Miracle-dz/UniFAR-FTUs huggingface dataset:
Bayesian Cross Validation and WAIC for Predictive Prior Design in Regular Asymptotic Theory

Prior design is one of the most important problems in both statistics and machine learning. The cross validation (CV) and the widely applicable information criterion (WAIC) are predictive measures of the Bayesian estimation, however, it has been difficult to apply them to find the optimal prior because their mathematical properties in prior evaluation have been unknown and the region of the hyperparameters is too wide to be examined. In this paper, we derive a new formula by which the theoretical relation among CV, WAIC, and the generalization loss is clarified and the optimal hyperparameter can be directly found. By the formula, three facts are clarified about predictive prior design. Firstly, CV and WAIC have the same second order asymptotic expansion, hence they are asymptotically equivalent to each other as the optimizer of the hyperparameter. Secondly, the hyperparameter which minimizes CV or WAIC makes the average generalization loss to be minimized asymptotically but does not the random generalization loss. And lastly, by using the mathematical relation between priors, the variances of the optimized hyperparameters by CV and WAIC are made smaller with small computational costs. Also we show that the optimized hyperparameter by DIC or the marginal likelihood does not minimize the average or random generalization loss in general.
Color Fidelity in Computer Graphics: a Survey

The purpose of this paper is to make a state of the art for color fidelity in computer graphics. Color fidelity includes three steps. The first one is the spectral rendering phase which attributes a spectrum to each pixel of a picture. During the second step, a spectral data is transformed into a set of tristimulus values in the XYZ color space. The purpose of the third step, called Color Reproduction Function, is to determine the RGB values displayable on the screen, in such a way that subjective fidelity is reached. We especially detail the two last steps of the color fidelity process; we also point out the work still remaining to be done in this field and we propose some research ways.
The Effects of Sequencing and Producing Narrative Components of a Story on Reading Comprehension

The Effects of Sequencing and Producing Narrative Components of a Story on Reading Comprehension Kelly A. Mercorella In three experiments, I tested for the presence of the naming capability, the participants’ drawing responses of the stimuli learned in the absence of the visual stimulus, and the participants’ comprehension of texts with and without pictures present. In Experiment 1, I tested for the presence of naming and the drawing responses for the stimuli presented during the naming experience in the absence of the visual stimulus in 44 third and fourth grade participants. Results demonstrated that fewer instances of the naming capability, as well as the drawing response, were seen in students performing below grade-level in reading, than students performing on or above grade-level. I hypothesized that differences in reading performance may be due to the absence of a strong production response repertoire, which include behaviors such as drawing and speaking. Experiment 2 assessed whether or not the presence of visual stimuli during reading has an effect on the target participants’ comprehension of a story, as well as if the presence of the speaker component of naming and the drawing responses had an effect on comprehension. Results demonstrated that there were significant differences in comprehension scores between the three groups during the with pictures condition χ(2, N=44) = 24.38, p = 0.00 and the without pictures condition χ(2, N=44) = 33.47, p = 0.00. Furthermore, there was a significant correlation between mean comprehension scores and the number of components drawn correctly ρ (44) = . 412, p = . 005. These results are consistent with the theory that the visualization of events and characters in a story is necessary to facilitate reading comprehension. Experiment 3 employed a multiple probe design across 3 groups of 2 participants to test the effects of sequencing and producing narrative components of a story on the participants’ responses to comprehension questions for texts without pictures present. The participants were 6 third grade students who demonstrated belowgrade-level comprehension skills for texts without pictures present. Prior to the intervention, all participants exhibited lower comprehension scores for texts without pictures present than for texts with pictures present. Following the intervention, all participants’ comprehension scores for texts without pictures present increased, and 5 of the 6 participants showed increases in comprehension for texts with pictures present as well. Furthermore, following the intervention all participants demonstrated increases in the conditioned seeing repertoire, with 4 of the 6 participants meeting criterion level responding.
Exploiting frequency-selectivity in real-time multicast services over LTE networks

Long Term Evolution (LTE) is considered the most promising cellular system able to support the growing demand of multicast services (e.g., IPTV, video streaming) over mobile terminals. The design of effective strategies for the management of these applications is still an open issue, especially in scenarios where several multicast streams are simultaneously transmitted in a cell. In this paper we propose different resource allocation policies for the delivery of multicast scalable video flows. Such policies efficiently exploit the multi-user diversity and the frequency selectivity in order to match the requirements of both users and providers. The performance of the proposed strategies is analyzed through simulations by evaluating different cell deployment and user load environments and by focusing on spectral efficiency, throughput, fairness, and amount of resources needed for multicast service delivery. The last parameter is important in practical scenarios where multicast services share the available resources with other flows, e.g., unicast services.
Extractionbased on Space Folding Model and Application to Machine Learning

