Miracle-dz/UniFAR-FTUs huggingface dataset:
Electric Load Forecasting with Boosting based Sample Transfer

With the increasing adoption of renewable energy generation and different types of electronic devices, electric load forecasting, especially short-term load forecasting, has attracted more and more attention. Accurate short-term load forecasting is of signiﬁcant importance for the safety and efﬁciency of power grids. Deep learning-based models have shown impressive success on several applications, including short-term load forecasting. However, for several real-world scenarios, it may be very difﬁcult or even impossible to collect enough training data to learn a reliable machine learning model. Speciﬁcally, in this paper, we pro-pose a sample-based transfer learning algorithm to assist the learning performance of short-term load forecasting for houses with limited training data, e.g., newly built houses. The proposed method is evaluated on several real-world data sets and shows signiﬁcant improvement over the baselines.
2 Identifiability of Mixed kernel Gaussian Process Regression Model 2 . 1 Notation

Gaussian process regression (GPR) model is a popular nonparametric regression model. In GPR, features of the regression function such as varying degrees of smoothness and periodicities are modeled through combining various covarinace kernels, which are supposed to model certain effects (Bousquet et al., 2011; Gelman et al., 2013). The covariance kernels have unknown parameters which are estimated by the EM-algorithm or Markov Chain Monte Carlo. The estimated parameters are keys to the inference of the features of the regression functions, but identifiability of these parameters has not been investigated. In this paper, we prove identifiability of covariance kernel parameters in two radial basis mixed kernel GPR and radial basis and periodic mixed kernel GPR. We also provide some examples about non-identifiable cases in such mixed kernel GPRs.
The usage of fulvic acid and zeolite on the absorption of cadmium in Spinacia oleracea var. inermis.

Purpose: One of the problems in today's world is the pollution of the environment with heavy metals, which extraction of metals from mines, industrial and agricultural activities have caused significant pollution of soils and waters to metals. Research method: Fulvic acid at 15, 30, and 60 L ha-1 and zeolites at 1000, 2000, and 4000 kg ha-1 have formed the treatments, and the cadmium content was added to the region. The absorbance of cadmium, zinc, and nitrogen elements, as well as the dry weight of aerial organs and root of Spinacia oleracea var. inermis, was investigated. Findings: The application of fulvic acid and zeolite resulted in higher absorption of the elements and higher biomass weight compared to the control. In this case, fulvic acid at 30 L ha-1 and zeolite at 4000 kg ha-1had the best results own, and the accumulated amounts of cadmium and zinc in the root of the Spinach were higher than that of the aerial organs. Due to its acidic nature, fulvic acid caused higher mobility of elements than zeolite. Also, the application of the treatments caused better vegetative growth of the plant and moderation of the effects of stress. Limitations: There was no restriction on conducting this research. Originality/Value: Based on the results, the use of fulvic acid and zeolite can significantly increase the absorption of cadmium toxic metal from the organs of the Spinach plant and help the further release of this element from the soil.
Impact of Ambidextrous Leadership on Project Success with the Mediating Role of Innovation and Moderating Role of Self-Efficacy

The purpose of this research study is to empirically test the impact of ambidextrous leadership on project success with innovation as a mediator and self-efficacy as a moderator. The sample was drawn using convenient sampling technique. The data is collected through survey method, from branches of telecom firms in Pakistan (n = 327). The analysis is carried out by using SPSS v.25 and AMOS v.23 software’s to test hypothesis. The findings suggested that there is positive and significant relationship between ambidextrous leadership and project success. For the projects to be successful, the leaders need to be ambidextrous by being explorative and exploitative according to the situation to meet challenges and overcome constraints. Innovation mediates the relationship between ambidextrous leadership and project success. Furthermore, results indicated that selfefficacy positively moderates the relationship between innovation and project success. Finally, some theoretical and practical implications on an organizational level, presenting certain guidelines to understand how ambidextrous leadership affects the success of projects within the organization, and research limitations and future directions are presented.
Proteasome: A Novel Potential Biomarker of Skin Cancer in Mouse Model

