Miracle-dz/UniFAR-FTUs huggingface dataset:
ROYAL FAMILY MEMBERS AND CORPORATE GOVERNANCE CHARACTERISTICS: THE IMPACT ON EARNINGS MANAGEMENT IN UAE

This study examines the association between Royal family members on the board of directors and earnings management in the United Arab Emirates (UAE). Panel data was sourced from the annual and corporate governance reports of companies listed in two leading markets in the UAE: Abu Dhabi Exchange Security (ADX) and Dubai Financial Market (DFM). Final data resulted in 437 observations for the period from 2011 to 2018. The findings of this study concluded that Royal family members on the board of UAE listed companies is negatively associated with earnings management. This study provides evidence of the role played by elite groups (Royal family members) in the UAE in enhancing the role played by the board of directors. The study also found that board meetings and audit committee meetings are positively associated with earnings management, while audit committee expertise and profitability negatively associated with earnings management. However, board size, board independence, audit committee independence, firm size and firm leverage had insignificant association with earnings management. The paper contributes to the existing theory and empirical evidence of how internal governance mechanisms add value to the company by reducing earnings management and enhancing the quality of financial reporting. ____________________________________
Testbed for thermal and performance analysis in MPSoC platforms

—Many modern computing platforms in the safety-critical domains are based on heterogeneous Multiprocessor System-on-Chip (MPSoC). Such computing platforms are expected to guarantee high-performance within a strict thermal envelope. This paper introduces a testbed for thermal and performance analysis. The testbed allows the users to develop advanced scheduling and resource allocation techniques aiming at ﬁnding an optimal trade-off between the peak temperature and the achieved performance. This paper presents a new, open-source Thermobench tool for data collection and analysis of user-deﬁned workloads. Furthermore, a methodology for shortening the time needed for the data collection is proposed. Experiments show that a signiﬁcant amount of time can be saved. Speciﬁcally, time reduction from 60 minutes to 15 minutes is achieved with the i.MX8 MPSoC by NXP while running a set of user-deﬁned benchmarks that stress CPU, GPU, and different levels of the memory hierarchy.
The Brooklyn Multi-Interaction Corpus for Analyzing Variation in Entrainment Behavior

We present the Brooklyn Multi-Interaction Corpus (B-MIC), a collection of dyadic conversations designed to identify speaker traits and conversation contexts that cause variations in entrainment behavior. B-MIC pairs each participant with multiple partners for an object placement game and open-ended discussions, as well as with a Wizard of Oz for a baseline of their speech. In addition to fully transcribed recordings, it includes demographic information and four completed psychological questionnaires for each subject and turn annotations for perceived emotion and acoustic outliers. This enables the study of speakers’ entrainment behavior in different contexts and the sources of variation in this behavior. In this paper, we introduce B-MIC and describe our collection, annotation, and preprocessing methodologies. We report a preliminary study demonstrating varied entrainment behavior across different conversation types and discuss the rich potential for future work on the corpus.
Global land surface 250 m 8 d fraction of absorbed photosynthetically active radiation (FAPAR) product from 2000 to 2021

