Construction of quasi-cyclic self-dual codes

There is a one-to-one correspondence between $\ell$-quasi-cyclic codes over a finite field $\mathbb F_q$ and linear codes over a ring $R = \mathbb F_q[Y]/(Y^m-1)$. Using this correspondence, we prove that every $\ell$-quasi-cyclic self-dual code of length $m\ell$ over a finite field $\mathbb F_q$ can be obtained by the {\it building-up} construction, provided that char $(\mathbb F_q)=2$ or $q \equiv 1 \pmod 4$, $m$ is a prime $p$, and $q$ is a primitive element of $\mathbb F_p$. We determine possible weight enumerators of a binary $\ell$-quasi-cyclic self-dual code of length $p\ell$ (with $p$ a prime) in terms of divisibility by $p$. We improve the result of [3] by constructing new binary cubic (i.e., $\ell$-quasi-cyclic codes of length $3\ell$) optimal self-dual codes of lengths $30, 36, 42, 48$ (Type I), 54 and 66. We also find quasi-cyclic optimal self-dual codes of lengths 40, 50, and 60. When $m=5$, we obtain a new 8-quasi-cyclic self-dual $[40, 20, 12]$ code over $\mathbb F_3$ and a new 6-quasi-cyclic self-dual $[30, 15, 10]$ code over $\mathbb F_4$. When $m=7$, we find a new 4-quasi-cyclic self-dual $[28, 14, 9]$ code over $\mathbb F_4$ and a new 6-quasi-cyclic self-dual $[42,21,12]$ code over $\mathbb F_4$.Comment: 25 pages, 2 tables; Finite Fields and Their Applications, 201

Using GOES-16 ABI data to detect convection, estimate latent heating, and initiate convection in a high resolution model

2021 Spring.Includes bibliographical references.Convective-scale data assimilation has received more attention in recent years as spatial resolution of forecast models has become finer and more observation data are available at such fine scale. Significant amounts of observation data are available over the globe, but only a limited number of observations are assimilated in operational forecast models in the most effective way. One of the most important observation data for predicting precipitation is radar reflectivity from ground-based radars as it provides three-dimensional structure of precipitation. Many operational models use these data to create cloud analysis and initiate convection. In High-Resolution Rapid Refresh (HRRR), the cloud permitting operational model at National Oceanic and Atmospheric Administration (NOAA) that is responsible for short term forecasts over the Contiguous United States (CONUS), latent heating is derived from ground-based radars and added in the observed convective regions to initiate convection. Even though adding heating is shown to improve forecasts of convection, this cannot be done over ocean or mountainous regions where radar data is not available. Geostationary data are available regardless of radar coverage and its data are provided in similar spatial and temporal resolution as ground-based radar. Currently, geostationary data are only used as a source of cloud top information or atmospheric motion vectors due to lack of vertical information. However, Geostationary Operational Environmental Satellites (GOES)-16 and -17 have high temporal resolution data that can compensate the lack of vertical information. From loops of one-minute visible images, convective clouds can be detected by finding a region with a constant bubbling. Therefore, this dissertation seeks a way to use these high temporal resolution GOES-16 data to mimic what radars do over land. In the first two papers presented in the dissertation, two methods are proposed to detect convection using one-minute GOES-16 Advanced Baseline Imager (ABI) data. The first method explicitly calculates Tb decrease or lumpiness of reflectance data and finds convective regions. The second paper tries to automate this process using machine learning method. Results from both methods are comparable to radar product, but the machine learning model seems to detect more convective regions than the conventional method. In the third paper, latent heating profiles for convective clouds are estimated from GOES-16. Once a convective cloud is detected, latent heating profiles corresponding to cloud top temperature of the convective cloud is searched from the lookup table created using model simulations. This technique is similar to spaceborne radar inferred latent heating developed for National Aeronautics and Space Administration (NASA)'s Global Precipitation Measurement Mission (GPM). Latent heating assigned from GOES-16 is shown to be similar to latent heating derived from Next-Generation Radar (NEXRAD) once they are summed up over each cloud. Finally in the last paper, latent heating estimated by using the method from the third paper are assimilated into the Weather Research and Forecasting (WRF) model to examine impacts of using GOES-16 derived latent heating in initiating convection in the forecast model. Two case studies are presented to compare results using GOES-16 derived heating and NEXRAD derived heating. Results show that using GOES-16 derived heating sometimes produce deeper convection than it should, but it improves overall precipitation forecasts. This appears related to the much deeper column of heating assigned by GOES than the empirical relation used by the HRRR operational scheme. In addition, in a case when storms developed over Gulf of Mexico where radar data are not available, forecasts are improved using GOES-16 latent heating