Background: The ubiquitin-proteasome system is an important regulator of cell growth differentiation and apoptosis. Recently proteasome levels have been suggested as a marker of various cancer diseases. In this study, we investigate the involvement of proteasome in skin cancer. Methods: A mouse model of DMBA induced skin cancer was developed, papilloma or skin tumor confirmed by histopathological analysis. Proteasomes levels were measured using a sandwich ELISA test. The catalytic activity of the 20S proteasome was determined by measuring the fluorescence emitted after the cleavage of AMC. Results: The results suggested that while all mice in the carcinogenesis group developed tumorlike forms of papilloma, control mice did not show any tumor formation. The histological skin analysis of carcinogenesis mice showed change, illustrated by hyperkeratosis, together with an irregular proliferation, and acanthosis in the epidermis. The serum proteasome and catalytic activity detected in the control group was much lower compared to those obtained in carcinogenesis group. Furthermore, we also noted that a higher level of intracellular proteasome concentration and an important catalytic activity were detected in the carcinogenesis group in comparison to control groups. Conclusion: The proteasome may represent a novel marker of skin cancer. It could be a key element in the differentiation of normal cells from malignant cells, and may be useful in monitoring the clinical development of malignant cells.
Dynamic Capabilities in Small Firms: The Role of Practicing Accountants in Southern Africa

The dramatic social changes have resulted in the need for SMMEs in South Africa to have dynamic capability and agile structures. However, due to the cost of hiring full time professional managers, certified public accountants or practicing accountants are required to fill this gap by offering dynamic capability and agility advisory services to their clients such as small firms so that they will be able to sense, seize opportunities and transform or reconfigure their products and service offering to remain viable and competitive in the fast-changing technological era.
Stable Sampling in Repeated Games

Abstract This paper considers finitely repeated games played by procedurally rational players, who sample their available alternatives and use realized payoffs as a basis for strategy selection. The corresponding solution concept is that of (payoff) sampling equilibrium, which is a distribution over strategies that is self-replicating under the sampling procedure. Sampling equilibria are rest points of a disequilibrium dynamic process, and stability with respect to this process can be used as an equilibrium selection criterion. The structure of stable sampling equilibria in symmetric, finitely repeated games is characterized, and illustrated with applications to cooperation and coordination over time.
News Entropy

We introduce the concept of 'news entropy' to characterise the relationship between news coverage and the economy. Intuitively, news entropy decreases as the news focus on a smaller set of pressing topics. We observe that news entropy exhibits clear negative spikes close to important economic, financial, and political events. Investigating the effect of changes in news entropy, we find that decreases are associated with two key features: an increase in uncertainty measures and a macroeconomic contraction. The variable is priced in the cross-section of stock returns and low news entropy is associated with increased stock price volatility at the firm level.
Understanding the Formation Mechanism of Void Lattice Under Irradiation: From Collision Cascades to Self-Assembled Nanovoids

The formation of periodic arrangements of voids is an interesting phenomenon occurring in materials under neutron irradiation, usually replicating the symmetry and crystallographic orientation of the host matrix, hence called “void lattice”. Here, taking tungsten as an example, we explore the formation mechanism of the void lattice using an object kinetic Monte Carlo (OKMC) model through the collision cascades simulated using molecular dynamics method. Specifically, the detailed processes from the chaotic neutron irradiation defects to the observable void lattice are obtained via OKMC simulation, which is a prerequisite for understanding its formation mechanism. The formation of the void lattice could be divided into three stages: nucleation, incubation and growth. Both the one-dimensional (1D) migration of SIAs and the fraction of clustered vacancies in cascades play a critical role in the emergence of the void lattice. On the one hand, the 1D migration of SIAs leads to the mutual protection of voids aligned in <111> directions. The self-shielded voids may therefore grow faster compared to the unaligned ones. On the other hand, a moderate fraction of clustered vacancies in cascades guarantees a stable rate for both nucleation and growth of voids. Once the density of the <111> aligned voids reaches a critical value, the dissolution rate of the unaligned voids will overwhelm their nucleation rate, leading to the formation of void-free channels and thus the void lattice. Our results reveal the interrelated mechanism of the 1D migration of SIAs and intra-cascade vacancy clustering for the formation of the void lattice under neutron irradiation, which improves our fundamental understanding of the self-assembled microstructures in irradiated materials.
Evaluation of Information Leadership Share as Price Discovery Measure

