Miracle-dz/UniFAR-FTUs huggingface dataset:
Técnicas de Previsão: Aplicação a Redes Celulares Forecasting Techniques: Application to Cellular Networks

Due to the growing competitiveness and aggressiveness of the market, the network operators’ strategy is increasingly based on improving infrastructures and optimizing existing resources, in a way that provides the best experience to the user. To do this, the operator analyzes the Key Performance Indicators (KPIs) and uses forecasting methods to predict and plan the modifications needed in the network. With this as basis, this work focuses on the study and analysis of different forecasting methods and their implementation in Python, so that the operator can obtain automate real-time predictions of the future behavior of his network.
Abstract. Effects of fast‐track anesthesia (FTA) on miR‐1 and neuropeptides in serum of patients undergoing cardiac surgery were investigated. A total of 147 patients who underwent cardiac surgery at Jining No. 1 people's Hospital

Effects of fast‐track anesthesia (FTA) on miR‐1 and neuropeptides in serum of patients undergoing cardiac surgery were investigated. A total of 147 patients who underwent cardiac surgery at Jining No. 1 people's Hospital from August 2015 to July 2018 were selected. There were 72 patients who received the FTA technology during cardiac surgery in the intervention group, and 75 patients who received routine anesthesia during cardiac surgery in the control group. Venous blood was, respectively, collected before anesthesia (T0), 30 min after artery opening (T1), 60 min after artery opening (T2), and 180 min after artery opening (T3). Expression of serum miR‐1 in patients at T0 to T3 were detected by real‐time fluorescence quantita‐ tive PCR. Expression of neuropeptide indexes such as neuron‐specific enolase (NSE), S100β protein (S100β), and amyloid β‐protein (Aβ) in serum of patients in the two groups at T0 to T3 were detected by ELISA, and the correla‐ tion of expression of serum miR‐1, serum NSE, S100β and Aβ was analyzed. There was no significant difference in the expression of serum miR‐1 between the two groups at T0 (P>0.05). There was no significant difference in the expres‐ sion of NSE, S100β and Aβ between the two groups at T0 (P>0.05). Expression of serum NSE, S100β and Aβ in both groups increased gradually, and expression of serum NSE, S100β and Aβ in the intervention group were significantly lower than those in the control group at T1‐T3 (P<0.05). There was a positive correlation between expression of serum miR‐1, serum NSE, S100β and Aβ (r=0.773, P<0.05; r=0.683, P<0.05; r=0.769, P<0.05). Application of the FTA technology in cardiac surgery can effectively reduce the level of serum miR‐1 in patients undergoing surgical treatment and improve their neurological function.
A Study of Translation Look Aside Buffer Structures for Low Power Consumption

Modern microprocessors consume large amounts of energy, majority of which comes from the processor’s clock and memory hierarchy. One particular area that can be explored for possible power reduction is the translation lookaside buffer (TLB). TLBs are small caches used to speed up virtual-to-physical address translation. The aim of this study is to design and implement different TLB design structures using VHDL. The structures are laid-out using 0.25 μm CMOS standard cells and then analyzed and characterized in terms of area, performance and power consumption. Results show that, compared to the different structures considered in this study, fully associative structures consume the least amount of power and produce the lowest miss rate. Banked associative structures, on the other hand, occupy the smallest silicon area, with a power consumption that is slightly higher than that of a fully associative structure.
The Role of Policy, Innovation and Markets

Chinese companies have become major technology producers, with the largest share of their output exported. This paper examines the development of solar PV and wind energy technology component (WETC) exports from China and the competitive position of the country`s renewable energy industry. We also describe the government’s renewable energy policy and its success in renewable electricity generation as well as increasing renewable energy innovation and foreign knowledge accumulation, which may drive export performance. We aim at empirically identifying determinants of Chinese solar PV and WETC exports. We estimate an augmented gravity trade model using maximum likelihood estimation. Besides controlling for standard variables derived from the gravity literature, we consider additional explanatory factors by accounting for market, policy and innovation effects steaming from both importing countries and China. We use a panel dataset representing annual bilateral trade flows of 43 countries from the developed and developing world that imported solar PV and WETCs from China between 1996 and 2008. The analysis shows that while the national market remained small for solar PV, the industry successfully entered foreign markets. The export performance of firms producing WETC increased but remained relatively small while the country developed a large home market. Empirical results indicate that high income countries, with a large renewable energy market and demand side policy support scheme, in terms of incentive tariffs, are increasingly importing solar PV components from China. We show that trade costs have a negative impact on exports of solar PV components but not WETC. Additionally, we find a positive impact of research and development (R&D) appropriation growth, especially from provincial governments in China, but no evidence that bilateral knowledge transfer and indigenous innovation affect exports.
Silence and Disclosure: A Transcendental Phenomenological Study into the Experience of Workplace Coaching whilst facing Stressful Life Events At Home

