Miracle-dz/UniFAR-FTUs huggingface dataset:
Attachment Theory and the Psychoanalytic Process

Contributors. Foreword Philip Mollon. Acknowledgements. Chapter 1 Introduction: reclaiming Bowlbya s contribution to psychoanalysis, Mario Marrone and Mauricio Cortina. Part 1 Clinical Dimensions. Chapter 2 Attachment theory, transference and the psychoanalytic process, Mauricio Cortina and Mario Marrone. Chapter 3 Empathy and sensitive responsiveness, Malcolm Pines and Mario Marrone. Chapter 4 Implications of attachment theory for developing a therapeutic alliance and insight in psychoanalytic psychotherapy, Tirril Harris. Chapter 5 The significance of the exploration of the patienta s attachment history for psychoanlytic psychotherapy, Sandra Weiner. Chapter 6 Clinical uses of the Adult Attachment Interview, Howard Steele and Miriam Steele. Chapter 7 Patient--Therapist attachment: impact on the therapeutic process and outcome, Diana Diamond, John F Clarkin K Chase Stovall--McClough, Kenneth N Levy, Pamela A Foelsch, Hilary Levine and Frank E Yeomans. Chapter 8 Integrating attachment and social character approaches to clinical training: case studiess from a Mexican Nahuatl village, Sonia Gojman de Millan and Salvador Millan. Chapter 9 Applications of attachment theory to the treatment of latency age children, June W Sroufe. Chapter 10 Revisiting Freud in the light of attachment theory: Little Hana s father -- oedipal rival or attachment figure? Luis J Juri. Chapter 11 Attachment and bereavement, Luis J Juri and Mario Marrone. Part 2 Theoretical Considerations. Chapter 12 Defensive process, emotions and internal working models: a perspective from attachment theory and contemporary models of the mind, Mauricio Cortina. Chapter 13 Attachment theory as a bridge between cognitive science and psychdynamic theory, Jean Knox. Chapter 14 The psychoanalytic process in the light of attachment theory, Rafael Cristobal. Chapter 15 Disorganized attachment, motivational systems and metacognitive monitoring in the treatment of a patient with boderline syndrome, Giovanni Liotti and Bruno Intreccialagli. Chapter 16 Attachment and intimacy in adult relationships, Hugo Bleichmar. Chapter Part 3 Attachment, Sexuality and the Body. Chapter 17 Attachment, trauma and the body, Nicola Diamond. Chapter 18 Sexual disorder and attachment: a developmental systems approach, Mary Gales Shane, Morton Shane and Estelle Shane. Chapter 19 Sexuality and attachment: a passionate relationship or a marriage of convenience? Doris K Silverman. Appendix The infant and adult attachment categories, June W Sroufe. Index.
Analog integrated 2-D optical flow sensor with programmable pixels

We present a framework for real-time visual motion perception consisting of an analog VLSI optical flow sensor with reconfigurable pixels, connected in feedback with a controlling processor. The 2-D sensor array is composed of motion processing pixels that can be individually recruited to form dynamic ensembles that collectively compute visual motion. The flexible framework lends itself to the emulation of multi-layer recurrent network architectures for high-level processing of visual motion. In particular, attentional modulation can easily be incorporated in the visual motion processing. We show a simple example of visual tracking that demonstrates the potential of the framework.
Precise Error Analysis of the $\ell_2$-LASSO

A classical problem that arises in numerous signal processing applications asks for the reconstruction of an unknown, $k$-sparse signal $x_0\in R^n$ from underdetermined, noisy, linear measurements $y=Ax_0+z\in R^m$. One standard approach is to solve the following convex program $\hat x=\arg\min_x \|y-Ax\|_2 + \lambda \|x\|_1$, which is known as the $\ell_2$-LASSO. We assume that the entries of the sensing matrix $A$ and of the noise vector $z$ are i.i. d Gaussian with variances $1/m$ and $\sigma^2$. In the large system limit when the problem dimensions grow to infinity, but in constant rates, we \emph{precisely} characterize the limiting behavior of the normalized squared-error $\|\hat x-x_0\|^2_2/\sigma^2$. Our numerical illustrations validate our theoretical predictions.
Tradeoff Issues for CCH/SCH Duty Cycle for IEEE 802.11p Single Channel Devices

