Set Of Codes Research Articles

XSUN-2022/SUSD3D n/γ sensitivity-uncertainty code package with recent JEFF-3.3 and ENDF/B-VIII.0 covariance data

A new version of the XSUN-2022 computer code package is under preparation to be released through NEA Data Bank which includes the latest updates of the SUSD3D sensitivity/uncertainty (S/U) code and the corresponding cross section and covariance matrix libraries based on the JEFF-3.3, ENDF/B-VIII.0 and FENDL-3.2 evaluations. The XSUN code package is a user–computer interface environment, written in Xbase++ ® and using Compiler 1.90.331 and Alaska 32-Bit Linker, for user-friendly pre- and post-processing of the input and output data for a complete and self-consistent set of deterministic codes. The flowchart integrates the codes TRANSX, PARTISN, and SUSD3D, all available from the OECD/NEA Data Bank and RSICC. The codes are used for the preparation of multigroup nuclear cross-section, neutron and gamma transport calculations for criticality and shielding calculations and nuclear data sensitivity and uncertainty calculations, respectively. XSUN-2022 is the 3rdreleased version, after XSUN-2013 and -2017.

Peculiarities of CI processor fitting in children with auditory neuropathy spectrum disorders

The peculiarities of cochlear implant (CI) processor fitting in children with auditory neuropathy spectrum disorders (ANSD) were investigated. At the 1-st fitting of the CI processor a standard protocol of parameters was used in all patients, including patients with cochlear nerve hypoplasia. After the initial fitting session, the behavioral tonal thresholds with CI in 55% of patients were 30-35 dB, in 32.% of patients - 40-50 dB. After 3-6 months, 65% of children with ANSD showed significant progress in auditory-speech development, which made it possible to use the standard protocol of tuning parameters for them with the most comfortable and threshold levels of electrical stimulation adjusted according to the child's reactions. The best dynamics was observed in 2 children with presynaptic ANSD with a confirmed DFNB9 (OTOF) gene mutation. In 35% of children, there was no progress in distinguishing speech signals and instability of reactions to sounds persisted after 6 months using of CI and speech therapy training, despite the low tonal thresholds of hearing. In these children the coding strategy was changed, the stimulation frequency was reduced, and the pulse width was increased. This helped to improve the discrimination of sounds with CI and progress in the child's speech development. The results demonstrate that children with ANSD require more frequent correction of CI processor settings: 1st year - every 3 months, then at least 2 times a year until the optimal coding strategy and settings are achieved. To predict the effectiveness of CI and determine the optimal tactics for setting up the CI processor in patients with ANSD, the preoperative examination should include MRI of the cerebellopontine angle to detect anomalies of the cochlear nerve and genetic examination to identify mutations that cause hearing impairment in patients with ANSD.

Classifying Astronomical Transients Using Only Host Galaxy Photometry

The Legacy Survey of Space and Time (LSST) at the Vera C. Rubin Observatory will discover tens of thousands of extragalactic transients each night. The high volume of alerts demands immediate classification of transient types in order to prioritize observational follow-ups before events fade away. We use host galaxy features to classify transients, thereby providing classification upon discovery. In contrast to past work that focused on distinguishing Type Ia and core-collapse supernovae (SNe) using host galaxy features that are not always accessible (e.g., morphology), we determine the relative likelihood across 12 transient classes based on only 19 host apparent magnitudes and colors from 10 optical and IR photometric bands. We develop both binary and multiclass classifiers, using kernel density estimation to estimate the underlying distribution of host galaxy properties for each transient class. Even in this pilot study, and ignoring relative differences in transient class frequencies, we distinguish eight transient classes at purities significantly above the 8.3% baseline (based on a classifier that assigns labels uniformly and at random): tidal disruption events (TDEs; 48% ± 27%, where ± indicates the 95% confidence limit), SNe Ia-91bg (32% ± 18%), SNe Ia-91T (23% ± 11%), SNe Ib (23% ± 13%), SNe II (17% ± 2%), SNe IIn (17% ± 6%), SNe II P (16% ± 4%), and SNe Ia (10% ± 1%). We demonstrate that our model is applicable to LSST and estimate that our approach can accurately classify 59% of LSST alerts expected each year for SNe Ia, Ia-91bg, II, Ibc, SLSN-I, and TDEs. Our code and data set are publicly available.