Traditionally the price discovery is measured by information share (IS) and component share (CS) measures. Recently, Putnins (2013) proposed another measure called information leadership share (ILS) measure based on IS and CS measures. This paper performs an evaluation of ILS and finds some issues with it. Therefore, we suggest that researchers continue using the IS and CS related measures instead of the ILS measure.
Is Cardano a Serious Rival to Ethereum?

Cardano was launched in October 2017 and by May 2021 has been operational for 44 months. Comparison with its closest rival, Ethereum, reveals that their prices are highly correlated but the change in daily closing prices do not always move in unison. Cardano is also more volatile than Ethereum and In terms of growth Cardano is lagging behind. Cardano’s only saving grace is its transaction fees, which are considerably lower than Ethereum. However, care must be taken in understanding the structure of any data source. In this case three data sources are used and results vary depending on the precision of the price data, particularly Cardano which for a number of years did not trade above one dollar.
H OW DOES THE S POTIFY API C OMPARE TO THE M USIC E MOTION R ECOGNITION S TATE - OF - THE -A RT ?

Features are arguably the key factor to any machine learning problem. Over the decades, myriads of audio features and recently feature-learning approaches have been tested in Music Emotion Recognition (MER) with scarce im-provements. Here, we shed some light on the suitability of the audio features provided by the Spotify API, the leading music streaming service, when applied to MER. To this end, 12 Spotify API features were obtained for 704 of our 900-song dataset, annotated in terms of Russell’s quadrants. These are compared to emotionally-relevant features obtained previously, using feature ranking and emotion classification experiments. We verified that energy, valence and acousticness features from Spotify are highly relevant to MER. However, the 12-feature set is unable to meet the performance of the features available in the state-of-the-art (58.5% vs. 74.7% F1-measure). Combining Spotify and state-of-the-art sets leads to small improve-ments with fewer features (top5: +2.3%, top10: +1.1%), while not improving the highest results (100 features). From this we conclude that Spotify provides some higher-level emotionally-relevant features. Such extractors are desirable, since they are closer to human concepts and al-low for interpretable rules to be extracted (harder with hun-dreds
A REVIEW ON INTRUSION DETECTION SYSTEM USING CLASSIFICATION TECHNIQUE

Intrusion of cyber security are one of the main concerns in computer security, thus intrusion detection system are being developed. Intrusion Detection Systems (IDS) are now a standard component in network security framework and is essential to protect computer systems and network from various attacks. In past few years, intrusion detection using data mining has captured the attention of researchers. Every researcher proposes a different algorithm for distinct categories. Constructing classifier is another research challenge to build dynamic IDS. KDDCup 1999 intrusion detection dataset plays a vital role in calibrating intrusion detection system and is extensively used by the researchers working in the field of intrusion detection. This paper presents an overview of various IDS and detailed analysis of various techniques used for improving IDS. KeywordsSVM, ADTREE, NSL-KDD.
No one is immune to misinformation: An investigation of misinformation sharing by subscribers to a fact-checking newsletter