In this paper we investigate the ratio of dwelling time in control channel (CCH) and service channel (SCH) for single channel devices deploying WAVE (Wireless Access in Vehicular Environments) protocols. Based on analytical model for both channels and four traffic types we present tradeoffs in channel performance when CCH/SCH duty cycle is changing. Our results show that, when video traffic is absent, duty cycles smaller than 0.5 can offer satisfactory performance on CCH while running larger amount of revenue generating traffic on SCH.
The use of gene-fusions to determine membrane protein topology in Saccharomyces cerevisiae

Summary We have used protein-fusions to study in Saccharomyces cerevisiae the topology and integration of arginine permease. Since this membrane protein does not contain a cleavable signal sequence, we sought to identify the first internal signal by, initially, fusing the cytoplasmic enzyme, galactokinase, to various positions along the amino-terminal region, and then measuring in vitro the translocation of galactokinase across the membrane of the endoplasmic reticulum. Five fusion proteins were examined that contained, progressively, zero to four hydrophobic segments. The galactokinase moiety of fusion 5, but not fusions 1–4, was translocated. Fusion 4 differed from 5 by only the fourth hydrophobic segment, indicated that this region contains the first internal signal. From this we conclude that hydrophobic segment IV spans the membrane, and that the hydrophilic domain amino-terminal to it lies on the cytoplasmic side of the membrane. If any of the first three segments actually span the membrane, then they probably integrate by a mechanism that differs from segment IV.
Intelligent energy forecasting based on the correlation between solar radiation and consumption patterns

The increasing penetration of renewable generation brings a significant escalation of intermittency to the power and energy system. This variability requires a new degree of flexibility from the whole system. The active participation of small and medium players becomes essential in this context. This is only possible by using adequate forecasting techniques applied both to the consumption and to generation. However, the large number of incontrollable factors, such as the presence of consumers in the building, the luminosity, or external temperature, makes the forecasting of energy consumption an arduous task. This paper addresses the electrical energy consumption forecasting problem, by studying the correlation between the solar radiation and the electrical consumption of lights. This study is performed by means of three forecasting methods, namely a multi-layer perceptron artificial neural network, a support vector regression method, and a linear regression method. The performed studies are analyzed using data gathered from a real installation - campus of the Polytechnic of Porto, in real time.
Are Smaller Packets Less Likely to Be Lost?

Despite growth in internet link capacities and the efforts of traffic engineers, packet loss continues to be a reality in today's internet. While many tools for {\em active measurement} of packet loss have been developed over the years, applying these tools to a particular operational network context in order to most accurately estimate loss characteristics has remained a challenge. In this paper we examine the problem of selecting a probe packet size for active loss measurement. Prior work has used either small ({\em e.g.}, 40 byte) packets in order to minimize probing bandwidth, or larger ({\em e.g.}, 1000 byte) packets in order to increase the likelihood that a probe will be lost if congestion is taking place along a path. We examine these rules of thumb in a systematic way through a series of controlled laboratory experiments. We find that a--perhaps {\em the}--key system parameter is whether network queue limits are specified in terms of packets or bytes. Our results suggest that if queue limits are specified in terms of packets, there may be no clear optimal probe packet size that yields the most accurate results. On the other hand, if queue limits are specified in bytes, a packet size chosen based on the average packet size of aggregate background traffic leads to the most accurate loss average estimate.
Comparison of Three Methods of Targeting the Subthalamic Nucleus for Chronic Stimulation in Parkinson’s Disease

