Miracle-dz/UniFAR-FTUs huggingface dataset:
More on the relation between the two physically inequivalent decompositions of the nucleon spin

The recent controversy on the nucleon spin decomposition problem is critically overviewed. We argue that there exist two and only two physically inequivalent gauge-invariant decompositions of the longitudinal nucleon spin, contrary to the rapidly spreading view in the QCD spin physics community that there are infinitely many decompositions of the nucleon spin.
I2MB: Intelligent Immersive Multimedia Broadcast in Next-Generation Cellular Networks

The popularity of immersive multimedia content is prevalent and the consumption of 360° videos is increasing rapidly in varied domains. The broadcast of such content in cellular networks will be challenging in terms of dynamic content adaptation and efficient resource allocation to serve heterogeneous consumers. In this work, we propose an intelligent immersive new radio multimedia broadcast multicast system (NR-MBMS), I2MB, for next-generation cellular networks. I2MB intelligently forecasts the users’ viewing angle and the 360° video tiles to be broadcast beforehand using long short-term memory network. We define broadcast areas by using modified K-means clustering. The complex multivariable optimization problem that integrates efficient adaptive 360-degree video encoding and tiled broadcast using optimized transmission parameters is defined as as a Markov decision process (MDP). In a dense urban scenario with a large MBSFN (multimedia broadcast multicast service single frequency network) synchronization area, the state and action space dimensionality is very high, in which the solution is obtained by using deep deterministic policy gradient (DDPG) algorithm. I2MB incorporates deep reinforcement learning based radio resource allocation (modulation-coding scheme and frequency-time resource blocks) and tiled video encoding to maximize the viewport video quality experienced by the broadcast mobile users. I2MB provides improved immersive video broadcast streaming quality while serving a higher number of mobile users. Adaptive encoding of 360° video tiles and radio resource allocation are performed based on users’ forecasted viewing angle, spatial distribution, channel conditions, and service request. The performance evaluation of our proposed scheme, I2MB, shows considerable gains in viewport quality (46.83%) and number of users served (30.52%), over a recent state-of-the-art method VRCAST.
Lessons Learned from adopting modular construction in Brazil: A startup journey

Although Modular Construction (MC) is already known in Brazil mainly to due the fast construction of health facilities during the Covid 19 pandemic, very few developers and building companies have adopted it as an ordinary strategy. Previous studies do not focus on analysing how start-up companies can contribute to MC establishment in the country. This study aims to describe the journey of a local start-up company in adopting MC following a descriptive case-study approach. The findings suggest that the following drivers are crucial for the survival and success of a start-up MC company (i) the development of a variety of products that fits market segments and ensuring a minimal sale revenue regularly to pay fixed costs until it manages to capture investment to scale its business; (ii) taking into consideration it takes time and practice first on a smaller scale before going to larger projects, and multi-story modular buildings; (iii) training of skilled professionals and education on design and detailing on MC building technologies; (iv) standardization of building components, product and design to certain levels is inevitable.
Incorporating Dual-Aware with Hierarchical Interactive Memory Networks for Task-Oriented Dialogue

Recent years, end-to-end task-oriented dialogue systems have made a remarkable breakthrough. However, existing dialogue models tend to equally summarize all the history as the context representation and apply memory networks to incorporate external knowledge. They neglect to highlight the latest request of users, which will cause the dialogue system to generate improper responses. In addition, it is insufficient for the original memory networks to interact between memories only at hop-level and difficult to extract more useful knowledge information. To address these issues, we propose a novel neural model which incorporates Dual-Aware with Hierarchical Inter-active Memory Networks (DA-HIMN). The dual-aware constituting static request-aware and dynamic KB-aware is responsible for capturing the latest request of users and collecting related knowledge information. Furthermore, we design a hierarchical interaction mechanism to augment the memory networks at layer-level to more adequately learn the knowledge representation. Our experimental results demonstrate that our model outperforms the baseline model on two task-oriented dialogue datasets in several evaluation metrics.
A coupled-adjoint method for high-fidelity aero-structural optimization

Abstract : A new integrated aero-structural design method for aerospace vehicles is presented. The approach combines an aero-structural analysis solver, a coupled aero-structural adjoint solver, a geometry engine, and an efficient gradient-based optimization algorithm. The aero-structural solver ensures accurate solutions by using high-fidelity models for the aerodynamics, structures, and coupling procedure. The coupled aero-structural adjoint solver is used to calculate the sensitivities of aerodynamic and structural cost functions with respect to both aerodynamic shape and structural variables. The aero-structural adjoint sensitivities are compared with those given by the complex-step derivative approximation and finite differences. The proposed method is shown to be both accurate and efficient, exhibiting a significant cost advantage when the gradient of a small number of functions with respect to a large number of design variables is needed. The optimization of a supersonic business jet configuration demonstrates the usefulness and importance of computing aero-structural sensitivities using the coupled-adjoint method.
One-Shot Speaker Adaptation Based on Initialization by Generative Adversarial Networks for TTS