Impacts of assimilating vertical velocity, latent heating, or hydrometeor water contents retrieved from a single reflectivity data set

2017 Spring.Includes bibliographical references.Assimilation of observation data in cloudy regions has been challenging due to the unknown properties of clouds such as cloud depth, cloud vertical profiles, or cloud drop size distributions. Attempts to assimilate data in cloudy regions generally assume a drop size distribution, but most assimilation systems fail to maintain consistency between models and the observation data, as each has its own set of assumptions. This study tries to retain the consistency between the forecast model and the retrieved data by developing a Bayesian retrieval scheme that uses the forecast model itself for the a-priori database. Through the retrieval algorithm, vertical profiles of three variables related to the development of tropical cyclones, including vertical velocity, latent heating, and hydrometeor water contents are derived from the same reflectivity observation. Vertical velocity and latent heating are variables related to dynamical processes of tropical cyclones, whereas hydrometeors are byproducts of those processes. Each retrieved variable is assimilated in the data assimilation system using a flow dependent forecast error covariance matrix. The simulations are compared to evaluate the respective impact of each variable in the assimilation system. In this study, the three assimilation experiments were conducted for two hurricane cases captured by the Global Precipitation Measurement (GPM) satellite: Hurricane Pali and Hurricane Jimena. Analyses from these two hurricane cases suggest that assimilating latent heating and hydrometeor water contents have similar impacts on the assimilation system while vertical velocity has less of an impact than the other two variables. Using these analyses as an initial condition for the forecast model reveals that the assimilations of retrieved latent heating and hydrometeor water contents were also able to improve the track forecast of Hurricane Jimena

Forgetting-aware Linear Bias for Attentive Knowledge Tracing

Knowledge Tracing (KT) aims to track proficiency based on a question-solving history, allowing us to offer a streamlined curriculum. Recent studies actively utilize attention-based mechanisms to capture the correlation between questions and combine it with the learner's characteristics for responses. However, our empirical study shows that existing attention-based KT models neglect the learner's forgetting behavior, especially as the interaction history becomes longer. This problem arises from the bias that overprioritizes the correlation of questions while inadvertently ignoring the impact of forgetting behavior. This paper proposes a simple-yet-effective solution, namely Forgetting-aware Linear Bias (FoLiBi), to reflect forgetting behavior as a linear bias. Despite its simplicity, FoLiBi is readily equipped with existing attentive KT models by effectively decomposing question correlations with forgetting behavior. FoLiBi plugged with several KT models yields a consistent improvement of up to 2.58% in AUC over state-of-the-art KT models on four benchmark datasets.Comment: In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (CIKM'23), 5 pages, 3 figures, 2 table

Design, Field Evaluation, and Traffic Analysis of a Competitive Autonomous Driving Model in a Congested Environment

