Miracle-dz/UniFAR-FTUs huggingface dataset:
MOTIVATION FOR CSR PRACTICES: EVIDENCE FROM FINANCIAL SERVICES INDUSTRY

They could be a variety of motivations for business corporations to voluntarily engage in CSR activities. The existing literature indicates that successful and profitable corporations spent significant amount of money to actively participate in social and philanthropic programmes. Therefore, this article seeks to explore the motivations behind CSR engagement by financial services industry in Malaysia. A quantitative survey design was adopted to gather primary data from senior managers of the corporations. A total of 376 responses were collected and analysed in this study. The result shows that financial industry in Malaysia placed non-economic element as the main motive for their involvement in CSR practices.
Semantic Textual Similarity with Siamese Neural Networks

Calculating the Semantic Textual Similarity (STS) is an important research area in natural language processing which plays a significant role in many applications such as question answering, document summarisation, information retrieval and information extraction. This paper evaluates Siamese recurrent architectures, a special type of neural networks, which are used here to measure STS. Several variants of the architecture are compared with existing methods
Diachronic Analysis of Entities by Exploiting Wikipedia Page revisions

In the last few years, the increasing availability of large corpora spanning several time periods has opened new opportunities for the diachronic analysis of language. This type of analysis can bring to the light not only linguistic phenomena related to the shift of word meanings over time, but it can also be used to study the impact that societal and cultural trends have on this language change. This paper introduces a new resource for performing the diachronic analysis of named entities built upon Wikipedia page revisions. This resource enables the analysis over time of changes in the relations between entities (concepts), surface forms (words), and the contexts surrounding entities and surface forms, by analysing the whole history of Wikipedia internal links. We provide some useful use cases that prove the impact of this resource on diachronic studies and delineate some possible future usage.
Irisin Improves Autophagy of Aged Hepatocytes via Increasing Telomerase Activity in Liver Injury

An aged liver has decreased reparative capacity during ischemia-reperfusion (IR) injury. A recent study showed that plasma irisin levels predict telomere length in healthy adults. The aim of the present study is to clarify the role of irisin, telomerase activity, and autophagy during hepatic IR in the elderly. To study this, hepatic IR was established in 22-month- and 3-month-old rats and primary hepatocytes were isolated. The results showed that the old rats exhibited more serious liver injury and lower levels of irisin expression, telomerase activity, autophagy ability, and mitochondrial function than young rats during hepatic IR. Irisin activated autophagy and improved mitochondrial function via increasing telomerase activity in aged hepatocytes. Inhibition of telomerase activity by BIBP1532 abolished the protective role of irisin in hepatocytes during hypoxia and reoxygenation. Additionally, this study proved irisin increased the telomerase activity via inhibition of the phosphorylation of JNK during hepatic IR. Administration of exogenous irisin significantly mitigated the inflammation, oxidative stress, apoptosis, and liver injury in an old rat model of hepatic IR. In conclusion, irisin improves autophagy of aged hepatocytes via increasing telomerase activity in hepatic IR. Irisin exhibits conspicuous benefits in increasing reparative capacity of an aged liver during hepatic IR.
On the Palaeozoic Tillite of the Adigrat Group (Tigrai, Ethiopia)

