Miracle-dz/UniFAR-FTUs huggingface dataset:
Algorithms for discovery of multiple Markov boundaries

Algorithms for Markov boundary discovery from data constitute an important recent development in machine learning, primarily because they offer a principled solution to the variable/feature selection problem and give insight on local causal structure. Over the last decade many sound algorithms have been proposed to identify a single Markov boundary of the response variable. Even though faithful distributions and, more broadly, distributions that satisfy the intersection property always have a single Markov boundary, other distributions/data sets may have multiple Markov boundaries of the response variable. The latter distributions/data sets are common in practical data-analytic applications, and there are several reasons why it is important to induce multiple Markov boundaries from such data. However, there are currently no sound and efficient algorithms that can accomplish this task. This paper describes a family of algorithms TIE* that can discover all Markov boundaries in a distribution. The broad applicability as well as efficiency of the new algorithmic family is demonstrated in an extensive benchmarking study that involved comparison with 26 state-of-the-art algorithms/variants in 15 data sets from a diversity of application domains.
Estimating the "Wrong" Graphical Model: Benefits in the Computation-Limited Setting

Consider the problem of joint parameter estimation and prediction in a Markov random field: that is, the model parameters are estimated on the basis of an initial set of data, and then the fitted model is used to perform prediction (e.g., smoothing, denoising, interpolation) on a new noisy observation. Working under the restriction of limited computation, we analyze a joint method in which the same convex variational relaxation is used to construct an M-estimator for fitting parameters, and to perform approximate marginalization for the prediction step. The key result of this paper is that in the computation-limited setting, using an inconsistent parameter estimator (i.e., an estimator that returns the "wrong" model even in the infinite data limit) is provably beneficial, since the resulting errors can partially compensate for errors made by using an approximate prediction technique. En route to this result, we analyze the asymptotic properties of M-estimators based on convex variational relaxations, and establish a Lipschitz stability property that holds for a broad class of convex variational methods. This stability result provides additional incentive, apart from the obvious benefit of unique global optima, for using message-passing methods based on convex variational relaxations. We show that joint estimation/prediction based on the reweighted sum-product algorithm substantially outperforms a commonly used heuristic based on ordinary sum-product.
Discriminative Clustering in Fisher Metrics

Discriminative clustering (DC) finds a Voronoi partitioning of a primary data space that, while consisting of local partitions, simultaneously maximizes information about auxiliary data categories. DC is useful in exploration and in finding more coarse or refined versions of already existing categori es. Theoretical results suggest that Voronoi partitions in the socalled Fisher metric would outperform partitions in the Euclidean metric. Here we use a local quadratic approximation of the Fi sher metric, derived from a conditional density estimator, in defining the partitions and show that the resulting algorithms outperform the conventional variants.
Psychometric Properties of the Psychosocial Assessment Tool-General in Adolescents and Young Adults With Sickle Cell Disease.

OBJECTIVES Adolescents and young adults (AYAs) with sickle cell disease (SCD) experience psychosocial factors that increase their risk for poor disease management and health outcomes. Routine assessment of psychosocial factors that perpetuate health disparities is recommended. The Psychosocial Assessment Tool 2. 0_General (PAT2.0_GEN) AYA is a psychosocial screener with potential clinical utility in AYAs with SCD. This article is a preliminary examination of the internal consistency and predictive validity of this measure in a sample of 45 AYAs with SCD. METHODS Participants completed the PAT2. 0_GEN AYA, Pediatric Quality of Life Inventory, and a demographics form; psychosocial referral data were also collected. RESULTS Internal consistency for the PAT2. 0_GEN AYA was acceptable except for the Family Beliefs (0.67) and Structure and Resources subscales (0.37). PAT2. 0_GEN AYA scores were associated with an increased likelihood of referral for intervention within 4 months. CONCLUSIONS The PAT2. 0_GEN AYA holds promise as a screener to identify psychosocial risk factors that may compromise health outcomes in AYAs with SCD.
Optimization of Scheduling Algorithms Parameters in a DiffServ Environment

In this paper, we evaluate the performance achieved by DWRR and WFQ, the widespread implementations of frame-based and sorted-priority schedulers respectively, in a high speed DiffServ environment. Using deterministic upper bounds of the end-to-end delay, we evaluate the schedulers behavior for a wide range of bandwidth configurations. Our analysis highlights the drawbacks of using frame-based scheduling with respect to sorted-priority when dealing with multiple classes of service. As a consequence, we propose a configuration of scheduler weights, which allows to contain the loss in performance of frame-based schemes, while minimizing the end-to-end delay for premium classes. Extensive simulations confirm our theoretical propositions and show the effectiveness of our method in satisfying hard quality of service requirements even in highly congested networks.
Infinitesimal deformations of symmetric simple modular Lie algebras and Lie superalgebras

