Miracle-dz/UniFAR-FTUs huggingface dataset:
Forecasting volatility and option pricing for exchange-rate dynamics: a comparison of models

This paper explores the applicability of static and dynamic models to capture the stylized facts of exchange-rate dynamics. The static models (mixture of distributions, compound Poisson process, generalized Student distribution) are compatible with leptokurtosis and can be characterized as scale-compounded distributions. The dynamic models (GARCH, GARCH-t, EGARCH, Markov-switching model), on the other hand, are compatible with both leptokurtosis and heteroskedasticity. In a comparison of the candidate models, it is found that the dynamic models do indeed achieve a better fit to the data than the static models. However, in forecasting experiments the dynamic models can outperform a 'naive' model of constant variances only with respect to unbiasedness but not with respect to precision. Furthermore, the paper examines the implications of the static and dynamic models for the pricing of foreign-currency options by simple simulations. Static models show significang option-price effect only when the maturity is short. GARCH and EGARCH models, on the other hand, imply options prices which are higher than Black-Scholes prices for the full range of moneyness. Only the Markov-switching model is compatible with the observed 'smile effects' on option markets.
Deciphering conserved identical sequences of mature miRNAs among six members of great apes

MicroRNAs (miRNAs) are a group of small RNA molecules which act as negative regulators of gene expression by controlling post-transcriptional regulation through binding to their corresponding mRNAs. Due to their small size, their nucleotide compositions are expected to be similar, but until now, the extent of similarity has not been reported in humans and their six phylogenetically closely related members of hominids. The present study allows direct comparison among six members of hominid species (Homosapiens, Gorillagorilla, Panpaniscus, Pongopygmaeus, Pantroglodytes and Symphalangussyndactylus) in terms of their miRNA repertoire, their evolutionary distance to human, as well as, the categorization of identical species-specific miRNAs. For this purpose, a total of 2694, 370, 157, 673, 590 and 10 mature miRNA sequences of Homosapiens, Gorillagorilla, Panpaniscus, Pongopygmaeus, Pantroglodytes and Symphalangussyndactylus respectively were retrieved from miRbase 22. A total of 12, 4, 4 and 3 conserved clusters with identical miRNA sequences that belong to the same gene families were found in Homosapiens, Gorillagorilla, Pongopygmaeus, Pantroglodytes respectively by neighbor-joining method using MEGA7 software. Interestingly, cross-species comparison has also shown a set of conserved identical miRNA sequences. Homologs of human mature miRNAs with 100% sequence identity are expected to have similar functions in the studied primates. Further in-vitro study is required to investigate common targets for identical miRNAs in the studied primates.
AESTHETICS APPRECIATION OF WOOD COLOUR AND PATTERNS BY COLORIMETRY: PART 1. COLORIMETRY THEORY FOR THE CIELAB SYSTEM

The colorimetry theory for the CIELab system today allows us to compute the chromatic coordinates of the raw timber colour along the wood - chain industry. The wood industry is nd strongly behind in such areas. Progress in the wood appearance description by means of colour and pattern characteristics would be suitable to classify the wood. It would also help to match pieces of wood in furniture and inside the houses, to study the wood colour change in wood ageing by photodecoloration or by drying operations, and to improve wood colour variability. Key Words: wood colour; colorimetry theory.
Building an Application Independent Natural Language Interface

Traditional approaches to building natural language (NL) interfaces typically use a semantic parser to parse the user command and convert it to a logical form, which is then translated to an executable action in an application. However, it is still challenging for a semantic parser to correctly parse natural language. For a different domain, the parser may need to be retrained or tuned, and a new translator also needs to be written to convert the logical forms to executable actions. In this work, we propose a novel and application independent approach to building NL interfaces that does not need a semantic parser or a translator. It is based on natural language to natural language matching and learning, where the representation of each action and each user command are both in natural language. To perform a user intended action, the system only needs to match the user command with the correct action representation, and then execute the corresponding action. The system also interactively learns new (paraphrased) commands for actions to expand the action representations over time. Our experimental results show the effectiveness of the proposed approach.
Transfer Learning from Transformers to Fake News Challenge Stance Detection (FNC-1) Task

Transformer models, trained and publicly released over the last couple of years, have proved effective in many NLP tasks. We wished to test their usefulness in particular on the stance detection task. We performed experiments on the data from the Fake News Challenge Stage 1 (FNC-1). We were indeed able to improve the reported SotA on the challenge, by exploiting the generalization power of large language models based on Transformer architecture. Specifically (1) we improved the FNC-1 best performing model adding BERT sentence embedding of input sequences as a model feature, (2) we fine-tuned BERT, XLNet, and RoBERTa transformers on FNC-1 extended dataset and obtained state-of-the-art results on FNC-1 task.
A Universal Method Based on Structure Subgraph Feature for Link Prediction over Dynamic Networks