LINNA: Likelihood Inference Neural Network Accelerator

Bayesian posterior inference of modern multi-probe cosmological analyses incurs massive computational costs. For instance, depending on the combinations of probes, a single posterior inference for the Dark Energy Survey (DES) data had a wall-clock time that ranged from 1 to 21 days using a state-of-the-art computing cluster with 100 cores. These computational costs have severe environmental impacts and the long wall-clock time slows scientific productivity. To address these difficulties, we introduce LINNA: the Likelihood Inference Neural Network Accelerator. Relative to the baseline DES analyses, LINNA reduces the computational cost associated with posterior inference by a factor of 8–50. If applied to the first-year cosmological analysis of Rubin Observatory's Legacy Survey of Space and Time (LSST Y1), we conservatively estimate that LINNA will save more than U.S. $300,000 on energy costs, while simultaneously reducing CO2 emission by 2,400 tons. To accomplish these reductions, LINNA automatically builds training data sets, creates neural network emulators, and produces a Markov chain that samples the posterior. We explicitly verify that LINNA accurately reproduces the first-year DES (DES Y1) cosmological constraints derived from a variety of different data vectors with our default code settings, without needing to retune the algorithm every time. Further, we find that LINNA is sufficient for enabling accurate and efficient sampling for LSST Y10 multi-probe analyses. We make LINNA publicly available at https://github.com/chto/linna, to enable others to perform fast and accurate posterior inference in contemporary cosmological analyses.

Generalized Construction of Z-Complementary Code Sets of Odd Length

Generalized Construction of Z-Complementary Code Sets of Odd Length

Glosa do Wyroku Sądu Najwyższego z dnia 15 października 2021 r., sygn. akt I CSKP 226/21

The Supreme Court ruled on 15 October 2021, ref. no. I CSKP 226/21, that the provisions of the Civil Code set out in Article 509 and subsequent articles are not applicable to assignment agreements (cession agreement) concerning EU funds. According to Article 509 of the Civil Code, a creditor may, without the debtor’s consent, assign a claim to a third party, unless this would be contrary to the law, a contractual condition, or the nature of the obligation. In commented judgement, the Supreme Court stated that an assignment agreement concluded between a beneficiary of EU funds (assignor, debt seller) and a third party (assignee, debt buyer) has legal effects with respect to the debtor, which is a public administration body granting subsidies from EU funds, only if it has been approved by this body. In the absence of approval, the public administration body is not obliged to perform such an agreement and the claim for a subsidy from EU funds, which debt seller transferred to debt buyer by means of an assignment agreement, may pay to the benefit of the debt seller. This unprecedented thesis, excluding the application of the Civil Code to assignment agreements relating to EU funds, leads to the question of its scope of application. Does the requirement set forth by the Supreme Court to approve assignment agreements by the public administration body apply only to the case at hand, or does it have universal character and should be applied to all assignment agreements concerning EU fund? A question arises as to the legal remedies available to the parties to such agreements to protect their rights under unapproved assignment agreements, who, on the date of their conclusion, could not have known about the requirement for their approval and thus fulfil the formalities required for this purpose. That issue will be addressed in this article.

The Concept of Thought from the Perspective of Cultural Codes in Kazakh Fiction

The modern linguistics interprets the old philosophical problem of the relationship between reality, thinking, and language in a completely new way: the world should be considered as a whole and comprehended through linguistic, mythological, religious, and artistic worldviews at once. The abstract concepts of the mental world represent a certain linguistic culture. This article describes a set of cultural codes that represent the mental concept of thought as similes in the contemporary Kazakh fiction. This linguaculturological analysis covered metaphors and similes found by continuous sampling in the Kazakh fiction of the XX century. The similes were tested for regular metaphorization models. The mental concept of thought in similes was presented through natural, biomorphic, and artefact cultural codes. The natural cultural code was represented by such conceptual metaphors as thought is wind / sunlight / fire / water. The zoomorphic metaphors of thought is horse / bird / insect were numerous in the biomorphic cultural code. The metaphor of thought is metal verbalized the artefact cultural code. In the linguistic consciousness of the Kazakhs, the concept of thought was verbalized as a divine gift or a living entity that has enough energy to take different material forms and move around all three worlds.