Over algebraically closed fields of positive characteristic, infinitesimal deformations of simple finite dimensional symmetric (the ones that with every root have its opposite of the same multiplicity) Lie algebras and Lie superalgebras are described for small ranks. The results are obtained by means of the Mathematica based code SuperLie. The infinitesimal deformation given by any odd cocycle is integrable. The moduli of the deformations form, in general, a supervariety. Not each even cocycle is integrable; but for those that are integrable, the global deforms (the results of deformations) are linear with respect to the parameter. In characteristic 2, the simple 3-dimensional Lie algebra admits a parametric family of non-isomorphic simple deforms. Some of Shen's "variations of G(2) theme" are interpreted as two global deforms corresponding to the several of the 20 infinitesimal deforms first found by Chebochko; we give their explicit form.
Personalized collaborative filtering recommender system using domain knowledge

In the current era of web applications such as e-retail business, the web services focused to provide personalized search systems to the targeted user intents based on the navigation patterns. Intelligent collaborative filtering recommender system tries to recommend the web pages considering the similar patterns of the other users along with the usage knowledge of the current user session. This recommender systems strategy lacks of the domain knowledge in comparing the usage patterns of the other users in serving with recommendations. This paper mainly focused on incorporating the domain knowledge and usage knowledge in personalization as well as in comparing the similar user patterns for recommender systems. This novel strategy builds a model to recommend the web pages that can help the new search scenarios and can improve the likelihood of a user towards the host website. Experimental results shown that the proposed novel strategy yields to gain in performance of the recommender system in terms of the quality of the web page recommendations.
Verification effectiveness in open-set speaker identification

Verification effectiveness in open-set, text-independent speaker identification is the authors' primary subject of concern. The study includes an analysis of the characteristics of this mode of speaker recognition and the potential causes of errors. The use of well-known score normalisation techniques for the purpose of enhancing the reliability of the process is described and their relative effectiveness is experimentally investigated. The experiments are based on the dataset proposed for the 1-speaker detection task of the NIST Speaker Recognition Evaluation 2003. On the basis of experimental results, it is demonstrated that significant benefits are achieved by using score normalisation in open-set identification, and that the level of this depends highly on the type of approach adopted. The results also show that better performance can be achieved by using the cohort normalisation methods. In particular, the unconstrained cohort method with a relatively small cohort size appears to outperform all other approaches.
CACTI 5.0

CACTI 5.0 is the latest major revision of the CACTI tool for modeling the dynamic power, access time, area, and leakage power of caches and other memories. CACTI 5.0 includes a number of major improvements over CACTI 4.0. First, as fabrication technologies enter the deep-submicron era, device and process parameter scaling has become non-linear. To better model this, the base technology modeling in CACTI 5.0 has been changed from simple linear scaling of the original CACTI 0.8 micron technology to models based on the ITRS roadmap. Second, embedded DRAM technology has become available from some vendors, and there is interest in 3D stacking of commodity DRAM with modern chip multiprocessors. As another major enhancement, CACTI 5.0 adds modeling support of DRAM memories. Third, to support the significant technology modeling changes above and to enable fair comparisons of SRAM and DRAM technology, the CACTI code base has been extensively rewritten to become more modular. At the same time, various circuit assumptions have been updated to be more relevant to modern design practice. Finally, numerous bug fixes and small feature additions have been made. For example, the cache organization assumed by CACTI is now output graphically to assist users in understanding the output generated by CACTI.
Initial Characterization of the Pig Skin Bacteriome and Its Effect on In Vitro Models of Wound Healing

Improved combat casualty care has increased survivability of traumatic wartime injuries, resulting in an emphasison longer‐term outcome such as wound healing. Recent advances in understanding the roles and composition of the humanskin microbiome have revealed a delicate interplay between resident microbes and combat wound healing. While pigs are the gold standard model for wound healing due to structural and healing similarities to human skin, the porcineskin microbiome has not been adequately elucidated. Moreover, no model organism has been established to examine the effects of novel pharmaceuticals on the skin microbiome in vivo. In the current study, we performed 16S rRNA gene sequencing on different anatomical locations from porcine skin, and utilized in vitro techniques to examine the paracrine effects of proteins released from these bacteria on human keratinocytes (NHEK) and fibroblasts (HDF). We found that the pig skin bacterial diversity varies in quantity and composition between subjects and anatomical locations. Moreover, t‐tests revealed significant effects on HDF and NHEK migration that was dependent on anatomical location. ELISA showed that, when cultured with NHEKs, bacterial releasate from certain anatomical locations reduced the amount of Vascular Endothelial Growth Factor present in the media and also induced NHEK apoptosis as determined via caspase‐3immunostaining. The results described herein further expand the knowledge of the cutaneous bacteriome of the healthy pig and suggest that naturally occurring bacterial flora may affect wound healing. This is particularly intriguing considering the amount of resources utilized for combat casualty wound care (i.e. healing, scarring, antibiotic resistance). Ultimately, if similarities between human and pig skin extend to the level of microbial diversity, the pig might be considered an ideal model for further investigations into the naturally occurring flora of the skin and its impact on wound healing process.
Alterations in white matter microstructure in women recovered from anorexia nervosa.

