Miracle-dz/UniFAR-FTUs huggingface dataset:
BIPOLAR MAGNETIC REGIONS ON THE SUN: GLOBAL ANALYSIS OF THE SOHO/MDI DATA SET

The magnetic flux that is generated by dynamo processes inside the Sun emerges in the form of bipolar magnetic regions. The properties of these directly observable signatures of the dynamo can be extracted from full-disk solar magnetograms. The most homogeneous, high-quality synoptic data set of solar magnetograms has been obtained with the Michelson Doppler Imager (MDI) instrument on the Solar and Heliospheric Observatory spacecraft during 1995–2011. We have developed an IDL program that has, when applied to the 73,838 magnetograms of the MDI data set, automatically identified 160,079 bipolar magnetic regions that span a range of scale sizes across nearly four orders of magnitude. The properties of each region have been extracted and statistically analyzed, in particular with respect to the polarity orientations of the bipolar regions, including their tilt-angle distributions and their violations of Hale's polarity law. The latitude variation of the average tilt angles (with respect to the E–W direction), which is known as Joy's law, is found to closely follow the relation 32. °1 × sin (latitude). There is no indication of a dependence on region size that one may expect if the tilts were produced by the Coriolis force during the buoyant rise of flux loops from the tachocline region. A few percent of all regions have orientations that violate Hale's polarity law. We show explicit examples, from different phases of the solar cycle, where well-defined medium-size bipolar regions with opposite polarity orientations occur side by side in the same latitude zone in the same magnetogram. Such oppositely oriented large bipolar regions cannot be part of the same toroidal flux system, but different flux systems must coexist at any given time in the same latitude zones. These examples are incompatible with the paradigm of coherent, subsurface toroidal flux ropes as the source of sunspots, and instead show that fluctuations must play a major role at all scales for the turbulent dynamo. To confirm the profound role of fluctuations at large scales, we show explicit examples in which large bipolar regions differ from the average Joy's law orientation by an amount between 90° and 100°. We see no observational support for a separation of scales or a division between a global and a local dynamo, since also the smallest scales in our sample retain a non-random component that significantly contributes to the accumulated emergence of a north–south dipole moment that will lead to the replacement of the old global poloidal field with a new one that has the opposite orientation.
A Quadrocopter Hovering Above a Person Wearing a Modified Cap

We here describe an application of a quadrocopter hovering stably above a person wearing a cap with 4 infrared LEDs. The application has originally been developed in the context of wearable computing. Our “wearable helicopter” hovers above the user. In further work, the system could send a bird’s eye view of the area to the operator, while the robot follows him. The entire aerial robot works autonomously, all processing is done onboard. We use a miniature quadrocopter with an inertial measurement unit and a low-cost Wii remote camera, i.e., commodity consumer hardware. This proof of concept gives insight into flight control. The syste m is evaluated in a series of experiments.
HMC-Sim: A Simulation Framework for Hybrid Memory Cube Devices

The recent advent of stacked die memory and logic technologies has lead to a resurgence in research associated with fundamental architectural techniques. Many architecture research projects begin with ample simulation of the target theoretical functions and approach. However, the logical and physical nature three-dimensional stacked devices, such as the Hybrid Memory Cube (HMC) specification, fundamentally do not align with traditional memory simulation techniques. As such, there currently exists a chasm in the capabilities of modern architectural simulation frameworks. This work introduces a new simulation framework developed specifically for the Hybrid Memory Cube specification. We present a set of novel techniques implemented on an associated development framework that provide an infrastructure to flexibly simulate one or more Hybrid Memory Cube stacked die memory devices attached to an arbitrary core processor. The goal of this development infrastructure is to provide architectural simulation frameworks the ability to begin migrating current banked DRAM memory models to stacked HMC-based designs without a reduction in simulation fidelity or functionality. In addition to the core simulation architecture, this work also presents a series of memory workload test results using the infrastructure that elicit device, vault and bank utilization trace data from within a theoretical device. These evaluations have confirmed that HMC-Sim can provide insightful guidance in designing and developing highly efficient systems, algorithms, and applications, considering the next-generation three-dimensional stacked memory devices. HMC-Sim is currently open source, licensed under a BSD-style license and is freely available to the community.
Accelerometry based classification of gait patterns using empirical mode decomposition

This paper describes accelerometry based classification of walking patterns. A feature extraction technique based on empirical mode decomposition (EMD) is proposed for the classification of unsupervised walking activities from accelerometry data. The front-end 20 dimensional features representing the gait patterns were obtained from the first three modes of decomposition of the acceleration data in anterior-posterior, medio-lateral, and vertical direction. The back-end of the system was a 64-mixture Gaussian Mixture Model (QMM) classifier. Overall classification accuracy of 96.02% was achieved for the five different human gait patterns including walking on flat surfaces, walking up and down paved ramps and walking up and down stairways.
“I am making it without you, dad”: Resilient academic identities of black female university students with absent fathers: An exploratory multiple case study

