Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust learning control for robotic manipulators with an extension to a class of non-linear systems

A robust learning control (RLC) scheme is developed for robotic manipulators by a synthesis of learning control and robust control methods. The non-linear learning control strategy is applied directly to the structured system uncertainties that can be separated and expressed as products of unknown but repeatable (over iterations) state-independent time functions and known state-dependent functions. The non-linear uncertain terms in robotic dynamics such as centrifugal, Coriolis and gravitational forces belong to this category. For unstructured uncertainties which may have non-repeatable factors but are limited by a set of known bounding functions as the only a priori knowledge, e.g the frictions of a robotic manipulator, robust control strategies such as variable structure control strategy can be applied to ensure global asymptotic stability. By virtue of the learning and robust properties, the new control system can easily fulfil control objectives that are difficult for either learning control or variable structure control alone to achieve satisfactorily. The proposed RLC scheme is further shown to be applicable to certain classes of non-linear uncertain systems which include robotic dynamics as asubset. Various important properties concerning learning control, such as the need for a resetting condition and derivative signals, whether using iterative control mode or repetitive control mode, are also made clear in relation to different control objectives and plant dynamics.
Joint optimization of rate, distortion, and decoding energy for HEVC intraframe coding

This paper presents a novel algorithm that aims at minimizing the required decoding energy by exploiting a general energy model for HEVC-decoder solutions. We incorporate the energy model into the HEVC encoder such that it is capable of constructing a bit stream whose decoding process consumes less energy than the decoding process of a conventional bit stream. To achieve this, we propose to extend the traditional Rate-Distortion-Optimization scheme to a Decoding-Energy-Rate-Distortion approach. To obtain fast encoding decisions in the optimization process, we derive a fixed relation between the quantization parameter and the Lagrange multiplier for energy optimization. Our experiments show that this concept is applicable for intraframe-coded videos and that for local playback as well as online streaming scenarios, up to 15% of the decoding energy can be saved at the expense of a bitrate increase of approximately the same magnitude.
Repositório ISCTE-IUL

Purpose – To analyses the effect of emotions obtained by oral reproduction of advertising slogans established via Voice Emotion Response software on brand recall by gender; and to show the relevance for marketing communication of combining “Human-Computer-Interaction (HCI)” with“affective computing (AC)” as part of their mission. Design/methodology/approach – A qualitative data analysis did the review of the scientific literature retrieved from Web-of-Science Core Collection (WoSCC), using CiteSpace’scientometric technique; the quantitative data analysis did the analyse of brand recall over a sample of Taiwan’ participants by “optimal data analysis”. Findings – Advertising effectiveness has a positive association with emotions; brand recall varies with gender; and “HCI” connected with “AC” is an emerging area of research. Research limitations/implications – The selection of articles obtained depend the terms used in WoSCC, and this study used of only five emotions. Still the richness of the data gives some compensation. Practical implications – Marketers involved with brands need a body of knowledge on which to base their marketing communication intelligence gathering and strategic planning. Originality/value - Provides exploratory research findings related to the use of automatic tools capable of mining emotions by gender in real time, that could enhance the feedback of customers toward their brands.
Entrepreneurial Orientation Diagnosis in SMEs : Some Conceptual and Methodological

There is an abundant literature about the individual entrepreneurship (entrepreneurs) and about corporate entrepreneurship within the organizations, but little is written about a related concept like entrepreneurial orientation in small businesses, and also, about how to envisage a diagnosis of this strategic orientation within SMEs. This paper will attempt to identify the dimensions of the construct of entrepreneurial orientation, and to announce the methodology to follow in order to measure these dimensions in relation to the most important characteristics of SMEs, (external environment, competencies of CEO, organizational structure, resources and capabilities of the firm.). It clearly appears that entrepreneurial orientation consists of three components: Innovativeness, proactiveness, and risk-taking of strategic leaders in these firms. An innovative small business will not be an entrepreneurial firm if it does not take risks or is not sufficiently proactive towards the competitors and the environment. The purpose is to conceive an operational diagnosis called Entrepreneurial Orientation Diagnosis (EOD) based on two-fold view: Opportunity-based and Resource-based view, and using a CEO’s self-perceptions approach of its components so that it will be tested in a selected sample of French and Tunisian SMEs. This study is still an on-going research. Directions and implications for future issues are discussed. Keys words: Entrepreneurship, entrepreneur, entrepreneurial orientation, innovativeness, risk-taking, proactiveness, Small and Medium-sized Enterprises, diagnosis, CEO’s perceptions.
Personality, Parasites, Political Attitudes, and Cooperation: A Model of How Infection Prevalence Influences Openness and Social Group Formation

