scholarly journals On the Impact of Affine Loop Transformations in Qubit Allocation

2021 ◽  
Vol 2 (3) ◽  
pp. 1-40
Author(s):  
Martin Kong
Keyword(s):  
Global Optimization ◽  
Quantum Circuits ◽  
Affine Transformations ◽  
Sliding Windows ◽  
Optimization Criteria ◽  
Loop Transformations ◽  
Domain Specific ◽  
Extensive Evaluation ◽  
Distance Minimization ◽  
The Impact

Most quantum compiler transformations and qubit allocation techniques to date are either peep-hole focused or rely on sliding windows that depend on a number of external parameters including the topology of the quantum processor. Thus, global optimization criteria are still lacking. In this article, we explore the synergies and impact of affine loop transformations in the context of qubit allocation and mapping. With this goal in mind, we designed and implemented AXL , a domain specific language and source-to-source compiler for quantum circuits that can be directly described with affine relations. We conduct an extensive evaluation spanning circuits from the recently introduced QUEKO benchmark suite, eight quantum circuits taken from the literature, three distinct coupling graphs, four affine transformations (including the Pluto dependence distance minimization and Feautrier’s minimum latency algorithms), four qubit allocators, and two back-end quantum compilers. Our results demonstrate that affine transformations using global optimization criteria can cooperate effectively in several scenarios with quantum qubit mapping algorithms to reduce the circuit depth, size and allocation time.

Emotions and health: The impact of emotions, emotions regulation, music, and acculturation on health

2008 ◽  
Vol 16 (3) ◽  
pp. 112-115 ◽  
Author(s):  
Stephan Bongard ◽  
Volker Hodapp ◽  
Sonja Rohrmann
Keyword(s):  
Stress Responses ◽  
Physiological Stress ◽  
Anger Expression ◽  
Domain Specific ◽  
Cardiovascular Stress ◽  
Expression Behavior ◽  
Cardio Vascular ◽  
And Coping ◽  
Relationship Of ◽  
The Impact

Abstract. Our unit investigates the relationship of emotional processes (experience, expression, and coping), their physiological correlates and possible health outcomes. We study domain specific anger expression behavior and associated cardio-vascular loads and found e.g. that particularly an open anger expression at work is associated with greater blood pressure. Furthermore, we demonstrated that women may be predisposed for the development of certain mental disorders because of their higher disgust sensitivity. We also pointed out that the suppression of negative emotions leads to increased physiological stress responses which results in a higher risk for cardiovascular diseases. We could show that relaxation as well as music activity like singing in a choir causes increases in the local immune parameter immunoglobuline A. Finally, we are investigating connections between migrants’ strategy of acculturation and health and found e.g. elevated cardiovascular stress responses in migrants when they where highly adapted to the German culture.

scholarly journals Mapping domain characteristics influencing Analytics initiatives: The example of Supply Chain Analytics

2020 ◽  
Vol 13 (1) ◽  
pp. 56
Author(s):  
Tino Herden
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Success Factors ◽  
Research Collaboration ◽  
Future Research ◽  
Structured Interviews ◽  
Chain Management ◽  
Domain Specific ◽  
The Status ◽  
The Impact

Purpose: Analytics research is increasingly divided by the domains Analytics is applied to. Literature offers little understanding whether aspects such as success factors, barriers and management of Analytics must be investigated domain-specific, while the execution of Analytics initiatives is similar across domains and similar issues occur. This article investigates characteristics of the execution of Analytics initiatives that are distinct in domains and can guide future research collaboration and focus. The research was conducted on the example of Logistics and Supply Chain Management and the respective domain-specific Analytics subfield of Supply Chain Analytics. The field of Logistics and Supply Chain Management has been recognized as early adopter of Analytics but has retracted to a midfield position comparing different domains.Design/methodology/approach: This research uses Grounded Theory based on 12 semi-structured Interviews creating a map of domain characteristics based of the paradigm scheme of Strauss and Corbin.Findings: A total of 34 characteristics of Analytics initiatives that distinguish domains in the execution of initiatives were identified, which are mapped and explained. As a blueprint for further research, the domain-specifics of Logistics and Supply Chain Management are presented and discussed.Originality/value: The results of this research stimulates cross domain research on Analytics issues and prompt research on the identified characteristics with broader understanding of the impact on Analytics initiatives. The also describe the status-quo of Analytics. Further, results help managers control the environment of initiatives and design more successful initiatives.

