Energized fluid forms in metal work

Not listed

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

DESIGNING COST-EFFECTIVE COARSE-GRAINED RECONFIGURABLE ARCHITECTURE

Application-specific optimization of embedded systems becomes inevitable to satisfy the market demand for designers to meet tighter constraints on cost, performance and power. On the other hand, the flexibility of a system is also important to accommodate the short time-to-market requirements for embedded systems. To compromise these incompatible demands, coarse-grained reconfigurable architecture (CGRA) has emerged as a suitable solution. A typical CGRA requires many processing elements (PEs) and a configuration cache for reconfiguration of its PE array. However, such a structure consumes significant area and power. Therefore, designing cost-effective CGRA has been a serious concern for reliability of CGRA-based embedded systems. As an effort to provide such cost-effective design, the first half of this work focuses on reducing power in the configuration cache. For power saving in the configuration cache, a low power reconfiguration technique is presented based on reusable context pipelining achieved by merging the concept of context reuse into context pipelining. In addition, we propose dynamic context compression capable of supporting only required bits of the context words set to enable and the redundant bits set to disable. Finally, we provide dynamic context management capable of reducing reduce power consumption in configuration cache by controlling a read/write operation of the redundant context words In the second part of this dissertation, we focus on designing a cost-effective PE array to reduce area and power. For area and power saving in a PE array, we devise a costeffective array fabric addresses novel rearrangement of processing elements and their interconnection designs to reduce area and power consumption. In addition, hierarchical reconfigurable computing arrays are proposed consisting of two reconfigurable computing blocks with two types of communication structure together. The two computing blocks have shared critical resources and such a sharing structure provides efficient communication interface between them with reducing overall area. Based on the proposed design approaches, a CGRA combining the multiple design schemes is shown to verify the synergy effect of the integrated approach. Experimental results show that the integrated approach reduces area by 23.07% and power by up to 72% when compared with the conventional CGRA

BubbleML: A Multi-Physics Dataset and Benchmarks for Machine Learning

In the field of phase change phenomena, the lack of accessible and diverse datasets suitable for machine learning (ML) training poses a significant challenge. Existing experimental datasets are often restricted, with limited availability and sparse ground truth data, impeding our understanding of this complex multiphysics phenomena. To bridge this gap, we present the BubbleML Dataset \footnote{\label{git_dataset}\url{https://github.com/HPCForge/BubbleML}} which leverages physics-driven simulations to provide accurate ground truth information for various boiling scenarios, encompassing nucleate pool boiling, flow boiling, and sub-cooled boiling. This extensive dataset covers a wide range of parameters, including varying gravity conditions, flow rates, sub-cooling levels, and wall superheat, comprising 79 simulations. BubbleML is validated against experimental observations and trends, establishing it as an invaluable resource for ML research. Furthermore, we showcase its potential to facilitate exploration of diverse downstream tasks by introducing two benchmarks: (a) optical flow analysis to capture bubble dynamics, and (b) operator networks for learning temperature dynamics. The BubbleML dataset and its benchmarks serve as a catalyst for advancements in ML-driven research on multiphysics phase change phenomena, enabling the development and comparison of state-of-the-art techniques and models.Comment: Submitted to Neurips Datasets and Benchmarks Track 202

Flud: a hybrid crowd-algorithm approach for visualizing biological networks

Modern experiments in many disciplines generate large quantities of network (graph) data. Researchers require aesthetic layouts of these networks that clearly convey the domain knowledge and meaning. However, the problem remains challenging due to multiple conflicting aesthetic criteria and complex domain-specific constraints. In this paper, we present a strategy for generating visualizations that can help network biologists understand the protein interactions that underlie processes that take place in the cell. Specifically, we have developed Flud, an online game with a purpose (GWAP) that allows humans with no expertise to design biologically meaningful graph layouts with the help of algorithmically generated suggestions. Further, we propose a novel hybrid approach for graph layout wherein crowdworkers and a simulated annealing algorithm build on each other's progress. To showcase the effectiveness of Flud, we recruited crowd workers on Amazon Mechanical Turk to lay out complex networks that represent signaling pathways. Our results show that the proposed hybrid approach outperforms state-of-the-art techniques for graphs with a large number of feedback loops. We also found that the algorithmically generated suggestions guided the players when they are stuck and helped them improve their score. Finally, we discuss broader implications for mixed-initiative interactions in human computation games.Comment: This manuscript is currently under revie

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