Miracle-dz/UniFAR-FTUs huggingface dataset:
Sexual differences rather than flight performance underlie movement and exploration in a tropical butterfly

1. Individual movement behaviour governs several routine processes, and may scale up to important ecological processes, including dispersal. However, movement is affected by a wealth of factors, including abiotic conditions, flight performance, and behavioural traits. Although it has been historically assumed that insect flight is in the first place ruled by physiology and morphology, researchers have only recently begun to understand the potentially important role of behavioural traits.
Do plants have a segregated germline?

For the last 100 years, it has been uncontroversial to state that the plant germline is set aside late in development, but there is surprisingly little evidence to support this view. In contrast, much evolutionary theory and several recent empirical studies seem to suggest the opposite—that the germlines of some and perhaps most plants may be set aside early in development. But is this really the case? How much does it matter? How can we reconcile the new evidence with existing knowledge of plant development? And is there a way to reliably establish the timing of germline segregation in both model and nonmodel plants? Answering these questions is vital to understanding one of the most fundamental aspects of plant development and evolution.
Linguistic-based computational treatment of textual entailment recognition

In this thesis, I investigate how lexical resources based on the organisation of lexical knowledge in classes which share common (syntactic, semantic, etc.) features support natural language processing and in particular symbolic recognition of textual entailment. First, I present a robust and wide coverage approach to lexico-structural verb paraphrase recognition based on Levin's (1993) classification of English verbs. Then, I show that by extending Levin's framework to general inference patterns, a classification of English adjectives can be obtained that compared with previous approaches, provides a more fine grained semantic characterisation of their inferential properties. Further, I develop a compositional semantic framework to assign a semantic representation to adjectives based on an ontologically promiscuous approach (Hobbs, 1985) and thereby supporting first order inference for all types of adjectives including extensional ones. Finally, I present a test suite for adjectival inference I developed as a resource for the evaluation of computational systems handling natural language inference. In der vorliegenden Dissertation habe ich untersucht, wie lexikalische Ressourcen, die auf der Gliederung lexikalischen Wissens in Klassen mit gemeinsamen Eigenschaften (lexikalische, semantische etc,) basieren, die computergestutzte Verarbeitung naturlicher Sprache und insbesondere die symbolische Erkennung von Entailment unterstutzen. Basierend auf Levins (1993) Klassifikation englischer Verben, wurde zuerst ein robuster, fur die Verarbeitung beliebiger Texte geeigneter Ansatz zur Paraphrasenerkennung vorgestellt. Dann habe ich aufgezeigt, dass man durch eine Erweiterung von Levins Systematik zur Behandlung allgemeiner Inferenzmuster, eine Klassifikation von englischen Adjektiven erhalt, die verglichen mit fruheren Ansatzen, eine feinkornige semantische Charakterisierung ihrer inferentiellen Eigenschaften gestattet und so die Basis fur die computergestutzte Behandlung von Inferenz bei Adjektiven bildet. Ein anderes beachtliches Ergebnis der vorliegenden Arbeit ist die Test Suite, die ich entwickelt habe und die als Ressource fur NPL Anwendungen, die Inferenzen (insbesondere Inferenzen bei Adjektiven) behandeln, genutzt werden kann. Durch die Konstruktion dieser Test Suite beabsichtige ich, den Weg fur die Schaffung von Ressourcen zu ebnen, die einen tieferen Einblick in die fur Inferenz verantwortlichen Phanomene ermoglichen.
Analysis of area spectral efficiency and energy efficiency in heterogeneous ultra-dense networks

With the exponential increasing of the mobile traffic from the wireless equipment, the cellular networks face a great challenge to meet the every-increasing throughput demand. A potential way is Ultra-Dense deployment of the small-cell in the existing macro-cell. By modeling the base stations (BSs) from both macro-cells and small-cells as homogeneous Poisson Point Process (PPP), we analytically evaluate the area spectral efficiency (ASE) and energy efficiency (EE) of downlink wireless system using stochastic geometry theory. It is shown that the ASE is increasing with the small cell density. It means that deploying more small-cells in the macro-cell coverage area is a feasible way to improve the system throughput. However, with the increasing of small cell density, the EE goes to an optimal point and then start to decrease. We introduce a firefly-inspired algorithm named Firefly Algorithm (FA) to analyze the ASE and the EE impacted by the BSs density and the transmitting power.
A Danish FrameNet Lexicon and an Annotated Corpus Used for Training and Evaluating a Semantic Frame Classifier