— A flat-lying sequence containing diamictite convincingly interpreted as tillites in the literature, occurs at the base of the (Mesozoic) Adigrat Sandstone in the Tigrai Region (Northern Ethiopia). The age of the tillite is controversial, two options (Ordovician and Permian) being put forward by previous authors. As an outcome of petrographic study, the pebbles displayed widespread, previously unreported occurrence of volcanics, thus strengthening the Permian options on the ground of regional geology. Isotopic analyses of C and O allowed comparison with tillites of the underlying, Neo-Proterozoic basement and proved the lack of the extreme excursion of δ13C that in the Neopoterozoic tillite were seen as supporting the “Snowball Earth” hypothesis for the “Sturtian” age glaciation. Rb-Sr radiometric dating was also performed and yielded results compatible with a Permian age, though facing difficulties connected with intimate intermingling of clasts and matrix, and deuteric alteration. riassunto. — Nella regione del Tigrai (Etiopia settentrionale) una sequenza detritica comprendente diamictiti, convincentemente interpretate come tilliti dagli autori precedenti, si interpone tra il basamento neo-proterozoico e la copertura mesozoica. Due datazioni della sequenza si incontrano in letteratura (Ordoviciano vs. Permiano). Lo studio petrografico ha dimostrato la estesa presenza di materiale vulcanico, rinforzando la opzione permiana in base a considerazioni di geologia regionale. L’analisi isotopica di C ed O ha riscontrato l’assenza della estrema escursione dei valori di δ13C che nelle tilliti del basamento hanno fornito supporto alla ipotesi “Snowball Earth”. Un tentativo di datazione col metodo Rb-Sr ha incontrato difficoltà costituite dall’intimo mescolamento di materiali diversi (clasti, matrice) e dall’alterazione, ma ha fornito risultati compatibili con un’età permiana.
Why so Negative? Belief Formation and Risk Taking in Boom and Bust Markets

What determines investors' risk-taking across macroeconomic cycles? Researchers have proposed rational expectations models that introduce countercyclical risk aversion to generate the empirically observed time variation in risk-taking. In this study, we test whether systematic deviations from rational expectations can cause the same observed investment pattern without assuming unstable risk preferences. We let subjects form beliefs in two different market environments which resemble key characteristics of boom and bust markets, followed by an independent investment task. Those subjects who learned in the negative domain form overly pessimistic beliefs and invest significantly less in an unrelated ambiguous investment option. However, similar investment patterns cannot be observed for an unrelated risky investment option, where expectations are fixed. The proposed mechanism presents an alternative explanation for time-varying risk-taking and provides new implications for both theory and policy makers.
Influenza C:a pilot study investigating the prevalence in Lancaster of a neglected respiratory virus

Influenza C is a virus found throughout the world that can cause respiratory illness, ranging from mild colds to pneumonias. Typically affecting younger children, it is a virus which can cause considerable illness and complications, and yet research into the virus is lacking. This study aimed to identify the prevalence of influenza C in Lancaster, and to determine the current seropositivity levels in the study population. 148 participants were recruited to the study – 77 asymptomatic and 71 symptomatic. Participants were asked to provide a nasal or nasopharyngeal swab and/or a serum sample. The swabs were analysed via polymerase chain reaction (PCR) and the serum samples by enzyme-linked immunosorbent assays (ELISA) and, of the samples tested via ELISA, 106 were positive (82%) and 23 were negative. Two swab samples also appeared positive for influenza C following PCR and so were sent for deep sequencing. A further seven mixed cDNA samples were also sent for deep sequencing to allow for comparison between different population groups etc. Overall, it was found that influenza C is prevalent in the Lancaster area, with the entire study population having some level of exposure to the virus previously, although only 82% of participants met the threshold to be classed as seropositive, and two participants were actively carrying the live influenza C virus. Further work needs to be done to analyse the seasonality of the virus and discover whether the virus has the same impact in the UK as it does in other parts of the world. As positive samples have been found and most of the population have influenza C antibodies, it provides a strong foundation for future work.
Text-Based Joint Prediction of Numeric and Categorical Attributes of Entities in Knowledge Bases

