Miracle-dz/UniFAR-FTUs huggingface dataset:
Software Engineering Practice in the Development of Deep Learning Applications

Deep-Learning(DL) applications have been widely employed to assist in various tasks. They are constructed based on a data-driven programming paradigm that is different from conventional software applications. Given the increasing popularity and importance of DL applications, software engineering practitioners have some techniques specifically for them. However, little research is conducted to identify the challenges and lacks in practice. To fill this gap, in this paper, we surveyed 195 practitioners to understand their insight and experience in the software engineering practice of DL applications. Specifically, we asked the respondents to identify lacks and challenges in the practice of the development life cycle of DL applications. The results present 13 findings that provide us with a better understanding of software engineering practice of DL applications. Further, we distil these findings into 7 actionable recommendations for software engineering researchers and practitioners to improve the development of DL applications.
Scalable comparison of JavaScript V8 bytecode traces

The comparison and alignment of runtime traces are essential, e.g., for semantic analysis or debugging. However, naive sequence alignment algorithms cannot address the needs of the modern web: (i) the bytecode generation process of V8 is not deterministic; (ii) bytecode traces are large. We present STRAC, a scalable and extensible tool tailored to compare bytecode traces generated by the V8 JavaScript engine. Given two V8 bytecode traces and a distance function between trace events, STRAC computes and provides the best alignment. The key insight is to split access between memory and disk. STRAC can identify semantically equivalent web pages and is capable of processing huge V8 bytecode traces whose order of magnitude matches today's web like https://2019.splashcon. org, which generates approx. 150k of V8 bytecode instructions.
Deep Value Model Predictive Control

In this paper, we introduce an actor-critic algorithm called Deep Value Model Predictive Control (DMPC), which combines model-based trajectory optimization with value function estimation. The DMPC actor is a Model Predictive Control (MPC) optimizer with an objective function defined in terms of a value function estimated by the critic. We show that our MPC actor is an importance sampler, which minimizes an upper bound of the cross-entropy to the state distribution of the optimal sampling policy. In our experiments with a Ballbot system, we show that our algorithm can work with sparse and binary reward signals to efficiently solve obstacle avoidance and target reaching tasks. Compared to previous work, we show that including the value function in the running cost of the trajectory optimizer speeds up the convergence. We also discuss the necessary strategies to robustify the algorithm in practice.
Perturbed minimizing movements of families of functionals

We consider the well-known minimizing-movement approach to the definition of a solution of gradient-flow type equations by means of an implicit Euler scheme depending on an energy and a dissipation term. We perturb the energy by considering a ($\Gamma$-converging) sequence and the dissipation by varying multiplicative terms. The scheme depends on two small parameters $\varepsilon$ and $\tau$, governing energy and time scales, respectively. We characterize the extreme cases when $\varepsilon/\tau$ and $\tau/\varepsilon$ converges to $0$ sufficiently fast, and exhibit a sufficient condition that guarantees that the limit is indeed independent of $\varepsilon$ and $\tau$. We give examples showing that this in general is not the case, and apply this approach to study some discrete approximations, the homogenization of wiggly energies and geometric crystalline flows obtained as limits of ferromagnetic energies.
Analysis of an Automated Machine Learning Approach in Brain Predictive Modelling: A data-driven approach to Predict Brain Age from Cortical Anatomical Measures

The use of machine learning (ML) algorithms has significantly increased in neuroscience. However, from the vast extent of possible ML algorithms, which one is the optimal model to predict the target variable? What are the hyperparameters for such a model? Given the plethora of possible answers to these questions, in the last years, automated machine learning (autoML) has been gaining attention. Here, we apply an autoML library called TPOT which uses a tree-based representation of machine learning pipelines and conducts a genetic-programming based approach to find the model and its hyperparameters that more closely predicts the subject's true age. To explore autoML and evaluate its efficacy within neuroimaging datasets, we chose a problem that has been the focus of previous extensive study: brain age prediction. Without any prior knowledge, TPOT was able to scan through the model space and create pipelines that outperformed the state-of-the-art accuracy for Freesurfer-based models using only thickness and volume information for anatomical structure. In particular, we compared the performance of TPOT (mean accuracy error (MAE): $4.612 \pm . 124$ years) and a Relevance Vector Regression (MAE $5.474 \pm .140$ years). TPOT also suggested interesting combinations of models that do not match the current most used models for brain prediction but generalise well to unseen data. AutoML showed promising results as a data-driven approach to find optimal models for neuroimaging applications.
Self-Paced Multi-Label Learning with Diversity