In this paper, we present an approach to efficiently compile a Danish FrameNet based on the Danish Thesaurus, focusing in particular on cognition and communication frames. The Danish FrameNet uses the frame and role inventory of the English FrameNet. We present the corresponding corpus annotations of frames and roles and show how our corpus can be used for training and evaluating a semantic frame classifier for cognition and communication frames. We also present results of cross-language transfer of a model trained on the English FrameNet. Our approach is significantly faster than building a lexicon from scratch, and we show that it is feasible to annotate Danish with frames developed for English, and finally, that frame annotations – even if limited in size at the current stage – are useful for automatic frame classification.
Client Tier Edge Tier Server Tier Packet Processor Sanity Check Penalty Table Upload Buffer Packet Processor Sanity Check Penalty Table Mixing Function Entropy Pool Packet Processor Packet Processor Buffer Check Local Cache Craft Reply Data Req

The generation of random numbers has traditionally been a task confined to the bounds of a single piece of hardware. However, with the rapid growth and proliferation of resource-constrained devices in the Internet of Things (IoT), standard methods of generating randomness encounter barriers that can limit their effectiveness. In this work, we explore the design, implementation, and efficacy of a Collaborative and Distributed Entropy Transfer protocol (CADET), which aims to move random number generation from an individual task to a collaborative one. Through the sharing of excess random data, devices that are unable to meet their own needs can be aided by contributions from other devices. We implement and test a proof-of-concept version of CADET on a testbed of 49 Raspberry Pi 3B single-board computers, which have been underclocked to emulate the resource constraints of IoT devices. Through this, we evaluate and demonstrate the efficacy and baseline performance of remote entropy protocols of this type, as well as highlight remaining research questions and challenges in this area.
Monetary Policy Preferences in the West African Monetary Zone: Evidence from Dynamic General Equilibrium Models

Anecdotal evidence shows that in spite of regional efforts towards achieving convergence, the single digit inflation objective has not been realized. The jury is still out on the dynamics of price level convergence and the monetary authorities’ relative weights in the loss function. Consequently, the overall objective of this study is to examine whether or not the relative weights in the monetary policy rule under optimal policy solution differ from the posterior estimates. The study applied a dynamic stochastic general equilibrium model using Bayesian estimation techniques to identify the policy-makers’ policy preferences. The results show that the posterior estimates show evidence of sticky prices with an average duration of price contracts of around 2 - 4 quarters. In addition, the coefficient of interest rate smoothing were high at 0.62, 0.52, 0.61, 0.79 and 0.66 for The Gambia, Ghana, Guinea, Nigeria and Sierra Leone, respectively, implying that the contemporaneous responsiveness of the nominal interest rates to inflation and output gap is dampened, an indication of a passive use of monetary policy as a demand management tool. Also, the impact of a change in interest rate on inflation is relatively non-persistent and short-lived. Overall, WAMZ member countries indicate preference for output stabilization.
Security improvement on a biometrics-based authentication protocol for multi-server environment

Recently, an biometrics-based multi-server authentication protocol was proposed by Odelu et al. It was proved that the scheme is secure against different possible known attacks and satisfies attractive security properties. In this paper, however, we prove that Odelu et al.'s scheme suffers from offline password guessing attack and fails to provide three-factor security. Further, we propose an improved scheme by applying fuzzy verifier to local password verification and employing Chebyshev chaotic map-based cryptography. Security analysis shows that our scheme not only maintains the advantages of the Odelu et al.'s scheme but also withstand the offline password guessing attack. As a result, our improved scheme can provide three-factor security and has better performance compared with Odelu et al.'s scheme.
Auxiliary Tasks in Multi-task Learning

Multi-task convolutional neural networks (CNNs) have shown impressive results for certain combinations of tasks, such as single-image depth estimation (SIDE) and semantic segmentation. This is achieved by pushing the network towards learning a robust representation that generalizes well to different atomic tasks. We extend this concept by adding auxiliary tasks, which are of minor relevance for the application, to the set of learned tasks. As a kind of additional regularization, they are expected to boost the performance of the ultimately desired main tasks. To study the proposed approach, we picked vision-based road scene understanding (RSU) as an exemplary application. Since multi-task learning requires specialized datasets, particularly when using extensive sets of tasks, we provide a multi-modal dataset for multi-task RSU, called synMT. More than 2.5 $\cdot$ 10^5 synthetic images, annotated with 21 different labels, were acquired from the video game Grand Theft Auto V (GTA V). Our proposed deep multi-task CNN architecture was trained on various combination of tasks using synMT. The experiments confirmed that auxiliary tasks can indeed boost network performance, both in terms of final results and training time.
A Swedish Cookie-Theft Corpus

