Miracle-dz/UniFAR-FTUs huggingface dataset:
The C. elegans proteome response to two protective Pseudomonas symbionts

The C. elegans natural microbiota isolates Pseudomonas lurida MYb11 and Pseudomonas fluorescens MYb115 protect the host against pathogens through distinct mechanisms. While P. lurida produces an antimicrobial compound and directly inhibits pathogen growth, P. fluorescens MYb115 protects the host without affecting pathogen growth. It is unknown how these two protective microbes affect host biological processes . We used a proteomics approach to elucidate the C. elegans response to MYb11 and MYb115. We found that both Pseudomonas isolates increase vitellogenin protein production in young adults, which confirms previous findings on the effect of microbiota on C. elegans reproductive timing. Moreover, the C. elegans responses to MYb11 and MYb115 exhibit common signatures with the response to other vitamin B 12 -producing bacteria, emphasizing the importance of vitamin B 12 in C. elegans -microbe metabolic interactions. We further analyzed signatures in the C. elegans response specific to MYb11 or MYb115. We provide evidence for distinct modification in lipid metabolism by both mutualistic microbes. We could identify activation of host pathogen defense responses as MYb11-specific proteome signature and demonstrate that the intermediate filament protein IFB-2 is required for MYb115-mediated protection. These results indicate that MYb11 not only produces an antimicrobial compound, but also activates host antimicrobial defenses, which together
Comparison of genomic-enabled cross selection criteria for the improvement of inbred line breeding populations

A crucial step in inbred plant breeding is the choice of mating design to derive high-performing inbred varieties while also maintaining a competitive breeding population to secure sufficient genetic gain in future generations. In practice, the mating design usually relies on crosses involving the best parental inbred lines to ensure high mean progeny performance. This excludes crosses involving lower performing but more complementary parents in terms of favorable alleles. We predicted crosses with putative outstanding progenies (high mean and high variance progeny distribution) using genomic prediction models to assess the value of top progeny. This study compared the benefits and drawbacks of seven genomic cross selection criteria (CSC) in terms of genetic gain for one trait and genetic diversity in the next generation. Six CSC were already published and we have proposed an improved CSC that can estimate the proportion of progeny above a threshold defined for the whole mating plan. We simulated mating designs optimized using different CSC and 835 elite parents from a real breeding program that were evaluated between 2000 and 2016. We applied constraints on parental contributions and genetic similarities between parents according to usual breeder practices. Our results showed that CSC based on progeny variance estimation increased the genetic value of superior progenies by up to 5% in the next generation compared to CSC based on the progeny mean estimation (i.e. parental genetic values) alone. It also increased the genetic gain (up to 4%) and/or maintained more genetic diversity at QTLs (up to 4% more genic variance when the marker effects were perfectly estimated).
Algorithmic Problems Arising in Posets and Permutations

Partially ordered sets and permutations are combinatorial structures having vast applications in theoretical computer science. In this thesis, we study various computational and algorithmic problems related to these structures. The first chapter of the thesis contains discussion about randomized fully polynomial approximation schemes obtained by employing Markov chain Monte Carlo. In this chapter we study various Markov chains that we call: the gladiator chain, the interval chain, and cube shuffling. Our objective is to identify some conditions that assure rapid mixing; and we obtain partial results. The gladiator chain is a biased random walk on the set of permutations. This chain is related to self organizing lists, and various versions of it have been studied. The interval chain is a random walk on the set of points in R whose coordinates respect a partial order. Since the sample space of the interval chain is continuous, many mixing techniques for discrete chains are not applicable to it. The cube shuffle chain is a generalization of H̊astad’s square shuffle. The importance of this chain is that it mixes in constant number of steps.
Bridging the Gap Between Intent and Outcome: Knowledge, Tools & Principles for Security-Minded Decision-Making