Abstract. The fraction of absorbed photosynthetically active radiation (FAPAR) is a critical land surface variable for carbon cycle modeling and ecological monitoring. Several global FAPAR products have been released and have become widely used; however, spatiotemporal inconsistency remains a large issue for the current products, and their spatial resolutions and accuracies can hardly meet the user requirements. An effective solution to improve the spatiotemporal continuity and accuracy of FAPAR products is to take better advantage of the temporal information in the satellite data using deep learning approaches. In this study, the latest version (V6) of the FAPAR product with a 250 m resolution was generated from Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data and other information, as part of the Global LAnd Surface Satellite (GLASS) product suite. In addition, it was aggregated to multiple coarser resolutions (up to 0.25∘ and monthly). Three existing global FAPAR products (MODIS Collection 6; GLASS V5; and PRoject for On-Board Autonomy–Vegetation, PROBA-V, V1) were used to generate the time-series training samples, which were used to develop a bidirectional long short-term memory (Bi-LSTM) model. Direct validation using high-resolution FAPAR maps from the Validation of Land European Remote sensing Instrument (VALERI) and ImagineS networks revealed that the GLASS V6 FAPAR product has a higher accuracy than PROBA-V, MODIS, and GLASS V5, with an R2 value of 0.80 and root-mean-square errors (RMSEs) of 0.10–0.11 at the 250 m, 500 m, and 3 km scales, and a higher percentage (72 %) of retrievals for meeting the accuracy requirement of 0.1. Global spatial evaluation and temporal comparison at the AmeriFlux and National Ecological Observatory Network (NEON) sites revealed that the GLASS V6 FAPAR has a greater spatiotemporal continuity and reflects the variations in the vegetation better than the GLASS V5 FAPAR. The higher quality of the GLASS V6 FAPAR is attributed to the ability of the Bi-LSTM model, which involves high-quality training samples and combines the strengths of the existing FAPAR products, as well as the temporal and spectral information from the MODIS surface reflectance data and other information. The 250 m 8 d GLASS V6 FAPAR product for 2020 is freely available at https://doi.org/10.5281/zenodo.6405564 and https://doi.org/10.5281/zenodo.6430925 (Ma, 2022a, b) as well as at the University of Maryland for 2000–2021 (http://glass.umd.edu/FAPAR/MODIS/250m, last access 1 November 2022).
Modelling the growth of atmospheric nitrous oxide using a global hierarchical inversion

Abstract. Nitrous oxide is a potent greenhouse gas (GHG) and ozone-depleting substance, whose atmospheric abundance has risen throughout the contemporary record. In this work, we carry out the first global hierarchical Bayesian inversion to solve for nitrous oxide emissions, which includes prior emissions with truncated Gaussian distributions and Gaussian model errors, in order to examine the drivers of the atmospheric surface growth rate. We show that both emissions and climatic variability are key drivers of variations in the surface nitrous oxide growth rate between 2011 and 2020. We derive increasing global nitrous oxide emissions, which are mainly driven by emissions between 0 and 30∘ N, with the highest emissions recorded in 2020. Our mean global total emissions for 2011–2020 of 17.2 (16.7–17.7 at the 95 % credible intervals) Tg N yr−1, comprising of 12.0 (11.2–12.8) Tg N yr−1 from land and 5.2 (4.5–5.9) Tg N yr−1 from ocean, agrees well with previous studies, but we find that emissions are poorly constrained for some regions of the world, particularly for the oceans. The prior emissions used in this and other previous work exhibit a seasonal cycle in the extra-tropical Northern Hemisphere that is out of phase with the posterior solution, and there is a substantial zonal redistribution of emissions from the prior to the posterior. Correctly characterizing the uncertainties in the system, for example in the prior emission fields, is crucial for deriving posterior fluxes that are consistent with observations. In this hierarchical inversion, the model-measurement discrepancy and the prior flux uncertainty are informed by the data, rather than solely through “expert judgement”. We show cases where this framework provides different plausible adjustments to the prior fluxes compared to inversions using widely adopted, fixed uncertainty constraints.
A Cross Time Slot Partial Interference Alignment Scheme in Two-Cell Relay Heterogeneous Networks

The interference of a two-cell two-hop multi-input–multi-output (MIMO) heterogeneous network where the cell-center user (CCU) coexists with the cell-edge user (CEU) is complex to characterize and costly to suppress. In downlink, the CEU is assumed to be served by a half-duplex relay with amplify-and-forward mode. To suppress the intra-cell and inter-cell interference, a cross time slot partial interference alignment (TPIA) scheme, which aligns the interference terms generated in different sub-time slots to the same subspace, is proposed in this paper. Furthermore, the feasibility condition of antenna configuration is analyzed. Simulation results indicate that the TPIA scheme outperforms other existing conventional techniques in achievable sum degrees of freedom and sum-rate performance.
Robust Beamforming Design for Sum Secrecy Rate Maximization in Multibeam Satellite Systems