One of the most important designs for a lot of machine learning methods is the determination of the similarity between instances. Especially the kernel matrix, which is also known as the Gram matrix, plays a central role in the kernel machines such as support vector machine. The simplest design of similarity function is to use the distances between instances or the Gaussian function based on them. It is easy to learn the model when the data distribution follows their label, in which the instances with same label are allocated near and those with different label are allocated far. However, when the data distribution is non-linear, it becomes difficult. This paper discusses the inner products of 2 non-orthogonal basis vectors and proposes the similarity between instances. This paper also proposes a space folding model for machine learning based on the proposed similarity. This paper applies the proposed method to pattern recognition problem and shows its effectiveness.
Multi-Phase QR ZCS Switched-Capacitor Bidirectional Power Converter for PV System Application

The multi-phase implementation in the QR (quasi resonant) ZCS (zero current switching) SC (switched capacitor) bidirectional DC-DC converter structure has been proposed to reduce current ripple, switching loss and significantly increase the converter efficiency and power density. This approach provides a more precise output voltage to obtain voltage conversion ratios from the double-mode versus half-mode to n-mode versus 1/n mode. This is accomplished by adding a different number of switched-capacitors and power MOSFET switches with a small series connected resonant inductor for forward and reverse schemes. The size and cost can be reduced when the proposed converter has been designed with the coupled inductors. The simulation and experimental results have been used to demonstrate the performance of the two-phase with and without coupled inductor interleaved QR ZCS SC converters for bidirectional power flow control application, and an extending structure for N-phase is mentioned.
Computer-Mediated Advertising : Consumers and Brands

This paper addresses computer-mediated advertising (CMA) as a source of brand image. It is proposed that CMA contributes to brand image by facilitating unique consumer-brand relationships. The 'quality' of these relationships largely depend on how advertisers leverage interactivity and computer intelligence during interaction. An analogy is drawn between CMA and service encounters and a framework incorporating principles from Dance's (1967) helix model of communication and Mehrabian's (1981) approach/avoidance theory is created to elucidate strategy and research issues.
The Effect of Cognitive Load on Gender Discrimination in Job Interviews

THE EFFECT OF COGNITIVE LOAD ON GENDER DISCRIMINATION IN JOB INTERVIEWS Sulan Kith Advisor: University of Guelph, 2018 Dr. Deborah Powell Interviews are cognitively-demanding tasks; and the load they put on interviewers may be exacerbated by a number of extraneous situational factors. High cognitive load may compromise the resistance to discrimination that otherwise distinguishes structured interviews from other interview methods, leading to heuristically-driven or discriminatory hiring decisions. The present study examined whether cognitive load influences interviewers’ likelihood to gender discriminate during job interviews, and whether the effect differs based on the measure of discrimination. In the study, participants completed an online interview simulation administrated through a crowdsourcing platform. During the simulation, participants were randomly assigned to either a high cognitive load or low cognitive load condition. All participants were asked to evaluate the interview responses of two equally-qualified, but differently-gendered candidates who were applying for a stereotypically-masculine or stereotypically-feminine job. Participants provided ratings of each candidates’ suitability, as well as a final hiring decision. The cognitive load manipulation was not found to affect the numeric ratings that participants gave the candidates; however, participants in the high cognitive load condition chose to hire the stereotypically gender-congruent candidate more frequently than participants in the low cognitive load condition. These results support the need for incorporating multi-step selection strategies that go beyond interviews, in order to help to prevent discrimination from influencing hiring decisions.
Preliminary Investigations intothe Development of Textile Based Temperature Sensor for Healthcare Applications

Human body temperature is an important sign of physical condition in terms of comfort, heat or cold stresses, and of performance. This paper presents the preliminary investigation into the design, manufacturing and testing of the textile based temperature sensor. This sensing fabric may be employed to measure the temperature of the human body on a continuous basis over extensive periods of time, outside the clinical environment. The sensing fabric was manufactured on an industrial scale flat-bed knitting machine by laying-in the sensing element (in the form of fine metal wire) into the double layer knitted structure. The operational principle of the sensing fabric is based on the inherent tendency of metal wire to change in its electrical resistance because of the change in its temperature. An experimental resistance-temperature relationship showed promising validation in comparison with their modeled counterparts.
MIMO Capacity Analysis Using Novel Adaptive Semi Blind Estimation Scheme for Spatial Channel