Like other disease outbreaks, the COVID-19 pandemic has led to the rapid generation and dissemination of misinformation and fake news. We investigated whether subscribers to a fact checking newsletter (n = 1397) were willing to share possible misinformation, and whether predictors of possible misinformation sharing are the same as for general samples. We also investigated predictors of willingness to have a COVID-19 vaccine and found that although vaccine acceptance was high on average, it decreased as a function of lower belief in science and higher conspiracy mentality. We found that 24% of participants had shared possible misinformation and that this was predicted by a lower belief in science. Like general samples, our participants were typically motivated to share possible misinformation due to interest in the information, or to seek a second opinion about claim veracity. However, even if information is shared in good faith and not for the purpose of deceiving or misleading others, the spread of misinformation is nevertheless highly problematic. Exposure to misinformation engenders faulty beliefs in others and undermines efforts to curtail the spread of COVID-19 by reducing adherence to social distancing measures and increasing vaccine hesitancy.
Native vs Prosthetic Valve Histoplasma capsulatum Infective Endocarditis: A Case Report and Systemic Literature Review Comparing Patient Presentation, Treatment Modalities, Clinical Outcomes, and Diagnostic Laboratory Testing

Abstract Histoplasma capsulatum is a rare cause of fungal endocarditis that affects both native and prosthetic valves. It is associated with a high mortality rate if not diagnosed early and treated with a combination of antifungal therapy and surgical intervention. We present a case of a 47-year-old man with histoplasmosis infective endocarditis. He was successfully treated with antifungal therapy and surgical replacement of the infected bioprosthetic aortic valve. Our systemic literature review includes 52 articles encompassing 60 individual cases of H. capsulatum infective endocarditis from 1940 to 2020. Patient presentations, diagnostic laboratory testing accuracy, treatment modalities, and patient outcomes comparing and contrasting native and prosthetic valve infection are described.
Idiopathic Intracranial Hypertension and Multiple Sclerosis Overlap

Idiopathic intracranial hypertension (IIH) and multiple sclerosis (MS) occur with a higher incidence in women of childbearing age and may be associated with other clinical entities. Both disease processes alter cerebrospinal fluid (CSF) dynamics and may present similarly with headache and visual changes. We report a case of a 33-year-old morbidly obese woman who developed progressive worsening blurry vision and unilateral temporal headache. She was found to have papilledema which prompted workup for intracranial hypertension. Her imaging and CSF findings were suggestive of a demyelinating process such as MS in addition to IIH.
Multidrug resistant superbugs in pyogenic infections: a study from Western Rajasthan, India

Introduction the crude mortality rate due to infectious diseases in India is approximately 417 per one lakh persons and pyogenic infections are one of the significant contributor. Poor antimicrobial stewardship in India has led to an increase in multidrug resistant superbugs in both community as well as hospital settings. The aim of this study was to identify the bacterial etiology of pyogenic infections and to evaluate their antimicrobial resistance profile. Methods this is a retrospective observational study from January, 2018 to December, 2018. A total 1851 samples, collected as a part of patient care were included in this study. Specimens were subjected to culture on Blood agar and MacConkey agar and incubated at 37°C for 48 hours. Species identification was done as per standard laboratory protocol. Antimicrobial susceptibility testing was performed using Kirby-Bauer disc diffusion according to Clinical and Laboratory Standards Institute guidelines. Results of total 1851 samples, culture was positive in 61.54%. A total 70.59%, Gram negative organisms were isolated followed by Gram positive cocci in 45.48%, yeast in 1.05%, coryneform bacteria in 0.79% and in one case, non-tubercular mycobacteria was isolated. Staphylococcus aureus (30.9%) was the predominant organism isolated. Most common multi drug resistant isolates were Klebsiella spp. (74.79%) and Acinetobacter spp. (74.32%). Conclusion this study gives an insight about the prevalence and common etiology of pyogenic infections along with their antimicrobial resistance profile in north western region of India. This study will contribute in formulating antibiotic stewardship program by selecting the antibiograms of pyogenic isolates.
An Image-based Generator Architecture for Synthetic Image Refinement