Shifting Sand: Reconnecting Social Work Values to Historical Biblical Foundations

The profession of Social Work, historically rooted in a Christian faith helping tradition, began a digression from its religious roots while struggling to differentiate itself from other social sciences and define a unique professional identity. To distance from an ambiguous reputation as a disorganized collection of liberal-minded but well-intended social interventions and practitioners, the profession took a positivist/scientist turn. In the ensuing absence of clear, foundational principles such as those historically defined with a Christian faith, the profession attempted to define a values base embedded in a set of ethical codes designed to regulate professional behaviors. Over multiple iterations, the code has grown in length and specificity in an attempt to balance diversity while defining morality. Despite these developments this paper contends that Social Work’s key values are so thoroughly engrained in a Judeo-Christian tradition that the fundamental values currently espoused by the profession mark a recapturing of its historical moral foundations.

An Experimental Study in Determining Basic Robotic Coding Skills of Pre-School Students with Intellectual Disabilities

Today, it can be said that coding has become a key competence for students and people working in many different fields in the business world. It is assumed that those who seek and develop new ways to learn-teach coding will be one step ahead. The educational use of coding started with the use of the Logo programming language in the 60s.This concept has started to revive in recent years with visual programming languages such as “Alice, its code, code.org and Scratch”. These visual programming structures enable young learners to write applications without having to learn the complex code structures of traditional programming languages. In this study to develop an experimental study to determine the basic robotic coding skills of preschool students with intellectual disabilities. The sample of the study consists of a total of 20 students with studying in pre-school education institutions in a city in Türkiye. The ages of the students are between 5 years old. Semi-experimental design, one of the experimental research designs, was used in the research. In this context, 1 experimental and 1 control group was formed with the random assignment method. The materials used in both the experimental and control groups were determined as the pre-school robotic coding set U-bot starter package.5 weeks were determined as the teaching process and a total of 12 activities that served the purpose were applied to the students. During the applications, the data of the students were collected with the help of the observation form, coding skill test and after the application, they were collected through focus group interviews. As a result, at the end of the 5-week period, it was observed that there were positive significant changes in the two dependent variables (academic achievement andlearning rate) that were used as a basis for measuring the learning skills of both the experimental group and the control group students.

Simulation of the effect of partial and total blockage of a sub-channel on two-phase flow through a 5 x 5 square rod bundle

The study of the effect of blockage on two-phase flow parameters in rod bundles is of significance to the prediction of the thermal-hydraulic behavior of fuel assembly under hypothetical accident conditions. The two-phase flow computational fluid dynamics method was used to simulate the fluid flow in 5 × 5 under partial (75%) and total blockage of a sub-channel. A scenario with no blockage was considered a baseline for further analysis into subsequent cases with blockages. The effect of the blockage on the pressure drop, void fraction, subchannel average velocities, and the turbulence intensity between the sub-channels were analyzed at different hydraulic diameters before and after the blockage, and compared. The 75% partial blockage of a single sub-channel increased the average pressure drop between 1Dh and 5Dh downstream by 0.47 kPa compared to the baseline scenario. Bubbles coalesce at the vicinity of the blockage. The effect of the blockage on other flow parameters extends to 46 Dh downstream of the blockages. The effect of these different blockage conditions can provide a reference for the parameter setting of the sub-channel analysis codes.

