) database which would be used to superimpose simulation results or route display on a paper map. In this paper, we propose a method called Robust Random Dot Markers (RRDM) that can robustly track coplanar random dot patterns, which can be used to address this problem. RRDM is based on the same idea of the Random Dot Markers (RDM) proposed by [Uchiyama and Saito 2011] and it can serve as fiducial markers as well as texture independent "natural markers". We conduct a series of experiments and show that RRDM is more robust than RDM in terms of jitter, perspective distortion, under and over detection of dots in the pattern. As an example of "natural marker", we show that RRDM can successfully register unprepared printed maps only with pure geographic features, i.e. road intersections coordinates, which we retrieve from a GIS. Our method does not suffer from the drawbacks of traditional "feature-point" based registration methods which mainly based on textures, since textures may change according to different maps.
Combination of Evidence-Based Classifiers for Text Categorization

Abstract -- In this paper we propose an evidential fusion approach to combining the decisions of text classifiers. These text classifiers are generated by four widely used learning algorithms: Support Vector Machine (SVM), kNN (Nearest Neighbour), kNN model-based approach (kNNM), and Rocchio on two text corpora. We first model each classifier output as a list of prioritized decisions and then divide it into the subsets of 2 and 3 decisions which are subsequently represented by the evidential structures in terms of triplet and quartet. We also develop the general formulae based on the Dempster- Shafer theory of evidence for combining such decisions. To validate our method various experiments have been carried out over the data sets of 20-newsgroup and Reuters-21578, and a comparative analysis with an alternative dichotomous structure and with majority voting have also been conducted to demonstrate the advantage of our approach in combining text classifiers.
Geographic Spatiotemporal Dynamic Model using Cellular Automata and Data Mining Techniques

Geospatial data and information availability has been increasing rapidly and has provided users with knowledge on entities change and movement in a system. Cellular Geography model applies Cellular Automata on Geographic data by defining transition rules to the data grid. This paper presents the techniques for extracting transition rule(s) from time series data grids, using multiple linear regression analysis. Clustering technique is applied to minimize the number of transition rules, which can be offered and chosen to change a new unknown grid. Each centroid of a cluster is associated with a transition rule and a grid of data. The chosen transition rule is associated with grid that has a minimum distance to the new data grid to be simulated. Validation of the model can be provided either quantitatively through an error measurement or qualitatively by visualizing the result of the simulation process. The visualization can also be more informative by adding the error information. Increasing number of cluster may give possibility to improve the simulation accuracy.
Predicting election trends with Twitter: Hillary Clinton versus Donald Trump

Forecasting opinion trends from real-time social media is the long-standing goal of modern-day big-data analytics. Despite its importance, there has been no conclusive scientific evidence so far that social media activity can capture the opinion of the general population at large. Here we develop analytic tools combining statistical physics of complex networks, percolation theory, natural language processing and machine learning classification to infer the opinion of Twitter users regarding the candidates of the 2016 US Presidential Election. Using a large-scale dataset of 73 million tweets collected from June 1st to September 1st 2016, we investigate the temporal social networks formed by the interactions among millions of Twitter users. We infer the support of each user to the presidential candidates and show that the resulting Twitter trends follow the New York Times National Polling Average, which represents an aggregate of hundreds of independent traditional polls, with remarkable accuracy (r=0.9). More importantly, the Twitter opinion trend forecasts the aggregated NYT polls by 6 to 15 days, showing that Twitter can be an early warning signal of global opinion trends at the national level. Our analytics [available at kcorelab.com] unleash the power of Twitter to predict social trends from elections, brands to political movements, and at a fraction of the cost of national polls.
An adaptive law for slope identification and force position regulation using motion variables

This work proposes an adaptive control law for the force position regulation problem under surface kinematic uncertainties. A compliant contact with friction is considered. The control law achieves exact regulation of force and position along the surface tangent by identifying the surface slope. The asymptotic stability of the closed loop system equilibrium point is proved in a local sense and is demonstrated by a simulation example
Interval-valued Matrix Factorization with Applications

In this paper, we propose the Interval-valued Matrix Factorization (IMF) framework. Matrix Factorization (MF) is a fundamental building block of data mining. MF techniques, such as Nonnegative Matrix Factorization (NMF) and Probabilistic Matrix Factorization (PMF), are widely used in applications of data mining. For example, NMF has shown its advantage in Face Analysis (FA) while PMF has been successfully applied to Collaborative Filtering (CF). In this paper, we analyze the data approximation in FA as well as CF applications and construct interval-valued matrices to capture these approximation phenomenons. We adapt basic NMF and PMF models to the interval-valued matrices and propose Interval-valued NMF (I-NMF) as well as Interval-valued PMF (I-PMF). We conduct extensive experiments to show that proposed I-NMF and I-PMF significantly outperform their single-valued counterparts in FA and CF applications.
Using on-body displays for extending the output of wearable devices

