Miracle-dz/UniFAR-FTUs huggingface dataset:
An Incremental Dialogue System for Learning Visually Grounded Language (demonstration system)

We present a multi-modal dialogue system for interactive learning of perceptually grounded word meanings from a human tutor. The system integrates an incremental, semantic, and bi-directional grammar framework – Dynamic Syntax and Type Theory with Records (DS-TTR1, (Eshghi et al., 2012; Kempson et al., 2001)) – with a set of visual classifiers that are learned throughout the interaction and which ground the semantic/contextual representations that it produces (c.f. Kennington & Schlangen (2015)) Our approach extends Dobnik et al. (2012) in integrating perception (vision in this case) and language within a single formal system: Type Theory with Records (TTR (Cooper, 2005)). The combination of deep semantic representations in TTR with an incremental grammar (Dynamic Syntax) allows for complex multi-turn dialogues to be parsed and generated (Eshghi et al., 2015). These include clarification interaction, corrections, ellipsis, and utterance continuations (see e.g. the dialogue in Fig. 1).
Heterogeneous Catalysts in the Process of Petrochemical Industry Byproducts Utilization

Search for effective heterogeneous catalysts for the olefin-containing fractions of liquid products of oil stock pyrolysis cooligomerization process has a great industrial and environmental importance. These fractions are the petrochemical industry wastes that require utilization. And cooligomeric products production from them is the most reasonable way of their utilization. These products have a wide range of applications. They are used in paint, rubber industry, papermaking, printing inks, corrosion-resistant coating, pavement, for woodworking and fiberboard production, for various building materials and composite materials production, etc. Manufacturing processes, based on the use of conventional homogeneous Friedel-Crafts and Ziegler-Natta catalysts, presuppose the formation of large quantities of environmentally hazardous waste water. The purpose of our research is to develop an effective technique of these petrochemical industry wastes utilization by means of their cooligomerization in the presence of heterogeneous catalysts. There are two ways to meet targets. The first one is the use of the supported Lewis acids (AlCl3, BF3, etc.), and another one is the use of oxide catalysts of the acid type. The last one means the natural clay materials, zeolites, complex oxide systems, etc. Catalyst supporting occurs due to coupling of Lewis acid with surface hydroxyl groups of support, resulting in Brønsted and Lewis active sites formation. Oxide catalysts, which are much cheaper than supported ones, have acidic Lewis and Brønsted active sites already in their natural form, or require additional activation by acids, as in the case of certain clays and zeolites.
Evaluation Unseen Test Set Selected Algorithm Selected Features Optimal Hyper-parameters Validated Model Algorithm Selection Features Selection Optimization Generalization

High levels of cognitive workload decreases human’s performance and leads to failures with catastrophic outcomes in risky missions. Today, reliable cognitive workload detection presents a common major challenge, since the workload is not directly observable. However, cognitive workload affects several physiological signals that can be measured noninvasively. The main goal of this work is to develop a reliable machine learning algorithm to identify the cognitive workload induced during rescue missions, which is evaluated through drone control simulation experiments. In addition, we aim to minimize the computing resources usage while maximizing the cognitive workload detection accuracy for a reliable real-time operation. We perform an experiment in which 24 subjects played a rescue mission simulator while respiration, electrocardiogram, photoplethysmogram, and skin temperature signals were measured. State-of-the-art feature-based machine learning algorithms are investigated for cognitive workload characterization using learning curves, data augmentation, and cross-validation techniques. The best classification algorithm is selected, optimized, and the most informative features are selected. Finally, the generalization power of the optimized model is evaluated on an unseen test set. We obtain an accuracy level of 86% on the new unseen datasets using the proposed and optimized eXtreme Gradient Boosting (XGB) algorithm. Then, we reduce the complexity of the machine learning model for future implementation on resource-constrained wearable embedded systems, by optimizing the model and selecting the 26 most important features. Overall, a generalizable and lowcomplexity machine learning model for cognitive workload detection based on physiological signals is presented for the first time in the literature.
1 Biomimetic Design of Protein Microspheres

Living cells in our body present striking complex structures and functions. Inspired by this, biomimetic design approaches for protein microspheres are discussed. The protein microspheres are synthesized using an ultrasonic method. Size control and surface morphology modification is achieved by modulating experimental parameters. The concentration of protein changes the size of microspheres. Different internal oil phase defines the morphology of surface. Nanoparticles are embedded inside and outside of the microspheres, yielding nanofunctionalization. Self-assembly for complex structures is also introduced using microspheres as build blocks. The designed microspheres and assembled structures are envisioned to have applications in biomedical, catalyst, and materials sciences. Keywords—Protein microspheres, Sonochemistry, Ultrasonic, Biomimetics, Colloidal self-assembly
Arg 399 Gln substitution in XRCC 1 as a prognostic and predictive biomarker for prostate cancer : Evidence from 8662 subjects and a structural analysis