scholarly journals Cross-Fertilizing Deep Web Analysis and Ontology Enrichment

2017 ◽  
Author(s):  
Marilena Oita ◽  
Antoine Amarilli ◽  
Pierre Senellart
Keyword(s):  
Domain Knowledge ◽  
Deep Web ◽  
Web Pages ◽  
Complete Understanding ◽  
Specific Knowledge ◽  
Domain Specific ◽  
Domain Specific Knowledge ◽  
Web Crawlers ◽  
New Perspective ◽  
The Impact

Deep Web databases, whose content is presented as dynamically-generated Web pages hidden behind forms, have mostly been left unindexed by search engine crawlers. In order to automatically explore this mass of information, many current techniques assume the existence of domain knowledge, which is costly to create and maintain. In this article, we present a new perspective on form understanding and deep Web data acquisition that does not require any domain-specific knowledge. Unlike previous approaches, we do not perform the various steps in the process (e.g., form understanding, record identification, attribute labeling) independently but integrate them to achieve a more complete understanding of deep Web sources. Through information extraction techniques and using the form itself for validation, we reconcile input and output schemas in a labeled graph which is further aligned with a generic ontology. The impact of this alignment is threefold: first, the resulting semantic infrastructure associated with the form can assist Web crawlers when probing the form for content indexing; second, attributes of response pages are labeled by matching known ontology instances, and relations between attributes are uncovered; and third, we enrich the generic ontology with facts from the deep Web.

scholarly journals An evaluation of the interpretability and predictive performance of the BayesR model for genomic prediction

2020 ◽  
Author(s):  
Fanny Mollandin ◽  
Andrea Rau ◽  
Pascal Croiseau
Keyword(s):  
Linkage Disequilibrium ◽  
Qtl Mapping ◽  
Effect Size ◽  
Genomic Prediction ◽  
Simulated Data ◽  
Predictive Performance ◽  
Real Data ◽  
Sliding Windows ◽  
Size Classes ◽  
The Impact

ABSTRACTTechnological advances and decreasing costs have led to the rise of increasingly dense genotyping data, making feasible the identification of potential causal markers. Custom genotyping chips, which combine medium-density genotypes with a custom genotype panel, can capitalize on these candidates to potentially yield improved accuracy and interpretability in genomic prediction. A particularly promising model to this end is BayesR, which divides markers into four effect size classes. BayesR has been shown to yield accurate predictions and promise for quantitative trait loci (QTL) mapping in real data applications, but an extensive benchmarking in simulated data is currently lacking. Based on a set of real genotypes, we generated simulated data under a variety of genetic architectures, phenotype heritabilities, and we evaluated the impact of excluding or including causal markers among the genotypes. We define several statistical criteria for QTL mapping, including several based on sliding windows to account for linkage disequilibrium. We compare and contrast these statistics and their ability to accurately prioritize known causal markers. Overall, we confirm the strong predictive performance for BayesR in moderately to highly heritable traits, particularly for 50k custom data. In cases of low heritability or weak linkage disequilibrium with the causal marker in 50k genotypes, QTL mapping is a challenge, regardless of the criterion used. BayesR is a promising approach to simultaneously obtain accurate predictions and interpretable classifications of SNPs into effect size classes. We illustrated the performance of BayesR in a variety of simulation scenarios, and compared the advantages and limitations of each.

scholarly journals Forensic Feature-Comparison Expertise: Statistical Learning Facilitates Visual Comparison Performance

2020 ◽  
Author(s):  
Bethany Growns ◽  
Kristy Martire
Keyword(s):  
Statistical Learning ◽  
High Performance ◽  
Comparison Task ◽  
Domain Specific ◽  
Visual Evidence ◽  
Visual Comparison ◽  
Distributional Information ◽  
Distributional Learning ◽  
The Impact ◽  
The Relationship