OBJECTIVE: The success of subthalamic nucleus (STN) surgery for Parkinson’s disease depends on accuracy in target determination. The objective of this study was to determine which of the following techniques was most accurate and precise in identifying the location for stimulation in STN deep brain stimulation surgery that is most clinically effective: direct targeting, indirect targeting using the positions of the anterior and posterior commissures, or a technique using the red nucleus (RN) as an internal fiducial marker. METHODS: We reviewed 14 patients with Parkinson’s disease treated with bilateral STN deep brain stimulation (28 STN targets). Electrode implantation was based on direct and indirect targeting using two-dimensional magnetic resonance imaging with refinement using microelectrode recording. Optimal settings, including the contacts used, were determined during the clinical follow-up. The position of the best contact was defined with postoperative magnetic resonance imaging. This location was compared with the modified direct, indirect, and RN-based targets. The mean distances between the targets and the final position of the optimal contact were calculated. The accuracy and variance of each target were analyzed. RESULTS: The mean position of the best contact was x = 12.12 (standard deviation [SD], 1.45 mm), y = −2.41 (SD, 1.63 mm), and z = −2.39 (SD, 1.49 mm) relative to the midcommissural point. The mean distance between the optimal contact position and the planned target was 3.19 mm (SD, 1.19 mm) using the RN-based method, 3.42 mm (SD, 1.34 mm) using indirect targeting, and 4.66 mm (SD, 1.33 mm) using a modified direct target. The mean distance between the optimal contact and the RN-based target was significantly smaller than the mean distance between the optimal contact and the direct target (post hoc with Tamhane’s correction, P < 0.001) but not between the optimal contact and the indirect target. The RN-based target had the smallest variance (F test, P < 0.001), indicating greater precision. CONCLUSION: The use of the RN as an internal fiducial marker for targeting the optimal region of STN stimulation was reliable and closely approximates the position of the electrode contact that provides the optimal clinical results.
Approximate feedback linearization: the cart-pole example

An existing nonlinear design methodology developed by Krener (1984, 1987), approximate feedback linearization, is applied to the control of underactuated single-input-single-output nonlinear systems. A computational approach to test for the order of linearization as well as a method to compute the approximate output function is derived. A method to simplify the dynamics using partial precompensation which uses the constraint equations of motion is presented. This approach is applied to a simple example. Simulation results showed a substantial improvement in the operating range of the linear controller by using this approach. <<ETX>>
Flow feature extraction for underwater robot localization: Preliminary results

Underwater robots conventionally use vision and sonar sensors for perception purposes, but recently bio-inspired sensors that can sense flow have been developed. In literature, flow sensing has been shown to provide useful information about an underwater object and its surroundings. In the light of this, we develop an underwater landmark recognition technique which is based on the extraction and comparison of compact flow features. The proposed features are based on frequency spectrum of a pressure signal acquired by a piezo-resistive sensor. We report experiments in semi-natural (human-made flume with obstacles) and natural (river) underwater conditions where the proposed technique successfully recognizes previously visited locations.
Supplementary Material to : Exploration of Empirical-Bayes Hierarchical Modelling for the Analysis of Genome-Wide Association Study Data

1 Mixture Model and Bayesian Model Interpretations Combining Equations 2.1 and 2.2 (Main Text), we can consider the empirical-Bayes hierarchical model as a mixed model with both fixed and random effects in the frequentist setting as given by: logit(pmi) = αm + ZmγXmi + Xmiτ tmm, where ZmγXmi would be considered a fixed effect and Xmiτ tmm a random effect. Alternatively, this model may be viewed in the Bayesian framework, where the posterior distribution for the unknown parameters given the data is given by:
Balancing different fitness levels in competitive exergames based on heart rate and performance

Combining exercises with game elements (exergaming) is a promising approach to increase physical activity of users not intrinsically motivated to exercise. Multiplayer games are particularly promising, since they add a social (competitive or collaborative) component. However, exercising together can become frustrating if users have vastly different abilities. In this paper, we review existing approaches for balancing differing physical abilities of players in competitive exergames, and based on this, we propose a novel exertion-based balancing formula. In contrast to previous work, we evaluate exertion-based balancing formulas with a moderately complex gameplay and show that it results in closer competitive gameplay and a more enjoyable game experience. The proposed formula can be applied to a wide variety of exergames and has the potential to increase compliance and long-term use of exergames.
Metabolic Syndrome, Inflammation and Atherosclerosis