In dynamic networks, links are annotated with timestamps showing the emerging time and the link prediction problem is to infer the future links in networks. Universal link prediction methods are highly demanded in various applications, which require universal link features that are feasible for multiple kinds of network topological structures and capable to address the difference of links with different timestamps. In this paper, we propose a novel link feature called Structure Subgraph Feature (SSF). The SSF is an outstanding link feature that is feasible to various dynamic networks due to the following superiorities: (1) the proposed structure subgraph is so far the most effective manner to represent surrounding topological features of target link and (2) the normalized influence well specifies the influence of multiple links and different timestamps in structure subgraph. We finally propose two link prediction methods by applying SSF to a linear regression model and a neural machine. Experimental results on real-world dynamic network datasets indicate that the SSF-based methods consistently provide top-class performance on various dynamic networks.
Machine Translation of Restaurant Reviews: New Corpus for Domain Adaptation and Robustness

We share a French-English parallel corpus of Foursquare restaurant reviews, and define a new task to encourage research on Neural Machine Translation robustness and domain adaptation, in a real-world scenario where better-quality MT would be greatly beneficial. We discuss the challenges of such user-generated content, and train good baseline models that build upon the latest techniques for MT robustness. We also perform an extensive evaluation (automatic and human) that shows significant improvements over existing online systems. Finally, we propose task-specific metrics based on sentiment analysis or translation accuracy of domain-specific polysemous words.
The Efficacy of Digital Case Scenario versus Paper Case Scenario on Clinical Reasoning in Problem Based Learning: A Systematic Review and Meta-Analysis

Introduction: In medical and clinical education, creating critical thinking and promoting clinical reasoning abilities are the highest aims and results of education. The main aim of this study was to assess the efficacy of digital case scenarios versus print/paper case scenarios on clinical reasoning in problem-based learning (PBL). If a study used the multimedia scenario case interventions, video case scenarios and online-guided scenarios as digital case PBL, we would consider it eligible. Method: The study was a systematic review and meta-analysis. A comprehensive search for all randomized controlled trials, systematic reviews and meta-analyses for digital case scenarios versus paper case scenarios in PBL were conducted using Medline (Ovid), Scopus, ISI Web of Science and CINAHL. Google Scholar was used to follow the bibliographies of the related papers to create an exhaustive data set. Search results were limited to the years 2003-2013. Results: Our searches yielded 65 hits. After initial screenings of the titles and abstracts, we assessed the full texts of studies. Five eligible studies with 222 students were included in the meta-analysis. The meta-analysis showed that both of the digital and paper-based scenarios have similar impacts on clinical reasoning. But the review of papers showed that 73% of students are more satisfied with digital scenarios in comparison with paper-based scenarios and found that digital scenarios are 90% more time saving than paper-based scenarios. Conclusion: According to the results of meta-analysis, efficacy of digital-based scenarios is similar to the efficacy of paper-based scenarios while simultaneously creating more satisfaction and saving the time of students and teachers; therefore, it is suggested that electronic PBL be used rather than paper-based in all levels of medical education.
Title Pronounced differences in signal processing and synaptic plasticity between piriform-hippocampal network stages : a prominent role for adenosine Permalink

The present study evaluated learning-related synaptic operations across the serial stages of the olfactory cortex-hippocampus network. Theta frequency stimulation produced very different time-varying responses in the Schaffer-commissural projections than in the lateral perforant path (LPP), an effect associated with distinctions in transmitter mobilization. Long-term potentiation (LTP) had a higher threshold in LPP field potential studies but not in voltage clamped neurons; coupled with input/output relationships, these results suggest that LTP threshold differences reflect the degree of input divergence. Theta pulse stimulation erased LTP in CA1 but not in the dentate gyrus (DG), although adenosine eliminated potentiation in both areas, suggesting that theta increases extracellular adenosine to a greater degree in CA1. Moreover, adenosine A1 receptor antagonism had larger effects on theta responses in CA1 than in the DG, and concentrations of ecto-5’-nucleotidase (CD73) were much higher in CA1. Input/output curves for two connections in the piriform cortex were similar to those for the LPP, whereas adenosine modulation again correlated with levels of CD73. In sum, multiple relays in a network extending from the piriform cortex through the hippocampus can be differentiated along three dimensions (input divergence, transmitter mobilization, adenosine modulation) that potently influence throughput and plasticity. A model that incorporates the regional differences, supplemented with data for three additional links, suggests that network output goes through three transitions during the processing of theta input. It is proposed that individuated relays allow the circuit to deal with different types of behavioural problems. C © 2015 The Authors. The Journal of Physiology C © 2015 The Physiological Society DOI: 10.1113/JP270398 2890 B. H. Trieu and others J Physiol 593.13 (Resubmitted 20 February 2015; accepted after revision 17 April 2015; first published online 22 May 2015) Corresponding authors G. Lynch or C. Gall: Department of Anatomy and Neurobiology, University of California at Irvine, Irvine, CA 92697-1275, USA. Email: glynch@uci.edu; cmgall@uci.edu Abbreviations A1R, adenosine A1 receptor; aCSF, artificial cerebral spinal fluid; ASSN, olfactory cortical associational system; 2-CAD, 6-amino-2-chloropurine riboside; CD73, ecto-5’-nucleotidase; DG, dentate gyrus; DPCPX, 8-cyclopentyl-1,3-dipropylxanthine; fEPSP, field EPSP; HFS, high-frequency stimulation; LEC, lateral entorhinal cortex; LOT, lateral olfactory tract; LPP, lateral perforant path; LTP, long-term potentiation; POM-1, sodium polyoxotungstate; PPF, paired-pulse facilitation; S-C, Schaffer-commissural; SO, stratum oriens; SR, stratum radiatum; TBS, theta burst
Continuous-flow synthesis of CF3-vinylic compounds via Heck type coupling and photoredox catalysis