Well-intentioned decisions—even ones intended to improve aggregate security— may inadvertently jeopardize security objectives. Adopting a stringent password composition policy ostensibly yields high-entropy passwords; however, such policies often drive users to reuse or write down passwords. Replacing URLs in emails with “safe” URLs that navigate through a gatekeeper service that vets them before granting user access may reduce user exposure to malware; however, it may backfire by reducing the user’s ability to parse the URL or by giving the user a false sense of security if user expectations misalign with the security checks delivered by the vetting process. A short timeout threshold may ensure the user is promptly logged out when the system detects they are away; however, if an infuriated user copes by inserting a USB stick in their computer to emulate mouse movements, then not only will the detection mechanism fail but the insertion of the USB stick may present a new attack surface. These examples highlight the disconnect between decision-maker intentions and decision outcomes. Our focus is on bridging this gap. This thesis explores six projects bound together by the core objective of empowering people to make decisions that achieve their security and privacy objectives. First, we use grounded theory to examine Amazon reviews of password logbooks and to obtain valuable insights into users’ password management beliefs, motivations, and behaviors. Second, we present a discrete-event simulation we built to assess the efficacy of password policies. Third, we explore the idea of supplementing language-theoretic
Sequent calculi and indexed categories as a foundation for logic programming

We present a semantic framework for sequent calculi which allows us to reason about properties of proofs. Using this framework we try to reconcile the proof theoretic approach to logic programming, essentially based on the concept of uniform proof, with the older model theoretic approach. Using abstract interpretation to model abstractions of proofs, we are able to port well known results for pure logic programming, in the field of abstract debugging and analysis, to richer languages such as hereditary Harrop formulas. While this approach is based on proof theory, there are cases of languages, like CLP, where models play a fundamental role. Therefore, we also propose a categorical semantic framework which is able to cope with both the syntax, the operational semantics and the model theory of a broad range of logic languages. A program is interpreted in an indexed category such that the base category contains all the possible states which can occur during the execution of the program (such as global constraints or type informations), while each fiber encodes the corresponding relevant logical properties. We define appropriate notions of categorical resolution and models, and we prove the related correctness and completeness properties. Corso Italia 40, 56125 Pisa, Italy. TEL: +39-050-887248. FAX: +39-050-887226. E-MAIL: amato@di.unipi.it. HOME PAGE: http://www.di.unipi.it/amato
Layout Optimization Method of Railway Transportation Route Based on Deep Convolution Neural Network

To improve the railway transportation capacity and maximize the benefits of railway transportation, a method for layout optimization of railway transportation route based on deep convolution neural network is proposed in this study. Considering the transportation cost of railway transportation and other factors, the layout model of railway transportation route is constructed. Based on improved ant colony algorithm, the layout model of railway transportation route was optimized, and multiple candidate railway transportation routes were output. Taking into account external information such as regional information, weather conditions and actual information of railway transportation routes, optimization of the candidate railway transportation routes obtained by the improved ant colony algorithm was performed based on deep convolution neural network, and the optimal railway transportation routes were output, and finally layout optimization of railway transportation routes was realized. The experimental results show that the proposed method can obtain the optimal railway transportation route, the shortest transportation length, and the least transportation time, maximizing the interests of railway transportation enterprises.
An efficient finite‐difference scheme for electromagnetic logging in 3D anisotropic inhomogeneous media

We consider a problem of computing the electromagnetic field in 3D anisotropic media for electromagnetic logging. The proposed finite-difference scheme for Maxwell equations has the following new features based on some recent and not so recent developments in numerical analysis: coercivity (i.e., the complete discrete analogy of all continuous equations in every grid cell, even for nondiagonal conductivity tensors), a special conductivity averaging that does not require the grid to be small compared to layering or fractures, and a spectrally optimal grid refinement minimizing the error at the receiver locations and optimizing the approximation of the boundary conditions at infinity. All of these features significantly reduce the grid size and accelerate the computation of electromagnetic logs in 3D geometries without sacrificing accuracy.
Path Planning Algorithms under the Link-Distance Metric

