Miracle-dz/UniFAR-FTUs huggingface dataset:
Model Similarity, Model Selection, and Attribute Classification

Selecting the most appropriate cognitive diagnosis model (CDM) for an item is a challenging process. Although general CDMs provide better model-data fit, specific CDMs have more straightforward interpretations, are more stable, and can provide more accurate classifications when used correctly. Recently, the Wald test has been proposed to determine at the item level whether a general CDM can be replaced by specific CDMs without a significant loss in model-data fit. The current study examines the practical consequence of the test by evaluating whether the attribute-vector classification based on CDMs selected by the Wald test is better than that based on general CDMs. Although the Wald test can detect the true underlying model for certain CDMs, it is yet unclear how effective it is at distinguishing among the wider range of CDMs found in the literature. This study investigates the relative similarity of the various CDMs through the use of the newly developed dissimiliarity index, and explores the implications for the Wald test. Simulations show that the Wald test cannot distinguish among additive models due to their inherent similarity, but this does not impede the ability of the test to provide higher correct classification rates than general CDMs, particularly when the sample size is small and items are of low quality. An empirical example is included to demonstrate the viability of the procedure.
Camera Color Accuracy Evaluated via Metamer Mismatch Moments

A novel method for evaluating the colorimetric accuracy of digital color cameras is proposed based on a new measure of the metamer mismatch body (MMB) induced by the change from the camera as an observer to the human standard observer. Previous methods of evaluating the colorimetric accuracy of a camera are the Luther the MMB relative to that of the convex hull of the spectral curve [3]. The latter method addresses problems of the previous two methods but, narrow. The method introduced here extends and improves upon the normalized-volume-of-the-MMB method by replacing the normalized volume with a normalized shape measure.
Towards Near-Data Processing of Compare Operations in 3D-Stacked Memory

The gap between the processing speed and memory access speed of the modern multi-core systems has become a bottleneck for the emerging data-intensive workloads. In this scenario, it has become a smarter idea to move some amount of computation closer to the data, thus stimulating the concept of near-data processing (NDP). Compare or scanning, the core operations of many applications, typically in a database, can leverage the benefits of NDP. We propose near-data compare unit (NDCU), a less-invasive hardware, that can be integrated with the existing ecosystem of the hybrid memory cube (HMC). While integrating NDCU, we have designed two full-system architectures, one is lighter NDP with no parallelism (NNP) and the second is NDP with vault level parallelism (NVLP). While the first architecture is more power and area efficient, the second one is very fast with negligible overheads. With the motive of carrying out scan operation, we have specifically implemented 'compare-n-hit', 'compare-n-count' and 'compare-n-max' operations on both row-store and column-store databases and found significant improvements over conventional CPU-based system. We get around 2.3x and 37x performance improvement in NNP and NVLP architectures respectively. In both the designs, we reduce the energy consumption by around 8x on an average.
VisIt-OSPRay: Toward an Exascale Volume Visualization System

Large-scale simulations can easily produce data in excess of what can be efﬁciently visualized using production visualization software, making it challenging for scientists to gain insights from the results of these simulations. This trend is expected to grow with exascale. To meet this challenge, and run on the highly parallel hardware being deployed on HPC system, rendering systems in production visualization software must be redesigned to perform well at these new scales and levels of parallelism. In this work, we present VisIt-OSPRay, a high-performance, scalable, hybrid-parallel rendering system in VisIt, using OSPRay and IceT, coupled with PIDX for scalable I/O. We examine the scalability and memory efﬁciency of this system and investigate further areas for improvement to prepare VisIt for upcoming exascale workloads
BIRITS: A Music Information Retrieval System Using Query-by-Playing Techniques

With the steady growth of the Internet many search websites have emerged, however most of these websites only allow you to perform the search using a textual interface. A more intuitive method for the user would be to use melody snippets of a song to perform this search. In this paper, we present a two-stage query-by-playing (QBP) system using symbolic representations where from a melody snippet of a song it is performed the retrieval of a set of similar songs. On the first stage we perform the alignments and calculate its result rank, which may have draws. Stage two was created to help solving this problem, where a classifier-based filter is applied on the results, reducing the number of draws. A new methodology for evaluating QBP is presented, aiming to understand the impact of the number of notes in the query and some alternatives to verify the robustness of the methods considering wrong and missing notes. In addition, a new method based on machine learning (ML) is presented to filter the results. In the experiments this method always improves the performance of queries regardless of the noise.
BRCA1‐associated mammary tumorigenesis is dependent on estrogen rather than progesterone signaling

