Miracle-dz/UniFAR-FTUs huggingface dataset:
Influence of the variation of fundamental constants on the primordial nucleosynthesis

We investigate the effect of a variation of fundamental constants on primordial element production in Big Bang nucleosynthesis (BBN). We focus on the effect of a possible change in the nucleon-nucleon interaction on nuclear reaction rates involving the A=5 (5Li and 5He) and A=8 (8Be) unstable nuclei. The reaction rates for 3He(d,p)4He and 3H(d,n)4He are dominated by the properties of broad analog resonances in 5Li and 5He compound nuclei respectively. While the triple-alpha process 4He(aa,g)12C is normally not effective in BBN, its rate is very sensitive to the position of the "Hoyle state" and could in principle be drastically affected if 8Be were stable during BBN. We found that the effect of the variation of constants on the 3He(d,p)4He, 3H(d,n)4He nd 4He(aa,g)12C reaction rates is not sufficient to induce a significant effect on BBN, even with a stable 8Be. The main influences come from the weak rates and the A=2, n(p,g)d, bottleneck reaction.
Game semantics for probabilistic modal μ-calculi

The probabilistic (or quantitative) modal μ-calculus is a fixed-point logic designed for expressing properties of probabilistic labeled transition systems (PLTS’s). Two semantics have been studied for this logic, both assigning to every process state a value in the interval [0, 1] representing the probability that the property expressed by the formula holds at the state. One semantics is denotational and the other is a game semantics, specified in terms of two-player stochastic games. The two semantics have been proved to coincide on all finite PLTS’s. A first contribution of the thesis is to extend this coincidence result to arbitrary PLTS’s. A shortcoming of the probabilistic μ-calculus is the lack of expressiveness required to encode other important temporal logics for PLTS’s such as Probabilistic Computation Tree Logic (PCTL). To address this limitation, we extend the logic with a new pair of operators: independent product and coproduct, and we show that the resulting logic can encode the qualitative fragment of PCTL. Moreover, a further extension of the logic, with the operation of truncated sum and its dual, is expressive enough to encode full PCTL. A major contribution of the thesis is the definition of appropriate game semantics for these extended probabilistic μ-calculi. This relies on the definition of a new class of games, called tree games, which generalize standard 2-player stochastic games. In tree games, a play can be split into concurrent subplays which continue their evolution independently. Surprisingly, this simple device supports the encoding of the whole class of imperfect-information games known as Blackwell games. Moreover, interesting open problems in game theory, such as qualitative determinacy for 2-player stochastic parity games, can be reformulated as determinacy problems for suitable classes of tree games. Our main technical result about tree games is a proof of determinacy for 2-player stochastic metaparity games, which is the class of tree games that we use to give game semantics to the extended probabilistic μ-calculi. In order to cope with measure-theoretic technicalities, the proof is carried out in ZFC set theory extended with Martin’s Axiom at the first uncountable cardinal (MAא1). The final result of the thesis shows that the game semantics of the extended logics coincides with the denotational semantics, for arbitrary PLTS’s. However, in contrast to the earlier coincidence result, which is proved in ZFC, the proof of coincidence for the extended calculi is once again carried out in ZFC + MAא1 .
CAD2Sketch

Concept sketches are ubiquitous in industrial design, as they allow designers to quickly depict imaginary 3D objects. To construct their sketches with accurate perspective, designers rely on longstanding drawing techniques, including the use of auxiliary construction lines to identify midpoints of perspective planes, to align points vertically and horizontally, and to project planar curves from one perspective plane to another. We present a method to synthesize such construction lines from CAD sequences. Importantly, our method balances the presence of construction lines with overall clutter, such that the resulting sketch is both well-constructed and readable, as professional designers are trained to do. In addition to generating sketches that are visually similar to real ones, we apply our method to synthesize a large quantity of paired sketches and normal maps, and show that the resulting dataset can be used to train a neural network to infer normals from concept sketches.1
Quantum Probability Estimation for Randomness with Quantum Side Information

We develop a quantum version of the probability estimation framework [arXiv:1709.06159] for randomness generation with quantum side information. We show that most of the properties of probability estimation hold for quantum probability estimation (QPE). This includes asymptotic optimality at constant error and randomness expansion with logarithmic input entropy. QPE is implemented by constructing model-dependent quantum estimation factors (QEFs), which yield statistical confidence upper bounds on data-conditional normalized R\'enyi powers. This leads to conditional min-entropy estimates for randomness generation. The bounds are valid for relevant models of sequences of experimental trials without requiring independent and identical or stationary behavior. QEFs may be adapted to changing conditions during the sequence and trials can be stopped any time, such as when the results so far are satisfactory. QEFs can be constructed from entropy estimators to improve the bounds for conditional min-entropy of classical-quantum states from the entropy accumulation framework [Dupuis, Fawzi and Renner, arXiv:1607.01796]. QEFs are applicable to a larger class of models, including models permitting measurement devices with super-quantum but non-signaling behaviors and semi-device dependent models. The improved bounds are relevant for finite data or error bounds of the form $e^{-\kappa s}$, where $s$ is the number of random bits produced. We give a general construction of entropy estimators based on maximum probability estimators, which exist for many configurations. For the class of $(k,2,2)$ Bell-test configurations we provide schemas for directly optimizing QEFs to overcome the limitations of entropy-estimator-based constructions. We obtain and apply QEFs for examples involving the $(2,2,2)$ Bell-test configuration to demonstrate substantial improvements in finite-data efficiency.
Introduction to Hierarchical Continuous Time Dynamic Modelling With ctsem