Proposed are alternative generator architectures for Boundary Equilibrium Generative Adversarial Networks, motivated by Learning from Simulated and Unsupervised Images through Adversarial Training. It disentangles the need for a noise-based latent space. The generator will operate mainly as a refiner network to gain a photo-realistic presentation of the given synthetic images. It also attempts to resolve the latent space's poorly understood properties by eliminating the need for noise injection and replacing it with an imagebased concept. The new flexible and simple generator architecture will also give the power to control the trade-off between restrictive refinement and expressiveness ability. Contrary to other available methods, this architecture will not require a paired or unpaired dataset of real and synthetic images for the training phase. Only a relatively small set of real images would suffice.
DeliData: A Dataset for Deliberation in Multi-party Problem Solving

Group deliberation enables people to collaborate and solve problems, however, it is understudied due to a lack of resources. To this end, we introduce the first publicly available dataset containing collaborative conversations on solving a well-established cognitive task, consisting of 500 group dialogues and 14k utterances. In 64% of these conversations, the group members are able to find a better solution than they had identified individually, and in 43.8% of the groups who had a correct answer as their final solution, none of the participants had solved the task correctly by themselves. Furthermore, we propose a novel annotation schema that captures deliberation cues and release all 14k utterances annotated with it. Finally, we use the proposed dataset to develop and evaluate two methods for generating deliberation utterances. The data collection platform, dataset and annotated corpus are publicly available at https://delibot.xyz.
Fog Simulation on Real LiDAR Point Clouds for 3D Object Detection in Adverse Weather

This work addresses the challenging task of LiDAR-based 3D object detection in foggy weather. Collecting and annotating data in such a scenario is very time, labor and cost intensive. In this paper, we tackle this problem by simulating physically accurate fog into clear-weather scenes, so that the abundant existing real datasets captured in clear weather can be repurposed for our task. Our contributions are twofold: 1) We develop a physically valid fog simulation method that is applicable to any LiDAR dataset. This unleashes the acquisition of large-scale foggy training data at no extra cost. These partially synthetic data can be used to improve the robustness of several perception methods, such as 3D object detection and tracking or simultaneous localization and mapping, on real foggy data. 2) Through extensive experiments with several state-of-the-art detection approaches, we show that our fog simulation can be leveraged to significantly improve the performance for 3D object detection in the presence of fog. Thus, we are the first to provide strong 3D object detection baselines on the Seeing Through Fog dataset. Our code is available at www.trace. ethz.ch/lidar fog simulation.
Study of the Inner Structure of the Molecular Torus in IRAS 08572+3915 NW with Velocity Decomposition of CO Rovibrational Absorption Lines

Understanding the inner structure of the clumpy molecular torus surrounding the active galactic nucleus is essential in revealing the forming mechanism. However, spatially resolving the torus is difficult because of its size of a few parsecs. Thus, to probe the clump conditions in the torus, we performed the velocity decomposition of the CO rovibrational absorption lines (Δv = 0 →1, ΔJ = ±1) at λ ∼ 4.67 μm observed toward an ultraluminous infrared galaxy IRAS 08572+3915 NW with the high-resolution spectroscopy (R ∼ 10,000) of Subaru Telescope. Consequently, we found that each transition had two outflowing components, i.e., (a) and (b), both at approximately ∼−160 km s−1, but with broad and narrow widths, and an inflowing component, i.e., (c), at approximately ∼+100 km s−1, which were attributed to the torus. The ratios of the velocity dispersions of each component led to those of the rotating radii around the black hole of R rot,a: R rot,b: R rot,c ≈ 1: 5: 17, indicating the torus where clumps are outflowing in the inner regions and inflowing in the outer regions if a hydrostatic disk with σV∝Rrot−0.5 is assumed. Based on the kinetic temperature of components (a) and (b) of ∼720 and ∼25 K, respectively, estimated from the level population, the temperature gradient is Tkin∝Rrot−2.1 . Magnetohydrodynamic models with large density fluctuations of two orders of magnitude or more are necessary to reproduce this gradient.