Hereditary breast cancers in BRCA1 mutation carriers are mostly estrogen receptor α (ERα)‐negative and progesterone receptor (PR)‐negative; however, hormone depletion via bilateral oophorectomy does result in a marked reduction in breast cancer risk, suggesting that BRCA1‐associated breast tumorigenesis is dependent on hormone signaling. We used geneticaly engineered mouse models to determine the individual influences of ERα and PR signaling on the development of BRCA1‐deficient breast cancer. In line with the human data, BRCA1‐deficient mouse mammary tumors are ERα‐negative, and bilateral ovariectomy leads to abrogation of mammary tumor development. Hormonal replacement experiments in ovariectomized mice showed that BRCA1‐deficient mammary tumor formation is promoted by estrogen but not by progesterone. In line with these data, mammary tumorigenesis was significantly delayed by the selective ERα downregulator fulvestrant, but not by the selective PR antagonist Org33628. Together, our results illustrate that BRCA1‐associated tumorigenesis is dependent on estrogen signaling rather than on progesterone signaling, and call into question the utility of PR antagonists as a tumor prevention strategy for BRCA1 mutation carriers. Copyright © 2018 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.
A Local Leader Election Protocol Applied to Decentralized Traffic Regulation

Connected vehicles are expected to improve traffic flow, by delivering relevant and precise information to the driver. This information may be received from other vehicles (Vehicle-to-Vehicle communication - V2V) or the infrastruc- ture (Infrastructure-to-Vehicle communication - I2V). Since infrastructure-side equipment (Road Side Units - RSUs) are costly and not extensively deployed on the road network yet, localized approaches can contribute to sustain relevant and useful information exchange between vehicles. In this paper, we propose and simulate a local traffic regulation protocol that aims to reduce traffic congestion especially in areas which are not covered by RSUs. Our approach is based on V2V communication only: connected vehicles exchange data collected from their sensing devices e.g. speed and position. The purpose of this protocol is to allow vehicles to participate in the election of a Local Leader, by choosing one vehicle among their neighborhood. The choice of a Local Leader will be based on predefined rules in order to guarantee a smoother traffic. The Local Leader will make his decisions, i.e. speed recommendation after a negotiation phase with all surrounding vehicles within a configurable diameter. Thus, the proposed approach will establish a traffic regulation scheme that is not dependent on the network infrastructure. Mi- croscopic simulations results, applied to a traffic flow composed of connected and non-connected vehicles show the benefits of such an approach.
The application of non-volatile look-up-table operations based on multilevel-cell of resistance switching random access memory

Resistance switching random access memory (RRAM) is deemed as an emerging memory which has drawn considerable attention. This paper presents an approach for the synthesis of multilevel-cell (MLC) RRAM-based circuits using look-up-table (LUT) operations to complete a multiplication procedure. The recently proposed MLC function of RRAM revealed that RRAM could have a multilevel stable resistance by adjusting the voltage pulse, which is assigned to it. The simulation results show that the new LUT has a calculation speed that is increased by 35.7% and an area that is decreased by 14%, when compared with the traditional one for 16-bit multiplier.
Detection of Multidimensional Co-Exclusion Patterns in Microbial Communities

Motivation Identification of complex relationships among members of microbial communities is key to understand and control the microbiota. Co-exclusion is arguably one of the most important patterns reflecting microorganisms’ intolerance to each other’s presence. Knowing these relations opens an opportunity to manipulate microbiotas, personalize anti-microbial and probiotic treatments as well as guide microbiota transplantation. The co-exclusion pattern however, cannot be appropriately described by a linear function nor its strength be estimated using covariance or (negative) Pearson and Spearman correlation coefficients. This manuscript proposes a way to quantify the strength and evaluate the statistical significance of co-exclusion patterns between two, three or more variables describing a microbiota and allows one to extend analysis beyond microorganism abundance by including other microbiome associated measurements such as, pH, temperature etc., as well as estimate the expected numbers of false positive co-exclusion patterns in a co-exclusion network. Results The implemented computational pipeline (CoEx) tested against 2,380 microbial profiles (samples) from The Human Microbiome Project resulted in body-site specific pairwise co-exclusion patterns. Availability C++ source code for calculation of the score and p-value for 2, 3, and 4 dimensional co-exclusion patterns as well as source code and executable files for the CoEx pipeline are available at https://scsb.utmb.edu/labgroups/fofanov/co-exclusion_in_microbial_communities. asp Contact lealbayr@utmb.edu Supplementary information Supplementary data are available at biorxiv online.
A Cyber-Physical Systems Approach for Implementing the Receding Horizon Optimal Power Flow in Smart Grids