Abstract What is the origin of individual differences in ideology and personality? According to the parasite stress hypothesis, the structure of a society and the values of individuals within it are both influenced by the prevalence of infectious disease within the society's geographical region. High levels of infection threat are associated with more ethnocentric and collectivist social structures and greater adherence to social norms, as well as with socially conservative political ideology and less open but more conscientious personalities. Here we use an agent‐based model to explore a specific opportunities‐parasites trade‐off (OPTO) hypothesis, according to which utility‐maximizing agents place themselves at an optimal point on a trade‐off between (a) the gains that may be achieved through accessing the resources of geographically or socially distant out‐group members through openness to out‐group interaction, and (b) the losses arising due to consequently increased risks of exotic infection to which immunity has not been developed. We examine the evolution of cooperation and the formation of social groups within social networks, and we show that the groups that spontaneously form exhibit greater local rather than global cooperative networks when levels of infection are high. It is suggested that the OPTO model offers a first step toward understanding the specific mechanisms through which environmental conditions may influence cognition, ideology, personality, and social organization.
The chimney technique for preserving the left subclavian artery in thoracic endovascular aortic repair

OBJECTIVES The objective of the present study was to evaluate short- and mid-term outcomes of the left subclavian artery (LSA) chimney stent implantation (LSACSI) during thoracic endovascular aortic repair (TEVAR), and to summarize our experience with this technique. METHODS From June 2010 to September 2012, 59 patients (49 men; mean age of 57.4 ± 13.3 years, range from 26 to 83 years) who underwent TEVAR and LSACSI were enrolled. Patients suffered from Stanford type B aortic dissection (n = 27), penetrating aortic ulcer (n = 18), aortic arch aneurysm (n = 9), pseudoaneurysm of the aortic arch (n = 4) and proximal type I endoleak after TEVAR of aortic dissection (n = 1). Elective settings were performed in 72% and emergent in 38% of all patients. Follow-up was performed at postoperative 3 months, 6 months and yearly thereafter. RESULTS The technical success rate was 98.3% (58/59), and 69 thoracic stent grafts were used. Sixty-two chimney stents, including 55 uncovered and 7 covered stents, were implanted in 59 LSAs. The overall immediate endoleak rate was 15.3% (9/59); type I endoleak was observed in 5 patients and type II in 4 patients. The difference in the immediate endoleak rate related to the anatomy between the outer and the inner curvature was statistically significant (35 vs 4%, P = 0.018). Chimney stent compression was observed in 3 patients and another stent was deployed inside the first one. Perioperative complications included stroke (3.4%, 2/59) and left upper limb ischaemia (1.7%, 1/59). The median follow-up period was 16.5 (range 1–39 months). The mortality rate during follow-up was 5.4% (3/56). Complications during follow-up included endoleak [overall, n = 8 (14.3%, 8/56); type I, n = 5; type II, n = 3], retrograde type A aortic dissection (n = 1), collapse (n = 3, 5.4%) or occlusion (n = 2, 3.6%) of the chimney stent. CONCLUSIONS Short- and mid-term results showed that it is feasible to preserve the patency of the LSA in TEVAR with the chimney technique for thoracic aortic pathologies close to the LSA. However, TEVAR combined with LSACSI was not advocated for lesions located at the outer curve of the aortic arch due to a high possibility of endoleak.
My Child and Me:A Qualitative Exploration of the Experiences of Parents Who Have Had a Child or Children Receive Psychological Therapy

Previous research has examined general attitudes towards mental health. These attitudes have tended to be stigmatised, which results in a negative impact upon the individuals who suffer from mental health difficulties. When parents have a child who suffers from a mental health difficulty, they are also affected by these stigmatising views, and there appears to be a culture of blame placed upon parents, particularly mothers. When a child needs psychological therapy, the effects that parents can have upon their child's therapy have been well researched, but the effects that children's mental health and the receipt of psychological therapy has upon the parents has been less researched, and there is a significant lack of research conducted around this area in the UK. This piece of research aimed to address this gap and to explore parents' experiences of having had a child or children receive psychological therapy. Eight mothers were interviewed all of whom had a child or children under the age of sixteen years receive psychological therapy. A semi-structured interview schedule was used, and the data was analysed using Interpretative Phenomenological Analysis. Four master themes emerged from the analysis which highlighted how the mothers made sense of having a child receive psychological therapy; the enhancement of relationships, exploring the meaning of having a child with mental health difficulties, the journey of therapy, and negotiating the role of parenthood. These themes are further discussed, and links are made to relevant previous research and literature. The challenges of conducting this research are discussed, along with the strengths and limitations of the study and the implications for the field of Counselling Psychology and practice. Ideas for future research are also presented. Keywords: parents, children, mental health, psychological therapy, IPA
The effects of transaction costs and human ca size : a simulation model approacR ital on Jirm