Organisations typically offer counselling to employees who experience stressful life events at home, and coaching to a much lesser extent. Yet my own experience suggests coaching can play a valuable role in support and action. A transcendental phenomenological methodology was adopted to gain vivid descriptions of the subjective experience of being coached, synthesised into an overarching essence. Findings highlight disclosure and boundaries were key themes, as was the interplay of coaching with concurrent therapeutic interventions. For organisations, the study supports the use of coaching as a tool for employee wellbeing and the creation of an emotionally literate culture.
Roma in higher education: Psychological and social capital as factors of academic success

Given that Roma students are underrepresented in higher education, and that few studies investigated personal and social resources of young Roma adults, our aim was to explore the psychological and social capital of Roma attending college and determine if there are differences compared to non-Roma students. In total, 89 Roma and 105 nonRoma college students filled in the questionnaire. Significant differences between the two groups were obtained for all components of psychological capital, except for self-efficacy. Roma students’ family financial and educational status was significantly more unfavorable than that of the non-Roma students. They received support from fewer persons to pursue higher education but have been more civically engaged and received more support from NGO representatives. We can conclude that, despite unfavorable family structure capital, Roma college students have developed a high level of psychological capital. Practical implications are furthered elaborated.
A Bayesian Approach to the Estimation of Metal Flows in Waste Streams

Metal contents in waste are normally presented without any measures of uncertainty, and, if such measures are given, they are normally based on subjective judgements. Moreover, it is difficult to analyse existing data sets by employing traditional statistical procedures. The number of analysed samples can be very small and the sampling strategy is often unknown. Outliers are common and it is not unusual that the standard deviation is larger than the mean. In this paper, we propose a method that combines subjective judgement of uncertainty with actual observations to establish uncertainty bounds. The Bayesian statistical framework is used to set up a probability model that reflects our scientific problem, and this provides a theoretical basis for the inference problem.
Incorporating Research Reports and Market Sentiment for Stock Excess Return Prediction: A Case of Mainland China

The prediction of stock excess returns is an important research topic for quantitative trading, and stock price prediction based on machine learning is receiving more and more attention. This article takes the data of Chinese A-shares from July 2014 to September 2017 as the research object, and proposes a method of stock excess return forecasting that combines research reports and investor sentiment. The proposed method measures individual stocks released by analysts, separates the two indicators of research report attention and rating sentiment, calculates investor sentiment based on external market factors, and uses the LSTM model to represent the time series characteristics of stocks. The results show that (1) the accuracy and F1 evaluation indicators are used, and the proposed algorithm is better than the benchmark algorithm. (2) The performance of deep learning LSTM algorithm is better than traditional machine learning algorithm SVM. (3) Investor sentiment as the initial hidden state of the model can improve the accuracy of the algorithm. (4) The attention of the split research report takes the two indicators of investor sentiment and price as the input of the model, which can effectively improve the performance of the model.
Nonsingular Fast Terminal Adaptive Neuro-sliding Mode Control for Spacecraft Formation Flying Systems

In this paper, a nonsingular fast terminal adaptive neurosliding mode control for spacecraft formation flying systems is investigated. First, a supertwisting disturbance observer is employed to estimate external disturbances in the system. Second, a fast nonsingular terminal sliding mode control law is proposed to guarantee the tracking errors of the spacecraft formation converge to zero in finite time. 'ird, for the unknown parts in the spacecraft formation flying dynamics, we proposed an adaptive neurosliding mode control law to compensate them. 'e proposed sliding mode control laws not only achieve the formation but also alleviate the effect of the chattering. Finally, simulations are used to demonstrate the effectiveness of the proposed control laws.
Design of Compact Circularly Polarized Antenna Using Sunshine-Shaped Slotted Patch

We propose a method to design compact circularly polarized (CP) antenna using a novel sunshine-shaped slotted radiating patch, which exhibits slow wave and other interesting properties. The structured radiating patch introduces adjustable coupling between a pair of orthogonal modes (TM<sub>10</sub> mode and TM<sub>01</sub> mode) to facilitate good axial ratio (AR) and reflection parameters of the antenna simultaneously in the radiating frequency band. A <inline-formula> <tex-math notation="LaTeX">$\pi $ </tex-math></inline-formula>/2 balun is employed to excite the TM<sub>10</sub> and TM<sub>01</sub> modes by using back feeding structure. Based on the proposed method, a compact left-hand CP antenna is designed, fabricated, and measured to verify the feasibility of the design method. The whole antenna is <inline-formula> <tex-math notation="LaTeX">$0.17\lambda _{0}\times 0.17\lambda _{0}$ </tex-math></inline-formula> in dimension (<inline-formula> <tex-math notation="LaTeX">$\lambda _{0}$ </tex-math></inline-formula> is the wavelength in free space) and the measured maximum gain is 5 dBic in the working band, and 3 dB AR bandwidth is 0.4% for left-hand CP.
Differentiable Neural Architecture Transformation for Reproducible Architecture Improvement