Two major challenges in securing reliable Optimal Power Flow (OPF) operations are: (i) fluctuations induced due to renewable generators and energy demand, and (ii) interaction and interoperability among the different entities. Addressing these issues requires handling both physical (e.g., power flows) and cyber aspects (computing and communication) of the energy grids, i.e, a cyber-physical systems (CPS) approach is necessitated. First, this investigation proposes a receding horizon control (RHC) based approach for solving OPF to deal with the uncertainties. It uses forecasts on renewable generation and demand and an optimization model solving a predictive control problem to secure energy balance while meeting the network constraints. Second, to handle the interoperability issues, a middleware using common information model (CIM) for exchanging information among applications and the associated profiles are presented. CIM profiles modelling various components and aspects of the RHC based OPF is proposed. In addition, a middleware architecture and services to collect information is discussed. The proposed CPS approach is illustrated in a distribution grid in Steinkjer, Norway having 85 nodes, 700 customers, three hydrogenerators, and various industrial loads. Our results demonstrate the benefits of CPS approach to implement OPF addressing also the interoperability issues.
Personal information documents for people with dementia: Healthcare staff ’s perceptions and experiences

Person-centred care is internationally recognised as best practice for the care of people with dementia. Personal information documents for people with dementia are proposed as a way to support person-centred care in healthcare settings. However, there is little research about how they are used in practice. The aim of this study was to analyse healthcare staff ’s perceptions and experiences of using personal information documents, mainly Alzheimer’s Society’s ‘This is me’, for people with dementia in healthcare settings. The method comprised a secondary thematic analysis of data from a qualitative study, of how a dementia awareness initiative affected care for people with dementia in one healthcare organisation. The data were collected through 12 focus groups (n = 58 participants) and 1 individual interview, conducted with a range of healthcare staff, both clinical and non-clinical. There are four themes presented: understanding the rationale for personal information documents; completing personal information documents; location for personal information documents and transfer between settings; impact of personal information documents in practice. The findings illuminated how healthcare staff use personal information documents in practice in ways that support person-centred care. Practical issues about the use of personal information documents were revealed and these may affect the optimal use of the documents in practice. The study indicated the need to complete personal information documents at an early stage following diagnosis of dementia, and the importance of embedding their use across care settings, to support communication and integrated care.
Microstructure and mechanical properties of different keratinous horns

Animal horns play an important role during intraspecific combat. This work investigates the microstructure and mechanical properties of horns from four representative ruminant species: the bighorn sheep (Ovis canadensis), domestic sheep (Ovis aries), mountain goat (Oreamnos americanus) and pronghorn (Antilocapra americana), aiming to understand the relation between evolved microstructures and mechanical properties. Microstructural similarity is found where disc-shaped keratin cells attach edge-to-edge along the growth direction of the horn core (longitudinal direction) forming a lamella; multiple lamellae are layered face to face along the impact direction (radial direction, perpendicular to horn core growth direction), forming a wavy pattern surrounding a common feature, the tubules. Differences among species include the number and shape of the tubules, the orientation of aligned lamellae and the shape of keratin cells. Water absorption tests reveal that the pronghorn horn has the largest water-absorbing ability due to the presence of nanopores in the keratin cells. The loading direction (compressive and tensile) and level of hydration vary among the horns from different species. The differences in mechanical properties among species may relate to their different fighting behaviours: high stiffness and strength in mountain goat to support the forces during stabbing; high tensile strength in pronghorn for interlocked pulling; impact energy absorption properties in domestic and bighorn sheep to protect the skull during butting. These design rules based on evolutionary modifications among species can be applied in synthetic materials to meet different mechanical requirements.
G2C2 – I. Homogeneous photometry for Galactic globular clusters in SDSS passbands

ABSTRACT We present g 0 and z 0 aperture photometry for 96 Galactic Globular Clusters, making this thelargest homogeneous catalog of photometry for these objects in the SDSS ﬁlter system. For asubset of 56 clusters we also provide photometry in r 0 and i . We carry out comparisons withprevious photometry as well as with the SDSS dataset. The data will be useful for a series ofapplications in Galactic and extragalactic astrophysics. Future papers will analyse the colour-metallicity relation, colour-magnitude diagrams, and structural parameters. The compilationof results based on this dataset will be collected in the Galactic Globular Cluster Catalog(G2C2). Key words: Galactic Globular Clusters 1 INTRODUCTIONGlobular clusters (hereafter GCs) formed during the earliestepisodes of star formation in galaxies. They are found in all but thesmallest dwarf galaxies, with massive galaxies hosting systems ofhundreds or thousands of clusters. The properties of globular clus-ters appear to be very homogeneous from one galaxy to the other(in terms of colour, luminosity distribution, etc.) and this impliesthat the formation of these objects has been intimately related tothe assembly of their parent galaxies (e.g., Harris 1991). Globu-lar clusters are living fossils of the Universe at high redshift (theirmass is similar to the Jeans mass at the epoch of recombination) andtherefore give a snapshot of conditions as prevailed at early epochs(see West et al. 2004; Brodie & Strader 2006 for reviews). The in-tegrated properties of globular clusters therefore provide us withinformation on the earliest stages of galaxy formation; the high in-trinsic luminosities of clusters means that they can be studied in de-
Including Uncertainty when Learning from Human Corrections

