Miracle-dz/UniFAR-FTUs huggingface dataset:
Working at Home in Greece: Unexplored Potential at Times of Social Distancing?

This paper investigates the incidence, trend and determinants of remote work in Greece. A crisis-stricken country in the years preceding the Covid-19 crisis, Greece entered the first wave of the public health shock as a laggard in digitalisation and remote work arrangements among European countries. While Covid-19 induced a spike in the use of remote work arrangements in many countries, this paper presents evidence that working from home (WfH) in Greece was subdued in the past decade. By analysing the profile of the job tasks and skill needs of Greek homeworkers, the paper also shows marked deviations in homeworking patterns and determinants in Greece, relative to other EU countries. This includes a higher prevalence of WfH among Greek females and non-nationals, limited use by young workers and families with children and a stronger relation with atypical work hours. While remote workers in Greece receive a 7% monthly wage premium, their jobs are found to involve standardised and moderate ICT tasks and to rely more on social serving tasks. The paper highlights that there is significant scope to enhance remote work in Greece, which can amount to up to 37% of all salaried jobs, subject to changing work organisation, norms and policies. In the coronavirus era, overcoming barriers to remote work will be key for the Greek labour market to adapt to social distances practices and digitalisation.
RELATIONSHIP BETWEEN NEW PRODUCT CHARACTERISTIC AND SALESPERSON ADOPTION IN MANUFACTURING FIRMS IN KENYA

This study examined the relationship between new product characteristics and new product adoption by the sales force. The specific objectives of the study were to examine the relationship between new product characteristics and product adoption by the sales person. The design of the research was cross sectional survey using the questionnaire to collect data. The target population was 250 sales managers and 550 salespersons, the sample comprised of 122 salespeople judgmentally sampled and 64 sales managers who were randomly sampled from manufacturing firms in Kenya. Data was analyzed using descriptive and inferential statistical tools. The findings show that there is a high correlation between product characteristics and product adoption by the salespeople. Specifically, relative advantage, compatibility, trialability and communicability showed strong correlation to product adoption by sales people while complexity had no significant relationship with salesperson adoption. The product characteristics significantly influence the adoption of products. Relative advantage is the best predictor of an innovation’s rate of adoption and is positively related to it. New products that are compatible with user’s previous values and its current practice are adopted. Complexity affects sales force adoption as the salesperson takes a long time and great effort to learn the new product well enough to explain it to customers. Innovations whose result can be seen and communicated are more easily adopted than those which are difficult to be communicated. For an innovation to be successfully adopted and used, the users must become aware of the innovation and know its benefits. A long term perspective can be achieved as immediate results can be balanced with long term sales relationships and outcomes. The salespersons should be motivated through offering of tangible rewards that will offer a sense of accomplishment, self actualization and self worth.
The Association Between Corporate Governance and Corporate Bond Liquidity: An Intraday Analysis

Extensive studies in the literature of market microstructure are dedicated to either verifying the informational efficiency of the market or exploring the relationship of relative efficiencies across different markets. However, little work has been done to examine the relation between asset liquidity and institutional details of firm and management team. The key research question of this study is whether or not the intraday corporate bond liquidity is associated with corporate internal governance, a governance mechanism stemming from within the top management team, shortly before earnings announcements when the information asymmetry is heightened. Building on the theoretical model of internal governance, the paper uncovers a hump-shaped relationship between internal governance measure and corporate bond liquidity before earnings announcements. The empirical results are generally persistent across a battery of robustness checks such as sensitivity check, subsample test and aggregation analysis. I employ the 2008 financial crisis as the main identification strategy. In face of the exogenous shock upon internal governance measure, the hump shape relationship still holds. This novel study contributes to the literature of financial economics, corporate governance and market microstructure. The study also provides evidence in support of the argument that the corporate bond market is informationally efficient.
Scaling Graph Neural Networks with Approximate PageRank

Graph neural networks (GNNs) have emerged as a powerful approach for solving many network mining tasks. However, learning on large graphs remains a challenge -- many recently proposed scalable GNN approaches rely on an expensive message-passing procedure to propagate information through the graph. We present the PPRGo model which utilizes an efficient approximation of information diffusion in GNNs resulting in significant speed gains while maintaining state-of-the-art prediction performance. In addition to being faster, PPRGo is inherently scalable, and can be trivially parallelized for large datasets like those found in industry settings. We demonstrate that PPRGo outperforms baselines in both distributed and single-machine training environments on a number of commonly used academic graphs. To better analyze the scalability of large-scale graph learning methods, we introduce a novel benchmark graph with 12.4 million nodes, 173 million edges, and 2.8 million node features. We show that training PPRGo from scratch and predicting labels for all nodes in this graph takes under 2 minutes on a single machine, far outpacing other baselines on the same graph. We discuss the practical application of PPRGo to solve large-scale node classification problems at Google.
Improved RIP-Based Bounds for Guaranteed Performance of Several Compressed Sensing Algorithms

