Miracle-dz/UniFAR-FTUs huggingface dataset:
Characteristics and scaling of energy and particle losses during Type I ELMs in JET H-modes

Recent experiments on the Type I ELMy H-mode regime performed at JET with improved diagnostics have expanded the range of parameters for the study of Type I ELM energy and particle losses. Deviations from the standard behaviour of such losses in some areas of the Type I ELMy H-mode operating space have revealed that the ELM losses are correlated with the parameters (density and temperature) of the pedestal plasma before the ELM crash, while other global ELM characteristics (such as ELM frequency) are a consequence of the ELM-driven energy and particle flux and of the in-between ELM energy and particle confinement. The relative Type I ELM plasma energy loss (to the pedestal energy) is found to correlate well with the collisionality of the pedestal plasma, showing a weak dependence on the method used to achieve those pedestal plasma parameters: plasma shaping, heating, pellet injection and impurity seeding. Effects of edge plasma collisionality and transport along the magnetic field on the Type I ELM particle and energy fluxes onto the divertor target have also been observed. Two possible physical mechanisms that may give rise to the observed collisionality dependence of ELM energy losses are proposed and their consistency with the experimental measurements investigated: collisionality dependence of the edge bootstrap current with its associated influence on the ELM MHD origin and the limitation of the ELM energy loss by the impedance of the divertor target sheath to energy flow during the ELM event.
The Impact of Organizational Crisis Preparedness on Firm Business Performance

Purpose – Organizations which are crisis prepared are generally considered better equipped to anticipate, detect, manage and make adequate decisions in times of crisis. Therefore, organizational crisis preparedness has strategic importance and significant influence on business. Additionally, very successful organizations are to a large extent crisis prepared, which implies the existence of close ties between general business success and organizational crisis preparedness. Hence, it is of managerial interest to gain insight into whether organizational crisis preparedness can be considered an important determinant of business success. This study aims to empirically explore the importance of medium and large sized firms’ organizational crisis preparedness and subsequent impact on their business success. Moreover, it aims to determine whether current organizational crisis preparedness and prospective organizational crisis preparedness statistically significantly influence firm’s organizational performance, measured through its two dimensions – business efficiency and business effectiveness. Design/Methodology/Approach – This study applies variance-based structural equation modeling PLS SEM on a set of empirically gathered results from a primary research survey. Findings and implications – This paper empirically confirmed the existence of statistically significant positive impact of organizational crisis preparedness on the business success. Limitations – Research sample size is adequate; however, it comprises multiple industries and does not fully reflect specific measures of organizational crisis preparedness of each single industry. Originality – Research provides interesting insight into the inadequately empirically explored concept of crisis management, along with providing recommendations for managers.
Segment for Restoration, Restore for Segmentation

Most state-of-the-art semantic segmentation or scene parsing approaches only achieve high accuracy rates in optimal weather conditions. The performance decrease enormously if images with unknown disturbances occur, which is less discussed but appears more in real applications. Most existing research works cast the handling of the challenging adverse conditions as a post-processing step of signal restoration or enhancement after sensing, then feed the restored images for visual understanding. However, the performance will largely depend on the quality of restoration or enhancement. Whether restoration-based approaches would actually boost the semantic segmentation performance remains questionable. In this paper, we propose a novel framework to tackle semantic segmentation and image restoration under adverse environmental conditions, named SR-Restore. The proposed approach contains two components: Semantically-Guided Adaptation, which exploits and leverages semantic information from degraded images then help to refine the segmentation; and Exemplar-Guided Synthesis, which synthesizes restored or enhanced images from semantic label maps given specific degraded exemplars. SR-Restore exploits the possibility of building connections of low-level image processing and high level computer vision tasks, achieving image restoration via segmentation refinement. Extensive experiments on several datasets demonstrate that our approach can not only improve the accuracy of high-level vision tasks with image adaption, but also boosts the perceptual quality and structural similarity of degraded images with image semantic guidance.
Nearest Neighbor Predictions of Realized Volatility for S&P 100 Options Trading