Physiology Research Centre, Kashan University of Medical Sciences, Kashan, Iran Department of Applied Cell Sciences, Faculty of Medicine, Kashan University of Medical Sciences, Kashan, Iran Anatomical Sciences Research Center, Kashan University of Medical Sciences, Kashan, Iran Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran Gametogenesis Research Center, Kashan University of Medical Sciences, Kashan, Iran Correspondence M. Karimian, Anatomical Sciences Research Center, Kashan University of Medical Sciences, Qotb‐e Ravandi Blvd, 8715988141, Kashan, Iran. Email: mdkarimian@gmail.com Funding information Kashan University of Medical Sciences, Grant/ Award Number: 95103
Financial Development Resource Curse in Resource-Rich Countries: The Role of Commodity Price Shocks

Why do commodity-dependent developing countries have typically lower levels of financial development than their peers? The literature has proposed many possible explanations, but it typically does not dwell on the deep mechanisms that drive such an outcome. In this paper, we argue that one of the main causes is the shocks to commodity prices. We test the hypothesis on 68 commodity-rich developing countries over the period 1980–2014, and we find strong evidence of the financial development resource curse through the channel of commodity price shocks, after controlling for other explanations found in the literature. The findings are robust to the different types of commodities, the nature of the shocks, various indicators of financial development, and alternative econometric methods. We also show how the impact of these shocks can be mitigated through good quality of governance. JEL Classification Numbers: E30, E44, G38.
HOT : a Higher Order Theory of Locality Xiaoya

The locality of a program may be quantified by the data footprint over a time period or by the miss ratio in a particular cache. The relation between data usage and miss frequency has not been precisely established. Neither is it known whether one metric contains more information than the other. This paper presents a theory with two constituents. The first is a set of complete derivations between five commonly used locality metrics including the footprint and miss ratio curve. The second is a condition for the footprint-to-miss-ratio conversion to be correct. The derivations use statistics on variable-length windows and different-size caches. The calculation is similar to differentiation and integration used to convert between highdegree polynomials. As an application, the paper gives a new sampling technique, which predicts the miss ratio for all cache sizes in near real time without special hardware support and without the exclusive use of the machine cache. The paper evaluates the technique for sequential and parallel programs as well as for a parallel mix of sequential programs. The research is supported in part by the National Science Foundation (Contract No. CCF-1116104, CCF-0963759, CNS-0834566), IBM CAS Faculty Fellowship, IBM CAS Student Fellowship and a grant from Huawei. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the funding organizations.
Acupuncture-Point Stimulation for Postoperative Pain Control

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An LPC Excitation Model using Wavelets 81 2 LPC Excitation

This paper presents a new model of linear predictive coding (LPC) excitation using wavelets for speech signals. The LPC excitation becomes a linear combination of a set of selfsimilar, orthonormal, band-pass signals with time localization and constant bandwidth in a logarithmic scale. Thus, the set of the coefficients in the linear combination represents the LPC excitation. The discrete wavelet transform (DWT) obtains the coefficients, having several asymmetrical and non-uniform distribution properties that are attractive for speech processing and compression. The properties include magnitude dependent sensitivity, scale dependent sensitivity, and limited frame length, which can be used for having low bit-rate speech. We show that eliminating 8.97% highest magnitude coefficients degrades speech quality down to 1.49dB SNR, while eliminating 27.51% lowest magnitude coefficient maintain speech quality at a level of 27.42 dB SNR. Furthermore eliminating 6.25% coefficients located at a scale associated with 175-630 Hz band severely degrades speech quality down to 4.20 dB SNR. Finally, our results show that optimal frame length for telephony applications is among 32, 64, or 128 samples.
Preference trial of internet-delivered cognitive behaviour therapy comparing standard weekly versus optional weekly therapist support