This study explored the resilient academic identity constructions of two black father-absent females who were academically successful postgraduate students at a South African University. Using resilience theory and an African metaphysical framework, the study highlights the conversational devices that these two female students used when constructing a resilient academic identity during in-depth conversational interviews with the primary researcher. The findings highlight how paternal absence and related socio-economic effects can serve as a path towards a resilient academic identity construction and be communicated as opportunities for academic success. The study offers novel insight into the phenomenon of paternal absence among two young black female students, how this absence did not seem to yield negative identity and academic achievement outcomes, and the conversational ways in which a resilient academic identity may be constructed.
Mining the Web for Sense Discrimination Patterns

In this paper we present a method for mining the Web in order to extract lexical patterns that help in discriminating the senses of a given polysemic word. These patters are defined as sets and sequences of words strongly related to each sense of the word. To discover the patterns, the method first determines the different senses of the word from a reference lexical database, and then it uses the set of synonyms from each sense as search patterns on the Web. The purpose is to create a corpus of usage cases per sense, downloading snippets via fast search engines. Finally, it applies a well-known association discovery data mining technique to select the most relevant lexical patterns for each word sense. The preliminary results indicate that making sense out of the Web is possible and the discovered patters should be of great benefit in tasks such as information retrieval and machine translation.
Applying Machine Learning Techniques to Improve Linux Process Scheduling

In this work we use Machine Learning (ML) techniques to learn the CPU time-slice utilization behavior of known programs in a Linux system. Learning is done by an analysis of certain static and dynamic attributes of the processes while they are being run. Our objective was to discover the most important static and dynamic attributes of the processes that can help best in prediction of CPU burst times which minimize the process TaT (Turn-around-Time). In our experimentation we modify the Linux Kernel scheduler (version 2.4.20-8) to allow scheduling with customized time slices. The "Waikato Environment for Knowledge Analysis" (Weka), an open source machine-learning tool is used to find the most suitable ML method to characterize our programs. We experimentally find that the C'4.5 Decision Tree algorithm most effectively solved the problem. We find that predictive scheduling could reduce TaT in the range of 1.4% to 5.8%. This was due to a reduction in the number of context switches needed to complete the process execution. We find our result interesting in the context that generally operating systems presently never make use of a program's previous execution history in their scheduling behavior.
NGSANE: a lightweight production informatics framework for high-throughput data analysis

Summary: The initial steps in the analysis of next-generation sequencing data can be automated by way of software ‘pipelines’. However, individual components depreciate rapidly because of the evolving technology and analysis methods, often rendering entire versions of production informatics pipelines obsolete. Constructing pipelines from Linux bash commands enables the use of hot swappable modular components as opposed to the more rigid program call wrapping by higher level languages, as implemented in comparable published pipelining systems. Here we present Next Generation Sequencing ANalysis for Enterprises (NGSANE), a Linux-based, high-performance-computing-enabled framework that minimizes overhead for set up and processing of new projects, yet maintains full flexibility of custom scripting when processing raw sequence data. Availability and implementation: Ngsane is implemented in bash and publicly available under BSD (3-Clause) licence via GitHub at https://github.com/BauerLab/ngsane. Contact: Denis.Bauer@csiro.au Supplementary information: Supplementary data are available at Bioinformatics online.
Regulation of the Receptor Specificity and Function of the Chemokine RANTES (Regulated on Activation, Normal T Cell Expressed and Secreted) by Dipeptidyl Peptidase IV (CD26)-mediated Cleavage

CD26 is a leukocyte activation marker that possesses dipeptidyl peptidase IV activity but whose natural substrates and immunological functions have not been clearly defined. Several chemo-kines, including RANTES (regulated on activation, normal T cell expressed and secreted), have now been shown to be substrates for recombinant soluble human CD26. The truncated RANTES(3–68) lacked the ability of native RANTES(1–68) to increase the cytosolic calcium concentration in human monocytes, but still induced this response in macrophages activated with macrophage colony-stimulating factor. Analysis of chemokine receptor messenger RNAs and patterns of desensitization of chemokine responses showed that the differential activity of the truncated molecule results from an altered receptor specificity. RANTES(3–68) showed a reduced activity, relative to that of RANTES(1–68), with cells expressing the recombinant CCR1 chemokine receptor, but retained the ability to stimulate CCR5 receptors and to inhibit the cytopathic effects of HIV-1. Our results indicate that CD26-mediated processing together with cell activation–induced changes in receptor expression provides an integrated mechanism for differential cell recruitment and for the regulation of target cell specificity of RANTES, and possibly other chemokines.
Models and frameworks of knowledge management: A literature review

