Miracle-dz/UniFAR-FTUs huggingface dataset:
Prognostic Significance of HPV DNA and p16INK4a in Anal Cancer: A Systematic Review and Meta-Analysis

We conducted a systematic review and meta-analysis of observational studies evaluating survival in patients with anal cancer, according to human papillomavirus (HPV) DNA, p16INK4a, and combined HPV DNA/p16INK4a status. We systematically searched PubMed, EMBASE, and Cochrane Library databases to identify studies published in English until July 25, 2018, directly providing or allowing estimation of survival of patients with anal cancer according to the presence of HPV DNA and/or overexpression of p16INK4a. We estimated pooled HRs and 95% confidence intervals (CI) for overall survival (OS) using a random-effects model. We included 16 studies, comprising 1,724 patients with anal cancer tested for HPV DNA (65% positive), and 567 patients tested for p16INK4a (87% positive). The pooled HR for OS was 0.54 (95% CI, 0.33–0.89) for HPV DNA positive versus negative, 0.37 (95% CI, 0.24–0.57) for p16INK4a positive versus negative, and 0.36 (95% CI, 0.22–0.58) for HPV DNA positive/p16INK4a positive versus HPV DNA positive/p16INK4a negative patients with anal cancer. Patients with HPV DNA or p16INK4a positive anal cancer have significantly better OS compared with HPV DNA or p16INK4a negative. This points to the possible value of HPV DNA and/or p16INK4a testing when planning the management and follow-up strategy for patients diagnosed with anal cancer.
The Effects of Community Home Visit and Peer Group Nutrition Intervention Delivery Platforms on Nutrition Outcomes in Low and Middle-Income Countries: A Systematic Review and Meta-Analysis

Weak delivery systems reduce the potential of evidence-supported interventions to improve nutrition. We synthesized the evidence for the effectiveness of nutrition-specific intervention delivery platforms for improving nutrition outcomes in low and middle-income countries (LMIC). A systematic literature search for studies published from 1997 to June 2018 resulted in the inclusion of 83 randomized controlled trials (RCTs), quasi-randomized, and controlled before–after studies across a variety of delivery platforms. In this paper, we report on meta-analysed outcomes for community health worker (CHW) home visits and mother/peer group delivery platforms. Compared to care as usual, CHW home visits increased early initiation of breastfeeding (EIBF) (OR: 1.50; 95% CI: 1.12, 1.99; n = 10 RCTs) and exclusive breastfeeding (EBF) (OR: 4.42; 95% CI: 2.28, 8.56; n = 9 RCTs) and mother/peer groups were effective for improving children’s minimum dietary diversity (OR: 2.34; 95% CI: 1.17, 4.70; n = 4) and minimum meal frequency (OR: 2.31; 95% CI: 1.61, 3.31; n = 3). Pooled estimates from studies using both home visit and group platforms showed positive results for EIBF (OR: 2.13; 95% CI: 1.12, 4.05; n = 9), EBF (OR: 2.43; 95% CI: 1.70, 3.46; n = 12), and < 5 wasting (OR: 0.77; 95% CI: 0.67, 0.89; n = 4). Our findings underscore the importance of interpersonal community platforms for improving infant and young child feeding practices and children’s nutritional status in LMICs.
Analyzing Relationship Between Unemployment and Growth For Selected OECD Countries Through Panel Data

Being able to examine the research question of this study, whether there is a correlation between unemployment and growth for selected certain OECD member countries, panel data econometrics have been practiced. In the light of empirical findings, while there is a relationship from unemployment to growth, on the other hand, there is no link from growth to unemployment has been reported. Although there is somehow a harmony with other studies existing in the literature in this area, there has been still some conflicts with the rest of them. In some of the reviews, conversely ours, the link from growth to unemployment has been explicitly reported. It is thought that the distinction between this study and other conflicting ones might have arisen from the sample or the method used. It was concluded that there would be no clear dynamics of the relationship between unemployment and growth. Therefore, it was observed that the selected sample and the method used were directly effective on the results. Key Words: Economic Growth, Unemployment, Panel Data Econometrics Introduction JEL Classification: O10, E24, C32
Randomized SPENs for Multi-Modal Prediction

Energy-based prediction defines the mapping from an input x to an output y implicitly, via minimization of an energy function E x (y). In certain machine learning tasks, such as information retrieval or recommender systems, we do not predict an individual value for y, but instead a set of diverse predictions. Unfortunately, extending energy-based prediction to this context is nontrivial. One available approach is to first learn the energy function by conditional density estimation of y given x, and then gather a set of predictions by sampling from the conditional distribution or finding its multiple modes. For structured prediction energy networks (Belanger & McCallum, 2016), density estimation is intractable, however, and approximate approaches require slow, difficult-to-tune MCMC. In response, this paper presents an alternative approach, where we directly learn a randomized predictor end-to-end such that it can be used to yield diverse sets of high-quality predictions.
How Can We Be So Dense? The Robustness of Highly Sparse Representations

