Miracle-dz/UniFAR-FTUs huggingface dataset:
Survival of L. casei BD II and L. plantarum WCFS1 in gastro-intestinal stresses and viability in mango juice during storage

Mango fruit (Mangifera indica L.) is a good source of beneficial nutrients and has been proven to have prebiotic potential. In addition, recent studies on novel probiotic strains Lactobacillus casei BD II and Lactobacillus plantarum WCFS1 suggest its applicability in the food industry. In this study, we report the suitability of mango as a raw material for production of a nondairy probiotic beverage and the resistance of L. casei BD II and L. plantarum WCFS1 to gastro-intestinal (GI) stresses. Prior to inoculation in mango juice (MJ), Lactobacillus strains were examined for their survival in acidic conditions (pH 1.0 and 3.0), presence of bile and digestive enzymes pepsin (at pH 2.0) and pancreatin (at pH 8.0). L. plantarum WCFS1 was found to be more tolerant at pH 1.0 and 3.0 at 0, 1, and 3 h as well as in the presence of digestive enzymes pepsin and pancreatin at 0 and 4h, compared to L. casei BD II (p<0.001); both strains were found to be tolerant against bile salts and showed growth in a modified media (1.0% w/v bile salts) after 48 h at 37°C. MJ containing 30.00% (w/v) mango puree was then inoculated with the two strains of lactic acid bacteria and stored at 25 ± 2°C and 4°C up to 4 weeks. Cell viability, change in total acidity computed as % lactic acid production and change in pH were monitored at weekly intervals. It was concluded that L. casei BD II and L. plantarum WCFS1 were able to rapidly utilize MJ for cell synthesis and lactic acid production without pH adjustment or addition of other nutrients and remained viable (≥10 6 cfu ml -1 ) up to 3 weeks and 4 weeks of storage at 4°C. Therefore, microencapsulation of the probiotic cells is highly recommended to enhance its viability when inoculated into fruit juice.
IMPACTS OF VISUALIZATION TOOLS ON MATHEMATICAL LEARNING IN TEACHER EDUCATION: A CRITICAL EVALUATION

This paper aims at evaluating students’ engagement with mathematics in a digital environment. It is a continuation of the previous projects that were carried out in 2016 and 2017 (Hadjerrouit, 2015, 2017). More specifically, the article focuses on the affordances and constraints of visualization tools and students’ learning of mathematics using SimReal. The concept of affordances is used as a theoretical lens to collect and analyse empirical data. SimReal is a visualization tool for teaching and learning mathematics for a wide range of topics. It uses a graphical calculator, video lessons and simulations, live streaming, and interactive simulations. SimReal has more than 5000 applications, exercises, and tasks in various areas of mathematics (Brekke, & Hogstad, 2011). The tool has been constantly improved to make the interface more user-friendly, and mathematical activities more motivating and engaging for students. The reason for using SimReal is that there is a huge interest in visualization tools in mathematics education, but there are few empirical studies in teacher education (McKenzie, & Clements, 2014; Macnab et al, 2012; Presmeg, 2012).
Bringing Gender Analysis and Resilience Analysis Together in Small Scale Fisheries Research: Challenges and Opportunities