Iterative hard thresholding (IHT) and compressive sampling matching pursuit (CoSaMP) are two types of mainstream compressed sensing algorithms using hard thresholding operators for signal recovery and approximation. The guaranteed performance for signal recovery via these algorithms has mainly been analyzed under the condition that the restricted isometry constant of a sensing matrix, denoted by $ \delta_K$ (where $K$ is an integer number), is smaller than a certain threshold value in the interval $(0,1). $ The condition $ \delta_{K}< \delta^*$ for some constant $ \delta^* \leq 1 $ ensuring the success of signal recovery with a specific algorithm is called the restricted-isometry-property-based (RIP-based) bound for guaranteed performance of the algorithm. At the moment, the best known RIP-based bound for the guaranteed recovery of $k$-sparse signals via IHT is $\delta_{3k}< 1/\sqrt{3}\approx 0.5774,$ and the bound for guaranteed recovery via CoSaMP is $\delta_{4k} < 0.4782. $ A fundamental question in this area is whether such theoretical results can be further improved. The purpose of this paper is to affirmatively answer this question and rigorously show that the RIP-based bounds for guaranteed performance of IHT can be significantly improved to $ \delta_{3k} < (\sqrt{5}-1)/2 \approx 0.618, $ and the bound for CoSaMP can be improved and pushed to $ \delta_{4k}< 0.5102. $ These improvements are achieved through a deep property of the hard thresholding operator.
Further Studies on Macrogyrodactylus polypteri, a Monogenean on the African Freshwater Fish Polypterus senegalus

Macrogyrodactylus polypteri Malmberg, 1957* is a viviparous monogenean found on the skin and fins of the African freshwater fish Polypterus senegalus Cuvier. It was first reported by Malmberg (1957) from Gambia. Khalil (1964, 19G9) and Amirthalingam (19C5) recorded it on the same host species and Saoud & Mageed (1969) found it on P. bichir in the Sudan. Prudhoe (1957) described a new genus and species, Neogyrodactylus congolensis, on the skin of Clarias lazera from the Congo but Yamaguti (1963) considered this genus to be a synonym of Macrogyrodactylus. A third species, M. clarii Gussev, 1961 was reported on Clarias sp. from south Ethiopia and Ghana. On the basis of one specimen M. latest Paperna, 1969 was reported on Lates nilotica from Ghana. So far the species of the genus are confined to African freshwater fishes.
Increasing the Trustworthiness of Deep Neural Networks via Accuracy Monitoring

Inference accuracy of deep neural networks (DNNs) is a crucial performance metric, but can vary greatly in practice subject to actual test datasets and is typically unknown due to the lack of ground truth labels. This has raised significant concerns with trustworthiness of DNNs, especially in safety-critical applications. In this paper, we address trustworthiness of DNNs by using post-hoc processing to monitor the true inference accuracy on a user's dataset. Concretely, we propose a neural network-based accuracy monitor model, which only takes the deployed DNN's softmax probability output as its input and directly predicts if the DNN's prediction result is correct or not, thus leading to an estimate of the true inference accuracy. The accuracy monitor model can be pre-trained on a dataset relevant to the target application of interest, and only needs to actively label a small portion (1% in our experiments) of the user's dataset for model transfer. For estimation robustness, we further employ an ensemble of monitor models based on the Monte-Carlo dropout method. We evaluate our approach on different deployed DNN models for image classification and traffic sign detection over multiple datasets (including adversarial samples). The result shows that our accuracy monitor model provides a close-to-true accuracy estimation and outperforms the existing baseline methods.
Detailed 3D structure of Orion A in dust with Gaia DR2

The unprecedented astrometry from Gaia’s second data release (DR2) provides us with an opportunity to study molecular clouds in the solar neighbourhood in detail. Extracting the wealth of information in these data remains a challenge, however. We have further improved our Gaussian-processes-based, three-dimensional dust mapping technique to allow us to study molecular clouds in more detail. These improvements include a significantly better scaling of the computational cost with the number of stars, and taking into account distance uncertainties to individual stars. Using Gaia DR2 astrometry together with the Two Micron All Sky Survey (2MASS) and the Wide-Field Infrared Survey Explorer (WISE) photometry for 30 000 stars, we infer the distribution of dust out to 600 pc in the direction of the Orion A molecular cloud. We identify a bubble-like structure in front of Orion A, centred at a distance of about 350 pc from the Sun. The main Orion A structure is visible at slightly larger distances, and we clearly see a tail extending over 100 pc that is curved and slightly inclined to the line of sight. The location of our foreground structure coincides with 5–10 Myr old stellar populations, suggesting a star formation episode that predates that of the Orion Nebula Cluster itself. We also identify the main structure of the Orion B molecular cloud, and in addition discover a background component to this at a distance of about 460 pc from the Sun. Finally, we associate our dust components at different distances with the plane-of-the-sky magnetic field orientation as mapped by Planck. This provides valuable information for modelling the magnetic field in three dimensions around star-forming regions.
Firmness measurement of stored 'Delicious' apples by sensory methods, Magness-Taylor, and sonic transmission