The increasing availability of intraday financial data has led to improvements in daily volatility forecasting through long-memory models of realized volatility. This paper demonstrates the merit of the non-parametric Nearest Neighbor (NN) approach for S&P 100 realized variance forecasting. A priori the NN approach is appealing because it can reproduce complex dynamic dependencies while largely avoiding misspecification and parameter estimation uncertainty, unlike model-based methods. We evaluate the forecasts through straddle trading profitability metrics and using conventional statistical accuracy criteria. The ranking of individual forecasts confirms that statistical accuracy does not have a one-to-one mapping into profitability. In turbulent markets, the NN forecasts lead to higher risk-adjusted profitability even though the model-based forecasts are statistically superior. In both calm and turbulent market conditions, the directional combination of NN and model-based forecasts is more profitable than any of the individual forecasts.
Exploring the Relationship between the Fast Food Environment and Obesity Rates in the US vs . Abroad: A Systematic Review

Obesity remains one of the greatest public health concerns in our modern-day society, necessitating an understanding of the underlying contributing factors. This systematic review examines the existing literature to better understand the relationship between the fast food environment and obesity rates within the United States (US) compared to other countries throughout the world. To be included in our review, studies must have been peer-reviewed, published in English, and had to include some measure of analysis of the fast food environment and rates of obesity. Based on the results of our review (n=46 studies), the findings were largely similar between US and non-US studies; in both cases, there were inconsistent associations between the fast food environment and rates of obesity/overweight. However, in terms of socioeconomic status (SES), our findings were consistent across both US and non-US studies; lower SES was associated with unfavourable fast food environments, higher concentrations of fast food restaurants, higher consumption of junk food, and higher obesity rates. Based on the results of our review, we conclude that more longitudinal research must be performed with consistent methodology in order to more clearly understand the role of the fast food environment in the development of obesity.
Detecting Point Outliers Using Prune-based Outlier Factor (PLOF)

Outlier detection (also known as anomaly detection or deviation detection) is a process of detecting data points in which their patterns deviate significantly from others. It is common to have outliers in industry applications, which could be generated by different causes such as human error, fraudulent activities, or system failure. Recently, density-based methods have shown promising results, particularly among which Local Outlier Factor (LOF) is arguably dominating. However, one of the major drawbacks of LOF is that it is computationally expensive. Motivated by the mentioned problem, this research presents a novel pruning-based procedure in which the execution time of LOF is reduced while the performance is maintained. A novel Prune-based Local Outlier Factor (PLOF) approach is proposed, in which prior to employing LOF, outlierness of each data instance is measured. Next, based on a threshold, data instances that require further investigation are separated and LOF score is only computed for these points. Extensive experiments have been conducted and results are promising. Comparison experiments with the original LOF and two state-of-the-art variants of LOF have shown that PLOF produces higher accuracy and precision while reducing execution time.
Emotional Ownership and Workplace Spirituality in Nigeria

Nigeria workplace has had accumulative incidences of gross laxity and unattached commitment to work due to the social divide between the goal of management and the interest of employees. This study focusses on achieving employees’ commitment in the banking sector of Nigeria economy in the midst ofthis social dislocation between management of banks and banks staff in Nigeria. To realize this objective, the banks in south south region were covered in this study. 342 respondent employees of the banks sampled from the region provided the data gathered and amongst the dimensions, employee involvement, incentive and workplace equality were tested using SPSS 15.0. Findings revealed a correlation of the dimensions of emotional ownership and workplace spirituality and recommended that employees should be involved, adequate incentive strategies and equality and fairplay should be encouraged to enshrine workplace spirituality in the banking sector in Nigeria.
Comparison between mixing and shaking techniques during the destabilization-hydrolysis of the acid mine drainage (AMD) using Ca(OH)2 and Mg(OH)2