ctsem allows for speciﬁcation and ﬁtting of a range of continuous and discrete time dynamic models with stochastic system noise. The models may include multiple indicators (dynamic factor analysis), multiple, interrelated, potentially higher order processes, and time dependent (varying within subject) and time independent (not varying within subject) covariates. Classic longitudinal models like latent growth curves and latent change score models are also possible. Version 1 of ctsem provided structural equation model based functionality by linking to the OpenMx software, allowing mixed eﬀects models (random means but ﬁxed regression and variance parameters) for multiple subjects. For version 2 of the R package ctsem , we include a Bayesian speciﬁ-cation and ﬁtting routine that uses the Stan probabilistic programming language, via the rstan package in R. This allows for all parameters of the dynamic model to individually vary, using an estimated population mean and variance, and any time independent covariate eﬀects, as a prior. Frequentist approaches with ctsem are documented in a forthcoming JSS publication (Driver, Voelkle, Oud, in press), and in R vignette form at https://cran.r-project.org/package=ctsem/vignettes/ctsem.pdf . The Bayesian approach is discussed more fully and conceptually in a preprint article available at https://www.researchgate. net/publication/310747801_Hierarchical_ Bayesian_Continuous_Time_Dynamic_Modeling , but here we provide more speciﬁcs on the software for getting started with the Bayesian approach included in version 2.
Universal Diﬀerential Equations Applied to Bioprocesses

Biomanufacturing is method of manufacturing using biological organisms to produce products and molecules for a wide-range of applications. Manufacturing of pharmaceutical products and precursors alone has generated more than $ 90 billion in sales in 2017, with Monoclonal Antibodies (mAb) being the primary product. Representing 60% of mAb biomanufacturing cell cultures, and 84% of approved products in the period of 2015-2018, Chinese Hamster Ovary (CHO) cells, and other mammalian cells are becoming the dominant organism used for biopharmaceutical production. The problem is that many biological organisms being used, and especially mammalian cell cultures, are not suﬃciently understood to allow for accurate prediction and control using mathematical models. Moreover, models are based on mechanisms and processes understood at lab-scale, with volumes of 3 . 5 L to 20 L whereas industrial volumes are frequently on the order of 20 , 000 L . At industrial-scale new processes and mechanisms become salient that are not captured by traditional ﬁrst-principles models. Furthermore, it is not clear how to incorporate many measured quantities into these ﬁrst-principles bioprocess models; e.g. pH, temperature, osmolality, and others.
Deep Learning Image-based Design of Graphene-reinforced Polyurethane Foams

Artificial Intelligence techniques have been rapidly progressing in the past decade and have become an important tool for discovery within the scientific community. The adoption of these techniques within the material science community has been critical in enabling high throughput research through rapid characterization and discovery of materials. Machine (Deep-) learning techniques are being used not only for characterizing materials but also for extrapolating structure-property relationships as well as for generating new structures from existing material structure data. We discuss two case studies where we demonstrate the use of deep learning techniques for modeling and optimizing the design of graphene-reinforced polyurethane (PU) foams. Specifically we demonstrate both forward and inverse design techniques through the use of image regression and Generative Adversarial Networks applied to SEM images of graphene-reinforced polyurethane foams. The dataset and analysis further opens up a wide application of AI for the microstructural studies of PU foams.
Effect of Methylammonium Iodide on the All-solution Prepared Methylammonium Bismuth Iodide Perovskite Solar Cells Performance