Speaker adaptation for personalizing text-to-speech (TTS) has become increasingly important. Herein, we propose a novel adaptation using a few seconds of data obtained from an un-seen speaker. We first use a speaker embedding lookup table to train a multi-speaker TTS model, wherein each speaker embedding in the lookup table contains information representing a speaker’s timbre. We propose an initial embedding predictor that extracts initial embedding suitable for the adaptation of unseen speakers. We use trained speaker embeddings to train the initial embedding predictor. Further, adversarial training is applied to improve the performance. After adversarial training, the initial embedding predictor infers the unseen speaker’s initial embedding, and it is fine-tuned. As the initial embedding contains timbre information of the unseen speaker, adaptation is achieved faster and with less data than with conventional meth-ods. We validate the performance with a mean opinion score (MOS) and demonstrate that adaptation is feasible with only 5 s of data.
An optimization technique for the development of two-dimensional steady turbulent boundary layer models

Abstract : A procedure for the development of a simple boundary layer turbulence model to account for different physical effects is described; the method is applied here to produce models for both pressure gradient and mainstream turbulence effects. Asymptotic theory is used to isolate the leading terms in an expansion for the mean velocity profile for high Reynolds numbers for both the inner and outer regions of a nominally steady two-dimensional boundary layer. The velocity profile in the outer layer satisfies a partial differential equation containing a Reynolds stress term and this term is modeled by a simple eddy viscosity function which contains two parameters. The velocity profile in the inner wall layer is modeled using an analytical expression which has been previously derived by consideration of the observed characteristics of the time-dependent flow in the wall layer and which contains a single independent parameter. For a self-similar flow, the outer layer equation becomes an ordinary differential equation; this equation is solved numerically and in conjunction with the analytical inner layer profile, a composite profile spanning the entire boundary layer is defined. This composite profile contains three parameters which may be adjusted systematically to obtain a best fit to a given set of experimental data. A computer optimization code is described in which any or all of the three profile parameters may be varied.
PCHIPOLATE: Stata module for piecewise cubic Hermite interpolation

pchipolate creates newvar by averaging non-missing values of yvar and using piecewise cubic Hermite interpolation of missing values of yvar, given xvar. This method is often described informally as "pchip". The piecewise cubics join smoothly, so that both the interpolated function and its first derivative are continuous. In addition, the interpolant is shape-preserving in the sense that it cannot overshoot locally; sections in which observed yvar is increasing, decreasing or constant with xvar remain so after interpolation, and local extremes (maxima, maxima) also remain so.
REAL OPTIONS FINANCIAL ANALYSIS FOR THE APPLICATION OF SOLID RAIN: THE CASE OF AVOCADO IN MICHOACÁN

The objective of this study was to perform a financial analysis of the use of solid rain ( potassium polyacrylate ) with the real options method, to deal with price volatility and water scarcity for avocado production ( Persea americana Mill.) in the state of Michoacán. The analysis is based on field data for extensive farming and a financial evaluation to decide the most convenient, economic, social and environmentally sustainable option for the project. The results from the study indicate that the incorporation of the polymer increased the efficiency in water use by 50%, decreased costs by 65%, and increased agricultural productivity by 30%. The cash flow (CF) at present value of the project with the option of investing in this technology is $3,261,608.13 and without the option it is $3,000,200.00. The Net Present Value (NPV) changes to $1,726,526.50, which elevates the value of the project by $57,326.50, so that the hypothesis of financial viability of the project is proven. The study concludes that it is possible to increase the profitability in the cultivation of avocado with the use of solid rain, in order to deal with water scarcity and price volatility in the study period.
Deep Reinforcement Learning for Optimal Sailing Upwind

We describe the application of deep reinforcement learning (DRL) methods to determine the optimal decision policy when sailing a sailboat towards a target point located upwind from the boat's current position, under the conditions of wind direction and speed that vary according to an unknown stochastic process, as is typical in real sailing races. A model of the dynamics of the sailboat is described together with a suitable choice of actions, in the form of a Markov decision process (MDP), which allows the application of a wide variety of DRL algorithms. Empirical results show that the learned policy outperforms baseline control algorithms that do not take into consideration the variability in wind strength and direction, and instead assume that the current wind conditions will persist indefinitely.
H FALL DETECTION SYSTEM USING YOLO VERSION 3