This letter investigates the secure transmission optimization in a multibeam satellite downlink network with multiple eavesdroppers, where each legitimate user is wiretapped by a corresponding eavesdropper. Our design objective is to maximize the sum secrecy rate of the multibeam satellite network in the presence of imperfect channel state information (CSI) of eavesdroppers, while guaranteeing the total transmit power constraint on-board the satellite. Due to the nonconvexity and intractability resulting from the eavesdroppers’ CSI uncertainty, a robust beamforming scheme is proposed to transform the initial optimization problem into a convex framework by jointly applying the Taylor expansion, S-Procedure, and Cauchy–Schwarz inequality. Meanwhile, an iterative algorithm is introduced to obtain the optimal solution. Finally, the validity and superiority of the proposed scheme are confirmed through comparisons with the existing nonrobust and perfect CSI approaches.
Tighter Bounds on Johnson Lindenstrauss Transforms

Johnson and Lindenstrauss (1984) proved that any finite set of data in a high dimensional space can be projected into a low dimensional space with the Euclidean metric information of the set being preserved within any desired accuracy. Such dimension reduction plays a critical role in many applications with massive data. There has been extensive effort in the literature on how to find explicit constructions of Johnson-Lindenstrauss projections. In this poster, we show how algebraic codes over finite fields can be used for fast Johnson-Lindenstrauss projections of data in high dimensional Euclidean spaces. Johnson Lindenstrauss Transform Problem Given data in a high dimensional space, we want to project the data to a low dimensional space so that the pairwise distances are preserved with high probability. Johnson Lindenstrauss Lemma •Let n be any positive integer, 0 ǫ] < δ. –A transformation matrix A with this property is called a Johnson Lindenstrauss Transformation.
Zero-dimensional philosophy

: Presented here is how the physical theory approach of theoretical physics can be demonstrated to be incomplete in explaining physical phenomena if indeed physical phenomena have key dimensional components that extend beyond the dimensional time-domain of time-now; although the past and the future cannot be proven to exist, for one has happened and the other has yet to happen, and reality is primarily defined physically in the time-domain of time-now as the action-principle realm, proposed here is that the idea of time-before and time-after should be if not are essential to a complete understanding of time. It follows therefore that to propose time to be linked with space as spacetime is to restrict one’s theoretic understanding of time and space to the datum reference of time-now and thence not acquire a full account of the theory of physical phenomena. This issue though is resolved with a philosophical analysis of time in extending the dimension of time to the virtual time-before and time-after time-domains consistent with our conscious ability of time, and then labelling such with a zero-dimensional mathematical theorem, by which process a resultant mathematical theorem can then more thoroughly explain the physical phenomena of time-now that the physical theory approach in physics has aimed to define.
Fundamental Limits on the Computational Accuracy of Resistive Crossbar-based In-memory Architectures

In-memory computing (IMC) architectures exhibit an intrinsic trade-off between computational accuracy and energy efficiency. This paper determines the fundamental limits on the compute SNR of MRAM-, ReRAM-, and FeFET-based crossbars by employing statistical signal and noise models. For a specific dot-product dimension N, the maximum compute SNR (SNRmax) is shown to occur at an optimum value of sensing resistance $R_{s}^{*}$ where clipping and quantization noise contributions from the analog-to-digital converter (ADC) are balanced out. SNRmax can be further improved by choosing devices with higher resistive contrast Roff/Ron, e.g., FeFET, but only until it attains a value in the range 12-15. Beyond this point, mismatch in the input digital-to-analog converters (DACs) and bitcell variations begin to dominate the compute SNR. Finally, by mapping a ResNet20 (CIFAR-10) network onto resistive crossbars, it is shown that the array-level compute SNR maximizing circuit parameters also maximizes the network-level accuracy.
Pre-Professional Internships and their Relationship with Employer Satisfaction of Accounting Students at a Technological Institute