In this work, we explore wearable on-body displays. These displays have the potential of extending the display space of smart watches to the user's body. Our research is motivated by wearable computing devices moving technology closer to the human. Today, smart watches offer functionalities similar to smart phones, yet at a smaller form factor. To cope with the limited display real-estate we propose to use on-body displays integrated with clothing to extend the available display space. We present a design space for on-body displays and explore users' location and visualization preferences. We also report on the design and implementation of a prototypical display system. We evaluated the prototype in a lab study with 16 participants, showing that on-body displays perform similar to current off-screen visualization techniques.
Manipulation of multiple objects in close proximity based on visual hierarchical relationships

This paper presents a method for daily assistive robots to manipulate objects whose contact relationships are important. In our approach, contact relationships between multiple objects such as “on” and “into” are estimated using the hierarchical relationships between the objects' regions in the images. The hierarchical states are used for failure recovery processes so that the target contact state is achieved. We tested our method in two daily tasks by HRP-2VZ humanoid robot: filing, and serving an egg with a spatula. These results suggested the effectiveness of our approach.
The Demon in Democracy: Totalitarian Temptations in Free Societies

tease. Some form of it will apparently continue after the present stage of critique is finished. Yet the political signs suggest not that present discontents are emerging from our successes but that we are ill suited to deal with discontent that emerges from failure. In a very different 2016 book called How Will Capitalism End? the German economic sociologist Wolfgang Streeck pointed to our economic stagnation, rising indebtedness, and rising inequality as signs that our economic system is not living up to its promises. If that is the case, we may once again be asking: which “discontents” saw our crisis coming?
Supplemental Information Extended Experimental Procedures

Bioinformatics for Drosophila IgSF, FnIII, and LRR-type Proteins We have built a list of all extracellular Drosophila IgSF, FnIII and LRR proteins (including the other L-Domain Superfamily proteins) (Table S1) based on previously published annotations of these families (Dolan et al. We also manually checked all proteins for expected features, such as signal peptides at their 5 0 ends, transmembrane helices and complete domain structures, resulting in selecting better splice variants for expression and improving gene predictions by combining neighboring genes into single proteins. Furthermore, in cases where expected protein features appeared to be incomplete or lacking (such as half an Ig domain at a gene boundary, or a missing signal peptide at the 5 0 end), or when predictions were weak, we checked orthologous genes in other available arthropod genomes to improve our gene predictions, gene boundaries and features such as signal peptides and transmem-brane helices. For the determination of signal peptides, transmembrane helices and GPI linkages, we used Phobius (Kä ll et al. and visual inspection of Kyte-Doolittle plots, coupled to searches in orthologous arthropod sequences when predictions were ambiguous. We also searched literature for reported instances of GPI modifications for proteins of interest. As a result, we have identified 129 IgSF, 59 FnIII and 71 LRR-containing Drosophila melanogaster proteins, making up a collection of 202 proteins (Figure 1, step 1 and Table S1), with the said domains residing in the extracellular compartment. There are 43 proteins that contain both extracellular IgSF and FnIII domains, 10 that contain both extracellular IgSF and LRR, and four that contain both extracellular FnIII and LRR domains (Figure 1). Overall, the Drosophila genome encodes for $3300 proteins with signal peptides, and $500 with a signal peptide (SP) and one transmembrane helix (TM) (Personal communication with Nick Grishin). The set of proteins we identified, which are mostly SP+1TM proteins, makes a significant portion ($30%) of all Drosophila SP+1TM proteins. For an overall analysis of all IgSF sequences, a multiple sequence alignment and a phylogenetic tree of all Drosophila IgSF (Figure S4) were created with Clustal Omega (Sievers et al., 2011). Phylogenetic trees are visualized and captured in Seaview (Gouy et al., 2010) and Archaeopteryx (Han and Zmasek, 2009). It should also be noted here that while IgSF and FnIII proteins are mostly extracellular, LRR protein family appears to be equally represented in the intracellular and extracellular milieu. Most cell surface LRR proteins contain …
Influence of StrenuousExerciseon AlbuminExcretion