Field of Knowledge Management is still growing in interest because it is believed to increase profits and competitive advantage of organization. Thus, KM research topic areas are increasingly varied and complex, especially in the creation of the models or frameworks that is intended for ease of application. Models and frameworks are created with specific approaches, while the methods depend on the perspective of inventor. We will present 30 KM models from five reviewers and 5 frameworks from three reviewers. Each model and framework described shortly, but clearly in the context, elements, processes and relationships. The aim is to provide an overview of KM models and frameworks. Hence, all researchers, academics and practitioners can understand and apply it according to the needs of organization.
Successful Contextual Integration of Loose Mental Associations As Evidenced by Emotional Conflict-Processing

Often we cannot resist emotional distraction, because emotions capture our attention. For example, in TV-commercials, tempting emotional voices add an emotional expression to a formerly neutral product. Here, we used a Stroop-like conflict paradigm as a tool to investigate whether emotional capture results in contextual integration of loose mental associations. Specifically, we tested whether the associatively connected meaning of an ignored auditory emotion with a non-emotional neutral visual target would yield a modulation of activation sensitive to emotional conflict in the brain. In an fMRI-study, nineteen participants detected the presence or absence of a little worm hidden in the picture of an apple, while ignoring a voice with an emotional sound of taste (delicious/disgusting). Our results indicate a modulation due to emotional conflict, pronounced most strongly when processing conflict in the context of disgust (conflict: disgust/no-worm vs. no conflict: disgust/worm). For conflict in the context of disgust, insula activity was increased, with activity correlating positively with reaction time in the conflict case. Conflict in the context of deliciousness resulted in increased amygdala activation, possibly due to the resulting “negative” emotion in incongruent versus congruent combinations. These results indicate that our associative stimulus-combinations showed a conflict-dependent modulation of activity in emotional brain areas. This shows that the emotional sounds were successfully contextually integrated with the loosely associated neutral pictures.
On the Multiplexing Gain of K-user Partially Connected Interference Channel

The multiplexing gain (MUXG) of $K$-user interference channel (IC) with partially connected interfering links is analyzed. The motivation for the partially connected IC comes from the fact that not all interferences are equally strong in practice. The MUXG is characterized as a function of the number ($K$) of users and the number ($N \geq 1$) of interfering links. Our analysis is mainly based on the interference alignment (IA) technique to mitigate interference. Our main results are as follows: One may expect that higher MUXG can be attained when some of interfering links do not exist. However, when $N$ is odd and $K=N+2$, the MUXG is not increased beyond the optimal MUXG of fully connected IC, which is $\frac{KM}{2}$. The number of interfering links has no influence on the achievable MUXG using IA, but affects the efficiency in terms of the number of required channel realizations: When N=1 or 2, the optimal MUXG of the fully connected IC is achievable with a finite number of channel realizations. In case of $N \geq 3$, however, the MUXG of $\frac{KM}{2}$ can be achieved asymptotically as the number of channel realizations tends to infinity.
Well-Formed Dependency to String translation with BTG Grammar

This paper proposes a well-formed dependency to string translation model with BTG grammar. By enabling the usage of wellformed sub-structures and allowing flexible reordering of them, our approach is effective to relieve the problems of parsing error and flatness in dependency structure. To utilize the well-formed dependency rules during decoding, we adapt the tree traversal decoding algorithm into a bottom-up CKY algorithm. And a lexicalized reordering model is used to encourage the proper combination of two neighbouring blocks. Experiment results demonstrate that our approach can effectively improve the performance by more than 2 BLEU score over the baseline.
Molecular classification of estrogens.

Estrogens are involved in a multiplicity of programmed events in target tissues e.g.: uterus, breast, and pituitary gland, and hormone-responsive tumors occur at these target sites. We have addressed the possibility that all of the estrogens do not produce the same conformation of estrogen receptor alpha (ER). A novel assay in vitro was used to activate the transforming growth factor alpha (TGF-alpha) gene in situ in MDA-MB-231 cells stably transfected with cDNA for D351 ER or D351G ER. Three estrogen types were used: estradiol, diethylstilbestrol, and a triphenylethylene (TPE) derivative of tamoxifen without the antiestrogenic side chain. Computer molecular modeling was used to interpret data. A flat estrogen such as estradiol or diethylstilbestrol can induce TGF-alpha through a correctly positioned activating function 2 (AF2) and bind SRC-1. The TPE did not activate AF2 but activated the TGF-alpha gene through AF2b. This was demonstrated because D351 but not D351G ER activated the TGF-alpha gene with the TPE. We propose two classes of estrogens with different ER complexes that may incorporate different coactivators to function. Phytoestrogens and environmental xenoestrogens will fall into different classes based on structure and may exhibit selective actions and carcinogenic potential based on different ER conformations.
Management of spatial data for visualization on mobile devices