Given the demands of the globalized and competitive job market, higher technical institutes must improve the training of technical professionals, especially in recent years when the COVID-19 pandemic has caused unemployment and underemployment. Pre-professional internship constitutes one of the fundamental components of quality technical education. In this study, the relationship between the performance in pre-professional internships, rated by the Instituto de Educación Superior Tecnológico Público Aenor Orrego, and the satisfaction of the expectations of the employer was studied in order to find the degree of congruence between the training entity and the requirements of the employers. The research is of a descriptive correlational type, since it considers the analysis of paired samples, without manipulating the variables. The results show a good, direct and positive correlation. The educational impact on employability would be notable if we consider the needs of employers; it would also contribute to the economic growth and welfare of the country.
Inducing Discourse Marker Inventories from Lexical Knowledge Graphs

Discourse marker inventories are important tools for the development of both discourse parsers and corpora with discourse annotations. In this paper we explore the potential of massively multilingual lexical knowledge graphs to induce multilingual discourse marker lexicons using concept propagation methods as previously developed in the context of translation inference across dictionaries. Given one or multiple source languages with discourse marker inventories that discourse relations as senses of potential discourse markers, as well as a large number of bilingual dictionaries that link them – directly or indirectly – with the target language, we specifically study to what extent discourse marker induction can benefit from the integration of information from different sources, the impact of sense granularity and what limiting factors may need to be considered. Our study uses discourse marker inventories from nine European languages normalized against the discourse relation inventory of the Penn Discourse Treebank (PDTB), as well as three collections of machine-readable dictionaries with different characteristics, so that the interplay of a large number of factors can be studied.
Empirical Analysis of Noising Scheme based Synthetic Data Generation for Automatic Post-editing

Automatic post-editing (APE) refers to a research field that aims to automatically correct errors included in the translation sentences derived by the machine translation system. This study has several limitations, considering the data acquisition, because there is no official dataset for most language pairs. Moreover, the amount of data is restricted even for language pairs in which official data has been released, such as WMT. To solve this problem and promote universal APE research regardless of APE data existence, this study proposes a method for automatically generating APE data based on a noising scheme from a parallel corpus. Particularly, we propose a human mimicking errors-based noising scheme that considers a practical correction process at the human level. We propose a precise inspection to attain high performance, and we derived the optimal noising schemes that show substantial effectiveness. Through these, we also demonstrate that depending on the type of noise, the noising scheme-based APE data generation may lead to inferior performance. In addition, we propose a dynamic noise injection strategy that enables the acquisition of a robust error correction capability and demonstrated its effectiveness by comparative analysis. This study enables obtaining a high performance APE model without human-generated data and can promote universal APE research for all language pairs targeting English.
Polarity of anomalous Hall effect hysteresis loops in † Pt / Co ‡ 15 / AF / † Co / Pt ‡ 15 ( AF = FeMn , NiO ) multilayers with perpendicular anisotropy

The effect of the metallic antiferromagnet (AF) g-FeMn and the AF-semiconductor NiO alloys on the polarity of anomalous Hall resistivity loops is examined in perpendicularly biased fPts2 nmd /Cos0.4 nmdg15/AFs3 nmd / fCos0.4 nmd /Pts2 nmdg15 (AF=NiO, FeMn) multilayers. The Hall resistivity exhibits negative polarity for AF=NiO and positive polarity for AF=FeMn. These differences are explained by the reduced spin-diffusion-length effects and the specular reflection of electrons at FeMn and NiO interfaces, respectively. In addition, it is shown that a sandwiched AF thin layer stabilizes the exchange-bias effect via interlayer coupling between top fCos0.4 nmd /Pts2 nmdg15 and bottomfPts2 nmd /Cos0.4 nmdg15 structures in multilayers with strong perpendicular magnetic anisotropy. © 2005 American Institute of Physics . [DOI: 10.1063/1.1825628 ]
Feature extraction for RGB-D cameras