The major challenge of learning from multi-label data has arisen from the overwhelming size of label space which makes this problem NP-hard. This problem can be alleviated by gradually involving easy to hard tags into the learning process. Besides, the utilization of a diversity maintenance approach avoids overfitting on a subset of easy labels. In this paper, we propose a self-paced multi-label learning with diversity (SPMLD) which aims to cover diverse labels with respect to its learning pace. In addition, the proposed framework is applied to an efficient correlation-based multi-label method. The non-convex objective function is optimized by an extension of the block coordinate descent algorithm. Empirical evaluations on real-world datasets with different dimensions of features and labels imply the effectiveness of the proposed predictive model.
Whole-body musculo-skeletal model V1

The main goal of this project was to build a basic musculo-skeletal model of the human body in order to be able of analyzing and simulating human motion. To this end, an OpenSim [4] musculo-skeletal model was modified and augmented based on previous works of the literature. The resulting model is also compatible with Pinocchio [3], an open source C++ library for efficient rigid multi-body dynamics computations based on revisited Roy Featherstone’s algorithms. This allows users for creating powerful simulations of whole-body human-based motions with anthropomorphic systems. Using Pinocchio with the model permits also to use state-of-the-art libraries written in C++ or python (e.g. from robotics, machine learning,.)
Detecting Image Brush Editing Using the Discarded Coefficients and Intentions

This paper describes a quick and simple method to detect brush editing in JPEG images. The novelty of the proposed method is based on detecting the discarded coefficients during the quantization of the image. Another novelty of this paper is the development of a subjective metric named intentions. The method directly analyzes the allegedly tampered image and generates a forgery mask indicating forgery evidence for each image block. The experiments show that our method works especially well in detecting brush strokes, and it works reasonably well with added captions and image splicing. However, the method is less effective detecting copy-moved and blurred regions. This means that our method can effectively contribute to implementing a complete imagetampering detection tool. The editing operations for which our method is less effective can be complemented with methods more adequate to detect them.
Determination of Benzoic and Sorbic Acids in Foods

The aim of this work was the analysis of benzoic acid and sorbic acid content in selected beverages and processed fruit and vegetable products. The analyses of these preservatives in 34 different food products (ketchups, carbonated and non-carbonated beverages, beverage concentrates) were performed. In this purpose, the conditions of analyzed compounds extraction from the food samples were optimized and method of analysis by reversed-phase high performance liquid chromatography method (RP-HPLC) was developed. Very good separation of the analytes was achieved using methanol-water-acetate buffer pH 4.4 (40 : 40 : 20) as a mobile phase.
Silhouettes based human action recognition by Procrustes analysis and Fisher vector encoding

Recently, human action recognition in videos has attracted much attention. This paper proposed a framework for human action recognition based on procrustes analysis and Fisher vector encoding. First, we apply a pose based feature extracted from silhouette image by employing Procrustes analysis and local preserving projection. It can preserve the discriminative shape information and local manifold structure of human pose and is invariant to translation, rotation and scaling. After the pose feature is extracted, a recognition framework based on Fisher vector encoding and multi-class supporting vector machine is employed for classifying the human action. Experimental results on benchmarks demonstrate the effectiveness of the proposed method.
Examining the economic consequences of concise integrated reports: a pitch