The widely-accepted policy imperative to enhance gender equity means that the demand for gender analysis is now increasingly orthodox in natural resource programming, including that for small-scale fisheries. Whilst the analysis of socialecological resilience has made valuable contributions to integrating social dimensions into research and policy-making on natural resource management, it has so far demonstrated limited success in effectively integrating considerations of gender equity. This paper reviews the challenges in and opportunities for bringing a gender analysis together with an analysis of social-ecological resilience in the context of policy-orientated small-scale fisheries research. We argue that gender analysis and the analysis of social-ecological resilience have divergent epistemological and methodological underpinnings and we show how these have shaped the progress in social-ecological resilience analysis in addressing gender, as well as the progress of gender analysis in addressing social-ecological resilience. We conclude that rather than searching for a single unifying framework for gender and resilience analysis, it will be more effective to pursue a plural solution in which closer engagement is fostered between gender analysis and resilience analysis whilst preserving the strengths of each approach. We argue that, alongside the ongoing work of better integrating intersectional gender-disaggregated data into analysis of social-ecological resilience, that developing theoretically and methodologically rigorous gender analysis about small scale fisheries and aquaculture in specific empirical contexts is an important component for brokering closer inter-disciplinarily. Accordingly, we elaborate three core principles and three practical starting points for doing this. Fostering constructive debate between analyses of gender and social-ecological resilience has the potential to generate a richer understanding of the ways in which gendered power relations mediate social-ecological resilience.
Translating Similar Languages: Role of Mutual Intelligibility in Multilingual Transformers

In this work we investigate different approaches to translate between similar languages despite low resource limitations. This work is done as the participation of the UBC NLP research group in the WMT 2019 Similar Languages Translation Shared Task. We participated in all language pairs and performed various experiments. We used a transformer architecture for all the models and used back-translation for one of the language pairs. We explore both bilingual and multi-lingual approaches. We describe the pre-processing, training, translation and results for each model. We also investigate the role of mutual intelligibility in model performance.
On The Usefulness of Self-Attention for Automatic Speech Recognition with Transformers

Self-attention models such as Transformers, which can capture temporal relationships without being limited by the distance between events, have given competitive speech recognition results. However, we note the range of the learned context increases from the lower to upper self-attention layers, whilst acoustic events often happen within short time spans in a left-to-right order. This leads to a question: for speech recognition, is a global view of the entire sequence useful for the upper self-attention encoder layers in Transformers? To investigate this, we train models with lower self-attention/upper feed-forward layers encoders on Wall Street Journal and Switchboard. Compared to baseline Transformers, no performance drop but minor gains are observed. We further developed a novel metric of the diagonality of attention matrices and found the learned diagonality indeed increases from the lower to upper encoder self-attention layers. We conclude the global view is unnecessary in training upper encoder layers.
A Translation of Weighted LTL Formulas to Weighted B{\" u}chi Automata over {\(\omega\)}-valuation Monoids

In this paper we introduce a weighted LTL over product omega-valuation monoids that satisfy specific properties. We also introduce weighted generalized Buchi automata with epsilon-transitions, as well as weighted Buchi automata with epsilon-transitions over product omega-valuation monoids and prove that these two models are expressively equivalent and also equivalent to weighted Buchi automata already introduced in the literature. We prove that every formula of a syntactic fragment of our logic can be effectively translated to a weighted generalized Buchi automaton with epsilon-transitions. For generalized product omega-valuation monoids that satisfy specific properties we define a weighted LTL, weighted generalized Buchi automata with epsilon-transitions, and weighted Buchi automata with epsilon-transitions, and we prove the aforementioned results for generalized product omega-valuation monoids as well. The translation of weighted LTL formulas to weighted generalized Buchi automata with epsilon-transitions is now obtained for a restricted syntactical fragment of the logic.
Pseudorapidity distributions of charged particles in pp($\rm\overline{p}$), p(d)A and AA collisions using Tsallis thermodynamics

The pseudorapidity distributions of charged particles measured in p+p($\rm \overline{p}$) collisions for energies ranging from $\sqrt{s_{NN}}=23.6$ GeV to 13 TeV, d+Au collisions at $\sqrt{s_{NN}}=200$ GeV, p+Pb collisions at $\sqrt{s_{NN}}= 5.02$ TeV and A+A collisions at RHIC and LHC are investigated in the fireball model with Tsallis thermodynamics. We assume that the rapidity axis is populated with fireballs following q-Gaussian distribution and the charged particles follow the Tsallis distribution in the fireball. The theoretical results are in good agreement with the experimental data for all the collision systems and centralities investigated. The collision energy and centrality dependence of the central position $y_0$ and its width $\sigma$ of the fireball distribution are also investigated. A possible application of the model to predict the charged particle pseudorapidity distributions for the system size scan program proposed recently for the STAR experiment at RHIC is proposed.
RMITB at TREC COVID 2020

