Miracle-dz/UniFAR-FTUs huggingface dataset:
Learned feature embeddings for non-line-of-sight imaging and recognition

Objects obscured by occluders are considered lost in the images acquired by conventional camera systems, prohibiting both visualization and understanding of such hidden objects. Non-line-of-sight methods (NLOS) aim at recovering information about hidden scenes, which could help make medical imaging less invasive, improve the safety of autonomous vehicles, and potentially enable capturing unprecedented high-definition RGB-D data sets that include geometry beyond the directly visible parts. Recent NLOS methods have demonstrated scene recovery from time-resolved pulse-illuminated measurements encoding occluded objects as faint indirect reflections. Unfortunately, these systems are fundamentally limited by the quartic intensity fall-off for diffuse scenes. With laser illumination limited by eye-safety limits, recovery algorithms must tackle this challenge by incorporating scene priors. However, existing NLOS reconstruction algorithms do not facilitate learning scene priors. Even if they did, datasets that allow for such supervision do not exist, and successful encoder-decoder networks and generative adversarial networks fail for real-world NLOS data. In this work, we close this gap by learning hidden scene feature representations tailored to both reconstruction and recognition tasks such as classification or object detection, while still relying on physical models at the feature level. We overcome the lack of real training data with a generalizable architecture that can be trained in simulation. We learn the differentiable scene representation jointly with the reconstruction task using a differentiable transient renderer in the objective, and demonstrate that it generalizes to unseen classes and unseen real-world scenes, unlike existing encoder-decoder architectures and generative adversarial networks. The proposed method allows for end-to-end training for different NLOS tasks, such as image reconstruction, classification, and object detection, while being memory-efficient and running at real-time rates. We demonstrate hidden view synthesis, RGB-D reconstruction, classification, and object detection in the hidden scene in an end-to-end fashion.
The Impact of Outsourcing Schools in Liberia to Bridge International Academies after Three Years1

Outsourcing the management of 23 randomly-selected government primary schools in Liberia to Bridge International Academies led to learning gains of 0. 35σ after three years, equivalent to reading roughly 2.2 additional words per minute. Beyond learning gains, Bridge increased dropout by more than half and reduced transition to secondary school (overall, Bridge had a -6.53 percentage point effect on the probability of being enrolled in any school after three years). Bridge had no statistically significant impact on corporal punishment and failed to reduced sexual abuse. Overall, any assessment of outsourcing public schools to Bridge must weigh its modest learning gains against its high operating costs and negative effects on access to education via increased dropout. 1We are grateful to the Minister George K. Werner and his team, Minister Prof. Ansu. D Sonii, Sr and his team, the Partnership Schools for Liberia (PSL) team, Susannah Hares, Robin Horn, and Joe Collins from Ark EPG, and the team at Social Finance for their commitment throughout this project to ensuring a rigorous and transparent evaluation of the PSL/LEAP program. We are especially grateful to Wayne A. Sandholtz for his collaboration in the early stages of this project and subsequent discussions. Thanks to Arja Dayal, Dackermue Dolo, and their team at Innovations for Poverty Action who led the data collection. Avi Ahuja, Miguel Ángel Jiménez-Gallardo, Dev Patel, Rony RodríguezRamírez, and Benjamin Tan provided excellent research assistance. We are grateful to Laura Johnson who provided guidance on the sexual violence survey module and protocol. A randomized controlled trials registry entry is available at: https://www.socialscienceregistry. org/trials/1501 as well as the pre-analysis plan. IRB approval was received from IPA (protocol #14227) and the University of Liberia (protocol #17-04-39) prior to any data collection. UCSD IRB approval (protocol #161605S) was received after the first round of data collection but before any other activities were undertaken. The evaluation was supported by the UBS Optimus Foundation, Aestus Trust, and the UK’s Economic and Social Research Council (grant number ES/P00604). Romero acknowledges financial support from the Asociación Mexicana de Cultura. The views expressed here are ours, and not those of the Ministry of Education of Liberia or our funders. All errors are our own.
UNDERSTANDING SOCIAL MEDIA EFFECTS ACROSS DIFFERENT PARTIES’ INTERACTIONS

This study examined the contagion effects of social media on different parties, such as producers, retailers, and consumer. Further, we empirically investigate the contagion effects of the social media and their ultimate impact on multiple performance meas - ures. The findings give new insights into the contagion effects of social media usage across the distribution channel and important social influence mechanisms that enhance these effects. In line with the hypothesis, efficient use of social media contributes to retailer and brand performance, and consumer–retailer loyalty. In light with the advancement of technologies and growth in social media applica - tions, this study provides a framework to promote usage by supply channel, which ultimately influences performance-related outcomes.
Spatial Disaggregation Using Geo-Referenced Social Media Data as Ancillary Information