Purpose - The purpose of this paper is to provide a discussion of the application of the Faff ( Design/methodology/approach - The author recounts her personal experience with completing the template for a pitch that examines reporting conciseness in the context of integrated reporting. Findings - The template was found useful in refining a research idea in a structured manner. Originality/value - The letter also demonstrates how the template was used as a research collaboration tool when engaging with collaborators on research projects.
A Locally Adaptive Bayesian Cubature Method

Bayesian cubature (BC) is a popular inferential perspective on the cubature of expensive integrands, wherein the integrand is emulated using a stochastic process model. Several approaches have been put forward to encode sequential adaptation (i.e. dependence on previous integrand evaluations) into this framework. However, these proposals have been limited to either estimating the parameters of a stationary covariance model or focusing computational resources on regions where large values are taken by the integrand. In contrast, many classical adaptive cubature methods focus computational resources on spatial regions in which local error estimates are largest. The contributions of this work are three-fold: First, we present a theoretical result that suggests there does not exist a direct Bayesian analogue of the classical adaptive trapezoidal method. Then we put forward a novel BC method that has empirically similar behaviour to the adaptive trapezoidal method. Finally we present evidence that the novel method provides improved cubature performance, relative to standard BC, in a detailed empirical assessment.
Novel Results on Series of Floor and Ceiling Functions

We propose and prove a couple of formulas and infinite series involving the floor and the ceiling functions. Formula relating to the difference of floor and ceiling functions is obtained using aforementioned formulas. Partial summations of floor and ceiling of qth roots of natural numbers are equated as simple formulas. Particular cases of the series are taken into consideration and it is proven that both the cases relate to the Riemann-Zeta function. Poles for the both series are mentioned and it is shown that even if both series individually fail to converge at the pole, their difference is convergent at the same. It is shown that our formulas reduce to the Gauss formula and the series reduce to the Riemann-Zeta for a particular value. Further some special cases and scope for future work are discussed.
Oxidative stress and antioxidative responses in plant e virus interactions

During plantevirus interactions, defence responses are linked to the accumulation of reactive oxygen species (ROS). Importantly, ROS play a dual role by (1) eliciting pathogen restriction and often localized death of host plant cells at infection sites and (2) as a diffusible signal that induces antioxidant and pathogenesis-related defence responses in adjacent plant cells. The outcome of these defences largely depends on the speed of host responses including early ROS accumulation at virus infection sites. Rapid host reactions may result in early virus elimination without any oxidative stress (i.e. a symptomless, extreme resistance). A slower host response allows a certain degree of virus replication and movement resulting in oxidative stress and programmed death of affected plant cells before conferring pathogen arrest (hypersensitive response, HR). On the other hand, delayed host attempts to elicit virus resistance result in an imbalance of antioxidative metabolism and massively stressed systemic plant tissues (e.g. systemic chlorotic or necrotic symptoms). The final consequence of these processes is a partial or almost complete loss of control over virus invasion (compatible infections). © 2015 Elsevier Ltd. All rights reserved.
Two Affine Scaling Methods for Solving Optimization Problems Regularized with an L 1-norm by Zhirong

In finance, the implied volatility surface is plotted against strike price and time to maturity. The shape of this volatility surface can be identified by fitting the model to what is actually observed in the market. The metric that is used to measure the discrepancy between the model and the market is usually defined by a mean squares of error of the model prices to the market prices. A regularization term can be added to this error metric to make the solution possess some desired properties. The discrepancy that we want to minimize is usually a highly nonlinear function of a set of model parameters with the regularization term. Typically monotonic decreasing algorithm is adopted to solve this minimization problem. Steepest descent or Newton type algorithms are two iterative methods but they are local, i.e., they use derivative information around the current iterate to find the next iterate. In order to ensure convergence, line search and trust region methods are two widely used globalization techniques. Motivated by the simplicity of Barzilai-Borwein method and the convergence properties brought by globalization techniques, we propose a new Scaled Gradient (SG) method for minimizing a differentiable function plus an L1-norm. This non-monotone iterative method only requires gradient information and safeguarded Barzilai-Borwein steplength is used in each iteration. An adaptive line search with the Armijo-type condition check is performed in each iteration to ensure convergence. Coleman, Li and Wang proposed another trust region approach in solving the same problem. We give a theoretical proof of the convergence of their algorithm. The objective of this thesis is to numerically investigate the performance of the SG method and establish global and local convergence properties of Coleman, Li and Wang’s trust region method proposed in [26]. Some future research directions are also given at the end of this thesis.
Crystal Structure and Luminescence Studies of Upconverting Nanoparticles