Collaboratively constructed knowledge bases play an important role in information systems, but are essentially always incomplete. Thus, a large number of models has been developed for Knowledge Base Completion, the task of predicting new attributes of entities given partial descriptions of these entities. Virtually all of these models either concentrate on numeric attributes () or they concentrate on categorical attributes (). In this paper, we propose a simple feed-forward neural architecture to jointly predict numeric and categorical attributes based on embeddings learned from textual occurrences of the entities in question. Following insights from multi-task learning, our hypothesis is that due to the correlations among attributes of different kinds, joint prediction improves over separate prediction. Our experiments on seven FreeBase domains show that this hypothesis is true of the two attribute types: we find substantial improvements for numeric attributes in the joint model, while performance remains largely unchanged for categorical attributes. Our analysis indicates that this is the case because categorical attributes, many of which describe membership in various classes, provide useful ‘background knowledge’ for numeric prediction, while this is true to a lesser degree in the inverse direction.
Privatisation prices: The role of prior restructuring and emerging private ownership structure

We investigate the impact of restructuring policies before sale and resulting private ownership structures on privatisation sales prices. We use a new dataset of companies privatised in Brazil between 1991 and 2004, covering 118 transactions observed in the period. Our results show that, after controlling for endogeneity, replacing the CEO is associated with an increase in privatisation net prices. Efficiency measures aimed at improving operating performance of the firms and voluntary labour downsizing schemes fail to improve premiums. On the other hand, restructuring measures such as debt absorption and compulsory labour downsizing schemes have a significant negative impact on net prices. We also find that prices are very sensitive to the level of competition in the auction and that the longer it takes to put the company on the block the lower the premium obtained. Finally, prices are shown to depend upon the resulting ownership structures. The more concentrated private ownership is, the higher prices are. Identity of owners on concentration is also shown to matter as domestic institutions and local corporations are associated with higher prices, while employee ownership concentration has a negative impact on premiums. This case suggests conflicts of interest. JEL classification:G32, H10, J45, O1
Quasi-Newton Waveform Iteration for Partitioned Fluid-Structure Interaction

We present novel coupling schemes for partitioned multi-physics simulation that combine four important aspects for strongly coupled problems: implicit coupling per time step, fast and robust acceleration of the corresponding iterative coupling, support for multi-rate time stepping, and higher-order convergence in time. To achieve this, we combine waveform relaxation -- a known method to achieve higher order in applications with split time stepping based on continuous representations of coupling variables in time -- with interface quasi-Newton coupling, which has been developed throughout the last decade and is generally accepted as a very robust iterative coupling method even for gluing together black-box simulation codes. We show convergence results (in terms of convergence of the iterative solver and in terms of approximation order in time) for two academic test cases -- a heat transfer scenario and a fluid-structure interaction simulation. We show that we achieve the expected approximation order and that our iterative method is competitive in terms of iteration counts with those designed for simpler first-order-in-time coupling.
Understanding star formation and feedback in small galaxies

Context. Low-luminosity, active star-forming blue compact galaxies (BCGs) are excellent laboratories for investigating the process of star formation on galactic scales and to probe the interplay between massive stars and the surrounding interstellar (or intergalactic) medium. Aims. We investigate the morphology, structure, and stellar content of BCG Mrk 900, as well as the excitation, ionization conditions, and kinematics of its H II regions and surrounding ionized gas. Methods. We obtained integral field observations of Mrk 900 working with the Visible Multi-Object Spectrograph at the Very Large Telescope. The observations were taken in the wavelength range 4150−7400 Å covering a field of view of 27″ × 27″ on the sky with a spatial sampling of 0. ″67. From the integral field data we built continuum, emission, and diagnostic line ratio maps and produced velocity and velocity dispersion maps. We also generated the integrated spectrum of the major H II regions and the nuclear area to determine reliable physical parameters and oxygen abundances. Integral field spectroscopy was complemented with deep broad-band photometry taken at the 2.5 m NOT telescope; the broad-band data, tracing the galaxy up to radius 4 kpc, allowed us to investigate the properties of the low surface brightness underlying stellar host. Results. We disentangle two different stellar components in Mrk 900: a young population, which resolves into individual stellar clusters with ages ∼5.5−6.6 Myr and extends about 1 kpc along the galaxy minor axis, is placed on top of a rather red and regular shaped underlying stellar host, several Gyr old. We find evidence of a substantial amount of dust and an inhomogeneous extinction pattern, with a dust lane crossing the central starburst. Mrk 900 displays overall rotation, although distorted in the central, starburst regions; the dispersion velocity map is highly inhomogeneous, with values increasing up to 60 km s−1 at the periphery of the SF regions, where we also find hints of the presence of shocks. Our observational results point to an interaction or merger with a low-mass object or infalling gas as plausible trigger mechanisms for the present starburst event.
Independent Control of Multiple Channels in Metasurface Devices