Language disturbances can be a diagnostic marker for neurodegenerative diseases, such as Alzheimer’s disease, at earlier stages. Connected speech analysis provides a non-invasive and easy-to-assess measure for determining aspects of the severity of language impairment. In this paper we focus on the development of a new corpus consisting of audio recordings of picture descriptions (including transcriptions) of the Cookie-theft, produced by Swedish speakers. The speech elicitation procedure provides an established method of obtaining highly constrained samples of connected speech that can allow us to study the intricate interactions between various linguistic levels and cognition. We chose the Cookie-theft picture since it’s a standardized test that has been used in various studies in the past, and therefore comparisons can be made based on previous research. This type of picture description task might be useful for detecting subtle language deficits in patients with subjective and mild cognitive impairment. The resulting corpus is a new, rich and multi-faceted resource for the investigation of linguistic characteristics of connected speech and a unique dataset that provides a rich resource for (future) research and experimentation in many areas, and of language impairment in particular. The information in the corpus can also be combined and correlated with other collected data about the speakers, such as neuropsychological tests, brain physiology and cerebrospinal fluid markers as well as imaging.
Expanding Abbreviations in a Strongly Inflected Language: Are Morphosyntactic Tags Sufficient?

In this paper, the problem of recovery of morphological information lost in abbreviated forms is addressed with a focus on highly inflected languages. Evidence is presented that the correct inflected form of an expanded abbreviation can in many cases be deduced solely from the morphosyntactic tags of the context. The prediction model is a deep bidirectional LSTM network with tag embedding. The training and evaluation data are gathered by finding the words which could have been abbreviated and using their corresponding morphosyntactic tags as the labels, while the tags of the context words are used as the input features for classification. The network is trained on over 10 million words from the Polish Sejm Corpus and achieves 74.2% prediction accuracy on a smaller, but more general National Corpus of Polish. The analysis of errors suggests that performance in this task may improve if some prior knowledge about the abbreviated word is incorporated into the model.
Architecture for the semi-automatic fabrication and assembly of thin-film based dielectric elastomer actuators

One problem related to the actuation principle of macroscopic dielectric elastomer actuators is the high voltage required, typically in the Kilovolt range, that imposes particular care in the insulation of the whole actuator from the surrounding environment. This high actuation voltage, however, can be drastically reduced if a thin film of dielectric elastomer is used. Despite this, the manufacture of a macroscopic stack-like actuator, starting from thin films of dielectric elastomer can present many manufacture difficulties, like the handling and the assembly of the films, the power distribution to hundreds or thousands of layers, the presence of defects in one single layer that can cause the complete failure of the whole actuator. In this paper, a fast, semi-automatic process is proposed for the manufacture of modular units of dielectric elastomer, each of them consisting of many layers of rolled thin dielectric film. All the manufactured units are independent and take their power from a lateral, compliant supply rail that contacts the sides the electroded layers. This design is very suitable for industrial production: each module can be independently tested and then assembled in a complete macroscopic actuator composed by an unlimited number of these modules. The simple assembly methodology and the semi-automatic manufacture process described in this paper allows the fabrication of multilayer stacked devices, that can be used both as contractile or expanding actuators.
Some Generalizations of Weibull Distribution and Related Processes

A new class of distributions containing Marshall-Olkin extended Weibull distribution is introduced. The role of this distribution in the study of minification process is established. A new class of distributions that includes the Laplace and Logistic distributions is introduced. Properties and generation of Marshall-Olkin extended Double Weibull distribution and Marshall-Olkin extended asymmetric double Weibull distribution are discussed. The distribution of daily stock price index of Bombay stock exchange (INDIA) is identified as generalised asymmetric double Weibull model and its estimation and predictions are done.
mrbsizeR : scale space multiresolution analysis in R

In the analysis of spatial fields or images (i.e. an object), scale space methods are often useful. When observed on different scales, distinct features of the object can be detected. Imagine the concept of a branch of a tree (Lindeberg [1994]): This concept makes sense on a scale from a few centimeters to a few meters. On a much smaller scale, one could describe the molecules that form the branch and on a much larger scale, it would be possible to describe the forest the tree grows in. The goal in scale space analysis is therefore to represent an object on different scales, and this is done by dividing it into a family of smooths. The smooths are made on different smoothing levels and each smooth provides information relevant at a particular scale, which makes it possible to extract scale-dependent features from the object.
Cross-lingual Terminology Extraction for Translation Quality Estimation

We explore ways of identifying terms from monolingual texts and integrate them into investigating the contribution of terminology to translation quality.The researchers proposed a supervised learning method using common statistical measures for termhood and unithood as features to train classiﬁers for identifying terms in cross-domain and cross-language settings. On its basis, sequences of words from source texts (STs) and target texts (TTs) are aligned naively through a fuzzy matching mechanism for identifying the correctly translated term equivalents in student translations. Correlation analyses further show that normalized term occurrences in translations have weak linear relationship with translation quality in term of usefulness/transfer, terminology/style, idiomatic writing and target mechanics and near-and above-strong relationship with the overall translation quality. This method has demonstrated some reliability in automatically identifying terms in human translations. However, drawbacks in handling low frequency terms and term variations shall be dealt in the future.
Ultrasound Cyclo-Plasty in Patients with Glaucoma: 1-Year Results from a Multicentre Prospective Study