Although statistical information on socio-economic activities is widely available, the data are often collected or released only at a relatively aggregated level. Using aggregated data usually masks important local hotspots, and overall tends to over-smooth spatial variations in impact. For these reasons, we often need to disaggregate the source data, in order to provide more localized estimates. In the context of spatial analysis, spatial disaggregation or spatial downscaling are techniques that can be used to transform data from a set of source zones into a set of target zones, with different geometry and with a higher general level of spatial resolution. This paper presents a spatial downscaling/disaggregation technique which combines state-of-the-art regression analysis procedures with the classic methods of dasymetric mapping and pycnophylactic interpolation. This procedure was used together with ancillary data, like population density, land coverage, nighttime light emissions, OpenStreetMap road density, or geo-referenced data extracted from Flickr, to disaggregate different types of socio-economic indicators.
Asset Allocation Implementation with Structured Guaranteed Investments-Proceedings AFIR 1996-Nürnberg, Germany

Starting with empirical studies which show, that at least in the long run, investments in stocks achieve a noticeably higher average return than investments in bonds, we discuss alternative innovative investments to manage the higher risks incorporated with stocks compared to bonds. We show, that all three described structured guaranteed instruments are equivalent from an economic point of view and that their main difference is their institutional setup, i.e. how the capitaland FX protection is build or tailored into the different investment products to fulfill different needs of different customer types. Building the bridge to the efficient market theory, where prices serve as proper signals for relevant information, and where marketversus single stock selection is regarded as superior, we show that structured guaranteed products provide a sound method to implement an asset allocation decision from fixed income instruments to equity markets in a highly efficient and cost effective way. This is especially the case for an international asset allocation, when for example domestic or foreign bonds are switched into international equity markets for the reason to achieve a higher return. We find that those investments provide an ideal method to manage or to reduce the tremendous increase in risk attached to this shift, even without selling the unwanted assets. We also show, that in this context the modern innovative investments can be understood as portfolio insurance instruments, where all three described products create economically comparable capitalor downside risk protection.
Experience and Job Satisfaction among Bachelor and Master Degree holder Head Teachers at Elementary Level in Pakistan

The general intent of this descriptive study was twofold. First, the study was to measure general job satisfaction of government elementary school head teachers in context of their qualification and experience. Secondly, the influence of the head teachers’ qualification and experience on their job satisfaction was considered. Survey design was used to obtain needed information. The head teachers working in government elementary schools at district Toba Tek Singh in the Punjab, Pakistan, were asked to take part in the survey. The Urdu version of the modified Minnesota Satisfaction Questionnaire (MSQ) was used as a research tool. The head teachers were generally satisfied with their positions and no job satisfaction differences among the head teachers were found for degree status. The head teachers with minimum (less than 11 years) and maximum (more than 15 years), experience were found significantly more satisfied than the head teachers with medium (11-15 years) experience. As the head teachers were found satisfied with their positions, therefore, policy makers and other concerned authorities should put forth effort to either maintain this level of satisfaction or increase it to a higher level in order to promote positive perceptions for this important position through an attachment of some more benefits to higher qualification. The results of the study show that head teachers with medium experience were less satisfied than the head teachers with minimum and maximum experience; therefore, it seems prudent therefore that government should make plans to enhance the level of job satisfaction of the medium experienced head teachers. Studies on satisfaction to investigate more predictors to job satisfaction should be conducted with a special focus on the reason why satisfaction increases with minimum and maximum experience while decreases with medium
Translative and Multiplicative Interpretation of Neutrosophic Cubic Set

In this paper, we introduce the idea of neutrosophic cubic translation (NCT) and neutrosophic cubic multiplication (NCM) and provide entirely new type of conditions for neutrosophic cubic translation and neutrosophic cubic multiplication on BF-algebra. This is the new kind of approach towards translation and multiplication which involves the indeterminacy membership function. We also define neutrosophic cubic magnified translation (NCMT) on BF-algebra which handles the neutrosophic cubic translation and neutrosophic cubic multiplication at the same time on membership function, indeterminacy membership function and non-membership function. We present the examples for better understanding of neutrosophic cubic translation, neutrosophic cubic multiplication, and neutrosophic cubic magnified translation, and investigate significant results of BF-ideal and BF-subalgebra by applying the ideas of NCT, NCM and NCMT. Intersection and union of neutrosophic cubic BF-ideals are also explained through this new type of translation and multiplication
THE EUROPEAN ELECTRICITY SYSTEM COUGHT BETWEEN AUTARKY ENDEVOURS