Acid mine drainage (AMD) is detrimental to both humans and the ecosystem, and contains sulphuric acid and heavy metals, which have to be removed by dosing the coagulants. A 200 mL sample of AMD, emanating from a mining area in South Africa was poured into 500 mL glass beakers or Erlenmeyer flasks and were dosed with 0.043 M Ca(OH)2, 0.043 M Mg(OH)2 and synthetic 0.043 M CaMg.2(OH)2 and treated in a jar test or shaking apparatus employing rapid agitation at 250 rpm for 2 min. Each batch of samples was allowed to settle for an hour after which the pH, conductivity and turbidity were measured. The results showed that the turbidity removal efficiency exhibited by Ca(OH)2 or Mg(OH)2, and synthetic CaMg.2(OH)2 dosage of a range 30 to 60 mL was identical all above 90%. Effective wastewater treatment is not necessarily dependent upon the pH but the ability of the coagulant to destabilize the double layer (high electronegativity) of the aqua-colloids coupled with optimal hydrolysis, precursor to adsorption. The Ca2+ and Mg2+ ions added to AMD sample do not only neutralize the solution, but also cause destabilization; whereas the anionic species (OH-) increases the pH of the system. The Ca2+ and Mg2+ ions in Ca(OH)2 and Mg(OH)2 added to AMD sample respectively did not only neutralize the solution, but also cause destabilization, whereas the unreacted OH- ions increase the pH of the system. The identical turbidity removal yielded by all reagents confirms that the CaMg2(OH)2 can be used as a replacement of CaMg(CO3)2. Turbidity removal in AMD sample with Ca(OH)2, Mg(OH)2 or CaMg.2(OH)2 dosages is of a physical nature as can be observed from the SEM images, showing sponge cake-like structure with dense flocs linked together. Key words: Coagulants, shaking, agitation, settle, pH, turbidity.
In-plane antiferromagnetic moments and magnetic polaron in the axion topological insulator candidate 
EuIn2As2

Topological insulator with antiferromagnetic order can serve as an ideal platform to realize axion electrodynamics. In this paper, we report a systematic study of the axion topological insulator candidate ${\mathrm{EuIn}}_{2}{\mathrm{As}}_{2}$. A linear energy dispersion across the Fermi level reveals a hole-type Fermi pocket. The orientation of the magnetic moment for ground state is determined within the $ab$-plane by anisotropic magnetic behavior. Besides long-range antiferromagnetic order, magnetization and magnetotransport measurements indicate existence of the ferromagnetic orders and ferromagnetic correlation, suggesting the formation of the magnetic polarons. These ferromagnetic clusters can persist above the antiferromagnetic transition leading to unconventional transport properties. Our results suggest multiple magnetic orders and states in ${\mathrm{EuIn}}_{2}{\mathrm{As}}_{2}$, which is vital to understanding its topological nature.
Operating Performance of Privatized Companies in Transition Economies - the Case of Poland, Hungary and the Czech Republic

We examine operating performance of 154 Polish, Hungarian and Czech companies that were fully or partially privatized between January 1990 and December 1998. Overall, our results are different from results of similar studies on operating performance of companies privatized in developed and other developing countries (Souza and Megginson, 1999; Boubakri and Cosset, 1998). For example, privatized firms in our sample did not manage to increase profitability, and significantly reduced efficiency and output in the post-privatization period. Enterprises privatized through mass privatization programs (Czech SOEs) achieved lower profitability in the post-privatization period compared to their counterparts privatized through case-by-case method. Czech companies have also maintained much higher bank borrowings after privatizations then their Polish and Hungarian counterparts. We further document that private sector IPOs underperform their privatization counterparts in terms of profitability, efficiency, capital investments and output. Finally, firms' size does not seem to influence key performance measures in selected countries.
A Crowdsourcing Framework for On-Device Federated Learning