Emerging evidence from research trials suggests that Internet-delivered cognitive behaviour therapy (ICBT) produces similar symptom improvements whether patients receive weekly therapist support (standard support) or therapist support only when requested (optional support). It remains unknown, however, how many patients receiving ICBT as part of routine clinical care would prefer optional support compared to standard support and how outcomes compare when patients select their preferred treatment option. In this uncontrolled trial, we investigated patient preference and outcomes for standard versus optional support among patients with depression and or anxiety who were offered an 8-week transdiagnostic ICBT intervention in routine care. Of 401 patients accepted for ICBT, 22% selected optional support and 78% selected standard support. At assessment, patients who selected optional support had lower symptoms of anxiety and panic than patients who selected standard support. At post-treatment, both groups achieved similar large improvements in symptoms of anxiety and depression, with improvements sustained at 3-month follow-up. Patients receiving optional support sent and received fewer messages compared to patients receiving standard support. This study demonstrates the potential of optional therapist support to meet the needs and preferences of patients and to also reduce therapist costs in routine care.
MEDICA-ODONTOLOGICA THERMOPLASTIC BIOACTIVE COMPOSITE – With Special Reference to Dissolution Behaviour and Tissue Response by

Tiina Ranne (former Jaakkola) Thermoplastic bioactive composite – with special reference to dissolution behaviour and tissue response Department of Prosthetic Dentistry and Biomaterials Research, Instititute of Dentistry, University of Turku, Turku, Finland. Annales Universitatis Turkuensis, Turku, Finland, 2008. Bioactive glasses are surface-active ceramic materials which support and accelerate bone growth in the body. During the healing of a bone fracture or a large bone defect, fixation is often needed. The aim of this thesis was to determine the dissolution behaviour and biocompatibility of a composite consisting of poly(ε-caprolactone-co-DL-lactide) and bioactive glass (S53P4). In addition the applicability as an injectable material straight to a bone defect was assessed. In in vitro tests the dissolution behaviour of plain copolymer and composites containing bioactive glass granules was evaluated, as well as surface reactivity and the material’s capability to form apatite in simulated body fluid (SBF). The human fibroblast proliferation was tested on materials in cell culture. In in vivo experiments, toxicological tests, material degradation and tissue reactions were tested both in subcutaneous space and in experimental bone defects. The composites containing bioactive glass formed a unified layer of apatite on their surface in SBF. The size and amount of glass granules affected the degradation of polymer matrix, as well the material’s surface reactivity. In cell culture on the test materials the human gingival fibroblasts proliferated and matured faster compared with control materials. In in vitro tests a connective tissue capsule was formed around the specimens, and became thinner in the course of time. Foreign body cell reactions in toxicological tests were mild. In experimental bone defects the specimens with a high concentration of small bioactive glass granules (<45 μm) formed a dense apatite surface layer that restricted the bone ingrowth to material. The range of large glass granules (90-315 μm) with high concentrations formed the best bonding with bone, but slow degradation on the copolymer restricted the bone growth only in the superficial layers. In these studies, the handling properties of the material proved to be good and tissue reactions were mild. The reactivity of bioactive glass was retained inside the copolymer matrix, thus enabling bone conductivity with composites. However, the copolymer was noticed to degradate too slowly compared with the bone healing. Therefore, the porosity of the material should be increased in order to improve tissue healing.
EFFECT OF VARIOUS ZINC CONCENTRATIONS ON SOME MORPHOLOGICAL PARAMETERS AT TOMATO ( Lycopersicon esculentum Mill . )

Cvetanovska, L. , Spasenoski, M. (2000/1). Effect of various zinc concentrations on some morphological parameters at tomato (Lycopersicon esculentum Mill.). Ekol. Za{t. @ivot. Sred. , Vol. 7, No.1-2, 61-66, Skopje. The investigations have been carried out with tomato plants cv.Saint Pierre grown in hydroponics on modified Hogland's nutrient solution with different Zn concentrations. In the course of these investigations has been established that the optimal Zn concentrations of treatment which had a favorable effect on the biometric indices, on macro and microelement content, varied within a range od 0,67-3,34 mM. Concentrations of 13,40 as well as 1339,42 mM zinc were toxic and inchibited greatly growth, sugar and chloroplast pigments production in abovegrounds parts.
Explorer TEAD 3 , implicated by association to grilsing in Atlantic salmon

With the recent finding that the vestigial-like family member 3 (Vgll3) gene is associated with early maturation, we decided to re-examine previously reported quantitative trait loci (QTL) and genome wide association (GWA) studies, involved with early maturation, to see if Vgll3 was the source of any of these associations. Using a TaqMan assay for Vgll3, we were able to verify that a prominent QTL for early maturation was indeed associated with Vgll3. Next, we used Vgll3 genotypes as a cofactor to re-examine for any additional QTLs or GWAs. Several were found at the chromosome-wide significance level. By examining interacting partners of Vgll3 from other organisms, we identified several candidate genes that may interact with Vgll3 in Atlantic salmon and effect early maturation. The location of these genes were determined from the genome sequence, while markers near QTL were examined to see if they were near any Vgll3 interacting genes. In this way, we identified TEAD3 as a putative gene also influencing early maturation in Atlantic salmon. A TaqMan assay was used to genotype TEAD3. These genotypes appear to be associated with early maturation.
MODELING AND FORECASTING EXCHANGE RATE VOLATILITY IN EEC COUNTRIES 1