Neural networks can be highly sensitive to noise and perturbations. In this paper we suggest that high dimensional sparse representations can lead to increased robustness to noise and interference. A key intuition we develop is that the ratio of the match volume around a sparse vector divided by the total representational space decreases exponentially with dimensionality, leading to highly robust matching with low interference from other patterns. We analyze efficient sparse networks containing both sparse weights and sparse activations. Simulations on MNIST, the Google Speech Command Dataset, and CIFAR-10 show that such networks demonstrate improved robustness to random noise compared to dense networks, while maintaining competitive accuracy. We propose that sparsity should be a core design constraint for creating highly robust networks.
Conventional Industrial Robotics Applied to the Process of Tomato Grafting Using the Splicing Technique

Horticultural grafting is routinely performed manually, demanding a high degree of concentration and requiring operators to withstand extreme humidity and temperature conditions. This article presents the results derived from adapting the splicing technique for tomato grafting, characterized by the coordinated work of two conventional anthropomorphic industrial robots with the support of low-cost passive auxiliary units for the transportation, handling, and conditioning of the seedlings. This work provides a new approach to improve the efficiency of tomato grafting. Six test rates were analyzed, which allowed the system to be evaluated across 900 grafted units, with gradual increases in the speed of robots work, operating from 80 grafts/hour to over 300 grafts/hour. The results obtained show that a higher number of grafts per hour than the number manually performed by skilled workers could be reached easily, with success rates of approximately 90% for working speeds around 210–240 grafts/hour.
Reinforced Curriculum Learning on Pre-trained Neural Machine Translation Models

The competitive performance of neural machine translation (NMT) critically relies on large amounts of training data. However, acquiring high-quality translation pairs requires expert knowledge and is costly. Therefore, how to best utilize a given dataset of samples with diverse quality and characteristics becomes an important yet understudied question in NMT. Curriculum learning methods have been introduced to NMT to optimize a model's performance by prescribing the data input order, based on heuristics such as the assessment of noise and difficulty levels. However, existing methods require training from scratch, while in practice most NMT models are pre-trained on big data already. Moreover, as heuristics, they do not generalize well. In this paper, we aim to learn a curriculum for improving a pre-trained NMT model by re-selecting influential data samples from the original training set and formulate this task as a reinforcement learning problem. Specifically, we propose a data selection framework based on Deterministic Actor-Critic, in which a critic network predicts the expected change of model performance due to a certain sample, while an actor network learns to select the best sample out of a random batch of samples presented to it. Experiments on several translation datasets show that our method can further improve the performance of NMT when original batch training reaches its ceiling, without using additional new training data, and significantly outperforms several strong baseline methods.
Risk reduction or private gain ? by

We examine the motives that shape an NGO’s decision to diversify the activities it undertakes. We identify two primary motivations, namely the aim to reduce idiosyncratic risks related to future funding and the desire to gain private benefits. We incorporate both in a theoretical framework and derive predictions regarding how an NGO responds to a change in incentives offered by their stakeholders. We predict that under an endogenous incentive scheme, the reaction of the agent will depend on the relative magnitude of the two motivators. We exploit the 2007 historic flood in Uganda and the surge in the international aid to affected areas to examine how Ugandan NGOs change their diversification behaviour following the change in incentives from contractual funding. We find diversification is negatively related to financial incentives from stakeholders. Combining with our theoretical predictions, the result suggests that NGOs diversify mainly to reduce risks rather than to capture private gains, including ability signalling, prestige, career concerns or altruism. Policies targeting financial stability for grassroots NGOs are highly recommended. JEL Classification: L25, L31
New record of Mastotermitidae from Fonseca Basin, Eocene-Oligocene boundary of southeastern Brazil

The first representative of the genus Mastotermes Froggatt, 1897 from the Cenozoic of the South America is described here based on seven specimens from the Fonseca Formation, Eocene-Oligocene boundary, Minas Gerais State, southeastern Brazil. Today, Mastotermes is geographically restricted to northern Australia, with only one relict species, Mastotermes darwiniensis Froggatt, 1897. However, its fossil record clearly shows a global distribution, with species ranging from the Cretaceous of Myanmar, Russia, Mongolia and China, throughout Cenozoic of mainland Europe and Miocene of Africa, Mesoamerica and the Caribbean. Along with Spagotermes costalimai Emerson, 1965, Mastotermes brasiliensis sp. n. is the second fossil termite recorded in the Fonseca strata. This discovery extends the paleo-distribution of this genus into Neotropical South America during the Paleogene.
A microfluidics study of surfactant flooding at optimum and under-optimum conditions