Search engine users rarely express an information need using the same query, and small differences in queries can lead to very different result sets. These user query variations have been exploited in past TREC CORE tracks to contribute diverse, highly-effective runs in offline evaluation campaigns with the goal of producing reusable test collections. In this paper, we document the query fusion runs submitted to the first and second round of TREC COVID, using ten queries per topic created by the first author. In our analysis, we focus primarily on the effects of having our second priority run omitted from the judgment pool. This run is of particular interest, as it surfaced a number of relevant documents that were not judged until later rounds of the task. If the additional judgments were included in the first round, the performance of this run increased by 35 rank positions when using RBP p=0.5, highlighting the importance of judgment depth and coverage in assessment tasks.
Explaining Deep Graph Networks with Molecular Counterfactuals

We present a novel approach to tackle explainability of deep graph networks in the context of molecule property prediction tasks, named MEG (Molecular Explanation Generator). We generate informative counterfactual explanations for a specific prediction under the form of (valid) compounds with high structural similarity and different predicted properties. We discuss preliminary results showing how the model can convey non-ML experts with key insights into the learning model focus in the neighborhood of a molecule.
Principles of Quantum Communication Theory: A Modern Approach

This is a preliminary version of a book in progress on the theory of quantum communication. We adopt an information-theoretic perspective throughout and give a comprehensive account of fundamental results in quantum communication theory from the past decade (and earlier), with an emphasis on the modern one-shot-to-asymptotic approach that underlies much of today's state-of-the-art research in this field. In Part I, we cover mathematical preliminaries and provide a detailed study of quantum mechanics from an information-theoretic perspective. We also provide an extensive and thorough review of the quantum entropy zoo, and we devote an entire chapter to the study of entanglement measures. Equipped with these essential tools, in Part II we study classical communication (with and without entanglement assistance), entanglement distillation, quantum communication, secret key distillation, and private communication. In Part III, we cover the latest developments in feedback-assisted communication tasks, such as quantum and classical feedback-assisted communication, LOCC-assisted quantum communication, and secret key agreement.
Low-symmetry two-dimensional 
BNP2
 and 
C2SiS
 structures with high and anisotropic carrier mobilities

We study the stability and electronic structure of previously unexplored two-dimensional (2D) ternary compounds BNP$_2$ and C$_2$SiS. Using $ab$ $initio$ density functional theory, we have identified four stable allotropes of each ternary compound and confirmed their stability by calculated phonon spectra and molecular dynamics simulations. Whereas all BNP$_2$ allotropes are semiconducting, we find C$_2$SiS, depending on the allotrope, to be semiconducting or semimetallic. The fundamental band gaps of the semiconducting allotropes we study range from $1.4$ eV to $2.2$ eV at the HSE06 level $0.5$ eV to $1.4$ eV at the PBE level and display carrier mobilities as high as $1.5{\times}10^5$ cm$^2$V$^{-1}$s$^{-1}$. Such high mobilities are quite uncommon in semiconductors with so wide band gaps. Structural ridges in the geometry of all allotropes cause a high anisotropy in their mechanical and transport properties, promising a wide range of applications in electronics and optoelectronics.
ROIAL: Region of Interest Active Learning for Characterizing Exoskeleton Gait Preference Landscapes