Multiple Input Multiple Output(MIMO) has been remained in much importance in recent past because of high capacity gain over a single antenna system. In this article, analysis over the capacity of the MIMO channel systems with spatial channel has been considered when the channel state information(CSI) is considered imperfect or partial. The dynamic CSI model has also been tried consisting channel mean and covariance which leads to the extracting of channel estimates and error covariance. Both parameters indicated the CSI quality since these are the functions of temporal correlation factor, and based on this, the model covers data from perfect to statistical CSI, either partial or full blind. It is found that in the case of partial & imperfect CSI, the capacity depends on the statistical properties of the error in the CSI. Based on the knowledge of statistical distribution of the deviations in CSI knowledge, a new estimation approach which maximizes a capacity of spatial channel model has been tried. The interference then interactively reduced by employing the iterative channel estimation and data detection approach.
European Society for Pediatric Gastroenterology, Hepatology, and Nutrition Guidelines for the Diagnosis of Coeliac Disease

Objective: Diagnostic criteria for coeliac disease (CD) from the European Society for Paediatric Gastroenterology, Hepatology, and Nutrition (ESPGHAN) were published in 1990. Since then, the autoantigen in CD, tissue transglutaminase, has been identified; the perception of CD has changed from that of a rather uncommon enteropathy to a common multiorgan disease strongly dependent on the haplotypes human leukocyte antigen (HLA)-DQ2 and HLA-DQ8; and CD-specific antibody tests have improved. Methods: A panel of 17 experts defined CD and developed new diagnostic criteria based on the Delphi process. Two groups of patients were defined with different diagnostic approaches to diagnose CD: children with symptoms suggestive of CD (group 1) and asymptomatic children at increased risk for CD (group 2). The 2004 National Institutes of Health/Agency for Healthcare Research and Quality report and a systematic literature search on antibody tests for CD in paediatric patients covering the years 2004 to 2009 was the basis for the evidence-based recommendations on CD-specific antibody testing. Results: In group 1, the diagnosis of CD is based on symptoms, positive serology, and histology that is consistent with CD. If immunoglobulin A anti-tissue transglutaminase type 2 antibody titers are high (>10 times the upper limit of normal), then the option is to diagnose CD without duodenal biopsies by applying a strict protocol with further laboratory tests. In group 2, the diagnosis of CD is based on positive serology and histology. HLA-DQ2 and HLA-DQ8 testing is valuable because CD is unlikely if both haplotypes are negative. Conclusions: The aim of the new guidelines was to achieve a high diagnostic accuracy and to reduce the burden for patients and their families. The performance of these guidelines in clinical practice should be evaluated prospectively.
Comparative Proteomic Analysis of Differentially Expressed Proteins in the Urine of Reservoir Hosts of Leptospirosis

Rattus norvegicus is a natural reservoir host for pathogenic species of Leptospira. Experimentally infected rats remain clinically normal, yet persistently excrete large numbers of leptospires from colonized renal tubules via urine, despite a specific host immune response. Whilst persistent renal colonization and shedding is facilitated in part by differential antigen expression by leptospires to evade host immune responses, there is limited understanding of kidney and urinary proteins expressed by the host that facilitates such biological equilibrium. Urine pellets were collected from experimentally infected rats shedding leptospires and compared to urine from non-infected controls spiked with in vitro cultivated leptospires for analysis by 2-D DIGE. Differentially expressed host proteins include membrane metallo endopeptidase, napsin A aspartic peptidase, vacuolar H+ATPase, kidney aminopeptidase and immunoglobulin G and A. Loa22, a virulence factor of Leptospira, as well as the GroEL, were increased in leptospires excreted in urine compared to in vitro cultivated leptospires. Urinary IgG from infected rats was specific for leptospires. Results confirm differential protein expression by both host and pathogen during chronic disease and include markers of kidney function and immunoglobulin which are potential biomarkers of infection.
Topic word selection for blogs by topic richness using web search result clustering

Blogs are one of popular services to publish and archive posts with personal opinions on the web. The topics of a blog can be used for classification, recommendation, opinion mining, and ranking, etc. In this paper, we propose a method for extracting important topic words from a blog. Our method selects topic words by measuring whether the blog includes rich content on the word. To measure the richness of a blog on candidate topic words, we compare web search results by the candidate words with the content of the blog. We used document clustering and cluster matching in order to compare them.
A Physically Derived Water Content/Permittivity Calibration Model for Coarse-Textured, Layered Soils