In analogy with electromagnetic networks which connect multiple input-output ports, metasurfaces can be considered as multi-port devices capable of providing different functionalities for waves of different polarizations illuminating the surface from different directions. The main challenge in the design of such multichannel metasurfaces is to ensure independent and full control of the electromagnetic response for each channel ensuring the fulilment of the boundary condition at the metasurface. In this work, we demonstrate that by properly engineering the evanescent fields excited at each port (that is, for all possible illumination directions), it is possible to independently control the reflection or transmission for all different illuminations. We develop a fully analytical method to analyze and synthesize general space-modulated impedance metasurfaces, engineering strong spatial dispersion. This method, combined with mathematical optimization, allows us to find a surface impedance profile that simultaneously ensures the desired electromagnetic responses at each port. We validate the technique via the design of phase-controlled multichannel retroreflectors. In addition, we demonstrate that the method is rather powerful in the design of other functional metasurfaces such as multifunctional reflectors and multichannel perfect absorbers.
LTP: A New Active Learning Strategy for Bert-CRF Based Named Entity Recognition

In recent years, deep learning has achieved great success in many natural language processing tasks including named entity recognition. The shortcoming is that a large amount of manually-annotated data is usually required. Previous studies have demonstrated that active learning could elaborately reduce the cost of data annotation, but there is still plenty of room for improvement. In real applications we found existing uncertainty-based active learning strategies have two shortcomings. Firstly, these strategies prefer to choose long sequence explicitly or implicitly, which increase the annotation burden of annotators. Secondly, some strategies need to invade the model and modify to generate some additional information for sample selection, which will increase the workload of the developer and increase the training/prediction time of the model. In this paper, we first examine traditional active learning strategies in a specific case of BiLstm-CRF that has widely used in named entity recognition on several typical datasets. Then we propose an uncertainty-based active learning strategy called Lowest Token Probability (LTP) which combines the input and output of CRF to select informative instance. LTP is simple and powerful strategy that does not favor long sequences and does not need to invade the model. We test LTP on multiple datasets, and the experiments show that LTP performs slightly better than traditional strategies with obviously less annotation tokens on both sentence-level accuracy and entity-level F1-score. Related code have been release on this https URL
Don’t Forget The Past: Recurrent Depth Estimation from Monocular Video

Autonomous cars need continuously updated depth information. Thus far, depth is mostly estimated independently for a single frame at a time, even if the method starts from video input. Our method produces a time series of depth maps, which makes it an ideal candidate for online learning approaches. In particular, we put three different types of depth estimation (supervised depth prediction, self-supervised depth prediction, and self-supervised depth completion) into a common framework. We integrate the corresponding networks with a ConvLSTM such that the spatiotemporal structures of depth across frames can be exploited to yield a more accurate depth estimation. Our method is flexible. It can be applied to monocular videos only or be combined with different types of sparse depth patterns. We carefully study the architecture of the recurrent network and its training strategy. We are first to successfully exploit recurrent networks for real-time self-supervised monocular depth estimation and completion. Extensive experiments show that our recurrent method outperforms its image-based counterpart consistently and significantly in both self-supervised scenarios. It also outperforms previous depth estimation methods of the three popular groups. Please refer to our webpage11https://www.trace. ethz.ch/publications/2020/rec_depth_estimation/ for details.
Training Progressively Binarizing Deep Networks using FPGAs