The methylammonium bismuth iodide (MBI), a promising lead (Pb)-free perovskite solar cells (PeSC’s) material, is suitable for photovoltaic applications due to less toxic and good stability. Herein, the effect of MAI (methylammonium iodide) on the structural, morphological, optical properties and solar cells performance of bismuth-perovskite solar cells (Bi-PeSC’s) by all-solution processed multi-step spin coating is investigated. The scanning electron microscope (SEM) morphology visually depicts that with the increase of methylammonium iodide (MAI) precursor molar ratio in bismuth (III) iodide (BiI3) x [= [MAI]/([MAI]+[BiI3])] from x=0 to x=0.8, the morphological phase changed significantly. Likewise, the x-ray diffraction (XRD) peak of BiI3 at 12.78o (003) is tremendously changed in the phase and intensity. At the same time, the solar cell performances exhibit gradual increment as increasing the content of MAI in BiI3 and the open circuit voltage (Voc) is exceeded four-times of the minimum MAI molar ratio. The maximum Voc obtained by multilayer Bi-PeSC’s is 0.34V and an efficiency shows an increment up to 0.0037% at MAI molar ratio, x=0.8. In brief, our findings suggested the improvement of Bi-perovskite absorber layer in the future.
On-Line Indoor Positioning System For Monitoring Elderly People Using Li-Fi Technology

: This study aims to present an indoor positioning system that can locate a target in space. The created system has the flexibility to be modified online for precisely locating and monitoring an elder in an indoor environment by using Li-Fi technology. The designed system is based on a novel hybrid technique. It has different steps. Firstly, the combination method is used to divide the entire anchors into different groups. Secondly, a weighted least squares (WLS) positioning approach is employed to compute the patient position by each group. Then, anchor selection is used online to select the group having the best positioning accuracy using the mean square error (MSE) metric. Finally, the selected group is chosen to relocate the patient using its estimated position as an initial point for the maximum likelihood (ML) positioning approach. The simulation results show that the proposed algorithm outperforms the WLS and conventional ML approaches.
Secrecy Performance of Correlated α - µ Fading Channels

—This paper investigates the secrecy performance of the classical Wyner’s wiretap model, where the main channel and eavesdropper channel experience correlated α - µ fading. Novel and exact expressions for the average secrecy capacity and secrecy outage probability are derived for the considered realistic scenario. The effect of correlation has been studied on the secrecy performance. Useful insights into the system performance are obtained through the asymptotic analysis.
The CTSimU software toolbox for CT-related image processing and quality assessment

A software toolbox is introduced that addresses several needs common to computed tomography (CT). Built for the WIPANO CTSimU project [1] to serve as the reference implementation for its image processing and evaluation tasks, it provides a Python 3 interface that is adaptable to many conceivable applications. Foremost, the toolbox features a pipeline architecture for sequential 2D image processing tasks, such as flat field corrections and image binning, and enables the user to create their own processing modules. Beyond that, it provides means to measure line profiles and image quality assessment algorithms to calculate modulation transfer functions (MTF) or to determine the interpolated basic spatial resolution (iSRb) using a duplex wire image [2]. It can also be used to calculate projection matrices for the reconstruction of scans with arbitrary industrial CT geometries and trajectories. The CTSimU project defined a framework of projection- and volume-based test scenarios [3] for the qualification of radiographic simulation software towards its use in dimensional metrology. The toolbox implements the necessary evaluation routines and generates reports for all projection-based tests.
Predicting Students’ Performance by Learning Analytics

Predicting Students’ Performance by Learning Analytics by Sandeep Subhash Madnaik The field of Learning Analytics (LA) has many applications in today’s technology and online driven education. Learning Analytics is a multidisciplinary topic for learning purposes that uses machine learning, statistic, and visualization techniques [1]. We can harness academic performance data of various components in a course, along with the data background of each student (learner), and other features that might affect his/her academic performance. This collected data then can be fed to a system with the task to predict the final academic performance of the student, e.g., the final grade. Moreover, it allows students to monitor and self-assess their progress throughout their studies and periodically perform a self-evaluation. From the educators’ perspective, predicting student grades can help them be proactive, in guiding students towards areas that need improvement. Moreover, this study also takes into consideration social factors that might affect students’ performance.
Generalized Least-Squares Regressions IV: Theory and Classi(cid:2)cation Using Generalized Means

(cid:151)The theory of generalized least-squares is reformu- lated here using the notion of generalized means. The generalized least-squares problem seeks a line which minimizes the average generalized mean of the square deviations in x and y . The notion of a generalized mean is equivalent to the generating function concept of the previous papers but allows for a more robust understanding and has an already existing literature. Generalized means are applied to the task of constructing more examples, simplifying the theory, and further classifying generalized least-squares regressions.
An Implementation of Parallel Processing in Pricing and Hedging High-Dimensional American Options

We present a parallel implementation of an existing method for computing prices and deltas of high dimensional American Options as a method of overcoming some of the known issues of computational time cost associated with the known method. The known method utilizes a sequence of Neural networks which learn the diﬀerence of the option pricing functions between timesteps by using a least squares residual derived from a backwards stochastic diﬀerential equation. Our method reduces the computational complexity of the known algorithm by the square of a known factor, at the cost of a limited reduction in accuracy. Our method also continues to compute prices and deltas across the same space-time as what is presented in the previous literature and is thus usable in the construction of delta-hedging processes for market traders. The numerical simulations of the method show that we reach signiﬁcant reductions in the wall clock time required to complete the algorithm while still outputting accurate results within a certain statistical signiﬁcance.
Lipid-Based Intelligent Vehicle Capabilitized with Physical and Physiological Activation