of the Dissertation Path Planning Algorithms under the Link-Distance Metric by David Phillip Wagner Doctor of Philosophy in Computer Science Dartmouth College, Hanover, NH February 2006 Professor Robert Scot Drysdale, Co-Chair Professor Clifford Stein, Co-Chair The Traveling Salesman Problem and the Shortest Path Problem are famous problems in computer science which have been well studied when the objective is measured using the Euclidean distance. Here we examine these geometric problems under a different set of optimization criteria. Rather than considering the total distance traversed by a path, this thesis looks at reducing the number of times a turn is made along that path, or equivalently, at reducing the number of straight lines in the path. Minimizing this objective value, known as the link-distance, is useful in situations where continuing in a given direction is cheap, while turning is a relatively expensive operation. Applications exist in VLSI, robotics, wireless communications, space travel, and other fields where it is desirable to reduce the number of turns. This thesis examines rectilinear and non-rectilinear variants of the Traveling Salesman Problem under this metric. The objective of these problems is to find a path visiting a set of points which has the smallest number of bends. A 2-approximation algorithm is given for the rectilinear problem, while for the non-rectilinear problem, an O(log n)-approximation algorithm is given. The latter problem is also shown to be NP-Complete.
Enhanced Regular Expression as a DGL for Generation of Synthetic Big Data

Synthetic data generation is generally used in performance evaluation and function tests in data-intensive applications, as well as in various areas of data analytics, such as privacy-preserving data publishing (PPDP) and statistical disclosure limit/control. A significant amount of research has been conducted on tools and languages for data generation. However, existing tools and languages have been developed for specific purposes and are unsuitable for other domains. In this article, we propose a regular expression-based data generation language (DGL) for flexible big data generation. To achieve a general-purpose and powerful DGL, we enhanced the standard regular expressions to support the data domain, type/format inference, sequence and random generation, probability distributions, and resource reference. To efficiently implement the proposed language, we propose caching techniques for both the intermediate and database queries. We evaluated the proposed improvement experimentally.
Dog-Species Classification through CycleGAN and Standard Data Augmentation

In the image field, data augmentation refers to increasing the amount of data through an editing method such as rotating or cropping a photo. In this study, a generative adversarial network (GAN) image was created using CycleGAN, and various colors of dogs were reflected through data augmentation. In particular, dog data from the Stanford Dogs Dataset and Oxford-IIIT Pet Dataset were used, and 10 breeds of dog, corresponding to 300 images each, were selected. Subsequently, a GAN image was generated using CycleGAN, and four learning groups were established: 2,000 original photos (group I); 2,000 original photos + 1,000 GAN images (group II); 3,000 original photos (group III); and 3,000 original photos + 1,000 GAN images (group IV). The amount of data in each learning group was augmented using existing data augmentation methods such as rotating, cropping, erasing, and distorting. The augmented photo data were used to train the MobileNet_v3_Large, ResNet-152, InceptionResNet_v2, and NASNet_Large frameworks to evaluate the classification accuracy and loss. The top-3 accuracy for each deep neural network model was as follows: MobileNet_v3_Large of 86.4% (group I), 85.4% (group II), 90.4% (group III), and 89.2% (group IV); ResNet-152 of 82.4% (group I), 83.7% (group II), 84.7% (group III), and 84.9% (group IV); InceptionResNet_v2 of 90.7% (group I), 88.4% (group II), 93.3% (group III), and 93.1% (group IV); and NASNet_Large of 85% (group I), 88.1% (group II), 91.8% (group III), and 92% (group IV). The InceptionResNet_v2 model exhibited the highest image classification accuracy, and the NASNet_Large model exhibited the highest increase in the accuracy owing to data augmentation.
A Deep Learning-Based Image Semantic Segmentation Algorithm

This paper is an attempt to design segmentation method based on fully convolutional networks (FCN) and attention mechanism. The first five layers of the Visual Geometry Group (VGG) 16 network serve as the coding part in the semantic segmentation network structure with the convolutional layer used to replace pooling to reduce loss of image feature extraction information. The up-sampling and deconvolution unit of the FCN is then used as the decoding part in the semantic segmentation network. In the deconvolution process, the skip structure is used to fuse different levels of information and the attention mechanism is incorporated to reduce accuracy loss. Finally, the segmentation results are obtained through pixel layer classification. The results show that our method outperforms the comparison methods in mean pixel accuracy (MPA) and mean intersection over union (MIOU).
Comparison of Discrete Sources in Radio and Hα Surveys of the Magellanic Clouds and the Potential for the New Hα Survey