Forensic feature-comparison examiners in select disciplines are more accurate than novices when comparing visual evidence samples. This paper examines a key cognitive mechanism that may contribute to this superior visual comparison performance: the ability to learn how often stimuli occur in the environment (distributional statistical learning). We examined the relation-ship between distributional learning and visual comparison performance, and the impact of training about the diagnosticity of distributional information in visual comparison tasks. We compared performance between novices given no training (uninformed novices; n = 32), accu-rate training (informed novices; n = 32) or inaccurate training (misinformed novices; n = 32) in Experiment 1; and between forensic examiners (n = 26), informed novices (n = 29) and unin-formed novices (n = 27) in Experiment 2. Across both experiments, forensic examiners and nov-ices performed significantly above chance in a visual comparison task where distributional learning was required for high performance. However, informed novices outperformed all par-ticipants and only their visual comparison performance was significantly associated with their distributional learning. It is likely that forensic examiners’ expertise is domain-specific and doesn’t generalise to novel visual comparison tasks. Nevertheless, diagnosticity training could be critical to the relationship between distributional learning and visual comparison performance.

scholarly journals Domain Specific Self-Efficacy Mediates the Impact of Pain Catastrophizing on Pain and Disability in Overweight and Obese Osteoarthritis Patients

2008 ◽  
Vol 9 (10) ◽  
pp. 912-919 ◽  
Author(s):  
Rebecca A. Shelby ◽  
Tamara J. Somers ◽  
Francis J. Keefe ◽  
Jennifer J. Pells ◽  
Kim E. Dixon ◽  
...  
Keyword(s):  
Self Efficacy ◽  
Pain Catastrophizing ◽  
Domain Specific ◽  
The Impact

Adaptive Stochastic Resonance Method for Early Fault Detection of Rotating Machinery Based on Alpha Stable Distribution

2014 ◽  
Vol 618 ◽  
pp. 458-462
Author(s):  
Gang Yu ◽  
Ye Chen
Keyword(s):  
Fault Detection ◽  
Stochastic Resonance ◽  
Stable Distribution ◽  
Signal To Noise Ratio ◽  
Resonance Method ◽  
Rotating Machinery ◽  
Sliding Windows ◽  
Alpha Stable Distribution ◽  
Early Fault Detection ◽  
The Impact

This paper proposes an adaptive stochastic resonance (SR) method based on alpha stable distribution for early fault detection of rotating machinery. By analyzing the SR characteristic of the impact signal based on sliding windows, SR can improve the signal to noise ratio and is suitable for early fault detection of rotating machinery. Alpha stable distribution is an effective tool for characterizing impact signals, therefore parameter alpha can be used as the evaluating parameter of SR. Through simulation study, the effectiveness of the proposed method has been verified.

How Innovativeness Influences Internet Shopping

2005 ◽  
pp. 323-336
Author(s):  
Ronald E. Goldsmith ◽  
Barbara A. Lafferty
Keyword(s):  
Individual Difference ◽  
Personality Trait ◽  
Internet Shopping ◽  
Buying Behavior ◽  
Domain Specific ◽  
New Ideas ◽  
The Impact ◽  
Online Buying

Innovativeness is a willingness to try new things. Combined with other factors, innovativeness leads consumers to be among the first to adopt new ideas, products, and practices, such as buying online. As an individual difference trait, innovativeness can be conceptualized at three levels of generality: a global personality trait, general marketplace innovativeness, and domain-specific innovativeness. The present study assessed the impact of the first and third of these types of innovativeness on cyber-shopping. Data were collected through a survey of 303 consumers. Four hypotheses were proposed and supported. Global innovativeness and domain-specific innovativeness (online innovativeness) were positively correlated with each other and with amount of online buying. Domain-specific innovativeness also mediated the influence of global innovativeness on online buying behavior. Moreover, the relationships were weakly moderated by gender of respondent. These findings are consistent with previous studies and help to explain how personality influences online buying.

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