Disclaimer This document contains a student thesis (bachelor's or master's), as authored by a student at Eindhoven University of Technology. Student theses are made available in the TU/e repository upon obtaining the required degree. The grade received is not published on the document as presented in the repository. The required complexity or quality of research of student theses may vary by program, and the required minimum study period may vary in duration.
A Stepwise, Label-based Approach for Improving the Adversarial Training in Unsupervised Video Summarization

In this paper we present our work on improving the efficiency of adversarial training for unsupervised video summarization. Our starting point is the SUM-GAN model, which creates a representative summary based on the intuition that such a summary should make it possible to reconstruct a video that is indistinguishable from the original one. We build on a publicly available implementation of a variation of this model, that includes a linear compression layer to reduce the number of learned parameters and applies an incremental approach for training the different components of the architecture. After assessing the impact of these changes to the model's performance, we propose a stepwise, label-based learning process to improve the training efficiency of the adversarial part of the model. Before evaluating our model's efficiency, we perform a thorough study with respect to the used evaluation protocols and we examine the possible performance on two benchmarking datasets, namely SumMe and TVSum. Experimental evaluations and comparisons with the state of the art highlight the competitiveness of the proposed method. An ablation study indicates the benefit of each applied change on the model's performance, and points out the advantageous role of the introduced stepwise, label-based training strategy on the learning efficiency of the adversarial part of the architecture.
Fast Approximations of Betweenness Centrality with Graph Neural Networks

Betweenness centrality is an important measure to find out influential nodes in networks in terms of information spread and connectivity. However, the exact calculation of betweenness centrality is computationally expensive. Although researchers have proposed approximation methods, they are either less efficient, or suboptimal, or both. In this paper, we present a Graph Neural Network(GNN) based inductive framework which uses constrained message passing of node features to approximate betweenness centrality. As far as we know, we are the first to propose a GNN based model to accomplish this task. We demonstrate that our approach dramatically outperforms current techniques while taking less amount of time through extensive experiments on a series of real-world datasets.
A Comparative Study of Estimating Articulatory Movements from Phoneme Sequences and Acoustic Features

Unlike phoneme sequences, movements of speech articulators (lips, tongue, jaw, velum) and the resultant acoustic signal are known to encode not only the linguistic message but also carry para-linguistic information. While several works exist for estimating articulatory movement from acoustic signals, little is known to what extent articulatory movements can be predicted only from linguistic information, i.e., phoneme sequence. In this work, we estimate articulatory movements from three different input representations: R1) acoustic signal, R2) phoneme sequence, R3) phoneme sequence with timing information. While an attention network is used for estimating articulatory movement in the case of R2, BLSTM network is used for R1 and R3. Experiments with ten subjects’ acoustic-articulatory data reveal that the estimation techniques achieve an average correlation coefficient of 0.85, 0.81, and 0.81 in the case of R1, R2, and R3 respectively. This indicates that attention network, although uses only phoneme sequence (R2) without any timing information, results in an estimation performance similar to that using rich acoustic signal (R1), suggesting that articulatory motion is primarily driven by the linguistic message. The correlation coefficient is further improved to 0.88 when R1 and R3 are used together for estimating articulatory movements.
Learned Bloom-Filter for an Efficient Name Lookup in Information-Centric Networking