Additional index words. Malus domestica, ripeness, quality, postharvest Abstract. 'Delicious' apples (Malus domestica Borkh.) from five major U.S. production areas were tested after ≈3 months of commercial storage. Soluble solids concentration (SSC), titratable acidity (TA), Magness-Taylor (MT) firmness, and sonic transmission spectra were compared with ripeness (maturity in trade terminology) scores assigned by six U.S. Dept. of Agriculture (USDA)-licensed apple inspectors according to USDA Grades and Standards inspec- tion procedures. USDA ripeness categories are defined by textural and flavor terms. Inspectors in this test used visual, manual, oral, and auditory sensations to make their judgments, but firmness was the paramount characteristic judged. SSC and TA did not correlate with inspectors' scores, MT, or sonic measurements and thus are not satisfactory indices of ripeness for stored apples. Sonic resonance functions correlated significantly with mean inspectors' scores and with MT firmness. Inspectors' scores correlated slightly better with MT firmness than with sonic terms. MT is destructive and site-specific; in contrast, sonic measurements are nondestructive and representative of the entire fruit. Apple production in the United States in 1987 was nearly 4.9 million tons, valued at more than $906 million (U.S. Dept. Agr., 1989). Effective merchandising of such a volume of fruit requires effective sorting for several characteristics, particularly those in the Grade Standards (U.S. Dept. Agr., 1976). Domestic and foreign buyers both continuously demand better quality and a more uniform degree of ripeness. Renewed interest in apple ripeness (maturity in trade termi- nology) by the Agricultural Marketing Service (AMS) caused us to reevaluate simple tests that AMS inspectors could use and to examine the characteristics and methods that inspectors pres- ently use. It also afforded an opportunity to reexamine sonic vibrational characteristics for measuring texture in view of re- cent advances in electronic instruments and computers. SSC and TA content of apples are known to change with maturation and ripening. Texture often is equated with quality in apples, with firm, crisp apples being the ideal. Firmness is also frequently used as a measure of maturity and ripeness. In fact, the USDA-AMS Standards for maturity/ripeness cate- gories for fresh apples are Immature, Hard, Firm, Firm-ripe, Ripe, and Overripe (U.S. Dept. Agr., 1976). AMS inspectors rely mostly on firmness to make those assessments, particularly on apples that have been stored. Firmness is most commonly measured by a puncture test using a MT type fruit firmness tester (Abbott et al., 1976; Blanpied et al., 1978; Bourne, 1974; Magness and Taylor, 1925). Pro- vided that the probe geometry is comparable to that of the orig-
The surprisingly carbon-rich environment of the S-type star W Aql,

Context. W Aql is an asymptotic giant branch (AGB) star with an atmospheric elemental abundance ratio C/O ≈ 0.98. It has previously been reported to have circumstellar molecular abundances intermediate between those of M-type and C-type AGB stars, which respectively have C/O < 1 and C/O > 1. This intermediate status is considered typical for S-type stars, although our understanding of the chemical content of their circumstellar envelopes is currently rather limited. Aims. We aim to assess the reported intermediate status of W Aql by analysing the line emission of molecules that have never before been observed towards this star. Methods. We performed observations in the frequency range 159−268 GHz with the SEPIA/B5 and PI230 instruments on the APEX telescope. We made abundance estimates through direct comparison to available spectra towards a number of well-studied AGB stars and based on rotational diagram analysis in the case of one molecule. Results. From a compilation of our abundance estimates and those found in the literature for two M-type (R Dor, IK Tau), two S-type (χ Cyg, W Aql), and two C-type stars (V Aql, IRC +10 216), we conclude that the circumstellar environment of W Aql appears considerably closer to that of a C-type AGB star than to that of an M-type AGB star. In particular, we detect emission from C2H, SiC2, SiN, and HC3N, molecules previously only detected towards the circumstellar environment of C-type stars. This conclusion, based on the chemistry of the gaseous component of the circumstellar environment, is further supported by reports in the literature on the presence of atmospheric molecular bands and spectral features of dust species which are typical for C-type AGB stars. Although our observations mainly trace species in the outer regions of the circumstellar environment, our conclusion matches closely that based on recent chemical equilibrium models for the inner wind of S-type stars: the atmospheric and circumstellar chemistry of S-type stars likely resembles that of C-type AGB stars much more closely than that of M-type AGB stars. Conclusions. Further observational investigation of the gaseous circumstellar chemistry of S-type stars is required to characterise its dependence on the atmospheric C/O. Non-equilibrium chemical models of the circumstellar environment of AGB stars need to address the particular class of S-type stars and the chemical variety that is induced by the range in atmospheric C/O.
Disentangled Graph Collaborative Filtering