Nanoparticles doped with lanthanide ions that are capable of upconverted radiation promise great potential as exogenous luminescent biomarkers in biomedical imaging. In this work, hexagonal structured core NaYF4:Yb 3+,Tm3+ and core-shell NaYF4:Yb 3+,Tm3+ @NaYF4 upconverting nanoparticles (UCNPs) with ranging from ∼43 nm to ∼66 nm in size are successfully synthesized by a modified solvothermal synthesis method. The work presented in this thesis aims at the crystal structure analysis of core NaYF4:Yb 3+,Tm3+ and core-shell NaYF4:Yb ,Tm@NaYF4 UCNPs under varying thermal conditions. X-ray diffraction (XRD) and transmission electron microscopy (TEM) techniques were utilized to characterize size, phase and shape of UCNPs. Moreover, thermal stability of the crystal phase of UCNPs was investigated. Thus, UCNPs were exposed to a post heating treatment where the temperature was gradually increased from room temperature to 700◦C. The XRD patterns of UCNPs at varying temperature levels show that UCNPs are thermally stable up to ∼300◦C. Additionally, at the temperature level of 590◦C core UCNPs began to undergo a phase transformation from hexagonal phase (β) to cubic phase (α). The low quantum yield (QY) of upconversion (UC) emissions in UCNPs is always considered as a challenging problem that hinders UCNPs’ utilization in bioimaging. Therefore, another aim of this work is pointed to figure out a successful way that improves QY of core UCNPs. Indeed, a significant increase in QY of near infrared (NIR) UC emission in core-shell UCNPs was accomplished under the excitation of 975 nm light. Simulations promise that it is feasible to increase QY up to ∼2.4% by millisecond pulse excitation with carefully selected pulse parameters. Furthermore, NIR UC signal gain, ∼8.7, is experimentally achieved by the pulsed excitation that had a 2 Hz repetition rate, a 20 ms pulse width, a 4% duty cycle with ∼0.12 Wcm−2 average power density.
A feature-ranking framework for IoT device classification

IoT market is rapidly changing the cyber threat landscape. The challenges to security and privacy arise not only because IoT devices are large in number, but also because IoT devices are heterogeneous in type and functionality. Machine learning algorithms are attractive methods to solve various problems such as device identification, anomaly detection, and attack detection. Often, all available features extracted from network traffic are fed as input to train the models, which in practice is not regarded as the best approach. Associated with features are different kinds of cost, such as costs for obtaining the data, extracting and storing features, compute resources to run a model with high dimensional features, etc. Instead, if a smaller set of features could achieve performance close to that obtained with all features, that might help to reduce cost as well as to make better interpretation of results. In this work, we address the problem of selecting features extracted from IoT network traffic, based on the utility of a feature in achieving the goal of the machine learning models. We develop a unifying framework of fundamental statistical tests for ranking features. We specifically consider the use case of IoT device classification, and demonstrate the effectiveness of our framework by evaluating it using different classifiers on traffic obtained from real IoT devices.
ESTABLISHMENT OF AN EFFICIENT MEDIUM FOR ANTHER CULTURE OF RICE THROUGH COMPARATIVE EXPERIMENTS ON THE NITROGEN SOURCES