to try and ascertain the relative importance of different soil properties for calibration. This approach has good Many empirical formulas relating TDR-measured permittivity (Ka) utilityforfindingsimpleandpracticalequationsthatcan to volumetric water content () have been presented owing to the lack of a robust and accurate physically derived model describing this beusedtoestimate.However,thelimitationofsuchan relationship across a range of soils. Soil-specific calibrations are often approach is set by the required input parameters deinfeasible due to the time-consuming gravimetric sampling required scribing the soil attributes used in the analysis. for adequate calibration. In this work we propose a sample scale model An alternative approach to understand the permittivfor the Ka– relationship in coarse-grained media using physically ityresponseofunsaturatedporousmediaisthe‘bottombased pore-scale, or calibrated two-point, anchoring. Materials tested up’ approach. This approach begins at the grain-scale include mono-size glass spheres and quartz sand grains in addition to and endeavors to reconstruct the sample-scale response two sandy soils. The performance of the model was comparable with based on the grain-scale properties. Maxwell (1881) dethe empirical models of Topp et al. (1980) for the different media. veloped a mixing model approach relevant for describThe model accounts for particle shape and bulk density using a twoing the electrical conductivity of heterogeneous sands. phase pore-scale mixing model and refers to a wetting or draining profile with a sharp wetting or drying front. Our measurements indicate Since then many dielectric-mixing models have been the absenceof dielectrichysteresis forthe narrowsize distributionma- proposed for different applications and geometries (Looterials studied. An alternate calibration approach only requires the yenga, 1965; de Loor, 1968; Dobson et al., 1985; Hilfer, measured soil effective permittivities for dry (edry) and saturated (esat) 1991; Heimovaara et al., 1994; Friedman, 1997; Friedconditions (i.e., two-phase mixtures) and knowledge of the bulk den- man, 1998; Hilhorst et al., 2000). This approach has been sity. We recommend a general value of 2.8 for edry in soils with pre- used extensively in the geophysics literature where satudominantly quartz mineralogy; the model then requiring only the esat rated rocks present the simpler case of a two-phase meto develop a soil specific calibration. The results provide insight into dium (Sen et al., 1981; Kenyon, 1984). The extension theappropriate‘refractiveindex’modelingoflayered(wetting/drying) of these methods to the problem of three-phase, unsatusoil profiles with the grain-scale modeled two-phase permittivity proratedsoilspresentsadifficultconceptualandmathematviding bounds for the sample-scale three-phase porous medium. ical problem. Friedman (1998) applied an effective medium approach using two types of configurations for the air, water, and solid phases with some success. In that
Semantic Relation Discovery by Using Co-occurrence Information

Motivated by the need of constructing a knowledge base for a patient-centred question-answering system, the potential of exploiting co-occurrence data to infer non-ontological semantic relations out of these statistical associations is explored. The UMLS concept co-occurrence table MRCOC is used as a data source. This data provides, for each co-occurrence record, a profile of MeSH subheading profiles. This is used as an additional source of semantic information from which we generate hypotheses for more specific semantic relations. An initial experiment was performed, limited to the study of disease-substance associations. For validation 20 diseases were selected and annotated by experts regarding treatment and prevention. The results showed good precision values (82 for prevention, 72 for treatment), but unsatisfactory values for recall (67 for prevention, 45 for treatment) for this particular use case.
Face numbers of manifolds with boundary

We study face numbers of simplicial complexes that triangulate manifolds (or even normal pseudomanifolds) with boundary. Specifically, we establish a sharp lower bound on the number of interior edges of a simplicial normal pseudomanifold with boundary in terms of the number of interior vertices and relative Betti numbers. Moreover, for triangulations of manifolds with boundary all of whose vertex links have the weak Lefschetz property, we extend this result to sharp lower bounds on the number of higher-dimensional interior faces. Along the way we develop a version of Bagchi and Datta's $\sigma$- and $\mu$-numbers for the case of relative simplicial complexes and prove stronger versions of the above statements with the Betti numbers replaced by the $\mu$-numbers. Our results provide natural generalizations of known theorems and conjectures for closed manifolds and appear to be new even for the case of a ball.
Approximate medians and other quantiles in one pass and with limited memory

We present new algorithms for computing approximate quantiles of large datasets in a single pass. The approximation guarantees are explicit, and apply for arbitrary value distributions and arrival distributions of the dataset. The main memory requirements are smaller than those reported earlier by an order of magnitude. We also discuss methods that couple the approximation algorithms with random sampling to further reduce memory requirements. With sampling, the approximation guarantees are explicit but probabilistic, i.e. they apply with respect to a (user controlled) confidence parameter. We present the algorithms, their theoretical analysis and simulation results on different datasets.
Sampling Based Range Partition Methods for Big Data Analytics

Big Data Analytics requires partitioning datasets into thousands of partitions according to a specific set of keys so that different machines can process different partitions in parallel. Range partition is one of the ways to partition the data that is needed whenever global ordering is required. It partitions the data according to a pre-defined set of exclusive and continuous ranges that covers the entire domain of the partition key. Providing high-quality (approximately equal-sized) partitions is a key problem for the big data analytics because the job latency is determined by the most loaded node. This problem is especially challenging because typically no statistics about the key distribution over machines for an input dataset is available at the beginning of a range partition. The system needs to find a way to determine the partition boundaries that is both cost-effective and accurate. This paper presents a weightedsampling based approach, implemented in Cosmos–the cloud infrastructure for big data analytics used by Microsoft Online Service Division. The approach has been used by many jobs daily and was found to be both efficient and providing desired partition quality.