OBJECTIVE A recent study of ill individuals with anorexia nervosa (AN) reported microstructural alterations in white matter integrity including lower fractional anisotropy and higher mean diffusivity. This study was designed to determine whether such alterations exist in long-term recovered AN individuals and to examine potential associations with underlying AN traits. METHOD Twelve adult women recovered from restricting-type AN and 10 control women were studied using diffusion tensor imaging. RESULTS Overall, there was no significant fractional anisotropy alteration in recovered AN, in contrast to a prior study reporting lower fractional anisotropy in ill AN. Further, recovered AN showed lower mean diffusivity in frontal, parietal and cingulum white matter relative to control women, contrary to elevated mean diffusivity previously reported in ill AN. Lower longitudinal diffusivity in recovered AN was associated with higher harm avoidance. However, more severe illness history was associated with worse white matter integrity after recovery in the same direction as reported in prior work. DISCUSSION Our findings suggest that fractional anisotropy in recovered AN is not different from controls, however, a novel pattern of lower mean diffusivity was evidenced in recovered AN, and this alteration was associated with harm avoidance. Notably, severity of illness history may have long-term consequences, emphasizing the importance of aggressive treatment.
Interaction between Presenilin 1 and Ubiquilin 1 as Detected by Fluorescence Lifetime Imaging Microscopy and a High-throughput Fluorescent Plate Reader*

Presenilin 1 (PS1) in its active heterodimeric form is the catalytic center of the γ-secretase complex, an enzymatic activity that cleaves amyloid precursor protein (APP) to produce amyloid β (Aβ). Ubiquilin 1 is a recently described PS1 interacting protein, the overexpression of which increases PS1 holoprotein levels and leads to reduced levels of functionally active PS1 heterodimer. In addition, it has been suggested that splice variants of the UBQLN1 gene are associated with an increased risk of developing Alzheimer disease (AD). However, it is still unclear whether PS1 and ubiquilin 1 interact when expressed at endogenous levels under normal physiological conditions. Here, we employ three novel fluorescence resonance energy transfer-based techniques to investigate the interaction between PS1 and ubiquilin 1 in intact cells. We consistently find that the ubiquilin 1 N terminus is in close proximity to several epitopes on PS1. We show that ubiquilin 1 interacts both with PS1 holoprotein and heterodimer and that the interaction between PS1 and ubiquilin 1 takes place near the cell surface. Furthermore, we show that the PS1-ubiquilin 1 interaction can be detected between endogenous proteins in primary neurons in vitro as well as in brain tissue of healthy controls and Alzheimer disease patients, providing evidence of its physiological relevance.
Mode-matching analysis for double-layered substrate integrated and rectangular waveguide filter technology

The adaption of Mode Matching Techniques (MMTs) for an E-plane double-layer discontinuity is presented. The bottom layer is realized in substrate integrated waveguide (SIW) and the top layer in conventional waveguide technology. The calculations are validated by comparison with data from commercial field solvers. The verification shows a good match and qualifies the MMT routine as a suitable method to prototype magnetically coupled, double layer, mixed technology filters.
Design and Validation of a Relative Trust Model 1

When considering intelligent agents that interact with humans, having an idea of the trust levels of the human, for example in other agents or services, can be of great importance. Most models of human trust that exist assume trust in one trustee is independent of trust in another trustee. The model introduced here addresses so-called relative trust. The idea of relative trust is that trust in a certain trustee not only depends on the experiences with that trustee, but also on trustees that are perceived competitors of that trustee. Such models for relative trust contain parameters to represent the specific dependency between trust for different trustees. In order to tailor the model towards a specific human, dedicated parameter estimation techniques are used. The validation shows that such a model for relative trust is able to predict human trust based behaviour significantly better compared to a benchmark model. Work presented in this paper is a significant extension of the works published in: Hoogendoorn, M. , Jaffry, S.W. , and Maanen, P.-P. van. , Validation and Verification of Agent Models for Trust: Independent compared to Relative Trust. Proc. of the International Conference on Trust Management, (TM'11). Advances in Information and Communication Technology, vol. 358, Springer Verlag, 2011, pp. 35-50. Hoogendoorn, M. , Jaffry, S.W. , and Treur, J., An Adaptive Agent Model Estimating Human Trust in Information Sources. In: Baeza-Yates, R. , Lang, J., Mitra, S. , Parsons, S. , Pasi, G. (eds.), Proceedings of the 9th International Conference on Intelligent Agent Technology, IAT'09. IEEE Computer Society Press, 2009, pp. 458-465.
Digital image correlation method (DICM) application in speckle phase-shift of shear speckle defect detection