A Tutorial on Bayesian Latent Class Analysis Using JAGS

This tutorial introduces readers to latent class analysis (LCA) as a model-based approach to understand the unobserved heterogeneity in a population. Given the growing popularity of LCA, we aim to equip readers with theoretical fundamentals as well as computational tools. We outline some potential pitfalls of LCA and suggest related solutions. Moreover, we demonstrate how to conduct frequentist and Bayesian LCA in R with real and simulated data. To ease learning, the analysis is broken down into a series of simple steps. Beyond the simple LCA, two extensions including mixed-model LCA and growth curve LCA are provided to aid readers’ transition to more advanced models. The complete R code and data set are provided. 

Doped low-density parity-check codes

In this paper, we propose a doping approach to lower the error floor of Low-Density Parity-Check (LDPC) codes. The doping component is a short block code in which the information bits are selected from the coded bits of the dominant trapping sets of the LDPC code. Accordingly, an algorithm for selecting the information bits of the short code is proposed, and a specific two-stage decoding algorithm is presented. Simulation results demonstrate that the proposed doped LDPC code achieves up to 2.0 ​dB gain compared with the original LDPC code at a frame error rate of 10−6. Furthermore, the proposed design can lower the error floor of original LDPC codes.

Encoder Blind Combinatorial Compressed Sensing

In its most elementary form, compressed sensing studies the design of decoding algorithms to recover a sufficiently sparse vector or code from a lower dimensional linear measurement vector. Typically it is assumed that the decoder has access to the encoder matrix, which in the combinatorial case is sparse and binary. In this paper we consider the problem of designing a decoder to recover a set of sparse codes from their linear measurements alone, that is without access to encoder matrix. To this end we study the matrix factorisation task of recovering both the encoder and sparse coding matrices from the associated linear measurement matrix. The contribution of this paper is a computationally efficient decoding algorithm, Decoder-Expander Based Factorisation, with strong performance guarantees. Under mild assumptions on the sparse coding matrix and by deploying a novel random encoder matrix, we prove that Decoder-Expander Based Factorisation recovers both the encoder and sparse coding matrix at the optimal measurement rate with high probability and from a near optimal number of measurement vectors. In addition, our experiments demonstrate the efficacy and computational efficiency of our algorithm in practice. Beyond compressed sensing, our results may be of interest for researchers working in areas as diverse as linear sketching, coding theory, matrix compression and dictionary learning.

Fireside Chat on the Asian American, Native Hawaiian, and Pacific Islander Special Edition of the Health Equity Journal Featuring Authors.

Fireside Chat on the Asian American, Native Hawaiian, and Pacific Islander Special Edition of the Health Equity Journal Featuring Authors.

Facilitated low-rank multi-view subspace clustering

Low-rank multi-view subspace clustering has recently attracted increasing attention in the multi-view learning research. Despite significant progress, most existing approaches still suffer from two issues. First, they mostly focus on exploiting the low-rank consistency across multiple views, but often ignore the low-rank structure within each view. Second, they often encounter the expensive time overhead, typically owing to the costs of matrix inversion and singular value decomposition (SVD) at each iteration. In light of this, we propose an efficient and effective approach termed Facilitated Low-rank Multi-view Subspace Clustering (FLMSC) in this paper. In terms of efficiency, our approach factorizes the view-specific representation matrix into two small factor matrices, i.e., an orthogonal dictionary and a latent representation, which mitigates the computation cost of solving SVD problems. In terms of effectiveness, our approach preserves the latent low-rank structure within each view, while at the same time encourages the structural consistency across different views by imposing an agreement term. Based on this, our approach can fully explore the underlying subspace structure of multiple views, so as to better serve for the following spectral clustering. Besides, a facilitated iterative algorithm is developed for the resultant optimization problem, upon which the matrix inversion operation is substantially accelerated during iterations. Experimental results on a variety of multi-view data sets demonstrate the effectiveness and efficiency of our approach. The source code and data sets are available at: https://www.researchgate.net/publication/365349930_FLMSC_v1.