Role of CA 125 as a tumor marker in ovarian carcinoma, Obstet Gynecol 1986;67:473-7. 11. Brioschi PA, BischofP. Rapin C, De Roten M, Irion 0, Krauer F. Longitudinal study of CEA and CA 125 in ovarian cancer. Gynecol Oncol 1985;21:1-6. 12. Altaras MM, Goldberg GL, Levin W, Darge L, Bloch B, Smith JA. The value of cancer antigen 125 as a tumor marker in malignant germ cell tumors of the ovary. Gynecol Oncol 1986;25:150-9. 13. Petersifi WA, BagleyCM, Smith MR. CA-125 use as a tumor marker with mixed mesodermal tumors of the femalegenital tract. Cancer 1986;58:2625-7. 14. Atack DB, Nisker JA, Allen H}I, Tustanofi’ EF, L.evin L. CA 125surveillance andsecond-looklaparotomy in ovarian carcinoma. Am J Obstet Gynecol 1986;154:287-9. 15. Niloff JM, Bast Jr Re, Schaetzl EM, Knapp RC. Predictive value of CA 125 antigen levels in second-look procedures for ovarian cancer. Am J Obstet Gynecol 1985;151:981-6. 16. Shelley MD, Fish RG. Evaluation of an inununoradiometric assay for the detection of an ovarian tumour marker, CA 125, in serum. Ann Clin Biochem 1986;23:292-6. 17. Packageinsert, Abbott CA 125-EIA Monoclonal. North Chica-
Received signal strength indication (RSSI) code assessment for wireless sensors network (WSN) deployed Raspberry-Pi

Wireless sensor network (WSN) is commonly used for localization applications. Through sniffing receive signal strength indicator (RSSI) in WSN system, localization and connection to access point highest RSSI can be done automatically. In this paper, we propose Raspberry-Pi (RasPi), based best access point selection method utilizing RSSI metric. The RasPi brings the advantages of a personal computer (PC) to the domain of sensor network, which makes it the perfect platform for interfacing with a wide variety of external peripherals. This work aims to investigate various source codes deployed on RasPi for localization purpose by sniffing the RSSI metric. Consequently, comparative analysis of its key elements and performances with some of the currently available wireless sensor nodes have shown that despite few disadvantages, RasPi remains an inexpensive single board computer (SBC) which has been used very successfully in sensor network domain and diverse range of research applications.
How to make destructive updates less destructive

We present a safe embedding of mutable data structures in functional languages. With safety we mean that confluence and (in some sense) referential transparency are maintained. We develop a static criterion based on abstract interpretation which checks that any side-effect which a function may exert via a destructive update remains invisible. The technique opens up the possibility of designing safe and efficient wide-spectrum languages which combine functional and imperative language constructs.
An overview of discourses of skilled immigrants and “Canadian experience”: An English-language print media analysis

This paper was originally published in the CERIS Working Paper Series. We thank CERIS for sharing it with us. The research team would like to thank those who participated in the expert advisory board and consultation sessions and provided valuable feedback on our preliminary results. The research team is thankful for the contributions made by research assistants in the earlier phase of this project: Elena Chou, Fritz Pino, Willa Liu, Anuppriya Sriskandarajah, Nina Rongavilla, Judee Duran, and Yufei (Fiona) Wang.
International Symposium on Cluster Computing and the Grid User Group-based Workload Analysis and Modelling

Knowledge about the workload is an important aspect for scheduling of resources as parallel computers or Grid components. As the scheduling quality highly depends on the characteristics of the workload running on such resources, a representative workload model is significantfor performance evaluation. Previous approaches on workload modelling mainly focused on methods that use statistical distributions to fit the overall workload characteristics. Therefore, the individual association and correlation to users or groups are usually lost. However, job scheduling for single parallel installations as well asfor Grid systems started to focus more on the quality of service for specific user groups. Here, detailed knowledge of the individual user characteristic and preference is necessary for developing appropriate scheduling strategies. In the absence of a large information base ofactual workloads, the adequate modelling ofsubmission behaviors is sought. In this paper; we propose a new workload model, called MUGM (Mixed User Group Model), which maintains the characteristics of individual user groups. The MUGM method has been fiurther evaluated by simulations and shown to yield good results.
Constraint-Directed Search: A Case Study of Job-Shop Scheduling

Abstract : This thesis investigates the problem of constraint-directed reasoning in the job-shop scheduling domain. The job-shop scheduling problem is defined as: selecting a sequence of operations whose execution results in the completion of an order, and assigning times (i.e., start and end times) and resources to each operation. The number of possible schedules grows exponentially with the number of orders, alternative production plans, substitutable resources, and possible times to assign resources and perform operations. The acceptability of a particular schedule depends not only on the availability of alternatives, but on other knowledge such as organizational goals, physical limitations of resources, causal restrictions amongst resources and operations, availability of resources, and preferences amongst alternatives. By viewing the scheduling problem from a constraint-directed search perspective, much of this knowledge can be viewed as constraints on the schedule generation and selection process. In this thesis, we present a system called ISIS. ISIS uses a constraint-directed search paradigm to solve the scheduling problem. ISIS provides: a knowledge representation language (SRL) for modeling organizations and their constraints; hierarchical, constraint-directed scheduling of orders, which includes: constraint-directed bounding of the solution space; context-sensitive selection of constraints, and weighted interpretation of constraints; analytic and generative constraint relaxation; and techniques for the diagnosis of poor schedules.
Model reduction and process analysis of biological models