Federated learning (FL) rests on the notion of training a global model in a decentralized manner. Under this setting, mobile devices perform computations on their local data before uploading the required updates to improve the global model. However, when the participating clients implement an uncoordinated computation strategy, the difficulty is to handle the communication efficiency (i.e., the number of communications per iteration) while exchanging the model parameters during aggregation. Therefore, a key challenge in FL is how users participate to build a high-quality global model with communication efficiency. We tackle this issue by formulating a utility maximization problem, and propose a novel crowdsourcing framework to leverage FL that considers the communication efficiency during parameters exchange. First, we show an incentive-based interaction between the crowdsourcing platform and the participating client’s independent strategies for training a global learning model, where each side maximizes its own benefit. We formulate a two-stage Stackelberg game to analyze such scenario and find the game’s equilibria. Second, we formalize an admission control scheme for participating clients to ensure a level of local accuracy. Simulated results demonstrate the efficacy of our proposed solution with up to 22% gain in the offered reward.
Exploring Superconductivity under Strong Coupling with the Vacuum Electromagnetic Field

Light-matter interactions have generated considerable interest as a means to manipulate material properties. Light-induced superconductivity has been demonstrated using pulsed lasers. An attractive alternative possibility is to exploit strong light-matter interactions arising by coupling phonons to the vacuum electromagnetic field of a cavity mode as has been suggested and theoretically studied. Here we explore this possibility for two very different superconductors, namely YBCO (YBa$_2$Cu$_3$O$_{6+x}$) and Rb$_3$C$_{60}$, coupled to surface plasmon polaritons, using a novel cooperative effect based on the presence of a strongly coupled vibrational environment allowing efficient dressing of the otherwise weakly coupled phonon bands of these compounds. By placing the superconductor-surface plasmon system in a SQUID magnetometer, we find that the superconducting transition temperatures ($T_{c}$) for both compounds are modified in the absence of any external laser field. For YBCO, $T_{c}$ decreases from 92 K to 86 K while for Rb$_3$C$_{60}$, it increases from 30 K to 45 K at normal pressures. In the latter case, a simple theoretical framework is provided to understand these results based on an enhancement of the electron-phonon coupling. This proof-of-principle study opens a new tool box to not only modify superconducting materials but also to understand the mechanistic details of different superconductors.
Test Metrics for Recurrent Neural Networks

Recurrent neural networks (RNNs) have been applied to a broad range of application areas such as natural language processing, drug discovery, and video recognition. This paper develops a coverage-guided test framework, including three test metrics and a mutation-based test case generation method, for the validation of a major class of RNNs, i.e., long short-term memory networks (LSTMs). The test metrics are designed with respect to the internal structures of the LSTM layers to quantify the information of the forget gate, the one-step information change of an aggregate hidden state, and the multi-step information evolution of positive and negative aggregate hidden state, respectively. We apply the test framework to several typical LSTM applications, including a network trained on IMDB movie reviews for sentiment analysis, a network trained on MNIST dataset for image classification, and a network trained on a lipophilicity dataset for scientific machine learning. Our experimental results show that the coverage-guided testing can be used to not only extensively exploit the behaviour of the LSTM layer in order to discover the safety loopholes (such as adversarial examples) but also help understand the internal mechanism of how the LSTM layer processes data.
Inclusion of Blend of L-lysine, DL-Methionine and Vitamins D3, E in Diets for Broiler; Effects on Growth Performance, Feed Intake and Conversion and Water Intake