It is difficult for humans to efficiently teach robots how to correctly perform a task. One intuitive solution is for the robot to iteratively learn the human's preferences from corrections, where the human improves the robot's current behavior at each iteration. When learning from corrections, we argue that while the robot should estimate the most likely human preferences, it should also know what it does not know, and integrate this uncertainty when making decisions. We advance the state-of-the-art by introducing a Kalman filter for learning from corrections: this approach also maintains the uncertainty of the estimated human preferences. Next, we demonstrate how uncertainty can be leveraged for active learning and risk-sensitive deployment. Our results indicate that maintaining and leveraging uncertainty leads to faster learning from human corrections.
Modeling and optimization of kanban controlled manufacturing systems with GSPN including QN

We investigate the kanban assignment problem for assembly kanban systems. We use REMO, a general purpose tool for optimization. REMO includes several algorithms like hill climbing and genetic algorithms and has an easily adaptable interface to performance analysis tools. As we try to find an optimal kanban assignment with respect to certain performance measures of the system, a fast performance analysis is a crucial factor for sensible and successful application of optimization algorithms. For this purpose we introduce Petri nets including queueing nets (PNiQ) as modeling formalism especially suited for optimization of arbitrary kanban systems. At modeling level, PNiQ allow the use of both the concise description of queueing nets where possible and the notation of stochastic Petri nets where needed, e.g. to model fork/join needed for the matching of kanbans and parts. Approximate performance analysis is carried out by decomposition and aggregation of the queueing net parts. This technique provides a fast numerical solution even for large systems as important requirement for the application of optimization algorithms. The optimization not only yields optimal kanban assignments for various kanban systems but also a common pattern in the set of solutions can be recognized.
An investigation of the effects of n-gram length in scanpath analysis for eye-tracking research

Scanpath analysis is a controversial and important topic in eye tracking research. Previous work has shown the value of scanpath analysis in perceptual tasks; little research has examined its utility for understanding human reasoning in complex tasks. Here, we analyze n-grams, which are continuous ordered subsequences of participants' scanpaths. In particular we studied the length of n-grams that are most appropriate for this form of analysis. We reuse datasets from previous studies of human cognition, medical diagnosis and art, systematically analyzing the frequency of n-grams of increasing length, and compare this approach with a string alignment-based method. The results show that subsequences of four or more areas of interest may not be of value for finding patterns that distinguish between two groups. The study is the first to systematically define the parameters of the length of n-gram suitable for analysis, using an approach that holds across diverse domains.
The Effects of Moderate Physical Exercise on Adult Cognition: A Systematic Review

Background: Physical exercise is a systematic sequence of movements executed with a predefined purpose. This muscular activity impacts not only on circulatory adaptations, but also neuronal integration with the potential to influence cognition. The aim of this review was to determine whether the literature supports the idea that physical exercise promotes cognitive benefits in healthy adults. Methods: A systematic search for relevant articles was performed according to the Preferred Reporting Items for Systematic Review and Meta-Analysis criteria using available databases (PubMed, LILACS, Scopus, Web of Science, The Cochrane Library, OpenGrey, Google Scholar and CENTRAL). The search terms included “humans” or “adults” or “cognition” or “awareness” or “cognitive dissonance” or “cognitive reserve” or “comprehension” or “consciousness” and “motor activity” or “exercise” or “physical fitness,” and not “aged” or “nervous system diseases,” with the purpose of finding associations between moderate physical exercise and cognition. A methodological quality and risk of bias unit assessed the eligibility of articles. Results: A total of 7179 articles were identified. Following review and quality assessment, three articles were identified to fulfill the inclusion criteria. An association between moderate physical exercise and cognition was observed. Improvements in cognitive parameters such as reduced simple reaction time, improved response precision and working memory were identified among the included articles. Conclusion: This systematic review found that moderate physical exercise improves cognition.