Assisted Cement Log Interpretation Using Machine Learning

Summary The Assisted Cement Log Interpretation Project has used machine learning (ML) to create a tool that interprets cement logs by predicting a predefined set of annular condition codes used in the cement log interpretation process. The development of a cement log interpretation tool speeds up the log interpretation process and enables expert knowledge to be efficiently shared when training new professionals. By using high-quality and consistent training data sets, the project has trained a model that will support unbiased and consistent interpretations over time. The tool consists of a training and a prediction tool integrated with cased-hole logging interpretation software. By containerizing the code using an “API First” design principle (API: application programming interface), the applicability of this add-on tool is broad. The ML model is trained using selected and engineered features from cement logs, and the tool predicts an annular condition code according to the cement classification system for each depth segment in the log. The interpreters can easily fetch a complete cement log interpretation prediction for the log and use that as a template for their final interpretation. The ML model can easily be retrained with new data sets to improve accuracy even further. To improve cement log interpretation consistency in the industry, the code will be made available as open source.

Levy Equilibrium Optimizer algorithm for the DNA storage code set.

The generation of massive data puts forward higher requirements for storage technology. DNA storage is a new storage technology which uses biological macromolecule DNA as information carrier. Compared with traditional silicon-based storage, DNA storage has the advantages of large capacity, high density, low energy consumption and high durability. DNA coding is to store data information with as few base sequences as possible without errors. Coding is a key technology in DNA storage, and its results directly affect the performance of storage and the integrity of data reading and writing. In this paper, a Levy Equilibrium Optimizer (LEO) algorithm is proposed to construct a DNA storage code set that satisfies combinatorial constraints. The performance of the proposed algorithm is tested on 13 benchmark functions, and 4 new global optima are obtained. Under the same constraints, the DNA storage code set is constructed. Compared with previous work, the lower bound of DNA storage code set is improved by 4-13%.

Prediction of Early Disease Progression at 24 Months (POD24) Using Pre-Treatment Biopsies from Patients with Follicular Lymphoma (FL) Treated with Immunochemotherapy (IC)

Background: Gene expression profiling has been successfully used to interrogate FL biology related to overall survival alone or in combination with clinical parameters and mutation profiles such as the M7-FLIPI. However, none of these has ever been specifically trained against the most reproducible and pertinent predictor of excess mortality: early progression of disease by 24 months (POD24). Nor has any assay been validated on a clinical platform for testing patients in a CAP-CLIA environment. We therefore undertook a discovery analysis and also investigated published gene signatures to determine the best method for upfront prediction of POD24 with clinical progression (not transformation). Methods: For model generation, we assembled and harmonized gene expression data from frozen tissues from 377 total FL cases treated with immunochemotherapy (IC) from the Lymphoma/Leukemia Molecular Profiling Project (LLMPP; N=80, unpublished, Affymetrix), British Columbia (N=137, Silva et al, 2018, DASL Illumina), and PRIMA cohorts (N=160, S. Huet et al, 2018, Affymetrix) and performed in silico analysis to identify the most promising candidate genes to predict POD24. In addition to the identified genes, we also included all "IR1/IR2" genes (S. Dave 2004) and "PRIMA23" (Huet) genes and other published genes of interest totaling 144 genes related to tumor, microenvironment, and housekeeping functions. We used these 144 genes as the basis for an nCounter code set (NanoString, Seattle, WA), and then evaluated their expression in formalin-fixed, paraffin-embedded (FFPE) tissues from a training set of 57 matching cases from the LLMPP cohort and an independent set of 59 independent samples from the University of Iowa/Mayo Clinic SPORE Molecular Epidemiology Resource (MER). Comparing the FFPE NanoString results from the LLMPP training set to their matched fresh frozen Affymetrix results identified, 48 of the 144 candidate genes for which the two platforms showed agreement with correlation > 0.5). Multiple prediction architectures were compared using these 48 genes and optimized via cross-validation on this training set to develop a final locked down POD24 predictor ("CCP-32") based on a linear combination of the expression of 32 selected informative genes and an additional 19 housekeeping genes. Additionally, for comparison, the Huet profiling scores were calculated on the LLMPP training samples and a cut-point, optimized to select patients with POD24 as a dichotomous endpoint. These two predictors were then applied to an independent set of 174 FFPE samples from the MER population-based cohort. Samples with very low signal in the normalization genes were excluded (8/116 training and 17/174 validation). Samples from patients that were event free until they experienced transformation or were lost to follow-up in less than 24 months after IC were treated as censored and were thus excluded (2/116 training, 6/174 validation). Results: When applied to the MER FFPE validation set, the CCP-32 predictor placed 30% in a poor prognosis group with an observed POD24 rate of 56% while the remaining 70% samples had an observed 18% POD24 - an enrichment that was statistically significant (p=2.9x10-5). A training-optimized cut of the Huet profiling score performed similarly on the validation set with 44% placed in the poor prognosis group, with an observed POD24 rate of 48%, while the remaining 56% had an observed POD24 of 15% (p=1.5x10-5) Conclusions: Starting with an agnostic in silico analysis of data from frozen FL tissue to identify predictive genes, also including known gene sets pertinent to FL, we first identified genes that correlated best in FFPE tissues using digital expression profiling to develop an algorithm specifically trained to detect POD24 due to progression (not transformation) on a clinical platform using routinely processed samples. A novel predictor and the Huet profiling score were each able to detect a biological subset that was heavily enriched in the approximately 30-44% of FL patients at risk for POD24. Additional cohort analyses are underway. Integration of gene mutations and other genetic factors will also be explored to determine whether these can improve the predictive value with a long-term goal of creating a combined assay that could be suitable for use in a clinical laboratory to select high risk patients for clinical trials.