The information name replaces traditional IP address as the identity of the network transmission is a typical feature of Information-Centric Networking (ICN). Therefore, designing efficient lookup algorithms of information names becomes a new challenge. For this reason, we propose an efficient name lookup structure for ICN, called Learned Bloom-Filter Lookup, which combines Recurrent Neural Networks (RNN) with standard Bloom filter to improve lookup efficiency. In our scheme, RNN trains the element set and non-element set, which are used to obtain the pre-filtering of names. Moreover, we look up the contents by using the backup Bloom filter, which can improve the accuracy of the search. In addition, we evaluate the performance of the proposed algorithm by experimental simulations. Compared with the Bloom-Hash method, our results show that our method can reduce the false positive rate. Furthermore, the memory required by our method is less than the Bloom-Hash method.
Joint Communication and Computation Resource Allocation for Cloud-Edge Collaborative System

In this paper, we investigate the latency minimization resource allocation problem in a hierarchical cloud-edge coexistence system by optimally splitting tasks for partial cloud computing and partial edge computing. A joint communication and computation resource allocation problem is first formulated and the structural characteristics are further analyzed. Next, by defining two novel parameters: the normalized backhaul communication capacity and the normalized cloud computation capacity, an optimal task splitting strategy is developed. With the help of these definitions, the joint communication and computation resource allocation policy can be devised in closed-form. Finally, numerical results demonstrate that the proposed collaborative cloud-edge computing scheme performs better than some baseline schemes in terms of minimizing the end-to-end latency of mobile devices.
Students’ Behavior When Connecting to the LMS: A Case Study at UNED

One of the main advantages of the use of Information and Communication Technologies (ICTs) in distance or online education, is the information that could be obtained regarding the activity of students during the learning process. At the same time, the advantages for students are clear since ICTs allow them to access to information on the courses they follow from anywhere at any time. Knowing how students connect to the Learning Management System (LMS) could help professors to better schedule online activities during the day, the week or throughout the academic year, as well as to determine the tools they need to log in, etc. This behavior could be different depending on several parameters. The aim of this study is to find differences with other studies and possible patterns of students when connecting to the LMS, specifically with task-based methodologies, in order to have information that could help professors, as mentioned. The results show that the number of connections (log sessions) to LMS is lower than in studies using other methodologies. Furthermore, the average connection time (session) per student is also shorter. Moreover, students reduce their activity in the LMS as the academic year progresses or the years enrolled increase.
HyperEar: Indoor Remote Object Finding with a Single Phone

Finding a small object (e.g., keys or a wallet) in an indoor environment (e.g., in a house or an office) can be frustrating. In this paper, we propose an innovative system, called HyperEar, to localize such an object using only one single smartphone, based on enhanced time-difference-of-arrival (TDoA) measurements over acoustic signals issued from the object. One major challenge is the hardware limitations of a Commercial-Off-The-Shelf (COTS) phone with a short separation between the two microphones and the low sampling rate of such microphones. HyperEar enhances the accuracy of TDoA measurements by virtually increasing distances between microphones through sliding the phone in the air. HyperEar requires no communication for synchronization between the phone and the object and is a low-cost and easy-to-use system. We evaluate the performance of HyperEar via extensive experiments in various indoor conditions and the results demonstrate that, for an object of 7m away, HyperEar can achieve a mean localization accuracy of about 15cm when the object in normal indoor environments.
Distilling Pixel-Wise Feature Similarities for Semantic Segmentation

Among the neural network compression techniques, knowledge distillation is an effective one which forces a simpler student network to mimic the output of a larger teacher network. However, most of such model distillation methods focus on the image-level classification task. Directly adapting these methods to the task of semantic segmentation only brings marginal improvements. In this paper, we propose a simple, yet effective knowledge representation referred to as pixel-wise feature similarities (PFS) to tackle the challenging distillation problem of semantic segmentation. The developed PFS encodes spatial structural information for each pixel location of the high-level convolutional features, which helps guide the distillation process in an easier way. Furthermore, a novel weighted pixel-level soft prediction imitation approach is proposed to enable the student network to selectively mimic the teacher network's output, according to their pixel-wise knowledge-gaps. Extensive experiments are conducted on the challenging datasets of Pascal VOC 2012, ADE20K and Pascal Context. Our approach brings significant performance improvements compared to several strong baselines and achieves new state-of-the-art results.
Multi-Stage Document Ranking with BERT

The advent of deep neural networks pre-trained via language modeling tasks has spurred a number of successful applications in natural language processing. This work explores one such popular model, BERT, in the context of document ranking. We propose two variants, called monoBERT and duoBERT, that formulate the ranking problem as pointwise and pairwise classification, respectively. These two models are arranged in a multi-stage ranking architecture to form an end-to-end search system. One major advantage of this design is the ability to trade off quality against latency by controlling the admission of candidates into each pipeline stage, and by doing so, we are able to find operating points that offer a good balance between these two competing metrics. On two large-scale datasets, MS MARCO and TREC CAR, experiments show that our model produces results that are either at or comparable to the state of the art. Ablation studies show the contributions of each component and characterize the latency/quality tradeoff space.