Abstract We present a comparison between the latest Parkes radio surveys (Filipović et al. 1995, 1996, 1997) and Hα surveys of the Magellanic Clouds (Kennicutt & Hodge 1986). We have found 180 discrete sources in common for the Large Magellanic Cloud (LMC) and 40 in the field of the Small Magellanic Cloud (SMC). Most of these sources (95%) are HII regions and supernova remnants (SNRs). A comparison of the radio and Hα flux densities shows a very good correlation and we note that many of the Magellanic Clouds SNRs are embedded in HII regions.
Types for the Chain of Trust: No (Loader) Write Left Behind

The software chain of trust starts with a chain of loaders. Software is just as reliant on the sequence of loaders that ultimately setup its runtime environment as it is on the libraries with which it shares its address space and offloads tasks onto. Loaders, and especially bootloaders, act as the keystone of trust, and yet their formal security properties – which should be a part of any solid bootloader design – are both underappreciated and not well understood. This is especially problematic given the increasing adoption of loader-based code signing and execution enforcement mechanisms. My thesis digs deeply into how loaders have failed to earn our trustworthiness and how they may continue to harbor vulnerabilities even after memory corruption-based vulnerabilities lose their prevalence. In order to address these issues, I propose a memory region-based type system that allows us to better model a loader’s intentions and thus mediate its behavior. More specifically, I show how a loader’s execution can be broken down into a sequence of typed phases, each semantically classified as either a bookkeeping, loading, or a patching substage, while sections of memory are grouped into semantically related regions and assigned a type, based on their intended use, by which policy access decisions are made. I demonstrate the feasibility of this technique by applying it to Das U-Boot, a well-known and widely-used bootloader, with minimal changes to the bootloader’s implementation. In order to do so, I designed and developed an extensive bootloader instrumentation suite to help analyze a bootloader’s behaviors, construct a policy, and completely mediate operations, thereby enforcing behaviors governed by the type system’s policy.
Nonstationarity of the mean and the hurst Phenomenon

Hurst (1957), Klemes (1974, 1975), and Potter (1975, 1976a, 1976b) show that nonstationarity of the mean provides a possible explanation of the so-called Hurst phenomenon; O'Connell (1971) and Wallis and O'Connell (1973 )show that this phenomenon can also be explained with a mixed autoregressive- moving average (Arma) process. These two alternate explanations can be quite similar; in fact, both Hurst's (1957) model and a model suggested by Klemes (1974) and Potter (1975) have correlation structure identical to an Arma (1,1) process. A mixture model for shifting levels is proposed, and it is shown that the models of Hurst and Klemes and Potter are special cases.
The lift on an aerofoil in starting flow

An analysis is given of the initial development of the lift on an aerofoil in inviscid starting flow. It is shown that because of the spiral shape of the vortex sheet shed initially from the trailing edge the lift and drag are both singular at the start of impulsive motion. This result is in contrast with the prediction of finite forces by methods that assume the vortex sheet to be initially planar. The effect of a steady rate of change of incidence following the sudden onset of transverse (heaving) motion of an aerofoil in a steady stream is also discussed.
Double hierarchical generalized linear models (with discussion)

Summary. We propose a class of double hierarchical generalized linear models in which random effects can be specified for both the mean and dispersion. Heteroscedasticity between clusters can be modelled by introducing random effects in the dispersion model, as is heterogeneity between clusters in the mean model. This class will, among other things, enable models with heavy‐tailed distributions to be explored, providing robust estimation against outliers. The h‐likelihood provides a unified framework for this new class of models and gives a single algorithm for fitting all members of the class. This algorithm does not require quadrature or prior probabilities.
Folic Acid–Chitosan Oligosaccharide Conjugates Decorated Nanodiamond as Potential Carriers for the Oral Delivery of Doxorubicin