Recently, Neural Architecture Search (NAS) methods are introduced and show impressive performance on many benchmarks. Among those NAS studies, Neural Architecture Transformer (NAT) aims to improve the given neural architecture to have better performance while maintaining computational costs. However, NAT has limitations about a lack of reproducibility. In this paper, we propose differentiable neural architecture transformation that is reproducible and efficient. The proposed method shows stable performance on various architectures. Extensive reproducibility experiments on two datasets, i.e., CIFAR-10 and Tiny Imagenet, present that the proposed method definitely outperforms NAT and be applicable to other models and datasets.
Will Dependency Conflicts Affect My Program's Semantics?

Java projects are often built on top of various third-party libraries. If multiple versions of a library exist on the classpath, JVM will only load one version and shadow the others, which we refer to as <italic>dependency conflicts</italic>. This would give rise to <italic>semantic conflict</italic> (SC) issues, if the library APIs referenced by a project have identical method signatures but inconsistent semantics across the loaded and shadowed versions of libraries. SC issues are difficult for developers to diagnose in practice, since understanding them typically requires domain knowledge. Although adapting the existing test generation technique for dependency conflict issues, <sc>Riddle</sc>, to detect SC issues is feasible, its effectiveness is greatly compromised. This is mainly because <sc>Riddle</sc> randomly generates test inputs, while the SC issues typically require specific arguments in the tests to be exposed. To address that, we conducted an empirical study of 316 real SC issues to understand the characteristics of such specific arguments in the test cases that can capture the SC issues. Inspired by our empirical findings, we propose an automated testing technique <sc>Sensor</sc>, which synthesizes test cases using ingredients from the project under test to trigger inconsistent behaviors of the APIs with the same signatures in conflicting library versions. Our evaluation results show that <sc>Sensor</sc> is effective and useful: it achieved a <inline-formula><tex-math notation="LaTeX">$Precision$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>P</mml:mi><mml:mi>r</mml:mi><mml:mi>e</mml:mi><mml:mi>c</mml:mi><mml:mi>i</mml:mi><mml:mi>s</mml:mi><mml:mi>i</mml:mi><mml:mi>o</mml:mi><mml:mi>n</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href="yu-ieq1-3057767.gif"/></alternatives></inline-formula> of 0.898 and a <inline-formula><tex-math notation="LaTeX">$Recall$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>R</mml:mi><mml:mi>e</mml:mi><mml:mi>c</mml:mi><mml:mi>a</mml:mi><mml:mi>l</mml:mi><mml:mi>l</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href="yu-ieq2-3057767.gif"/></alternatives></inline-formula> of 0.725 on open-source projects and a <inline-formula><tex-math notation="LaTeX">$Precision$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>P</mml:mi><mml:mi>r</mml:mi><mml:mi>e</mml:mi><mml:mi>c</mml:mi><mml:mi>i</mml:mi><mml:mi>s</mml:mi><mml:mi>i</mml:mi><mml:mi>o</mml:mi><mml:mi>n</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href="yu-ieq3-3057767.gif"/></alternatives></inline-formula> of 0.821 on industrial projects; it detected 306 semantic conflict issues in 50 projects, 70.4 percent of which had been confirmed as real bugs, and 84.2 percent of the confirmed issues have been fixed quickly.
Quantifying treatment differences in confirmatory trials under non-proportional hazards

Proportional hazards are a common assumption when designing confirmatory clinical trials in oncology. With the emergence of immunotherapy and novel targeted therapies, departure from the proportional hazard assumption is not rare in nowadays clinical research. Under non-proportional hazards, the hazard ratio does not have a straightforward clinical interpretation, and the log-rank test is no longer the most powerful statistical test even though it is still valid. Nevertheless, the log-rank test and the hazard ratio are still the primary analysis tools, and traditional approaches such as sample size increase are still proposed to account for the impact of non-proportional hazards. The weighed log-rank test and the test based on the restricted mean survival time (RMST) are receiving a lot of attention as a potential alternative to the log-rank test. We conduct a simulation study comparing the performance and operating characteristics of the log-rank test, the weighted log-rank test and the test based on the RMST, including a treatment effect estimation, under different non-proportional hazards patterns. Results show that, under non-proportional hazards, the hazard ratio and weighted hazard ratio have no straightforward clinical interpretation whereas the RMST ratio can be interpreted regardless of the proportional hazards assumption. In terms of power, the RMST achieves a similar performance when compared to the log-rank test.
Leveraging Multimodal Behavioral Analytics for Automated Job Interview Performance Assessment and Feedback