This main objective of this paper is to examine the properties of the GARCH model and its usefulness in modeling and forecasting the volatility of exchange rate movements in selected EEC countries (Romania, Hungary and Serbia). The daily returns of exchange rates on Hungarian forint (HUF), Romanian lei (ROL) and Serbian dinar (RSD), all against the US dollar are analyzed during the period 03. January 2000 to 15. April 2013 in respect. In order to measure the involved risk, symmetric and asymmetric GARCH models are applied. The accuracy of exchange rate volatility forecast is evaluated through reference to the most commonly used criteria. These include a Mincer-Zarnowitz regression based test, Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Diebold and Mariano test (DM test). The results of Mincer-Zarnowitz regression test for selected exchange rate return series showed a clear lack of explanotory power and sub-optimality of the TGARCH model. The results of the Mean Absolute Error (MAE) and the Root Mean Square Error (RMSE) for the forecasted volatility showed that symmetric model better predict conditional variance of the exchange rate returns, but estimating results indicating that the parameters of forecasts are not satisfactory, i.e. models have little predictive power. Results for Diebold-Mariano test results for Diebold-Mariano test showed that symmetric model outperforming TGRACH forecast in case of Hungarian forint and Serbian dinar sample series, and that only in case of Romania lei TGARCH outperforming the GARCH forecast.
MODELING , AND PERFORMANCE COMPARISON OF FEEDING TECHNIQUES FOR A MICROSTRIP PATCH ANTENNA

Microstrip patch antennas has a variety of feeding technique applicable to them. It can be categorized in accordance to the main power transfer mechanism from the feed line to the patch. Contacting feeds investigated in this work are coaxial probe feed and transmission line feed; while noncontacting feeds which are proximity-coupled-fed and the aperture-coupled-fed. This work is an effort to design, model, simulate, fabricate and measure all four different types of microstrip antenna’s of noncontacting feed and contacting feed techniques on a similar sized, rectangular patch. Simulation is done using the circuit model (CM) derived from the Transmission Line Model (TLM), and is compared with another simulation set of feeding methods produced using the Method of Moments (MoM). Both methods are simulated on Microwave Office. This design intends to focus on studying the differences in measured and simulated parameters of the patch and its respective feeds, simulate it using MoM, and finally, the fabrication process. Radiation measurements are also presented. Designs for each feeding technique achieved the best return loss (RL) at the desired frequency range of 2.4 GHz. The fabricated hardware produced good RL, bandwidth (BW), and comparable radiation performance compared against simulation using MoM. All antennas produced maximum E-and H-plane coand crosspolarization difference in the magnitude of -18 dB and half-power beam widths (HPBW) in the magnitude of 90.
Sentiment analysis of Finnish movie reviews

Sentiment analysis is by now a well-researched subject within the field of Natural Language Processing. Due to technological advancements in computer strength and an explosion of freely available textual data in a semi-structured format online, the costs for conducting research on sentiment analysis have drastically decreased. A majority of the research has been conducted on data in English, and hence the best practices within the field are being formed around data in English. That also includes resources like programming libraries, sentiment lexicons and annotated corpuses for model training. Hence, it is important to fully understand if the same practices can be applied to all languages or if there are fundamental differences between languages regarding how to find the most sentiment-loaded features of a text. The purpose of this thesis is to examine whether a morphologically complex language, such as Finnish, requires different preprocessing methods than a morphologically simple language, such as English, in order to find the most sentiment-loaded features to be used in a sentiment analysis. Finnish has several properties that make it different than English. The number of unique words, the lack of adpositions and the much more frequent use of case endings are all features of the Finnish language that may have an impact on the result of a sentiment analysis system. To answer this question, two datasets (one in Finnish and the other in English) were used to create an experiment where it could be observed if the two datasets respond differently to different preprocessing techniques. The most common preprocessing techniques used in prior research were used to create five different preprocessing settings. The two datasets were processed five times each with the different preprocessing settings chosen and a Naïve Bayes Classifier was trained and tested on each of the five versions of both datasets. The results show that the only notable difference between the Finnish and the English dataset in terms of classification results, was that using only lemmatized adjectives as features are more fruitful when dealing with textual data in English. This can be a result