Differences in optimal firm size may only be explained by heterogeneity amongst enterprises and the markets in which they operate. Therefore, the concept of the representative enterprise from the traditional theory of the firm is not helpful in explaining size differences. Differences in firm size may better be explained using recent developments in labour economics that stress the heterogeneity of workers and enterprises. In this paper, we exploit these new developments in labour economics by building a simulation model of the firm, which explicitly considers the link between internal and external labour markets and the resulting worker flows. Simulations with the model show how factors that account for differences in transaction costs and for heterogeneity of workers generate enterprises of different sizes.
Selective coding strategy for unicast composite networks

Consider a composite unicast relay network where the channel statistic is randomly drawn from a set of conditional distributions indexed by θ ϵ Θ, which is assumed to be unknown at the source, fully known at the destination and only partly known at the relays. Commonly, the coding strategy at each relay is fixed regardless of its channel measurement. A novel coding for unicast composite networks with multiple relays is introduced. This enables the relays to select dynamically-based on its channel measurement - the best coding scheme between compress-and-forward (CF) and decode-and-forward (DF). As a part of the main result, a generalization of Noisy Network Coding is shown for the case of unicast general networks where the relays are divided between those using DF and CF coding. Furthermore, the relays using DF scheme can exploit the help of those based on CF scheme via offset coding. It is demonstrated via numerical results that this novel coding, referred to as Selective Coding Strategy (SCS), outperforms conventional coding schemes.
Psychometric Properties of the German School Refusal Assessment Scale–Revised

The School Refusal Assessment Scale–Revised (SRAS-R) is a widely used measure of the functional conditions leading to school absenteeism. In the present study, we developed a German version of the SRAS-R and investigated its psychometric properties in a clinical sample (N = 62) of referred school absentees aged 11 to 17 years. Results of an exploratory factor analysis revealed a four-factor solution for the self-rated version and a three-factor solution for the parent-rated version. All four subscales of the final 22-item German SRAS-R were internally consistent. Convergent and discriminant validity were shown by significant correlations with other scales assessing internalizing and externalizing symptoms. Overall, the German SRAS-R is a valuable instrument for assessing school absenting behavior. The results should be interpreted conservatively due to the limited sample size.
An Empirical Comparison of Discretization Methods

Many machine learning and neurally inspired algorithms are limited, at least in their pure form, to working with nominal data. However, for many real-world problems, some provision must be made to support processing of continuously valued data. This paper presents empirical results obtained by using six different discretization methods as preprocessors to three different supervised learners on several real-world problems. No discretization technique clearly outperforms the others. Also, discretization as a preprocessing step is in many cases found to be inferior to direct handling of continuously valued data. These results suggest that machine learning algorithms should be designed to directly handle continuously valued data rather than relying on preprocessing or ad hoc techniques.
Recurrent Signature Patterns in HIV-1 B Clade Envelope Glycoproteins Associated with either Early or Chronic Infections

Here we have identified HIV-1 B clade Envelope (Env) amino acid signatures from early in infection that may be favored at transmission, as well as patterns of recurrent mutation in chronic infection that may reflect common pathways of immune evasion. To accomplish this, we compared thousands of sequences derived by single genome amplification from several hundred individuals that were sampled either early in infection or were chronically infected. Samples were divided at the outset into hypothesis-forming and validation sets, and we used phylogenetically corrected statistical strategies to identify signatures, systematically scanning all of Env. Signatures included single amino acids, glycosylation motifs, and multi-site patterns based on functional or structural groupings of amino acids. We identified signatures near the CCR5 co-receptor-binding region, near the CD4 binding site, and in the signal peptide and cytoplasmic domain, which may influence Env expression and processing. Two signatures patterns associated with transmission were particularly interesting. The first was the most statistically robust signature, located in position 12 in the signal peptide. The second was the loss of an N-linked glycosylation site at positions 413–415; the presence of this site has been recently found to be associated with escape from potent and broad neutralizing antibodies, consistent with enabling a common pathway for immune escape during chronic infection. Its recurrent loss in early infection suggests it may impact fitness at the time of transmission or during early viral expansion. The signature patterns we identified implicate Env expression levels in selection at viral transmission or in early expansion, and suggest that immune evasion patterns that recur in many individuals during chronic infection when antibodies are present can be selected against when the infection is being established prior to the adaptive immune response.
Fast Private Set Operations with SEPIA