The laser shear speckle technique has a distinct advantage in non-destructive testing (NDT) field. Our main objective of our investigation has been to obtain some knowledge of digital image correlation method (DICM) applicated in speckle shearing phase-shift for damage detection. In many cases it uses laser speckle shearing technique with subtract to gain the fringe pattern, which method processes fast, but it can only gain low quality image. Meanwhile, the method of subtraction requires high quality of the original image, these features make it not suitable for high precision applications in many situations. This paper brought forth a method, which combined digital image correlation method (DICM) and phase-shifting method, to achieve shearography speckle phase shifting image in the tire defect detection according according to phase shifting image. The digital image speckle correlation technique can greatly reduce the requirements on original image quality and the number of the original images, and these features makes it easier to gain clear phase image combined with phase-shifting technique.
Adaptive output feedback control of linear systems preceded by unknown backlash-like hysteresis

In this paper, a new scheme is proposed to address an output feedback control problem: control of a class of uncertain linear systems preceded by unknown backlash-like hysteresis nonlinearities, where the hysteresis is modelled by a differential equation as in (C.Y. Su et al., 2000). The controller designed by using backstepping technique consists of a new robust control law and a new estimator to estimate the unknown parameters. For the implementation of the controller, no knowledge is assumed on the bounds of unknown system parameters and the effect contributed by the hysteresis. It is shown that all the signals are bounded. A bound for the truncated L/sub 2/ norm of the tracking error is obtained as a function of design parameters. Simulation studies also verify the effectiveness of the proposed scheme.
Categorization of a Song Using an Ant Colony Algorithm

Every day, the available mass of information other than text (such as image, video and sound) increases. These audio files may contain speech, music or other sounds are diverse and increasingly common in individuals of major media: radio, television and internet. This information would be irrelevant if our ability to efficiently access did not increase as well. For maximum benefit, we need tools that allow us to search, sort, store, index, update and analyze the available data. We also need tools helping us to find in a reasonable time the desired information. One of the promising areas is the audio indexing databases. This paper addresses the problem of indexing database containing songs to enable their effective exploitation. Since, we are interested with songs databases, it is necessary to exploit the specific structure of the song in with each part plays a specific role. We propose to use the title and the artist particularities (in fact each artist tends to compose or sing a specific genre of music). In this article, we present our experiments in automated song categorization, where we suggest the use of an ant colony algorithm. A naive Bayes algorithm is used as a baseline in our tests.
A STUDY ON SPATIAL CHARACTERISTICS OF AN INDOOR MULTI-ANTENNA CHANNEL

Sewoong Kwon ∗ , SeungHyun Ryu ∗ , ByoungSun Lee ∗ , Jeho Seok ∗∗ , Cheol Mun ∗∗∗ , Young Joong Yoon ∗ * is with Center for Information Technology Microwave and antenna lab. , Department of Electrical and Electronics Engineering, Yonsei University ** is with Ministry of Information and Communication Radio Research Laboratory *** is with Department of Electronic Communication Engineering, Chungju National University
Electrical and Computer Engineering 11-1-2007 MapReduce with Communication Overlap ( MaRCO )

MapReduce is a programming model from Google for cluster-based computing in domains such as search engines, machine learning, and data mining. MapReduce provides automatic data management and fault tolerance to improve programmability of clusters. MapReduce’s execution model includes an all-map-to-all-reduce communication, called the shuffle, across the network bisection. Some MapReductions move large amounts of data (e.g., as much as the input data), stressing the bisection bandwidth and introducing significant runtime overhead. Optimizing such shuffle-heavy MapReductions is important because (1) they include key applications (e.g., inverted indexing for search engines and data clustering for machine learning) and (2) they run longer than shuffle-light MapReductions (e.g., 5x longer). In MapReduce, the asynchronous nature of the shuffle results in some overlap between the shuffle and map. Unfortunately, this overlap is insufficient in shuffle-heavy MapReductions. We propose MapReduce with communicationoverlap (MaRCO) to achieve nearly full overlap via the novel idea of including the reduce in the overlap. While MapReduce lazily performs reduce computation only after receiving all the map data, MaRCO employs eager reduce to process partial data from some map tasks while overlapping with other map tasks’ communication. MaRCO’s approach of hiding the latency of the inevitably high shuffle volume of shuffle-heavy MapReductions is fundamental for achieving performance. We implement MaRCO in Hadoop’s MapReduce and show that on a 128-node Amazon EC2 cluster, MaRCO achieves 23% average speedup over Hadoop for shuffle-heavy MapReductions.