Understanding the dynamical behavior of biological networks is complicated due to their large number of components and interactions. We present a method to analyse key processes for the system behavior, based on the a priori knowledge of the system trajectory and the simplification of mathematical models of these networks. The method consists of the model decomposition into biologically meaningful processes, whose activity or inactivity is evaluated during the time evolution of the system. The structure of the model is reduced to the core mechanisms involving active processes only. We assess the quality of the reduction by means of global relative errors and apply our method to two models of the circadian rhythm in Drosophila and the influence of RKIP on the ERK signaling pathway.
Design and analysis of a framework for real-time vision-based SLAM using Rao-Blackwellised particle filters

This paper addresses the problem of simultaneous localization and mapping (SLAM) using vision-based sensing. We present and analyse an implementation of a Rao- Blackwellised particle filter (RBPF) that uses stereo vision to localize a camera and 3D landmarks as the camera moves through an unknown environment. Our implementation is robust, can operate in real-time, and can operate without odometric or inertial measurements. Furthermore, our approach supports a 6-degree-of-freedom pose representation, vision-based ego-motion estimation, adaptive resampling, monocular operation, and a selection of odometry-based, observation-based, and mixture (combining local and global pose estimation) proposal distributions. This paper also examines the run-time behavior of efficiently designed RBPFs, providing an extensive empirical analysis of the memory and processing characteristics of RBPFs for vision-based SLAM. Finally, we present experimental results demonstrating the accuracy and efficiency of our approach.
XMM-NEWTON OBSERVATIONS OF THREE HIGH REDSHIFT RADIO GALAXIES

We present the results of XMM-Newton observations of three high-redshift powerful radio galaxies 3C 184, 3C 292 and 3C 322. Although none of the sources lies in as rich an X-rayemitting environment as is seen for some powerful radio galaxies at low redshift, the environments provide sufficient pressure to confine the radio lobes. The weak gas emission is particularly interesting for 3C 184, where a gravitational arc is s een, suggesting the presence of a massive cluster. Here Chandradata complement the XMM-Newtonmeasurements by spatially separating X-rays from the extended atmosphere, the nucleus and the small-scale radio source. For 3C 292 the X-ray-emitting gas has a temperature of �2 keV and luminosity of 6.5×10 43 erg s 1 , characteristic of a poor cluster. In all three cases, struc tures where the magneticfield strength can be estimated through combining measureme nts of radio-synchrotron and inverse-Compton-X-ray emission, are consistent with being in a state of minimum total energy. 3C 184 and 3C 292 (and possibly 3C 322) have a heavily absorbed component of nuclear —
Cytotoxic Activity of CD48 Monoclonal Antibodies Against Human Lymphoma Cells

CD48 is a cell surface, glycosylphosphatidylinositol-linked glycoprotein, and a potential target for treatment of leukemia and lymphoma. Two anti-CD48 mAbs, murine HuLy-m3 and human IgG1-N2A, were compared in cellular as- says using a human lymphoma cell line (Raji) for their ability to inhibit cell growth and induce apoptosis. In vitro studies revealed both HuLy-m3 and IgG1-N2A mAbs were able to induce potent growth inhibition, reflected by a reduction in vi- able cells of approximately 70% compared to controls after 90 h. Furthermore, Raji cells treated with IgG1-N2A showed evidence of apoptosis, including increased ethidium bromide uptake, cell shrinkage and chromosomal DNA degradation.
Preventing Service Oriented Denial of Service (PreSODoS): A Proposed Approach

Today Web services have grown in context of both business to business (B2B) and business to customer (B2C) applications. Web services are the most popular mode of implementing service oriented architecture (SOA). With this growth and acceptance in the industry, the role of security is crucial. Most of the existing security mechanisms in Web services like XML encryption, digital signatures, user tokens etc. provide security on one basic assumption that source of the request is legitimate. But a typical denial of service attacker can use these sources as reflectors and play around with the contents of a Web service body to create an attack scenario. In this paper, we propose PreSODoS - a framework to detect and prevent XML based denial of service (XDoS) attacks on Web services based applications. The framework relies on content introspection to detect any XDoS possibility. We use a Patricia trie based representation so that the schemas and the request messages can be compared and validated in a performance efficient manner. PreSODoS is capable of detecting any repetitive request message and sense an attack scenario and trigger corresponding prevention mechanisms