Defining the Pakistani Nation in History Education: An Examination of Pakistan Studies Textbooks

Master'sMASTER OF ART

Folded Polynomial Codes for Coded Distributed AA⊤AA^\top-Type Matrix Multiplication

In this paper, due to the important value in practical applications, we consider the coded distributed matrix multiplication problem of computing $AA^\top$ in a distributed computing system with $N$ worker nodes and a master node, where the input matrices $A$ and $A^\top$ are partitioned into $p$-by-$m$ and $m$-by-$p$ blocks of equal-size sub-matrices respectively. For effective straggler mitigation, we propose a novel computation strategy, named \emph{folded polynomial code}, which is obtained by modifying the entangled polynomial codes. Moreover, we characterize a lower bound on the optimal recovery threshold among all linear computation strategies when the underlying field is real number field, and our folded polynomial codes can achieve this bound in the case of $m=1$. Compared with all known computation strategies for coded distributed matrix multiplication, our folded polynomial codes outperform them in terms of recovery threshold, download cost and decoding complexity.Comment: 14 pages, 2 tabl

Reliability-oriented adaptive switching frequency scheme for modular multilevel converters

Modular multilevel converters (MMCs) are widely utilized in medium voltage grid-connected applications, typically employing carrier phase shift modulation. However, the high switching frequency associated with this modulation scheme often increases power losses and thermal stress on semiconductor devices, negatively impacting their efficiency and reliability. In this paper, we propose an adaptive switching frequency scheme that divides the carrier frequency into several discrete zones based on load conditions. Through analytical evaluation of the carrier frequency, our proposed method optimizes it to meet power quality and capacitor voltage ripple requirements, effectively reducing power losses and thermal stress. A simulation case study based on a 15-MVA MMC demonstrates a remarkable 21% reduction in annual power losses and a 12% reduction in annual damage, thereby improving efficiency and reliability. Additionally, experimental measurements conducted on a 15-kW downscale platform validate around 10% reduction in power losses while fulfilling power quality and capacitor voltage ripple requirements

Domain Adaptive Semantic Segmentation by Optimal Transport

Scene segmentation is widely used in the field of autonomous driving for environment perception, and semantic scene segmentation (3S) has received a great deal of attention due to the richness of the semantic information it contains. It aims to assign labels to pixels in an image, thus enabling automatic image labeling. Current approaches are mainly based on convolutional neural networks (CNN), but they rely on a large number of labels. Therefore, how to use a small size of labeled data to achieve semantic segmentation becomes more and more important. In this paper, we propose a domain adaptation (DA) framework based on optimal transport (OT) and attention mechanism to address this issue. Concretely, first we generate the output space via CNN due to its superiority of feature representation. Second, we utilize OT to achieve a more robust alignment of source and target domains in output space, where the OT plan defines a well attention mechanism to improve the adaptation of the model. In particular, with OT, the number of network parameters has been reduced and the network has been better interpretable. Third, to better describe the multi-scale property of features, we construct a multi-scale segmentation network to perform domain adaptation. Finally, in order to verify the performance of our proposed method, we conduct experimental comparison with three benchmark and four SOTA methods on three scene datasets, and the mean intersection-over-union (mIOU) has been significant improved, and visualization results under multiple domain adaptation scenarios also show that our proposed method has better performance than compared semantic segmentation methods

The interplay between thermodynamics and kinetics in the solid-state synthesis of layered oxides.

In the synthesis of inorganic materials, reactions often yield non-equilibrium kinetic byproducts instead of the thermodynamic equilibrium phase. Understanding the competition between thermodynamics and kinetics is a fundamental step towards the rational synthesis of target materials. Here, we use in situ synchrotron X-ray diffraction to investigate the multistage crystallization pathways of the important two-layer (P2) sodium oxides Na0.67MO2 (M = Co, Mn). We observe a series of fast non-equilibrium phase transformations through metastable three-layer O3, O3' and P3 phases before formation of the equilibrium two-layer P2 polymorph. We present a theoretical framework to rationalize the observed phase progression, demonstrating that even though P2 is the equilibrium phase, compositionally unconstrained reactions between powder precursors favour the formation of non-equilibrium three-layered intermediates. These insights can guide the choice of precursors and parameters employed in the solid-state synthesis of ceramic materials, and constitutes a step forward in unravelling the complex interplay between thermodynamics and kinetics during materials synthesis