Intelligent drug delivery system based on “stimulus-response” mode emerging a promising perspective in next generation lipid-based nanoparticle. Here, we classify signal sources into physical and physiological stimulation according to their origin. The physical signals include temperature, ultrasound, and electromagnetic wave, while physiological signals involve pH, redox condition, and associated proteins. We first summarize external physical response from three main points about efficiency, particle state, and on-demand release. Afterwards, we describe how to design drug delivery using the physiological environment in vivo and present different current application methods. Lastly, we draw a vision of possible future development.
Novel Point Cloud Quality Improvement Method

Is it possible to enhance point cloud accuracy and density by merging Lidar with photogrammetry technologies? Lidar data can penetrate trees and measure shadow areas to produce a very accurate point cloud. Passive imaging cameras derive a more detailed 3D model and encode point clouds with multispectral information, resulting in a useful coloured point cloud classification. What if were possible to merge these technologies? What effect might neutral density filters have on point cloud colours? This article explores quantitative and qualitative point cloud enhancement in more detail.
Reducing Memory-Bus Energy Consumption of GPUs via Software-Based Bit-Flip Minimization

Energy consumption is a major concern in high-performance computing. One important contributing factor is the number of times the wires are charged and discharged, i.e., how often they switch from ‘0’ to ‘1’ and vice versa. We describe a software technique to minimize this switching activity in GPUs, thereby lowering the energy usage. Our technique targets the memory bus, which comprises many high-capacitance wires that are frequently used. Our approach is to strategically change data values in the source code such that loading and storing them yields fewer bit flips. The new values are guaranteed to produce the same control flow and program output. Measurements on GPUs from two generations show that our technique allows programmers to save up to 9.3% of the whole-GPU energy consumption and 1.2% on average across eight graph-analytics CUDA codes without impacting performance.
Maximally Supersymmetric AdS Solutions in Gauged Supergravity

In this thesis we study maximally supersymmetric solutions of gauged supergravity theories, with special focus on anti-de Sitter solutions. The latter are relevant in the context of the AdS/CFT correspondence. In the first part we classify all maximally supersymmetric solutions of all gauged or deformed supergravity theories in D ≥ 3 space-time dimensions. Without background fluxes the space-time background has to be either flat or AdSD. Solutions with non-trivial fluxes are only possible for a small class of theories and we derive a simple criterion for their existence. These solutions coincide with those of the corresponding ungauged theories, therefore the known list of maximally supersymmetric solutions is exhaustive. In the second part we exclusively study maximally supersymmetric AdSD solutions of gauged supergravities in dimensions D ≥ 4. We show that such solutions can only exist if the gauge group after spontaneous symmetry breaking is of the form HR × Hmat. This resembles the structure of the global symmetry groups of the holographically dual SCFTs, where HR is interpreted as the R-symmetry. Moreover, we discuss possible supersymmetry preserving continuous deformations. The moduli spaces spanned by these deformations correspond to the conformal manifolds of the dual SCFTs. Under the assumption that the scalar manifold of the supergravity is a symmetric homogeneous space we derive general conditions on the moduli. In particular, we show that they have to be singlets with respect to HR. Using these results we determine the AdS solutions of all gauged supergravities with more than 16 real supercharges. We show that almost all of them do not This article contains the Ph.D. thesis of the author, submitted at the University of Hamburg in August 2017. The full text of the thesis is also available at http://ediss.sub. uni-hamburg.de/volltexte/2017/8801/. From November 1, 2017: Centre de Physique Théorique, École Polytechnique, CNRS, 91128 Palaiseau, France (severin.luest@polytechnique.edu).
A SURVEY OF SOFTWARE REQUIREMENTS SPECIFICATION AMBIGUITY

The quality of Software Requirements Specification (SRS) is critical. A good quality SRS must be clear, correct, consistent, unambiguous, modifiable, verifiable and traceable. Ambiguity is difficult to tackle; therefore, requirements elicitation technique should be effective. Published material related to SRS issues discusses ambiguity as the most conversed problem. In this survey, our focus is on one of the major quality issues i.e. ambiguity. There are many solutions to resolve SRS ambiguity but, there is no prescribed classification of these solutions exists. The survey to provide a summary of the huge research contributions in the form of developments and new techniques, models, and methodologies that have been recommended to control the SRS ambiguity and magnify the benefits of addressing SRS ambiguity in development projects. To organize this enormous work by researchers, we try to provide the main concepts and associations that together represent the field of SRS ambiguity. The study is important for further assessments of possible solutions to SRS ambiguity for the improvement of SRS that helps researchers and experts to compare these techniques for better results.