— In this paper, With the increase of the elderly population, the phenomenon of the elderly falling at home or out is more and more common. Therefore, fall detection is of great significance for the health protection of the elderly. Throughout the research of fall detection at home and abroad, most of the fall detection based on video monitoring is complex and redundant, which affects the real-time and accuracy of detection. Given the above problems, this paper proposes a fall detection method based on a video in a complex environment, aiming to detect fall behavior more accurately and quickly. The main work of this paper is as follows: firstly, the YOLOv3 network model is proposed for the detection algorithm. Secondly, the human fall detection data set is constructed by referring to the Pascal VOC data set format. Then, the algorithm model is optimized and trained in GPU (graphic processing unit) deep learning server. Finally, a comparison of test results with our YOLOv3 network model and other detection algorithms shows that our detection algorithm has a good recognition effect.
ORIENT: Submodular Mutual Information Measures for Data Subset Selection under Distribution Shift

Real-world machine-learning applications require robust models that generalize well to distribution shift settings, which is typical in real-world situations. Domain adaptation techniques aim to address this issue of distribution shift by minimizing the disparities between domains to ensure that the model trained on the source domain performs well on the target domain. Nevertheless, the existing domain adaptation methods are computationally very expensive. In this work, we aim to improve the efficiency of existing supervised domain adaptation (SDA) methods by using a subset of source data that is similar to target data for faster model training. Specifically, we propose O RIENT , a subset selection framework that uses the submodular mutual information (SMI) functions to select a source data subset similar to the target data for faster training. Additionally, we demonstrate how existing robust subset selection strategies, such as G LISTER , G RAD M ATCH , and C RAIG , when used with a held-out query set, fit within our proposed framework and demonstrate the connections with them. Finally, we empirically demonstrate that SDA approaches like d -SNE, CCSA, and standard Cross-entropy training, when employed together with O RIENT , achieve a) faster training and b) better performance on the target data.
Towards Balanced Information Propagation in Social Media

As people increasingly rely on social media platforms such as Twitter to consume information, there are significant concerns about the diversity of news consumption. Users may narrow their attention to posts which reinforce their pre-existing views, which could lead to a more fragmented society. Aiming to combat this, earlier work divided news on a given story into high consensus and low consensus posts, based on how similar reactions can be expected from users with different political views: high consensus news elicits similar reactions, whereas low consensus news elicits different reactions from readers depending on their political leanings. In this work, we propose and quantify the benefits of a strategy to spread high consensus news across readers with diverse political leanings. We first compile a dataset and make the following three key observations: (1) low consensus news is more likely to remain within subgroups of users with similar political leanings, whereas high consensus news spreads more across subgroups; (2) high consensus news posted by neutral publishers spreads more equally across subgroups; and (3) users that get the information from other users instead of the publishers, get an even more biased exposure to news. Then, we propose a strategy that spreads high consensus news through neutral publishers, and quantify the significant decrease in the disparity of users’ news exposure. Our extensive experiments on Twitter shows that seeding high consensus information with neutral publishers is an effective way to achieve high spread with little disparity regarding political leaning.
Phyto-phospholipid Complexes (Phytosomes): A Novel Option to Enhance the Bioavailability of Plants' Pharmacologically Active Chemical Components

: Phytosomes a little “cell-like structure” which is made up of a molecular complex of phospholipids with phytoconstituents. They serve as a link between traditional and novel formulations. Phytosomes, which are lipid-based nanocarriers, have a crucial function in strengthening the pharmacokinetic and pharmacodynamic properties of herbal-derived polyphenolic compounds, making this nanotechnology a promising tool for the development of novel formulations. Bioavailability is a significant concern in improving bio-efficacy in the transport of dietary phytochemicals. Phospholipids work as emulsifiers by increasing the hydrophilicity and lipophilicity of phytoconstituents. For enhanced absorption and bioavailability of natural phytoconstituents, a balance of hydrophilicity (helps phytoconstituents dissolve in digestive fluids) and lipophilicity (helps phytoconstituents to penetrate lipid-based cell membranes) is essential. The enormous potential of emerging nanotechnology in the delivery of bioactive phytochemicals is examined, with a special focus on phytosomes as a novel lipid-based nanocarrier. The current review represents an overview of the vesicular system and its application, with emphasis on formulation consideration and characterization of the phyto-phospholipid complex.
Building an Open, Robust, and Stable Voting-Based Domain Top List

Domain top lists serve as critical resources for the Internet measurement, security, and privacy research communities. Hundreds of prior research studies have used these lists as a set of supposedly popular domains to investigate. However, existing top lists exhibit numerous issues, including a lack of transparency into the list data sources and construction methods, high volatility, and easy ranking manipulation. Despite these ﬂaws, these top lists remain widely used today due to a lack of suitable alternatives. In this paper, we systematically explore the construction of a domain top list from scratch. Using an extensive passive DNS dataset, we investigate different top list design considerations. As a product of our exploration, we produce a voting-based domain ranking method where we quantify the domain preferences of individual IP addresses, and then determine a global ranking across addresses through a voting mechanism. We empirically evaluate our top list design, demonstrating that it achieves better stability and manipulation resistance than existing top lists, while serving as an open and transparent ranking method that other researchers can use or adapt.
Nanotechnology in prosthodontics: A game changer