The European member states agreed on ambitious climate targets of 80-95% reduction of green-house gas emissions by 2050. Next to these ambitious goals, the European member states agreed on the target of a joint market for electricity spanning over the whole of Europe. In contrast to those earlier commitment the member states are striving for autarky of their electricity system, neglecting the interconnected nature of the European electricity grid. At the same time, we see more and more countries which are in serious danger of failing their own green-house gas emission and renewable targets for the upcoming years of 2020 and 2030.
A NOTE ON GENERAL RADICALS OF PARAGRADED RINGS

We discuss the general theory of radicals of paragraded rings, establish that the ADS-Theorem holds, and characterize paragraded normal radicals. It is known that any special radical of a ring can be described by the appropriate class of modules over that ring. In this note we show that all special paragraded radicals of paragraded rings can be described by the appropriate class of their paragraded modules.
Policy Evaluation : Assessing Innovation Systems and Programs 7

As is the case with all public policies, science, technology, and innovation programs and policies need proper evaluation. Evidence-based policy making has become increasingly important in recent years, with new techniques being developed to assess individual programs and policies and new methods being adopted to undertake cross-country comparisons of innovation systems. Evaluation has to take place throughout the policy process. Rigorous evaluation is not always easy to achieve, especially for developing countries, because it is resource and data intensive. The benefits, however, can be considerable and can enable public funds to be put to their best use. It is very difficult to measure the impacts and benefits of innovation policies. Many policies are related, and innovation systems are complex and evolve continuously. As a result, it is often hard to measure outcomes, but such evidence is crucial to establishing whether government policy successfully tackles market failures and provides a positive net stimulus to innovation. Effective evaluation programs should therefore seek to combine a range of evaluation methods, including national and international, quantitative and qualitative, and microand macrolevel. This chapter reviews a range of techniques used to gain information on innovation by firms and economies as a whole and to evaluate programs and policies. It first reviews several widely used international benchmarking indexes that attempt to quantify innovation or innovation capabilities of economies as a whole. The chapter then turns to look at innovation surveys as a means of
Electromagnet Separator of Different Particles

In this paper we simulate and realize an electromagnetic separator excited with an alternating current. And, we will to see the factors that influence the separation. This realization is used for the separation of the ferromagnetic and non ferromagnetic particles. The aim of this application is to recover the particles of non ferrous and ferrous from mixture by attractive magnetic force with big rate of separation. Experiments’ results and simulation of this separator, finally the parameters of separation are optimized with prediction method is presented in this paper.
Neural Networks Classification for Breast Cancer Analysis

Cancer is caused by a lump from a collection of cells that grows and attacks the surrounding tissue. Most breast lumps are benign, but benign breast lumps can increase the risk of developing breast cancer. Early detection can provide better handling. The diagnosis of breast cancer by a doctor is done by analyzing several factors. To help the doctor in diagnosing the data efficiently, this study implemented machine learning. The diagnosis was based on the features of digital image computation in the process of fine-needle aspiration (FNA) of a breast mass. The data came from 569 patients. The study used the neural networks classification method with multilayer perceptron algorithms. The results were obtained that the use of neural networks gave higher accuracy if it compared it in the ZeroR method. Their accuracies were 95.96%, and 62.74%, respectively.
Pricing and capital allocation for unit-linked life insurance contracts with minimum death guarantee

Concentrating on the minimum death guarantee, this paper first compares the actuarial and financial approaches for pricing guarantees in unitlinked life insurance contracts. We end up with the conclusion that, at least at the reinsurer level, only the actuarial approach can be consistently applied. Using Monte Carlo simulations, we then obtain the probability distribution of the future costs associated with this guarantee. This allows us to determine the necessary elements to fill in a cash flow model that is used to fix the price for this cover. Using a cash flow model raises some questions that are not completely resolved yet. How much capital should we allocate? How to release it? What is the cost of capital? In this paper, we propose simple solutions but the questions are still open.
Cross-View Gait Recognition Based on U-Net

Gait based recognition systems allow automatic subjects’ recognition by using the way of walking. However, the performance of these systems is often degraded by some covariate factors such as walking direction, appearance changes, occlusions, among others. From these, it has been shown that change in appearance is the most influent covariant by drastically affecting the recognition performance. Consequently, inspired by the great successes of GANs in image translation tasks, we propose a method of gait recognition using a conditional generative model to generate view-invariant features. The proposed method is evaluated on one of the largest datasets available under the variations of view, clothing and carrying conditions: CASIA gait database B. Experimental results show that the proposed method outperforms state-of-the-art methods specially in carrying-bag and wearing-coat sequences. The full implementation and trained networks are available at https://gitlab.com/IsRaTiAl/gait
Nighttime Light Predictions from Satellite Imagery

