Chemical and Biochemical Thermodynamics Reunification

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Bacillus spp. Probiotic Strains as a Potential Tool for Limiting the Use of Antibiotics, and Improving the Growth and Health of Pigs and Chickens

The pressure to increasingly optimize the breeding of livestock monogastric animals resulted in antimicrobials often being misused in an attempt to improve growth performance and counteract diseases in these animals, leading to an increase in the problem of antibiotic resistance. To tackle this problem, the use of probiotics, also known as direct in-feed microbials (DFM), seems to be one of the most promising strategies. Among probiotics, the interest in Bacillus strains has been intensively increased in recent decades in pigs and poultry. The aim of the present review was to evaluate the effectiveness of Bacillus strains as probiotics and as a potential strategy for reducing the misuse of antibiotics in monogastric animals. Thus, the potential modes of action, and the effects on the performance and health of pigs (weaning pigs, lactation and gestation sows) and broilers are discussed. These searches yielded 131 articles (published before January 2021). The present review showed that Bacillus strains could favor growth in terms of the average daily gain (ADG) of post-weaning piglets and broilers, and reduce the incidence of post-weaning diarrhea in pigs by 30% and mortality in broilers by 6–8%. The benefits of Bacillus strains on these parameters showed results comparable to the benefit obtained by the use of antibiotics. Furthermore, the use of Bacillus strains gives promising results in enhancing the local adaptative immune response and in reducing the oxidative stress of broilers. Fewer data were available regarding the effect on sows. Discordant effects have been reported regarding the effect on body weight (BW) and feed intake while a number of studies have supported the hypothesis that feeding probiotics to sows could benefit their reproductive performance, namely the BW and ADG of the litters. Taken all the above-mentioned facts together, this review confirmed the effectiveness of Bacillus strains as probiotics in young pigs and broilers, favoring their health and contributing to a reduction in the misuse of direct in-feed antibiotics. The continuous development and research regarding probiotics will support a decrease in the misuse of antibiotics in livestock production in order to endorse a more sustainable rearing system in the near future

Classifying Sequences of Extreme Length with Constant Memory Applied to Malware Detection

Recent works within machine learning have been tackling inputs of ever-increasing size, with cybersecurity presenting sequence classification problems of particularly extreme lengths. In the case of Windows executable malware detection, inputs may exceed $100$ MB, which corresponds to a time series with $T=100,000,000$ steps. To date, the closest approach to handling such a task is MalConv, a convolutional neural network capable of processing up to $T=2,000,000$ steps. The $\mathcal{O}(T)$ memory of CNNs has prevented further application of CNNs to malware. In this work, we develop a new approach to temporal max pooling that makes the required memory invariant to the sequence length $T$. This makes MalConv $116\times$ more memory efficient, and up to $25.8\times$ faster to train on its original dataset, while removing the input length restrictions to MalConv. We re-invest these gains into improving the MalConv architecture by developing a new Global Channel Gating design, giving us an attention mechanism capable of learning feature interactions across 100 million time steps in an efficient manner, a capability lacked by the original MalConv CNN. Our implementation can be found at https://github.com/NeuromorphicComputationResearchProgram/MalConv2Comment: To appear in AAAI 202

Gaseous Electronics

Contains reports on two research projects.Joint Services Electronics Programs (U. S. Army, U. S. Navy, and U. S. Air Force) under Contract DA 28-043-AMC-02536(E

Semi-supervised Classification of Malware Families Under Extreme Class Imbalance via Hierarchical Non-Negative Matrix Factorization with Automatic Model Selection

Identification of the family to which a malware specimen belongs is essential in understanding the behavior of the malware and developing mitigation strategies. Solutions proposed by prior work, however, are often not practicable due to the lack of realistic evaluation factors. These factors include learning under class imbalance, the ability to identify new malware, and the cost of production-quality labeled data. In practice, deployed models face prominent, rare, and new malware families. At the same time, obtaining a large quantity of up-to-date labeled malware for training a model can be expensive. In this paper, we address these problems and propose a novel hierarchical semi-supervised algorithm, which we call the HNMFk Classifier, that can be used in the early stages of the malware family labeling process. Our method is based on non-negative matrix factorization with automatic model selection, that is, with an estimation of the number of clusters. With HNMFk Classifier, we exploit the hierarchical structure of the malware data together with a semi-supervised setup, which enables us to classify malware families under conditions of extreme class imbalance. Our solution can perform abstaining predictions, or rejection option, which yields promising results in the identification of novel malware families and helps with maintaining the performance of the model when a low quantity of labeled data is used. We perform bulk classification of nearly 2,900 both rare and prominent malware families, through static analysis, using nearly 388,000 samples from the EMBER-2018 corpus. In our experiments, we surpass both supervised and semi-supervised baseline models with an F1 score of 0.80.Comment: Accepted at ACM TOP

Organization of Multinational Activities and Ownership Structure

We develop a model in which multinational investors decide about the modes of organization, the locations of production, and the markets to be served. Foreign investments are driven by market-seeking and cost-reducing motives. We further assume that investors face costs of control that vary among sectors and increase in distance. The results show that (i) production intensive sectors are more likely to operate a foreign business independent of the investment motive, (ii) that distance may have a non-monotonous effect on the likelihood of horizontal investments, and (iii) that globalization, if understood as reducing distance, leads to more integration