Oral administration of doxorubicin (DOX) is preferred but challenged owing to poor permeability in the gastrointestinal tract (GIT), efflux of P-glycoprotein, short residence time in the intestine, and rapid hydrolysis. Herein, folic acid–chitosan oligosaccharide conjugate (FA-COS)-modified hydroxylated nanodiamond (ND-OH) was designed to enhance the oral bioavailability of DOX. The carboxyl surface of ND was modified into hydroxyl terminal group to increase the colloidal stability of the system under different pH conditions in GIT. FA-COS modification could prolong retention time, endow the drug with sustained release properties, and actively target intestinal FA receptors. In contrast to DOX/ND-OH, the particle size of DOX/ND-OH/FA-COS increased from 189.5 ± 2.8 to 224.5 ± 1.4 nm, and the zeta potential reversed from − 9.1 ± 0.2 to 14.8 ± 0.4 mV. At 48 h, DOX/ND-OH and DOX/ND-OH/FA-COS released 69.07 ± 5.70% and 35.87 ± 5.64%, respectively. FA-COS modification effectively enhanced the cytotoxicity and intracellular uptake of ND-OH/DOX by Caco-2 cells and prolonged intestinal retention in rats. The internalization of DOX/ND-OH and DOX/ND-OH/FA-COS was mainly mediated by energy-dependent clathrin- and caveolae-mediated endocytosis pathways. Pharmacokinetic study demonstrated that the AUC_0−t of DOX/ND-OH and DOX/ND-OH/FA-COS was enhanced by 3.94- and 6.08-fold compared to DOX solution, respectively. These results illustrated that DOX/ND-OH/FA-COS could be an effective strategy to enhance the oral bioavailability of DOX. Graphical Abstract
Measurement of neutrino oscillations in atmospheric neutrinos with the IceCube DeepCore detector

Neutrinooszillationen sind ein sehr aktives Forschungsfeld. In den letzten Jahrzehnten haben viele Experimente das Phanomen untersucht und sind inzwischen zu Praazisionsmessungen vorangeschritten. Mit seiner Niederenergieerweiterung DeepCore kann das IceCube-Experiment zu diesem Forschungsfeld beitragen. IceCube ist ein 1 km^3 groses Tscherenkow-Neutrino-Teleskop, welches das tiefe, antarktische Eis des Sudpols als optisches Medium nutzt. DeepCore ist eine Erweiterung mit dichterer Instrumentierung im unteren Teil des IceCube-Teleskops. Diese dichte Instrumentierung ermoglicht den Nachweis von Neutrinos mit Energien ab einer Energieschwelle von etwa 10 GeV. Jedes Jahr werden Tausende von atmospharischen Neutrinos oberhalb dieser Schwelle in DeepCore detektiert. Eine Bestimmung der Energie der Neutrinos und des durch sie zuruckgelegten Weges durch die Erde ermoglicht die Messung von Neutrinooszillationen. In dieser Arbeit werden zunachst die Moglichkeiten von DeepCore diskutiert, Oszillationen auf unterschiedliche Weise zu messen. Das Verschwinden von Myon-Neutrinos wird als erfolgsversprechender Prozess ausgewahlt. Darauf folgt die Beschreibung einer Methode zur Identifizierung von Tscherenkow-Photonen, welche detektiert wurden, bevor sie gestreut wurden -sogenannte- direkte Photonen. Mit Hilfe dieser Photonen kann der Zenitwinkel der Myon-Neutrinos bestimmmt werden. Auch die Energie der Neutrinos wird rekonstruiert. In den Jahren 2011 und 2012 wurden innerhalb von 343 Tagen mit dieser Analyse 1487 Neutrinokandidaten mit Energien zwischen 7 GeV und 100 GeV in DeepCore gefunden. Vergleicht man diese Zahl mit der erwarteten Zahl vom atmospharischen Neutrinofluss ohne Oszillationen, so ergibt sich ein Defizit von etwa 500 Ereignissen. Die Osziallationsparameter, die die Daten am besten beschreiben, sind im Einklang mit den Parametern, die von anderen Experimenten veroffentlicht wurden.