Purpose: The aim of this study was to evaluate the safety and efficacy of ultrasound cyclo-plasty (UCP) for reducing intraocular pressure (IOP) in patients with glaucoma. Methods: This is a multicentre prospective study conducted in 3 Italian glaucoma centres. UCP was performed by EyeOP1, which delivers ultrasound beams using 6 piezoelectric transducers activated for 4/6 s (first generation) or 8 s (second generation). Primary outcomes were the mean IOP reduction and the rates of success after 1 year. Secondary outcomes were the mean IOP reduction at each follow-up, and the reduction of the number of hypotensive medications. Results: In total, 49 eyes from 47 patients were treated. One year postoperatively, the mean IOP had decreased from 27.7 ± 9.2 to 19.8 ± 6.9 mm Hg (p < 0.001), and the mean number of hypotensive drops and tablets had decreased from 3.2 and 0.5 to 2.3 and 0.2, respectively (p < 0.05). Postoperative IOP reduction was significantly related to preoperative IOP (r2 = 0.5034; p < 0.0001). Second-generation probes determined a significantly higher IOP reduction (p < 0.05). Qualified success was achieved in 25 eyes (51.1%) and complete success in 21 (42.9%), while failure was recorded in 12 (24.5%). Conclusions: UCP is safe and effective for reducing IOP. The procedure determines a greater IOP reduction in patients with higher preoperative IOP. Second-generation probes improve outcomes without detrimental effects on safety.
Impact of merger announcements on shareholder's wealth: An event case study of State bank of India

The banking sector has been witnessed a considerable growth in terms of Mergers & Acquisitions deals. The current study explores the implications of a merger announcement on shareholder's wealth by using the event case study of State Bank of India (SBI). SBI has been merged with two of its subsidiaries i.e. State Bank of Saurashtra and State Bank of Indore on August 13, 2008 and August 27, 2010 respectively. The effect of mergers has been analysed by calculating the abnormal returns earned by shareholders during the event period. The study concludes mixed evidences about the generation of shareholder's wealth after merger announcement. The significant abnormal returns for the merger of State Bank of Saurashtra around the event date infer the informational inefficiency. On the other hand, results for the merger of State Bank of Indore having less abnormal returns lead to conclude that the market price movements are not only affected by the current information, but also by how it relates to previous information and expectations.
Brain‐computer interfaces for post‐stroke motor rehabilitation: a meta‐analysis

Brain‐computer interfaces (BCIs) can provide sensory feedback of ongoing brain oscillations, enabling stroke survivors to modulate their sensorimotor rhythms purposefully. A number of recent clinical studies indicate that repeated use of such BCIs might trigger neurological recovery and hence improvement in motor function. Here, we provide a first meta‐analysis evaluating the clinical effectiveness of BCI‐based post‐stroke motor rehabilitation. Trials were identified using MEDLINE, CENTRAL, PEDro and by inspection of references in several review articles. We selected randomized controlled trials that used BCIs for post‐stroke motor rehabilitation and provided motor impairment scores before and after the intervention. A random‐effects inverse variance method was used to calculate the summary effect size. We initially identified 524 articles and, after removing duplicates, we screened titles and abstracts of 473 articles. We found 26 articles corresponding to BCI clinical trials, of these, there were nine studies that involved a total of 235 post‐stroke survivors that fulfilled the inclusion criterion (randomized controlled trials that examined motor performance as an outcome measure) for the meta‐analysis. Motor improvements, mostly quantified by the upper limb Fugl‐Meyer Assessment (FMA‐UE), exceeded the minimal clinically important difference (MCID=5.25) in six BCI studies, while such improvement was reached only in three control groups. Overall, the BCI training was associated with a standardized mean difference of 0.79 (95% CI: 0.37 to 1.20) in FMA‐UE compared to control conditions, which is in the range of medium to large summary effect size. In addition, several studies indicated BCI‐induced functional and structural neuroplasticity at a subclinical level. This suggests that BCI technology could be an effective intervention for post‐stroke upper limb rehabilitation. However, more studies with larger sample size are required to increase the reliability of these results.
Standardization of Call Prices and No-Arbitrage Properties with Extension to Negative Forwards Post Financial Crisis

After the recent financial crisis (also known as the "Credit Crunch'') it was observed that some market data exhibited non-zero probability for negative forward rates. In particular, options with zero strikes were quoted with non-zero prices. This led to the requirement to model and interpret option prices for negative strikes. In this note, irrespective of the asset classes, we provide a standardization technique for market call prices and derive the no-arbitrage properties that allow for negative strikes and forwards. Our approach works with non-deterministic discounting rates and multiple discounting curves that are increasingly becoming standard.