Behavioral cues play a significant part in human communication and cognitive perception. In most professional domains, employee recruitment policies are framed such that both professional skills and personality traits are adequately assessed. Hiring interviews are structured to evaluate expansively a potential employee’s suitability for the position - their professional qualifications, interpersonal skills, ability to perform in critical and stressful situations, in the presence of time and resource constraints, etc. Candidates, therefore, need to be aware of their positive and negative attributes and be mindful of behavioral cues that might have adverse effects on their success. We propose a multimodal analytical framework that analyzes the candidate in an interview scenario and provides feedback for predefined labels such as engagement, speaking rate, eye contact, etc. We perform a comprehensive analysis that includes the interviewee’s facial expressions, speech, and prosodic information, using the video, audio, and text transcripts obtained from the recorded interview. We use these multimodal data sources to construct a composite representation, which is used for training machine learning classifiers to predict the class labels. Such analysis is then used to provide constructive feedback to the interviewee for their behavioral cues and body language. Experimental validation showed that the proposed methodology achieved promising results.
Piecewise-Stationary Off-Policy Optimization

Off-policy learning is a framework for evaluating and optimizing policies without deploying them, from data collected by another policy. Real-world environments are typically non-stationary and the offline learned policies should adapt to these changes. To address this challenge, we study the novel problem of off-policy optimization in piecewise-stationary contextual bandits. Our proposed solution has two phases. In the offline learning phase, we partition logged data into categorical latent states and learn a near-optimal sub-policy for each state. In the online deployment phase, we adaptively switch between the learned sub-policies based on their performance. This approach is practical and analyzable, and we provide guarantees on both the quality of off-policy optimization and the regret during online deployment. To show the effectiveness of our approach, we compare it to state-of-the-art baselines on both synthetic and real-world datasets. Our approach outperforms methods that act only on observed context.
Modeling Implicit Communities using Spatio-Temporal Point Processes from Geo-tagged Event Traces

The location check-ins of users through various location-based services such as Foursquare, Twitter, and Facebook Places, etc., generate large traces of geo-tagged events. These event-traces often manifest in hidden (possibly overlapping) communities of users with similar interests. Inferring these implicit communities is crucial for forming user profiles for improvements in recommendation and prediction tasks. Given only time-stamped geo-tagged traces of users, can we find out these implicit communities, and characteristics of the underlying influence network? Can we use this network to improve the next location prediction task? In this paper, we focus on the problem of community detection as well as capturing the underlying diffusion process and propose a model COLAB based on Spatio-temporal point processes in continuous time but discrete space of locations that simultaneously models the implicit communities of users based on their check-in activities, without making use of their social network connections. COLAB captures the semantic features of the location, user-to-user influence along with spatial and temporal preferences of users. To learn the latent community of users and model parameters, we propose an algorithm based on stochastic variational inference. To the best of our knowledge, this is the first attempt at jointly modeling the diffusion process with activity-driven implicit communities. We demonstrate COLAB achieves up to 27% improvements in location prediction task over recent deep point-process based methods on geo-tagged event traces collected from Foursquare check-ins.
Structural Autoencoders Improve Representations for Generation and Transfer

We study the problem of structuring a learned representation to significantly improve performance without supervision. Unlike most methods which focus on using side information like weak supervision or defining new regularization objectives, we focus on improving the learned representation by structuring the architecture of the model. We propose a self-attention based architecture to make the encoder explicitly associate parts of the representation with parts of the input observation. Meanwhile, our structural decoder architecture encourages a hierarchical structure in the latent space, akin to structural causal models, and learns a natural ordering of the latent mechanisms. We demonstrate how these models learn a representation which improves results in a variety of downstream tasks including generation, disentanglement, and transfer using several challenging and natural image datasets.
Effective Hamiltonian for Stokes–anti-Stokes pair generation with pump and probe polarized modes

In the correlated Stokes--anti-Stokes scattering (SAS) an incident photon interacts with a Raman-active material, creating a Stokes photon and exciting a quantum vibrational mode in the medium, which is posteriorly annihilated on contact with a second incident photon, producing in turn an anti-Stokes photon. This can be accomplished by real and virtual processes. In the real process the quantum mode shared between the Stokes and anti-Stokes events is a real particle, whereas in virtual processes the pair formation is mediated by the exchange of virtual particles. Here, we introduce a Hamiltonian to describe the pair production in SAS scattering, for both types of process, when stimulated by two orthogonally polarized laser pulses in a pump-and-probe configuration. We also model the effect of the natural decay of the vibration created in the Stokes event and compute the probability of producing SAS pairs. Additionally, we follow the dynamics of the vibration by considering the Stokes and anti-Stokes fields as external reservoirs, obtaining thus a master equation for the reduced density matrix for the vibrational population. Finally, we compare our theoretical results to recently published experimental data.