While hardware implementations of inference routines for Binarized Neural Networks (BNNs) are plentiful, current realizations of efficient BNN hardware training accelerators, suitable for Internet of Things (IoT) edge devices, leave much to be desired. Conventional BNN hardware training accelerators perform forward and backward propagations with parameters adopting binary representations, and optimization using parameters adopting floating or fixed-point real-valued representations-requiring two distinct sets of network parameters. In this paper, we propose a hardware-friendly training method that, contrary to conventional methods, progressively binarizes a singular set of fixed-point network parameters, yielding notable reductions in power and resource utilizations. We use the Intel FPGA SDK for OpenCL development environment to train our progressively binarizing DNNs on an OpenVINO FPGA. We benchmark our training approach on both GPUs and FPGAs using CIFAR-10 and compare it to conventional BNNs.
To Transfer or Not to Transfer: Misclassification Attacks Against Transfer Learned Text Classifiers

Transfer learning --- transferring learned knowledge --- has brought a paradigm shift in the way models are trained. The lucrative benefits of improved accuracy and reduced training time have shown promise in training models with constrained computational resources and fewer training samples. Specifically, publicly available text-based models such as GloVe and BERT that are trained on large corpus of datasets have seen ubiquitous adoption in practice. In this paper, we ask, "can transfer learning in text prediction models be exploited to perform misclassification attacks? " As our main contribution, we present novel attack techniques that utilize unintended features learnt in the teacher (public) model to generate adversarial examples for student (downstream) models. To the best of our knowledge, ours is the first work to show that transfer learning from state-of-the-art word-based and sentence-based teacher models increase the susceptibility of student models to misclassification attacks. First, we propose a novel word-score based attack algorithm for generating adversarial examples against student models trained using context-free word-level embedding model. On binary classification tasks trained using the GloVe teacher model, we achieve an average attack accuracy of 97% for the IMDB Movie Reviews and 80% for the Fake News Detection. For multi-class tasks, we divide the Newsgroup dataset into 6 and 20 classes and achieve an average attack accuracy of 75% and 41% respectively. Next, we present length-based and sentence-based misclassification attacks for the Fake News Detection task trained using a context-aware BERT model and achieve 78% and 39% attack accuracy respectively. Thus, our results motivate the need for designing training techniques that are robust to unintended feature learning, specifically for transfer learned models.
Thaddäus Derfflinger’s Sunspot Observations during 1802–1824: A Primary Reference to Understand the Dalton Minimum

As we are heading toward the next solar cycle, presumably with a relatively small amplitude, it is of significant interest to reconstruct and describe the past secular minima on the basis of actual observations at the time. The Dalton Minimum is often considered one of the secular minima captured in the coverage of telescopic observations. Nevertheless, the reconstructions of the sunspot group number vary significantly, and the existing butterfly diagrams have a large data gap during the period. This is partially because most long-term observations at that time have remained unexplored in historical archives. Therefore, to improve our understanding on the Dalton Minimum, we have located two series of Thaddäus Derfflinger’s observational records spanning 1802–1824 (a summary manuscript and logbooks), as well as his Brander’s 5.5 feet azimuthal quadrant preserved in the Kremsmünster Observatory. We have revised the existing Derfflinger’s sunspot group number with Waldmeier classification, and eliminated all the existing “spotless days” to remove contaminations from solar elevation observations. We have reconstructed the butterfly diagram on the basis of his observations and illustrated sunspot distributions in both solar hemispheres. Our article aims to revise the trend of Derfflinger’s sunspot group number and to bridge a data gap of the existing butterfly diagrams around the Dalton Minimum. Our results confirm that the Dalton Minimum is significantly different from the Maunder Minimum, both in terms of cycle amplitudes and sunspot distributions. Therefore, the Dalton Minimum is more likely a secular minimum in the long-term solar activity, while further investigations for the observations at that time are required.