This paper investigates surfactant flooding using a microfluidic device. Its purpose is to validate the results obtained in recent core experiments reported by Janssen et al. (2019) and provide mechanistic interpretation. In these experiments authors injected a surfactant slug into sandstone cores, initially brought to residual oil saturation. Low oil/water IFT leads to oil mobilization and the formation of emulsions. Furthermore, authors found that oil is more effectively mobilized when the injected surfactant is at optimum salinity. In this study the following experiments are subsequently conducted in a microfluidic device to validate this optimum surfactant slug: primary drainage, waterflooding and surfactant flooding. In case of the surfactant flooding experiment, the micro-emulsion formation was observed at 1.5 PV and 1.25 PV for the under-optimum and optimum conditions respectively. In both conditions, an increased oil mobilization was obtained, compared to the waterflooding experiment. However, the optimum condition, with a slug salinity of 2.0 wt% NaCl outperformed the under-optimum condition with a slug salinity of 0.4 wt% NaCl in terms of oil recovery. The increased salinity in case of the optimum condition results in an lower IFT compared to the under-optimum condition, which in turn results in an higher capillary number. The obtained results indicate a dependency between the capillary number and the amount of oil droplets. The optimum capillary number can be found at the lowest oil saturation with the largest amount of droplets. Based on the results obtained from the conducted experiments on this microfluidic device, upscaling is considered to be a viable and educated recommendation.
A Sinkhorn-Knopp Fixed Point Problem ∗

We consider a fixed point problem that results from the Sinkhorn-Knopp Algorithm for transforming matrices into doubly stochastic matrices. We analytically find solutions for simpler cases, including the circulant case, we develop and prove a number of properties for the general case, and we examine how solutions to the fixed point problem relate to those of the Sinkhorn-Knopp Algorithm.
A Multimodal Event-Driven LSTM Model for Stock Prediction Using Online News

In finance, it is believed that market information, namely, fundamentals and news information, affects stock movements. Such media-aware stock movements essentially comprise a multimodal problem. Two unique challenges arise in processing these multimodal data. First, information from one data mode will interact with information from other data modes. A common strategy is to concatenate various data modes into one compound vector; however, this strategy ignores the interactions among different modes. The second challenge is the heterogeneity of the data in terms of sampling time. Specifically, fundamental data consist of continuous values sampled at fixed time intervals, whereas news information emerges randomly. This heterogeneity can cause valuable information to be partially missing or can distort the feature spaces. In addition, the study of media-aware stock movements in previous work has focused on the one-to-one problem, in which it is assumed that news affects only the performance of the stocks mentioned in the reports. However, news articles also impact related stocks and cause stock co-movements. In this article, we propose a tensor-based event-driven LSTM model to address these challenges. Experiments performed on the China securities market demonstrate the superiority of the proposed approach over state-of-the-art algorithms, including AZFinText, eMAQT, and TeSIA.
A Theoretical Perspective View on the Relationship between the EFQM Excellence Model and Innovation Activities in the Public Sector in the United Arab Emirates

This research proposes a theoretical perspective on the relationship between the EFQM Excellence Model and innovation activities, based on an extensive literature review. The outcomes suggest that the EFQM framework for innovation activities describes the success criteria for innovation activities and maps them to the EFQM Excellence Model, which ensures a balanced and comprehensive method to enforce the organization’s strategy. In addition, innovation activities are the essential element for an organization to gain knowledge in this new century. Innovation activities can enhance the efficiencies and effectiveness of production. While EFQM model practices are correlated with innovation, technological abilities and learning also further mediate the relationship.
Analysis of Area Efficiency of 12-bit Switched-Capacitor DAC Topologies used in SAR ADC

In this paper we present analysis of area efficiency of conventional binary-weighted and split-capacitor topologies of switched-capacitor DAC for SAR ADC. Although the main reason for usage of split-capacitor topologies is reducing the DAC’s area, the analysis showed that it is not always the case since the linearity parameters of split-capacitor topologies are more sensitive to parasitic effects. We based our analysis on differential realization of 12-bit switched capacitor digital-toanalog converter for 1 MS/s differential SAR ADC in 180 nm CMOS. However, the analysis can be applied to any SC DAC used in SAR ADCs. We used common mode voltage as a third reference voltage potential besides GND and VREF, which allowed us to reduce the DAC’s area while maintaining the linearity specifications. Monte Carlo simulations of the designed DAC with extracted parameters gave the 12-bit performance at 1 MS/s with the differential nonlinearity in LSBs DNLmax,mean/σDNLmax = +0.395/0.201 and integral nonlinearity INLmax,mean/σINLmax = +0.326/0.108.
Ecovillagers’ Assessment of Sustainability: Differing Perceptions of Technology as a Differing Account of Modernism