Private set operations allow correlation of sensitive data from multiple data owners. Although intensely researched, current solutions still exhibit limited scalability in terms of the supported maximum set size and number of sets. To address these issues, we propose a new approach to private set operations based on a combination of efficient secure multiparty computation and bloom filters, a spaceefficient probabilistic data structure for set representation. We design, implement and evaluate protocols for counting and non-counting set intersection, set union, threshold set union, weighted set intersection, and set cardinality estimation. Evaluation in realistic settings shows that our protocols are between twenty times and several orders of magnitudes faster than the state-of-the-art.
Interactions of meteoric smoke particles with sulphuric acid in the Earth's stratosphere

Abstract. Nano-sized meteoric smoke particles (MSPs) with iron-magnesium silicate compositions, formed in the upper mesosphere as a result of meteoric ablation, may remove sulphuric acid from the gas-phase above 40 km and may also affect the composition and behaviour of supercooled H 2 SO 4 -H 2 O droplets in the global stratospheric aerosol (Junge) layer. This study describes a time-resolved spectroscopic analysis of the evolution of the ferric (Fe 3+ ) ion originating from amorphous ferrous (Fe 2+ )-based silicate powders dissolved in varying Wt % sulphuric acid (30–75 %) solutions over a temperature range of 223–295 K. Complete dissolution of the particles was observed under all conditions. The first-order rate coefficient for dissolution decreases at higher Wt % and lower temperature, which is consistent with the increased solution viscosity limiting diffusion of H 2 SO 4 to the particle surfaces. Dissolution under stratospheric conditions should take less than a week, and is much faster than the dissolution of crystalline Fe 2+ compounds. The chemistry climate model UMSLIMCAT (based on the UKMO Unified Model ) was then used to study the transport of MSPs through the middle atmosphere. A series of model experiments were performed with different uptake coefficients. Setting the concentration of 1.5 nm radius MSPs at 80 km to 3000 cm −3 (based on rocket-borne charged particle measurements), the model matches the reported Wt % Fe values of 0.5–1.0 in Junge layer sulphate particles, and the MSP optical extinction between 40 and 75 km measured by a satellite-borne spectrometer, if the global meteoric input rate is about 20 tonnes per day. The model indicates that an uptake coefficient ≥0.01 is required to account for the observed two orders of magnitude depletion of H 2 SO 4 vapour above 40 km.
The Service Science of Climate Change Policy Analysis: applying Sceptre (the Spatial Climate Economic Policy Tool for Regional Equilibria)

The use of Computable General Equilibrium modelling in evidence-based policy requires an advanced policy making frame of reference, advanced understanding of neoclassical economics and advanced operations research capabilities. This paper examines developments in neoclassical economic models for the analysis of strategy and policy. Regions and industries have the ever-present challenge of building a future where production is competitive and employment is durable. In this context, the inhibitor effects of potential climate constraints on regional industries and bilateral trade is currently a topic of major concern to polity. Threats often bring opportunities and these are sometimes major disruptions to traditional industry structure. Therefore of equal interest to some policy makers are the strategic opportunities that a window of superior domestic productivity and resource expansiveness may bring to nations seeking a transformative boost in export performance. The Spatial Climate Economic Policy Tool for Regional Equilibria (Sceptre) is an intertemporal, multiregional general equilibrium model for investigating regional and industry strategies in the presence of global policies such as carbon emission constraints. In its simplest mode, Sceptre translates global climate policies to regional and commodity effects. This is achieved by bringing together traditional markets for commodities with new markets in carbon commodities. These new markets are emission permits trading and a technology function for carbon abatement and amelioration. A general equilibrium is settled by optimising a social welfare function, in the mode of a Negishi format, within a nonlinear economic-climate feedback loop. Both the technology function for carbon abatement and amelioration and the economic-climate feedback loop have precedent in William Nordhaus' DICE model. The social welfare function comprises regional economic expansion factors, which are developed in a multiregional context using a data envelopment or benchmarking technique successfully applied by Thjis ten Raa to single period national and bilateral models. In a novel intertemporal innovation, Sceptre draws together disciplines of economics and finance by substituting resource constraints with Dupont sales to asset ratios in order to dynamically link and mediate the stocks and flows of each commodity. This avoids the issue in Ramsey models that investment is merely an uncontrolled residual of production and consumption, and the issue in the Leontief Bmatrix approach that final industry assets are cannibalised. Regionally aggregated Make and Use matrices drawn from GTAP's Social Accounting Matrices are used in the underling economic model as regional-commodity production function tableaux. Outputs for policy
Dynamics of Training

A new method to calculate the full training process of a neural network is introduced. No sophisticated methods like the replica trick are used. The results are directly related to the actual number of training steps. Some results are presented here, like the maximal learning rate, an exact description of early stopping, and the necessary number of training steps. Further problems can be addressed with this approach.