Learning informative representations of users and items from the interaction data is of crucial importance to collaborative filtering (CF). Present embedding functions exploit user-item relationships to enrich the representations, evolving from a single user-item instance to the holistic interaction graph. Nevertheless, they largely model the relationships in a uniform manner, while neglecting the diversity of user intents on adopting the items, which could be to pass time, for interest, or shopping for others like families. Such uniform approach to model user interests easily results in suboptimal representations, failing to model diverse relationships and disentangle user intents in representations. In this work, we pay special attention to user-item relationships at the finer granularity of user intents. We hence devise a new model, Disentangled Graph Collaborative Filtering (DGCF), to disentangle these factors and yield disentangled representations. Specifically, by modeling a distribution over intents for each user-item interaction, we iteratively refine the intent-aware interaction graphs and representations. Meanwhile, we encourage independence of different intents. This leads to disentangled representations, effectively distilling information pertinent to each intent. We conduct extensive experiments on three benchmark datasets, and DGCF achieves significant improvements over several state-of-the-art models like NGCF, DisenGCN, and MacridVAE. Further analyses offer insights into the advantages of DGCF on the disentanglement of user intents and interpretability of representations. Our codes are available in https://github.com/ xiangwang1223/disentangled_graph_collaborative_filtering.
Inconsistencies Between Local and Global Measures of CME Radial Expansion as Revealed by Spacecraft Conjunctions

The radial expansion of coronal mass ejections (CMEs) is known to occur from remote observations, from the variation of their properties with radial distance, and from local in situ plasma measurements showing a decreasing speed profile throughout the magnetic ejecta (ME). However, little is known on how local measurements compare to global measurements of expansion. Here, we present results from the analysis of 42 CMEs measured in the inner heliosphere by two spacecraft in radial conjunction. The magnetic-field decrease with distance provides a measure of their global expansion. Near 1 au, the decrease in their bulk speed provides a measure of their local expansion. We find that these two measures have little relation with each other. We also investigate the relation between characteristics of CME expansion and CME properties. We find that the expansion depends on the initial magnetic-field strength inside the ME, but not significantly on the magnetic field inside the ME measured near 1 au. This is indirect evidence that CME expansion in the innermost heliosphere is driven by the high magnetic pressure inside the ME, while by the time the MEs reach 1 au, they are expanding due to the decrease in the solar-wind dynamic pressure with distance. We also determine the evolution of the ME tangential and normal magnetic-field components with distance, revealing significant deviations as compared to the expectations from force-free field configurations as well as some evidence that the front half of MEs expand at a faster rate than the back half.
On the Similarity between the Laplace and Neural Tangent Kernels

Recent theoretical work has shown that massively overparameterized neural networks are equivalent to kernel regressors that use Neural Tangent Kernels(NTK). Experiments show that these kernel methods perform similarly to real neural networks. Here we show that NTK for fully connected networks is closely related to the standard Laplace kernel. We show theoretically that for normalized data on the hypersphere both kernels have the same eigenfunctions and their eigenvalues decay polynomially at the same rate, implying that their Reproducing Kernel Hilbert Spaces (RKHS) include the same sets of functions. This means that both kernels give rise to classes of functions with the same smoothness properties. The two kernels differ for data off the hypersphere, but experiments indicate that when data is properly normalized these differences are not significant. Finally, we provide experiments on real data comparing NTK and the Laplace kernel, along with a larger class of{\gamma}-exponential kernels. We show that these perform almost identically. Our results suggest that much insight about neural networks can be obtained from analysis of the well-known Laplace kernel, which has a simple closed-form.
Strategies for Using Proximal Policy Optimization in Mobile Puzzle Games

While traditionally a labour intensive task, the testing of game content is progressively becoming more automated. Among the many directions in which this automation is taking shape, automatic play-testing is one of the most promising thanks also to advancements of many supervised and reinforcement learning (RL) algorithms. However these type of algorithms, while extremely powerful, often suffer in production environments due to issues with reliability and transparency in their training and usage. In this research work we are investigating and evaluating strategies to apply the popular RL method Proximal Policy Optimization (PPO) in a casual mobile puzzle game with a specific focus on improving its reliability in training and generalization during game playing. We have implemented and tested a number of different strategies against a real-world mobile puzzle game (Lily’s Garden from Tactile Games). We isolated the conditions that lead to a failure in either training or generalization during testing and we identified a few strategies to ensure a more stable behaviour of the algorithm in this game genre.