Risk Assessment and Mapping of Hand, Foot, and Mouth Disease at the County Level in Mainland China Using Spatiotemporal Zero-Inflated Bayesian Hierarchical Models

Hand, foot, and mouth disease (HFMD) is a worldwide infectious disease, prominent in China. China’s HFMD data are sparse with a large number of observed zeros across locations and over time. However, no previous studies have considered such a zero-inflated problem on HFMD’s spatiotemporal risk analysis and mapping, not to mention for the entire Mainland China at county level. Monthly county-level HFMD cases data combined with related climate and socioeconomic variables were collected. We developed four models, including spatiotemporal Poisson, negative binomial, zero-inflated Poisson (ZIP), and zero-inflated negative binomial (ZINB) models under the Bayesian hierarchical modeling framework to explore disease spatiotemporal patterns. The results showed that the spatiotemporal ZINB model performed best. Both climate and socioeconomic variables were identified as significant risk factors for increasing HFMD incidence. The relative risk (RR) of HFMD at the local scale showed nonlinear temporal trends and was considerably spatially clustered in Mainland China. The first complete county-level spatiotemporal relative risk maps of HFMD were generated by this study. The new findings provide great potential for national county-level HFMD prevention and control, and the improved spatiotemporal zero-inflated model offers new insights for epidemic data with the zero-inflated problem in environmental epidemiology and public health

ERNIE-UniX2: A Unified Cross-lingual Cross-modal Framework for Understanding and Generation

Recent cross-lingual cross-modal works attempt to extend Vision-Language Pre-training (VLP) models to non-English inputs and achieve impressive performance. However, these models focus only on understanding tasks utilizing encoder-only architecture. In this paper, we propose ERNIE-UniX2, a unified cross-lingual cross-modal pre-training framework for both generation and understanding tasks. ERNIE-UniX2 integrates multiple pre-training paradigms (e.g., contrastive learning and language modeling) based on encoder-decoder architecture and attempts to learn a better joint representation across languages and modalities. Furthermore, ERNIE-UniX2 can be seamlessly fine-tuned for varieties of generation and understanding downstream tasks. Pre-trained on both multilingual text-only and image-text datasets, ERNIE-UniX2 achieves SOTA results on various cross-lingual cross-modal generation and understanding tasks such as multimodal machine translation and multilingual visual question answering.Comment: 13 pages, 2 figure

A UPLC-MS/MS method for simultaneous determination of tiamulin and its metabolites in Crucian carp (Carassius carassius): an in vivo metabolism and tissue distribution study

Tiamulin (TML) has been studied and analyzed in pigs, cattle, chickens, ducks, and other domestic animals, however, its metabolic state in fish has not been well explored. This study investigated TML metabolism in Crucian carp (Carassius carassius). After intraperitoneal injection of TML into Crucian carp, ultra-high performance liquid chromatography with quadrupole and time-of-flight mass spectrometry (UPLC/Q-TOF MS) analysis, was conducted to identify TML metabolites. The UPLC/Q-TOF MS analysis and the relative molecular mass of the metabolites obtained from related literature identified five metabolites in Crucian carp. These metabolites were M1 (510.2908, C28H48NO5S+), M2 (510.2908, C28H48NO5S+), M3 (466.2750, C26H44NO4S+), M4 (482.2663, C26H44NO5S+), and M5 (482.2663, C26H44NO5S+). The enrichment and metabolism of TML and its metabolites in Crucian carp were investigated using the drug bath method combined with ultra-high performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). TML exhibited an overall trend of an initial increase followed by a decrease. Moreover, the drug enrichment rate was fast and reached saturation after two days. The bioconcentration factor of TML in Crucian carp was 3.01. However, the drug had a slow elimination rate, with its complete metabolism occurring after 20 days