Characterizing what types of exoskeleton gaits are comfortable for users, and understanding the science of walking more generally, require recovering a user’s utility landscape. Learning these landscapes is challenging, as walking trajectories are defined by numerous gait parameters, data collection from human trials is expensive, and user safety and comfort must be ensured. This work proposes the Region of Interest Active Learning (ROIAL) framework, which actively learns each user’s underlying utility function over a region of interest that ensures safety and comfort. ROIAL learns from ordinal and preference feedback, which are more reliable feedback mechanisms than absolute numerical scores. The algorithm’s performance is evaluated both in simulation and experimentally for three non-disabled subjects walking inside of a lower-body exoskeleton. ROIAL learns Bayesian posteriors that predict each exoskeleton user’s utility landscape across four exoskeleton gait parameters. The algorithm discovers both commonalities and discrepancies across users’ gait preferences and identifies the gait parameters that most influenced user feedback. These results demonstrate the feasibility of recovering gait utility landscapes from limited human trials.
Guarding Serverless Applications with SecLambda

As an emerging application paradigm, serverless computing attracts attention from more and more attackers. Unfortunately, security tools for conventional applications cannot be easily ported to serverless, and existing serverless security solutions are inadequate. In this paper, we present \emph{SecLambda}, an extensible security framework that leverages local function state and global application state to perform sophisticated security tasks to protect an application. We show how SecLambda can be used to achieve control flow integrity, credential protection, and rate limiting in serverless applications. We evaluate the performance overhead and security of SecLambda using realistic open-source applications, and our results suggest that SecLambda can mitigate several attacks while introducing relatively low performance overhead.
A Transfer Learning Approach for Dialogue Act Classification of GitHub Issue Comments

Social coding platforms, such as GitHub, serve as laboratories for studying collaborative problem solving in open source software development; a key feature is their ability to support issue reporting which is used by teams to discuss tasks and ideas. Analyzing the dialogue between team members, as expressed in issue comments, can yield important insights about the performance of virtual teams. This paper presents a transfer learning approach for performing dialogue act classification on issue comments. Since no large labeled corpus of GitHub issue comments exists, employing transfer learning enables us to leverage standard dialogue act datasets in combination with our own GitHub comment dataset. We compare the performance of several word and sentence level encoding models including Global Vectors for Word Representations (GloVe), Universal Sentence Encoder (USE), and Bidirectional Encoder Representations from Transformers (BERT). Being able to map the issue comments to dialogue acts is a useful stepping stone towards understanding cognitive team processes.
Formation of the largest galactic cores through binary scouring and gravitational wave recoil

Massive elliptical galaxies are typically observed to have central cores in their projected radial light profiles. Such cores have long been thought to form through `binary scouring' as supermassive black holes (SMBHs), brought in through mergers, form a hard binary and eject stars from the galactic centre. However, the most massive cores, like the ~3kpc core in A2261-BCG, remain challenging to explain in this way. In this paper, we run a suite of dry galaxy merger simulations to explore three different scenarios for central core formation in massive elliptical galaxies: `binary scouring', `tidal deposition' and `gravitational wave (GW) induced recoil'. Using the Griffin code, we self-consistently model the stars, dark matter and SMBHs in our merging galaxies, following the SMBH dynamics through to the formation of a hard binary. We find that we can only explain the large surface brightness core of A2261-BCG with a combination of a major merger that produces a small ~1kpc core through binary scouring, followed by the subsequent GW recoil of its SMBH that acts to grow the core size. We show that this same model can also explain the bright `knots' observed in the core region of A2261-BCG. A key prediction of this scenario is an offset SMBH surrounded by a compact cluster of bound stars. We note that a core in the surface brightness profile does not imply a three-dimensional region of (nearly) constant density, but rather a shallow cusp in space density scaling as $\sim r^{-0.5}$
High-throughput screening of the thermoelastic properties of ultra-high temperature ceramics

