Determining the cumulative energy demand and greenhouse gas emission of Swedish wheat flour : a life cycle analysis approach

Food production brought a tremendous impact on human society. However, there has been a lot of debate between organic and conventional farming. Producing enough food by maximizing the yield to feed the growing population has been the main goal of agriculture nowadays. This goal is achieved by applying different kinds of synthetic chemicals to improve the performance of crops in conventional farming. However, this leads to different environmental problems like soil degradation, loss of biodiversity, and disruption of healthy ecosystems. As a result, there is a growing demand for information on the environmental impact of food products from consumers and food supply chain participants. The main objective of the current study is to investigate the environmental impacts of organic and conventional wheat flour produced and supplied in Sweden, using life cycle analysis (LCA) and focusing on the global warming potential (GWP) and cumulative energy demand (CED). A cradle-to-gate LCA with the functional unit (FU) of 1 ton of wheat flour at the gate of the milling facility is conducted in this study. The results of the present study show that in terms of GWP, conventional systems have a higher emission compared to organic systems. As to energy demand, the two systems have almost similar results. The GWP for the conventional systems is 356 CO2-eq kg/FU while it is 249 CO2-eq kg/FU for the organic systems. The CED for the conventional system is 4025 MJ/FU while it is 3983 MJ/FU for the organic system. The farm activity is the hot spot stage for both conventional and organic systems. Overall, when considering environmental aspects, wheat flour from organic farming in Sweden is more sustainable than wheat flour from conventional farming systems. Increasing the yield for organic farming could improve further the environmental sustainability of organic wheat flour

Adversarial Weight Perturbation Improves Generalization in Graph Neural Network

A lot of theoretical and empirical evidence shows that the flatter local minima tend to improve generalization. Adversarial Weight Perturbation (AWP) is an emerging technique to efficiently and effectively find such minima. In AWP we minimize the loss w.r.t. a bounded worst-case perturbation of the model parameters thereby favoring local minima with a small loss in a neighborhood around them. The benefits of AWP, and more generally the connections between flatness and generalization, have been extensively studied for i.i.d. data such as images. In this paper, we extensively study this phenomenon for graph data. Along the way, we first derive a generalization bound for non-i.i.d. node classification tasks. Then we identify a vanishing-gradient issue with all existing formulations of AWP and we propose a new Weighted Truncated AWP (WT-AWP) to alleviate this issue. We show that regularizing graph neural networks with WT-AWP consistently improves both natural and robust generalization across many different graph learning tasks and models.Comment: AAAI 202

Towards architecture-level middleware-enabled exception handling of component-based systems

Exception handling is a practical and important way to improve the availability and reliability of a component-based system. The classical code-level exception handling approach is usually applied to the inside of a component, while some exceptions can only or properly be handled outside of the components. In this paper, we propose a middleware-enabled approach for exception handling at architecture level. Developers specify what exceptions should be handled and how to handle them with the support of middleware in an exception handling model, which is complementary to software architecture of the target system. This model will be interpreted at runtime by a middleware-enabled exception handling framework, which is responsible for catching and handling the specified exceptions mainly based on the common mechanisms provided by the middleware. The approach is demonstrated in JEE application servers and benchmarks. ? 2011 ACM.EI

Towards Robust Dataset Learning

Adversarial training has been actively studied in recent computer vision research to improve the robustness of models. However, due to the huge computational cost of generating adversarial samples, adversarial training methods are often slow. In this paper, we study the problem of learning a robust dataset such that any classifier naturally trained on the dataset is adversarially robust. Such a dataset benefits the downstream tasks as natural training is much faster than adversarial training, and demonstrates that the desired property of robustness is transferable between models and data. In this work, we propose a principled, tri-level optimization to formulate the robust dataset learning problem. We show that, under an abstraction model that characterizes robust vs. non-robust features, the proposed method provably learns a robust dataset. Extensive experiments on MNIST, CIFAR10, and TinyImageNet demostrate the effectiveness of our algorithm with different network initializations and architectures

PromptTTS: Controllable Text-to-Speech with Text Descriptions

Using a text description as prompt to guide the generation of text or images (e.g., GPT-3 or DALLE-2) has drawn wide attention recently. Beyond text and image generation, in this work, we explore the possibility of utilizing text descriptions to guide speech synthesis. Thus, we develop a text-to-speech (TTS) system (dubbed as PromptTTS) that takes a prompt with both style and content descriptions as input to synthesize the corresponding speech. Specifically, PromptTTS consists of a style encoder and a content encoder to extract the corresponding representations from the prompt, and a speech decoder to synthesize speech according to the extracted style and content representations. Compared with previous works in controllable TTS that require users to have acoustic knowledge to understand style factors such as prosody and pitch, PromptTTS is more user-friendly since text descriptions are a more natural way to express speech style (e.g., ''A lady whispers to her friend slowly''). Given that there is no TTS dataset with prompts, to benchmark the task of PromptTTS, we construct and release a dataset containing prompts with style and content information and the corresponding speech. Experiments show that PromptTTS can generate speech with precise style control and high speech quality. Audio samples and our dataset are publicly available.Comment: Submitted to ICASSP 202

Regulation of the Late Onset alzheimer’s Disease Associated HLA-DQA1/DRB1 Expression

(Genome-wide Association Studies) GWAS have identified ∼42 late-onset Alzheimer’s disease (LOAD)-associated loci, each of which contains multiple single nucleotide polymorphisms (SNPs) in linkage disequilibrium (LD) and most of these SNPs are in the non-coding region of human genome. However, how these SNPs regulate risk gene expression remains unknown. In this work, by using a set of novel techniques, we identified 6 functional SNPs (fSNPs) rs9271198, rs9271200, rs9281945, rs9271243, and rs9271247 on the LOAD-associated HLA-DRB1/DQA1 locus and 42 proteins specifically binding to five of these 6 fSNPs. As a proof of evidence, we verified the allele-specific binding of GATA2 and GATA3, ELAVL1 and HNRNPA0, ILF2 and ILF3, NFIB and NFIC, as well as CUX1 to these five fSNPs, respectively. Moreover, we demonstrate that all these nine proteins regulate the expression of both HLA-DQA1 and HLA-DRB1 in human microglial cells. The contribution of HLA class II to the susceptibility of LOAD is discussed