Analysis of the Representation of Frequent Clinical Attributes in the Unified Medical Language System.

Mapping clinical attributes from hospital information systems to standardized terminologies may allow their scientific reuse for multicenter studies. The Unified Medical Language System (UMLS) defines synonyms in different terminologies, which could be valuable for achieving semantic interoperability between different sites. Here we aim to explore the potential relevance of UMLS concepts and associated semantic relations for widely used clinical terminologies in a German university hospital. To semi-automatically examine a sample of the 200 most frequent codes from Erlangen University Hospital for three relevant terminologies, we implemented a script that queries their UMLS representation and associated mappings via a programming interface. We found that 94% of frequent diagnostic codes were available in UMLS, and that most of these codes could be mapped to other terminologies such as SNOMED CT. We observed that all examined laboratory codes were represented in UMLS, and that various translations to other languages were available for these concepts. The classification that is most widely used in German hospital for documenting clinical procedures was not originally represented in UMLS, but external mappings to SNOMED CT allowed identifying UMLS entries for 90.5% of frequent codes. Future research could extend this investigation to other code sets and terminologies, or study the potential utility of available mappings for specific applications.

Image-based geometric digital twinning for stone masonry elements

We present an image-based pipeline for generating geometrical digital twins (GDTs) of stone masonry elements with detail down to the stone level. For this purpose, we acquire RGB images of the individual stones and of the wall during the construction phase. In our framework, we use structure from motion (SfM) to first generate 3D source and destination models, which are then registered to form the GDT through non-linear least squares and 2D point feature correspondences detected on the SfM images. This method contrasts with traditional techniques that register point clouds using 3D point descriptors. Because of the robustness of image feature descriptors, we found that using 2D instead of 3D point features facilitates the automation of the GDT generation. To benchmark our algorithm, we compared the results through an Euclidean–distance-based proposed metric with a known 3D textured model from which images were synthetically generated. We show the robustness and feasibility of our method for full size elements, wherein GDTs were generated for dry-stone and stone-mortar systems. This study allows researchers to produce accurate representations of the 3D geometry of walls built for experimental research, reducing therefore uncertainties related to the stone size, shape and arrangement to a minimum when comparing 3D numerical simulations of these walls to experimental results. Codes and data sets are publicly available (https://github.com/eesd-epfl/stone_masonry_GDT and https://doi.org/10.5281/zenodo.7266587).