: Nanotechnology refers to the branch of science and engineering involved in designing and producing, structures and devices by manipulating atoms and molecules at nanoscale dimensions i.e. of the order of 100 nanometres or less. Due to a very high surface area to volume ratio of nanoparticles, new materials with superior properties and effects can be produced compared to their larger counterparts. The tremendously growing scientific and innovative research, has led to the development of nanomaterials with advanced bioapplications. Inorganic nanoparticles, exhibit significantly novel and improved physical, chemical and biological properties, phenomena and functionality due to their Nano scale size. The use of nano phasic and nanostructured materials is increasing manifold due to their potential for achieving specific processes and selectivity, especially in biological and pharmaceutical application. Specifically formulated nanomaterials have become very popular in the design and development of many dental materials so as to improve their chemical, physical and mechanical properties. Their unique phenomena enable the dentists to execute curative and reconstructive procedures at the cellular and molecular levels enabling the maintenance of near-perfect oral health and uplift the standard of medicine. This article tries to give an insight of the applications and recent developments of nano products with superior quality in the field of prosthodontics, Implant materials, Bone replacement materials and Maxillofacial prosthesis. The evolution of nanotechnology will help dentists with more precision made materials, prosthesis and equipments and to illustrate their potentially far-reaching impact on clinical dental practice. In this article, we have made an attempt to have a glimpse on impact of nanotechnology in the field of prosthodontics.
INTELLECTUAL PROPERTY RIGHTS KNOWLEDGE AND AWARENESS – ACADEMIC LEVEL EMPIRICAL ANALYSIS AND RECOMMENDATIONS

Commitment to Intellectual Property Rights (IPR) is a segment of students’ positive or negative behavior in the academic community. We define the negative behavior as students’ commitment to plagiarism and copyright violation. The overall objective of this paper is to give a recommendation to the non-IPR institutions that potentially show similar results in the level of awareness of students and their behavior regarding their academic program and teaching activities. This follows an empirical testing and analysis of the relationship between knowledge and the level of IPR awareness, and their influence over students’ behavior at a non-IPR committed Faculty. In this regard, the paper tests if students that have higher level of awareness and knowledge tend to demonstrate a positive behavior towards IPR more frequently. We use an online survey to collect data and construct scales to measure the level of awareness, knowledge and the type of behavior. Treating the constructed scales as categorical data, we apply log-linear analysis. Results showed that the higher the awareness level, the more frequent the positive behavior. On the other hand, the analysis of the knowledge of students does not provide clear findings; however, it is associated with the level of awareness. The negative behavior, as concluded from the sample, arises from the high cost of acquiring the basic studying material. Nevertheless, understanding students’ knowledge, awareness and behavior towards IPR could assist Faculties to implement policies for decreasing students’ negative behavior, promoting academic integrity and improving students’ ethics.
Crustal and uppermost mantle structure of the NW Namibia continental margin and the Walvis Ridge derived from ambient seismic noise

The Walvis Ridge (WR) is the most prominent hotspot track related to the opening in the South Atlantic Ocean. Several hypotheses have been developed to explain its origin and evolution. The presence of a massive magmatic structure at the landfall of the WR in Northwest Namibia raised speculation about the role of a hotspot during the opening of the South Atlantic ocean. To investigate its deeper velocity structure at the junction of the WR with the African continent was the focus of the amphibious seismological WALPASS experiment. In total 12 ocean-bottom seismometers and 28 broad-band land stations were installed between 2010 and 2012 to acquire seismological data. Here, we present the results of seismic ambient noise tomography to investigate to which extent the Tristan hotspot modiﬁed the crustal structure in the landward prolongation of the ridge and in the adjacent oceanic basins. For the tomography, vertical and hydrophone component cross correlations for > 300 d for OBS stations and between 1 and 2 yr for land stations data were analysed. More than 49 000 velocity measurements (742 dispersion curves) were inverted for group velocity maps at 75 individual signal periods, which then had been inverted for a regional 3-D shear wave velocity model. The resulting 3-D model reveals structural features of the crust related to the continent–ocean transition and its disturbance caused by the initial formation of the WR ∼ 130 Ma. We found relatively thick continental crust below Northwest Namibia and below the near-shore part of the WR, a strong asymmetry offshore with typical, thin oceanic crust in the Namibe Basin (crossing over into the Angola Basin further offshore) to the North and a wide zone of transitional crust towards the Walvis Basin south of the WR.