The blend comprising of L-lysine, DL-methionine and vitamins D3, E in diets evaluated for growth performance, water intake, feed consumption and conversion ratio and live body weight in broiler was investigated. The study was performed on 360 day old broiler chickens and divided into four groups i.e. A (control), B, C and D (30 chickens in each group) randomly. The birds were fed blend in the diets; group B (40gm / 50 kg feed), group C (80 gm / 50 kg feed) and group D (120 gm / 50 kg feed). The growth parameters of broilers were significant (P<0.05) high in the diet group D in comparison to C, B and A groups respectively. The broiler in group D consumed more feed (4303.20 g/bird) followed by the broiler in C, B and A groups respectively. The significant difference (P<0.05) was determined in water intake of broilers among the groups. The broiler in group D was gained more live body weight (2301.60 g/bird) followed by the broilers in C (2094.50), B (2009.00) and A (1922.20 g/bird). The broiler in group D resulted superior feed conversion efficiency (1.87); followed by the broilers in group C (1.97). The broiler in groups B and A (control) resulted in lowest feed conversion efficiency (1.99 and 2.00) respectively. Overall, the supplementation of the blend in feed 120gm / 50 kg significantly influence on the feed consumption, water intake, live body weight and growth performance in broiler chickens.
Relativistic hybrid stars with sequential first-order phase transitions and heavy-baryon envelopes

We compute the mass, radius and tidal deformability of stars containing phase transitions from hadronic to quark phase(s). These quantities are computed for three types of hadronic envelopes: purely nuclear, hyperonic, and $\mathrm{\ensuremath{\Delta}}$-resonance--hyperon admixed matter. We consider either a single first-order phase transition to a quark phase with a maximally stiff equation of state (EOS) or two sequential first-order phase transitions mimicking a transition from hadronic (H) to a quark matter phase followed by a second phase transition to another quark phase. Such a construct emulates the results of the computations of the EOS, which include two-flavor color-superconducting and color-flavor-locked superconducting phases at low and high density. We explore the parameter space which produces low-mass twin and triplet configurations where equal-mass stars have substantially different radii and tidal deformabilities. We demonstrate that while for purely hadronic stiff EOS the obtained maximum mass is inconsistent with the upper limit on this quantity placed by GW170817, the inclusion of the hyperonic and $\mathrm{\ensuremath{\Delta}}$-resonance degrees of freedom, as well as the deconfinement phase transition at sufficiently low density, produces a configuration of stars consistent with this limit. The obtained hybrid star configurations are in the mass range relevant for the interpretation of the GW170817 event. We compare our results for the tidal deformability with the limits inferred from GW170817 showing that the onset of non-nucleonic phases, such as $\mathrm{\ensuremath{\Delta}}$-resonance--hyperon admixed phase and/or the quark phase(s), are favored by these data if the nuclear EOS is stiff. Also, we show that low-mass twins and especially triplets proliferate the number of combinations of possible types of stars that can undergo a merger event, the maximal number being six in the case of triplets. The prospects for uncovering the first-order phase transition(s) to and in quark matter via measurements of tidal deformabilities in merger events are discussed.
Regularized Quantile Regression with Interactive Fixed Effects

I consider nuclear norm penalized quantile regression for large $N$ and large $T$ panel data models with interactive fixed effects. The estimator solves a convex minimization problem, not requiring pre-estimation of the (number of the) fixed effects. Uniform rates are obtained for both the slope coefficients and the low-rank common component of the interactive fixed effects. The rate of the latter is nearly optimal. To derive the rates, I show new results that establish uniform bounds of norms of certain random matrices of jump processes. These results may have independent interest. Finally, I conduct Monte Carlo simulations to illustrate the estimator's finite sample performance.
Sub-Optimal Local Minima Exist for Almost All Over-parameterized Neural Networks

Does over-parameterization eliminate sub-optimal local minima for neural network problems? On one hand, existing positive results do not prove the claim, but often weaker claims. On the other hand, existing negative results have strong assumptions on the activation functions and/or data samples, causing a large gap with positive results. It was unclear before whether there is a clean answer of "yes" or "no". In this paper, we answer this question with a strong negative result. In particular, we prove that for deep and over-parameterized networks, sub-optimal local minima exist for generic input data samples and generic nonlinear activation. This is the setting widely studied in the global landscape of over-parameterized networks, thus our result corrects a possible misconception that "over-parameterization eliminates sub-optimal local-min". Our construction is based on fundamental optimization analysis, and thus rather principled.