A proposed feature extraction method for RGB-D cameras is developed. The proposed method for feature extraction is based on a Histogram of oriented gradient HOG which is used to extract the features of RGB image alongside with Histogram of oriented depth HOD which extracts the depth features to find a new different feature vector that is better describe the image. The new feature extraction method is benchmarked by human action recognition of pause images and shows better performance than HOG and HOD
Lexical Resource Mapping via Translations

Aligning lexical resources that associate words with concepts in multiple languages increases the total amount of semantic information that can be leveraged for various NLP tasks. We present a translation-based approach to mapping concepts across diverse resources. Our methods depend only on multilingual lexicalization information. When applied to align WordNet/BabelNet to CLICS and OmegaWiki, our methods achieve state-of-the-art accuracy, without any dependence on other sources of semantic knowledge. Since each word-concept pair corresponds to a unique sense of the word, we also demonstrate that the mapping task can be framed as word sense disambiguation. To facilitate future work, we release a set of high-precision WordNet-CLICS alignments, produced by combining three different mapping methods.
Medical Crossing: a Cross-lingual Evaluation of Clinical Entity Linking

Medical data annotation requires highly qualified expertise. Despite the efforts devoted to medical entity linking in different languages, available data is very sparse in terms of both data volume and languages. In this work, we establish benchmarks for cross-lingual medical entity linking using clinical reports, clinical guidelines, and medical research papers. We present a test set filtering procedure designed to analyze the “hard cases” of entity linking approaching zero-shot cross-lingual transfer learning, evaluate state-of-the-art models, and draw several interesting conclusions based on our evaluation results.
Cyclops: an FSO-based wireless link for VR headsets

The ultimate goal of virtual reality (VR) is to create an experience indistinguishable from actual reality. To provide such a "life-like" experience, (i) the VR headset (VRH) should be wireless so that the user can move around freely, and (ii) the wireless link, connecting the VRH to a high-performance renderer, should support high data rates (tens to hundreds of Gbps). Industry is already pushing towards such wireless VRHs; however, these wireless links can only support a few Gbps rates. In general, current radio-frequency (RF) links (including mmWave) are not able to provide desired data rates. In this paper, we build a system, we call Cyclops, which uses free-space optical (FSO) technology to create a high-bandwidth VR wireless link. FSO links are capable of very high data rates (up to Tbps) due to the high frequencies of light waves and narrow beams. The main challenges in developing an effective FSO link are: (i) designing a link with sufficient movement tolerance, and (ii) developing a viable tracking and pointing (TP) mechanism which maintains the link while the VRH moves. As traditional TP approaches seem infeasible in our context, we develop a novel TP approach based on learning techniques, leveraging the VRH's inbuilt tracking system. We build robust 10 Gbps and 25Gbps link prototypes from commodity components, demonstrate their viability for expected movement speeds of a VRH, and show that, with certain custom-built components, we can support much higher movement speeds and bandwidths.
Windmill modelling consideration and factors influencing the stability of a grid-connected wind power based embedded generator

Summary form only given. Generation of electricity using wind power has received considerable attention worldwide in recent years. In order to investigate the impacts of the integration of wind farm into utilities' network various windmill models is developed. One such impact is related to the critical clearing time (CCT) of the wind power based embedded generators (WPBEGs). The work in this paper shows that oversimplification of the modelling of windmill mechanical drive train could introduce significant error in the value of the CCT that defines the stability limit of an integrated wind farm. This paper also reports investigation into the factors that influence the dynamic behaviour of the WPBEGs following network fault conditions. It is shown that wind farm CCT can be affected by various factors contributed by the host network. Results obtained from several case studies are presented and discussed. This investigation is conducted on a simulated grid-connected wind farm using EMTP.