Vector-based mapping is emerging as a preferred format in Location-based Services(LBS), because it can deliver an up-to-date and interactive map visualization. The Progressive Transmission(PT) technique has been developed to enable the ecient transmission of vector data over the internet by delivering various incremental levels of detail(LoD). However, it is still challenging to apply this technique in a mobile context due to many inherent limitations of mobile devices, such as small screen size, slow processors and limited memory. Taking account of these limitations, PT has been extended by developing a framework of ecient data management for the visualization of spatial data on mobile devices. A data generalization framework is proposed and implemented in a software application. This application can signi cantly reduce the volume of data for transmission and enable quick access to a simpli ed version of data while preserving appropriate visualization quality. Using volunteered geographic information as a case-study, the framework shows exibility in delivering up-to-date spatial information from dynamic data sources. Three models of PT are designed and implemented to transmit the additional LoD re nements: a full scale PT as an inverse of generalisation, a viewdependent PT, and a heuristic optimised view-dependent PT. These models are evaluated with user trials and application examples. The heuristic optimised view-dependent PT has shown a signi cant enhancement over the traditional PT in terms of bandwidth-saving and smoothness of transitions. A parallel data management strategy associated with three corresponding algorithms has been developed to handle LoD spatial data on mobile clients. This strategy enables the map rendering to be performed in parallel with a process which retrieves the data for the next map location the user will require. A viewdependent approach has been integrated to monitor the volume of each LoD for visible area. The demonstration of a exible rendering style shows its potential use in visualizing dynamic geoprocessed data. Future work may extend this to integrate topological constraints and semantic constraints for enhancing the vector map visualization.
Automatic Conversational Scene Analysis in Children with Asperger Syndrome/High-Functioning Autism and Typically Developing Peers

Individuals with Asperger syndrome/High Functioning Autism fail to spontaneously attribute mental states to the self and others, a life-long phenotypic characteristic known as mindblindness. We hypothesized that mindblindness would affect the dynamics of conversational interaction. Using generative models, in particular Gaussian mixture models and observed influence models, conversations were coded as interacting Markov processes, operating on novel speech/silence patterns, termed Steady Conversational Periods (SCPs). SCPs assume that whenever an agent's process changes state (e.g., from silence to speech), it causes a general transition of the entire conversational process, forcing inter-actant synchronization. SCPs fed into observed influence models, which captured the conversational dynamics of children and adolescents with Asperger syndrome/High Functioning Autism, and age-matched typically developing participants. Analyzing the parameters of the models by means of discriminative classifiers, the dialogs of patients were successfully distinguished from those of control participants. We conclude that meaning-free speech/silence sequences, reflecting inter-actant synchronization, at least partially encode typical and atypical conversational dynamics. This suggests a direct influence of theory of mind abilities onto basic speech initiative behavior.
Re-configuring inclusion, decolonising practice: Digital participation and learning in Black women’s community-led heritage

This article explores an innovative model of adult education within museums developed from a Black feminist approach. BAM! Sistahood! is a community-led project with regional heritage organisations, universities and women’s centres in the UK, that offers a holistic approach to heritage development. The ethos is to challenge the perpetuation of existing Eurocentric models of heritage and the historical exclusion experienced by black, Asian, minority ethnic and refugee (BAMER) women when accessing museums and archives. We argue that to be transformative, adult education processes must be sensitive to barriers in participation and encourage shared learning opportunities and change within institutions. Digital technology is an important feature, if configured carefully, enabling alternative models of women’s expertise to be documented and shared.
A mass model for estimating the gamma ray background of the Burst and Transient Source Experiment

Orbiting X-ray and gamma ray instruments are subject to large background count rates due to local particle fluxes in the space environment. The ability of an instrument to make calibrated measurements of the flux from a source of interest is highly dependent on accurately determining the background level. Wepresent here a method of calculating the energy dependent background flux for any point in the complete data set recorded by the Burst and Transient Source Experiment (BATSE) in its nine year mission. The BATSE Mass Model (BAMM) uses a Monte Carlo mass modelling approach to produce a data base of the gamma ray background which is then filtered to simulate the background count rate with a 2.048 s time resolution. This method is able to reduce the variations in the background flux by a factor of 8-10, effectively "flat-fielding" the detector response. With flat-fielded BATSE data it should be possible to use the Earth occultation technique to produce a hard X-ray all sky survey to the 1-2 mCrab sensitivity limit. BAMM is also capable of estimating the contribution to the spectra measured from gamma ray sources due to the reprocessing of source photons in inactive material surrounding a gamma ray detector. Possible applications of this aspect of the model in the area of Gamma Ray Burst spectral analysis are discussed.