The experiments have revealed that (NH4)2SO4 combined with KNO3 at low concentration is of advantage to the formation, growth and differentiation of pollen callus in rice, whereas the high concentration of (NH4)2SO4, whether used separately or in combination with KNO3, obviously inhibits the pollen callus formation. The optimum NH4+ concen- tration is about 7.0 mM (equal to 3.5 mM (NH4)2SO4). A basic medium containing 3.5 mM (NH4)2SO4 and 28 mM KNO3 as nitrogen sources has been established. On such medium the frequency of the pollen callus formation is higher than that on Millers me- dium, and the differentiation of shoot from pollen callus is satisfactory.
Differentiable Sparsification for Deep Neural Networks

Deep neural networks have relieved the feature engineering burden on human experts. However, comparable efforts are instead required to determine an effective architecture. In addition, as the sizes of networks have over-grown, a considerable amount of resources is also invested in reducing the sizes. The sparsification of an over-complete model addresses these problems as it removes redundant parameters or connections by pruning them away after training or encouraging them to become zero during training. However, these approaches are not fully differentiable and interrupt an end-to-end training process with the stochastic gradient descent in that they require either a parameter selection or soft-thresholding step. In this study, we proposed a fully differentiable sparsification method for deep neural networks, which allows parameters to be exactly zero during training. The proposed method can simultaneously learn the sparsified structure and weights of networks using the stochastic gradient descent.
INTEGRATED STRATEGY TO ENHANCE BIOGAS PRODUCTION FROM MANGO PEEL WASTE

The present study has successfully explored the use of pomegranate seed de-oiled cake as a major additive and micronutrients solution as a minor additive to enhance production of biogas and enrich its methane content, using mango peel as the initial substrate. While micronutrients solution has activated enzymes to carry out anaerobic digestion for optimal production of biogas and methane, pomegranate seed de-oiled cake has (a) provided buffering capacity for stabilized pH/alkalinity: acidity ratio, (b) reduced C:N ratio of the substrates to facilitate anaerobic digestion. Cumulatively effecting enhanced biogas generation (0.82 m 3 kg -1 VS fed) and its rich methane content (0.64 m 3 kg -1 VS fed).
Math . Phys . 15 ( 2011 ) 43 – 69 Invertible defects and isomorphisms of rational CFTs

Given two two-dimensional conformal field theories, a domain wall — or defect line — between them is called invertible if there is another defect with which it fuses to the identity defect. A defect is called topological if it is transparent to the stress tensor. A conformal isomorphism between the two CFTs is a linear isomorphism between their state spaces which preserves the stress tensor and is compatible with the operator product expansion. We show that for rational CFTs there is a one-to-one correspondence between invertible topological defects and conformal isomorphisms if both preserve the rational symmetry. This correspondence is compatible with composition. e-print archive: http://lanl.arXiv. org/abs/1004.4725 44 ALEXEI DAVYDOV, LIANG KONG AND INGO RUNKEL
A Survey on Deep-Learning based Techniques for Modeling and Estimation of MassiveMIMO Channels

\textit{Why does the literature consider the channel-state-information (CSI) as a 2/3-D image? What are the pros-and-cons of this consideration for accuracy-complexity trade-off? } Next generations of wireless communications require innumerable disciplines according to which a low-latency, low-traffic, high-throughput, high spectral-efficiency and low energy-consumption are guaranteed. Towards this end, the principle of massive multi-input multi-output (MaMIMO) is emerging which is conveniently deployed for millimeter wave (mmWave) bands. However, practical and realistic MaMIMO transceivers suffer from a huge range of challenging bottlenecks in design the majority of which belong to the issue of channel-estimation. Channel modeling and prediction in MaMIMO particularly suffer from computational complexity due to a high number of antenna sets and supported users. This complexity lies dominantly upon the feedback-overhead which even degrades the pilot-data trade-off in the uplink (UL)/downlink (DL) design. This comprehensive survey studies the novel deep-learning (DLg) driven techniques recently proposed in the literature which tackle the challenges discussed-above - which is for the first time. In addition, we consequently propose 7 open trends e.g. in the context of the lack of Q-learning in MaMIMO detection - for which we talk about a possible solution to the saddle-point in the 2-D pilot-data axis for a \textit{Stackelberg game} based scenario.