Ultra-high temperature ceramics, UHTCs, are a group of materials with high technological interest because their use in extreme environments. However, their characterization at high temperatures represents the main obstacle for their fast development. Obstacles are found from a experimental point of view, where only few laboratories around the world have the resources to test these materials under extreme conditions, and also from a theoretical point of view, where actual methods are extremely expensive. Here, a new theoretical high-throughput framework for the prediction of the thermoelastic properties of materials is introduced. This approach can be systematically applied to any kind of crystalline material, drastically reducing the computational cost of previous methodologies. Elastic constants for UHTCs have been calculated at a wide range of temperatures with excellent agreement with experimentally reported values. Moreover, other mechanical properties such a bulk modulus, shear modulus or Poisson ration have been also explored. Other frameworks with similar computational cost have been used only for predicting isotropic or averaged properties, however this new approach opens the door to the calculation of anisotropic properties at a very low computational cost.
Radiogenic Heating and Its Influence on Rocky Planet Dynamos and Habitability

The thermal evolution of rocky planets on geological timescales (Gyr) depends on the heat input from the long-lived radiogenic elements potassium, thorium, and uranium. Concentrations of the latter two in rocky planet mantles are likely to vary by up to an order of magnitude between different planetary systems because Th and U, like other heavy r-process elements, are produced by rare stellar processes. Here we discuss the effects of these variations on the thermal evolution of an Earth-size planet, using a 1D parameterized convection model. Assuming Th and U abundances consistent with geochemical models of the Bulk Silicate Earth based on chondritic meteorites, we find that Earth had just enough radiogenic heating to maintain a persistent dynamo. According to this model, Earth-like planets of stars with higher abundances of heavy r-process elements, indicated by the relative abundance of europium in their spectra, are likely to have lacked a dynamo for a significant fraction of their lifetimes, with potentially negative consequences for hosting a biosphere. Because the qualitative outcomes of our 1D model are strongly dependent on the treatment of viscosity, further investigations using fully 3D convection models are desirable.
Quantifying Power Flow Processes Mediated by Spin Currents

Energy propagation through spin currents has been expected to be one of the most promising ways to achieve dissipation-free energy propagation, but the power flow via spin currents has been unclear as to the efficiency of the power flow and where the power losses occur. Here, we comprehensively evaluate the spin current-mediated power flow process in the bilayer device consisting of ferromagnetic metal (FM) and non-magnetic metal (NM) layers by realizing experimental evaluations for each process from the microwave absorption to electromotive force (EMF) output. The absorption power by ferromagnetic resonance (FMR) of the thin FM layer during the EMF output is directly measured in operando using an antenna probe system. The transfer efficiency of the absorption power into the NM layer by spin pumping is estimated from strict linewidth evaluation of EMF spectra. The maximum transfer efficiency of the spin pumping power to the external load via the inverse spin Hall effect is determined to be 4.2*10-8 under 160mW microwave irradiation using an analysis model similar to that for solar cells. The main factors reducing the efficiency are found to be low resistivity of the NM layer and the interface loss. These findings enhance the applicability of spin current devices and contribute to the development of spin-based technologies.
Yang-Baxter Deformations of the $AdS_5\times T^{1,1}$ Superstring and their Backgrounds.

We consider three-parameter Yang-Baxter deformations of the $AdS_5\times T^{1,1}$ superstring for abelian $r$-matrices which are solutions of the classical Yang-Baxter equation. We find two new backgrounds which are dual to the dipole deformed Klebanov-Witten gauge theory and to the nonrelativistic Klebanov-Witten gauge theory with Schr\"odinger symmetry
Simplified quantum-secure-direct-communication protocol via enhanced super-dense coding

Quantum secure direct communication (QSDC) is one of the most important branches of quantum communication, and at the same time, a key tool within the quantum cryptography toolbox, for three reasons: a) it represents a complete quantum communication system, b) it constitutes a set of protocols, which, unlike the other quantum cryptography techniques, transmits secret information directly, i.e., without using a key, and c) it represents a complete and special paradigm inside quantum cryptography, which gives rise to a great variety of configurations for its practical implementation on all types of platforms, whether optical or based on superconductors. Here, we report the successful implementation of a simplified QSDC protocol on two free access quantum platforms.