Interactions between dietary chicory, gut microbiota and immune responses

This thesis provides a better understanding of interactions between diet, gut microbiota, and immune responses to a specific dietary fiber source, chicory (Cichorium intybus L). This was achieved by examining the impact of chicory fiber on animal performance, digestibility, gut development, commensal bacteria community structure in small and large intestine, and follow-up reactions with specific immune components, cytoprotective heat shock protein (HSP) 27 and 72, in vivo and in vitro. The impacts of dietary chicory on nutrient utilization, performance, and gut environment and morphology were investigated in chickens and young pigs. One-day-old chicks were fed cereal-based diets with inclusion of 60 or 120 g/kg chicory forage and/or root, with each forage diet derived from two harvests. Growing pigs were fed diets without and with inclusion of 80 or 160 g/kg chicory forage and/or root. The results showed that chicory inclusion maintained good animal performance and was accompanied by changes in gut morphology. Total tract apparent digestibility of non-starch polysaccharides (NSP) and uronic acid in broilers decreased with inclusion of 120 g/kg chicory, but not with inclusion of 60 g/kg. This indicates that chicory can be used as a palatable fiber source for broiler chickens and young pigs. Gut microbiota complexity and dietary NSP-induced changes in pigs were examined using terminal restriction fragment length polymorphism (T-RFLP). The analysis revealed four primary microbiota clusters: luminal and mucosal ileal microbiota and luminal and mucosal colonic microbiota. In the ileum, lactic acid bacteria (LAB) were dominant and responsive to inulin-type fructan. In the colon, bacteria belonging to clostridial cluster IV and XIVa responded to chicory pectin, whereas Prevotella was related to cereal xylan. Mapping of cytoprotective HSP27 and HSP72 occurrence in porcine gut revealed region- and cell type-specific features. Physiological expression of HSP72 was correlated with LAB, representing an important interplay between HSPs and commensal microbes. In-depth studies of interactions between lactobacilli and gut mucosa and their effects on barrier function and HSP expression revealed protective effects from lactobacilli by enhancing HSP and tight junction protein expression under pathogen challenge

Solar Tracker

The purpose of this project is to find how to maximize the amount of time that the solar panels receive direct sunlight or the nearest possible sunlight angles. The solar tracker is a device that can change the position of solar panels in both vertical and horizontal direction to increase solar energy absorption. The device uses a gear motor to drive the shaft that is fixed to the solar panel, so the solar panel can rotate in vertical direction. Based on the vertical sun position in Ellensburg, 85o E in the morning, 180o S in the noon, and 276o W in the evening. The motor, which is controlled by Arduino control chip will change the position of solar panel with a fixed angle to increase to amount of time solar panel faces direct sunlight. At the end of the day, Arduino control chip will control the motor to drive the solar panel back to the original position that will face the incoming morning sunlight for next day. Moreover, the horizontal direction of the solar panel faces have to change by hand since it only change a small angle each month. The testing result showed the panel to be perpendicular (90o) to incoming sunlight, with 2o deviation. This device increased the energy absorption by 20%

NetSentry: A deep learning approach to detecting incipient large-scale network attacks

Machine Learning (ML) techniques are increasingly adopted to tackle ever-evolving high-profile network attacks, including DDoS, botnet, and ransomware, due to their unique ability to extract complex patterns hidden in data streams. These approaches are however routinely validated with data collected in the same environment, and their performance degrades when deployed in different network topologies and/or applied on previously unseen traffic, as we uncover. This suggests malicious/benign behaviors are largely learned superficially and ML-based Network Intrusion Detection System (NIDS) need revisiting, to be effective in practice. In this paper we dive into the mechanics of large-scale network attacks, with a view to understanding how to use ML for Network Intrusion Detection (NID) in a principled way. We reveal that, although cyberattacks vary significantly in terms of payloads, vectors and targets, their early stages, which are critical to successful attack outcomes, share many similarities and exhibit important temporal correlations. Therefore, we treat NID as a time-sensitive task and propose NetSentry, perhaps the first of its kind NIDS that builds on Bidirectional Asymmetric LSTM (Bi-ALSTM), an original ensemble of sequential neural models, to detect network threats before they spread. We cross-evaluate NetSentry using two practical datasets, training on one and testing on the other, and demonstrate F1 score gains above 33% over the state-of-the-art, as well as up to 3 times higher rates of detecting attacks such as XSS and web bruteforce. Further, we put forward a novel data augmentation technique that boosts the generalization abilities of a broad range of supervised deep learning algorithms, leading to average F1 score gains above 35%

Cryptanalysis of the Randomized Version of a Lattice-Based Signature Scheme from PKC'08

International audienceIn PKC'08, Plantard, Susilo and Win proposed a lattice-based signature scheme, whose security is based on the hardness of the closest vector problem with the infinity norm (CVP∞). This signature scheme was proposed as a countermeasure against the Nguyen-Regev attack, which improves the security and the efficiency of the Goldreich, Goldwasser and Halevi scheme (GGH). Furthermore, to resist potential side channel attacks, the authors suggested modifying the determinis-tic signing algorithm to be randomized. In this paper, we propose a chosen message attack against the randomized version. Note that the randomized signing algorithm will generate different signature vectors in a relatively small cube for the same message, so the difference of any two signature vectors will be relatively short lattice vector. Once collecting enough such short difference vectors, we can recover the whole or the partial secret key by lattice reduction algorithms, which implies that the randomized version is insecure under the chosen message attack

A Survey on Surrogate-assisted Efficient Neural Architecture Search

Neural architecture search (NAS) has become increasingly popular in the deep learning community recently, mainly because it can provide an opportunity to allow interested users without rich expertise to benefit from the success of deep neural networks (DNNs). However, NAS is still laborious and time-consuming because a large number of performance estimations are required during the search process of NAS, and training DNNs is computationally intensive. To solve the major limitation of NAS, improving the efficiency of NAS is essential in the design of NAS. This paper begins with a brief introduction to the general framework of NAS. Then, the methods for evaluating network candidates under the proxy metrics are systematically discussed. This is followed by a description of surrogate-assisted NAS, which is divided into three different categories, namely Bayesian optimization for NAS, surrogate-assisted evolutionary algorithms for NAS, and MOP for NAS. Finally, remaining challenges and open research questions are discussed, and promising research topics are suggested in this emerging field.Comment: 18 pages, 7 figure

Concentration inequalities of MLE and robust MLE

The Maximum Likelihood Estimator (MLE) serves an important role in statistics and machine learning. In this article, for i.i.d. variables, we obtain constant-specified and sharp concentration inequalities and oracle inequalities for the MLE only under exponential moment conditions. Furthermore, in a robust setting, the sub-Gaussian type oracle inequalities of the log-truncated maximum likelihood estimator are derived under the second-moment condition

On the system-based design for steel frames using inelastic analysis

Design by inelastic analysis of overall system behaviour is permitted in several steel design specifications worldwide (e.g., the American Specification AISC360-10 and the Australian Specification AS4100-1998). Advanced inelastic analysis is better able to capture the system behavioural characteristics as they currently are understood. This paper presents a case study of the design of three planar steel structures using different design methods, including the Direct Analysis method in AISC360-10, the inelastic design method in AISC360-10, and the inelastic method (“advanced analysis”) in AS4100. The effects of structural ductility (capacity of load redistribution) and failure modes on the design results are discussed