The lack of reliable poverty data in developing countries poses a major challenge for making informed policy decisions and allocating resources effectively in those areas of the world. However, one good indicator of both economic development and population density is nighttime light intensity. In this paper, we present a method of predicting the nighttime light intensity distribution of a region given the corresponding daytime satellite image. We ﬁne-tune a Convolutional Neural Network (CNN) pre-trained on Ima-geNet, and then test the classiﬁer’s performance in predicting values of nighttime light intensity. Experiments show that we can achieve up to 70% accuracy on a three-class (low, medium, high nightlights intensity) classiﬁcation task. The eventual goal is to use the CNN features learned from the nightlights classiﬁcation task to predict indicators of interest to the international scientiﬁc community, such as poverty, wealth, or health outcomes.
Kinematics of Breaking Waves

One important kinematic properties of breaking waves is the wave celerity. Constant wave celerity has been used for the wave breaking criterion by many researchers. However, this approach does not consider the variation of wave celerity at different phases before breaking. Hence, this paper examines the aspects of the wave breaking criterion and dynamics of wave celerity before wave breaking. Breaking waves were generated using the JONSWAP focused spectrum and a semi-empirical formula for the wave celerity estimation was established.
Data-Aware Declarative Process Mining for Malware Detection

Mobile devices have become, in the last years, an essential tool used to perform daily activities. However, they also have become the target of continuous malware attacks usually coming out from new malware obtained as a variant of existing ones. For this reason, we suppose that by comparing the behavior of a new application with those of known malware applications it is possible to define it as malicious or trusted. According to this, the current study proposes an approach based on a data-aware declarative process mining technique to identify similarities and recurring patterns in the system call traces generated by a set of malicious mobile applications. The obtained characterization, represented by a set of declarative constraints within their data attributes, can be considered as a run-time fingerprint of a malware useful to evaluate the membership of a new application to a given malware family. The empirical validation of the proposed approach is performed on a dataset of more than 1200 trusted and malicious applications coming out from eight malware families and the obtained results show a very good discrimination ability.
A Speculative Trading Model of Electricity Market

The valiability of renewable energies is a major disadvantage because there is a characteristic that power supply must always be consistent with demand. In order to disseminate renewable energy in the power market, a mechanism to cope with this instability is indispensable. The unstable supply of renewable energy indicates that the cost for adjusting supply and demand will increase. It is necessary to be able to trade electricity until just before generated.
Foldmatch: Accurate and High Fidelity Garment Fitting Onto 3D Scans

In this paper, we propose a new template fitting method that can capture fine details of garments in target 3D scans of dressed human bodies. Matching the high fidelity details of such loose/tight-fit garments is a challenging task as they express intricate folds, creases, wrinkle patterns, and other high fidelity surface details. Our proposed method of non-rigid shape fitting – FoldMatch – uses physics-based particle dynamics to explicitly model the deformation of loose-fit garments and wrinkle vector fields for capturing clothing details. The 3D scan point cloud behaves as a collection of astrophysical particles, which attracts the points in template mesh and defines the template motion model. We use this point-based motion model to derive regularized deformation gradients for the template mesh. We show the parameterization of the wrinkle vector fields helps in the accurate shape fitting. Our method shows better performance than the stateof-the-art methods. We define several deformation and shape matching quality measurement metrics to evaluate FoldMatch on synthetic and real data sets.
A review on analytical methods for the determination of natural colorants (green) in food commodities and beverages

A wide variety of natural colorants are generally used for coloring food stuffs to make them more attractive, informative and reinstate the original color lost during the production process. However, in many countries the usage of natural colorants is limited due to their low stability. Green is one of the most important secondary colors as far as consumer goods are concerned due to looking fresh and natural. Chlorophyll and its metal derivatives like copper or zinc chlorophylls have been used as green colorant in food and pharmaceuticals. The regulatory authorities and food analysts have adopted precise analytical methods in assessing quality and safety of food products. These techniques mainly deal with determination of quality, quantity, colorant stability, safety and utility limits of colorants in food stuffs. These aspects are sensitively regulated by regulatory authorities as well as domestic and food suppliers. In this review, we discuss various extraction methods that are executed for extracting the chlorophyll and metallochlorophyllins from complex food commodities and beverages. In addition, different extraction methods used for food commodities have been presented. Exclusively the present manuscript reviews various analytical techniques used for the analysis of Na-Cu-Chlorophyllin (Na-Cu-Chl) employed in food products as food colorant.