There is great debate over how to measure progress towards ecological sustainability, and a number of approaches deployed by various groups to try and achieve it. One of these is the “ecovillage,” a form of communal living—the primary purpose of which is to enable a reduction in consumption through the collectivization of resources. This paper presents a case study of an ecovillage named Dancing Rabbit, which stands as an example counter to mainstream discourses on progress through the relatively successful approach to ecological sustainability. In the dwellings they have built, their resource consumption patterns, and the diverse attitudes towards technology use that they express, the ecovillagers in this study demonstrate narratives of progress which center an ecological motive over one of profitable innovation. Rather than rely on modernist assumptions of engineering our way to sustainable living through advanced technology or embrace primitivist notions of rejecting it all together, the case study of Dancing Rabbit presents an approach functioning as an alternative to that prevailing dichotomy.
Jurnal Ilmiah Bisnis, Pasar Modal, dan UMKMVolume. 2, No.1, Juni 2019

The main objective in this research is to test whether Company Size, Return on Assets, and Earnings per Share influence the practice of Income Smoothing. The data in this research is collected from 86 companies listed in manufacturing industry in Indonesia Stock Exchange. The period of the data collected is 3 years from 2013 – 2016. The research model used in this research is multiple regression where the classical assumption tests are conducted prior to find the result of the model by using SPSS 22. Quantitative approach is used in this research to test the raised hypotheses and solve the issues. In conclusion, the results of this research show that variable SIZE does have positive significant influence on Income Smoothing and variable ROA & EPS do not have significant influence on Income Smoothing.
A simple approach to determine the resonant frequencies of an elliptical microstrip antenna

A simple approach for the accurate determination of resonant frequencies of an elliptical microstrip antenna has been developed and presented in this paper. This approach takes into account the fringing area for the effective dielectric permittivity calculation. Numerical results of the resonant frequencies are presented and they are in good agreement with the available theoretical and experimental results. This approach is more efficient, simple and accurate than the existing methods.
A Note on the Gao, An, and Bai (2019) Uniform Mixture Model in the Case of Regression

We extend the Uniform Mixture Model of Gao, An and Bai (2019) to the case of linear regression. Gao, An and Bai (2019) proposed that to characterize the probability distributions of multimodal and irregular data observed in engineering, a Uniform Mixture Model can be used. This model is a weighted combination of multiple uniform distribution components. This case is of empirical interest since, in many instances, the distribution of the error term in a linear regression model cannot be assumed unimodal. Bayesian methods of inference organized around Markov Chain Monte Carlo are proposed. In a Monte Carlo experiment, significant efficiency gains are found in comparison to least squares justifying the use of the Uniform Mixture Model.
An Optimization Approach Considering User Utility for the PV-Storage Charging Station Planning Process

Based on the comprehensive utilization of energy storage, photovoltaic power generation, and intelligent charging piles, photovoltaic (PV)-storage charging stations can provide green energy for electric vehicles (EVs), which can significantly improve the green level of the transportation industry. However, there are many challenges in the PV-storage charging station planning process, making it theoretically and practically significant to study approaches to planning. This paper promotes a bi-level optimization planning approach for PV-storage charging stations. First, taking PV-storage charging stations and EV users as the upper- and lower-level problems, respectively, during the planning process, a bi-level optimization model for PV-storage charging stations considering user utility is established for capacity allocation and user behavior-based electricity pricing. Second, the model is converted into a single-level mixed-integer linear programming model using the piecewise linear utility function, Karush–Kuhn–Tucker (KKT) conditions, and linearization methods. Finally, to verify the validity of the proposed model and the solution algorithm, a commercial solver is used to solve the optimization model and obtain the planning scheme. The results show that the proposed bi-level optimization model can provide a more economical and reasonable planning scheme than the single-level model, and can reduce the investment cost by 8.84%, operation and maintenance cost by 13.23%, and increase net revenue by 5.11%.
SEISMIC BEHAVIOUR OF MASONRY DOMES AND VAULTS HAGIA SOPHIA IN ISTANBUL AND ST. FRANCIS IN ASSISI

The paper is divided in three parts. In the first part the history of the domes and their realisation are shortly presented: the Pantheon, Hagia Sophia, St. Mary of the Flower in Florence, St. Peter in Rome. In the second part the behaviour of Hagia Sophia is analysed through its history, the observation of the present damage and mathematical models. The study shows how the dome collapsed several times in its history. The third part shows the collapse of two vaults of the Basilica of St. Francis of Assisi hit by a strong earthquake on 26 September, 1997. The mathematical model and photographic records explain the mechanism of collapse. Finally the reinforcements have been achieved using composite materials with aramidic fibers to strengthen the deformed existing vaults.