Recently, numerous studies have investigated cooperative traffic systems using the communication among vehicle-to-everything (V2X). Unfortunately, when multiple autonomous vehicles are deployed while exposed to communication failure, there might be a conflict of ideal conditions between various autonomous vehicles leading to adversarial situation on the roads. In South Korea, virtual and real-world urban autonomous multi-vehicle races were held in March and November of 2021, respectively. During the competition, multiple vehicles were involved simultaneously, which required maneuvers such as overtaking low-speed vehicles, negotiating intersections, and obeying traffic laws. In this study, we introduce a fully autonomous driving software stack to deploy a competitive driving model, which enabled us to win the urban autonomous multi-vehicle races. We evaluate module-based systems such as navigation, perception, and planning in real and virtual environments. Additionally, an analysis of traffic is performed after collecting multiple vehicle position data over communication to gain additional insight into a multi-agent autonomous driving scenario. Finally, we propose a method for analyzing traffic in order to compare the spatial distribution of multiple autonomous vehicles. We study the similarity distribution between each team's driving log data to determine the impact of competitive autonomous driving on the traffic environment

Cyclin D1-CDK4 Controls Glucose Metabolism Independently of Cell Cycle Progression

Insulin constitutes a major evolutionarily conserved hormonal axis for maintaining glucose homeostasis1-3; dysregulation of this axis causes diabetes2,4. PGC-1α links insulin signaling to the expression of glucose and lipid metabolic genes5-7. GCN5 acetylates PGC-1α and suppresses its transcriptional activity, whereas SIRT1 deacetylates and activates PGC-1α8,9. Although insulin is a mitogenic signal in proliferative cells10,11, whether components of the cell cycle machinery contribute to insulin’s metabolic action is poorly understood. Herein, we report that insulin activates cyclin D1-CDK4, which, in turn, increases GCN5 acetyltransferase activity and suppresses hepatic glucose production independently of cell cycle progression. Through a cell-based high throughput chemical screen, we identified a CDK4 inhibitor that potently decreases PGC-1α acetylation. Insulin/GSK3β signaling induces cyclin D1 protein stability via sequestering cyclin D1 in the nucleus. In parallel, dietary amino acids increase hepatic cyclin D1 mRNA transcripts. Activated cyclin D1-CDK4 kinase phosphorylates and activates GCN5, which then acetylates and inhibits PGC-1α activity on gluconeogenic genes. Loss of hepatic cyclin D1 results in increased gluconeogenesis and hyperglycemia. In diabetic models, cyclin D1-CDK4 is chronically elevated and refractory to fasting/feeding transitions; nevertheless further activation of this kinase normalizes glycemia. Our findings show that insulin uses components of the cell cycle machinery in post-mitotic cells to control glucose homeostasis independently of cell division

Favorable prognosis in colorectal cancer patients with co-expression of c-MYC and ß-catenin

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.Abstract Background The purpose of our research was to determine the prognostic impact and clinicopathological feature of c-MYC and β-catenin overexpression in colorectal cancer (CRC) patients. Methods Using immunohistochemistry (IHC), we measured the c-MYC and β-catenin expression in 367 consecutive CRC patients retrospectively (cohort 1). Also, c-MYC expression was measured by mRNA in situ hybridization. Moreover, to analyze regional heterogeneity, three sites of CRC including the primary, distant and lymph node metastasis were evaluated in 176 advanced CRC patients (cohort 2). Results In cohort 1, c-MYC protein and mRNA overexpression and ß-catenin nuclear expression were found in 201 (54.8 %), 241 (65.7 %) and 221 (60.2 %) of 367 patients, respectively, each of which was associated with improved prognosis (P = 0.011, P = 0.012 and P = 0.033, respectively). Moreover, co-expression of c-MYC and ß-catenin was significantly correlated with longer survival by univariate (P = 0.012) and multivariate (P = 0.048) studies. Overexpression of c-MYC protein was associated with mRNA overexpression (ρ, 0.479; P  0.05). Conclusions Co-expression of c-MYC and ß-catenin was independently correlated with favorable prognosis in CRC patient. We concluded that the expression of c-MYC and ß-catenin might be useful predicting indicator of CRC patients prognosis