The inflammatory component of atherogenesis has been increasingly recognized over the last decade. Inflammation participates in all stages of atherosclerosis, not only during initiation and during evolution of lesions, but also with precipitation of acute thrombotic complications. The metabolic syndrome is associated with increased risk for development of both cardiovascular disease and type-2 diabetes in humans. Central obesity and insulin resistance are thought to represent common underlying factors of the syndrome, which features a chronic low-grade inflammatory state. Diagnosis of the metabolic syndrome occurs using defined threshold values for waist circumference, blood pressure, fasting glucose and dyslipidemia. The metabolic syndrome appears to affect a significant proportion of the population. Therapeutic approaches that reduce the levels of proinflammatory biomarkers and address traditional risk factors are particularly important in preventing cardiovascular disease and, potentially, diabetes. The primary management of metabolic syndrome involves healthy lifestyle promotion through moderate calorie restriction, moderate increase in physical activity and change in dietary composition. Treatment of individual components aims to control atherogenic dyslipidemia using fibrates and statins, elevated blood pressure, and hyperglycemia. While no single treatment for the metabolic syndrome as a whole yet exists, emerging therapies offer potential as future therapeutic approaches.
Development and validation of the first SSR markers for Mimosa scabrella Benth.

Mimosa scabrella Benth. , popularly known as ''bracatinga'', is a pioneer and endemic species of Brazil, occurring in Mixed Ombrophilous Forest associated with Brazilian Atlantic Rainforest biomes. It is a fast-growing tree of the Fabaceae family that facilitates the dynamics of ecological succession. SSR development, when there is no genome sequence, is time and labor intensive and there are no molecular markers for M. scabrella. We developed and validated the first microsatellite markers for this tetraploid species, evaluating mother trees and progenies. Using Illumina sequencing, we identified 290 SSR loci and 211 primer pairs. After 31 SSR loci PCR/agarose electrophoresis selection, a subset of 11 primer pairs was synthetized with fluorescence in the forward primer for PCR and capillary electrophoresis validation with leaf DNA of 33 adult and 411 progeny individuals. Polymorphic locus percentage was 36, 4 in 11 loci, 3 chloroplast SSRs, and 1 nuclear SSR. Allele number of polymorphic loci ranged from 2 to 11 alleles considering all sampling. All 11 primer pairs were also tested for cross-species amplification for five Fabaceae-Mimosoideae species, ranging from 2 loci transferred to Calliandra tweedii Benth. and all 11 loci transferred to Mimosa taimbensis Burkart. The assessed and validated SSR markers for M. scabrella are suitable and useful for analysis and population genetic studies.
Index Arbitrage and the Pricing Relationship between Australian Stock Index Futures and Their Underlying Shares

This paper examines the mispricing of Australian stock index futures. Exogenous and endogenous price volatility is confirmed to have a positive impact on the mispricing spread, after filtering out predictable time series components. More accurate pricing associated with surprise trading volume in the underlying stocks is consistent with arbitrageurs acting to narrow price disparities relative to the futures market. Ex-ante interest rate volatility is the primary source of risk faced by arbitrageurs and fluctuations in the market impact cost of opening index arbitrage positions influence the extent to which they drive prices towards theoretical fair values.
Oleic Acid and Octanoic Acid Sensing Capacity in Rainbow Trout Oncorhynchus mykiss Is Direct in Hypothalamus and Brockmann Bodies

In a previous study, we provided evidence for the presence in hypothalamus and Brockmann bodies (BB) of rainbow trout Oncorhynchus mykiss of sensing systems responding to changes in levels of oleic acid (long-chain fatty acid, LCFA) or octanoic acid (medium-chain fatty acid, MCFA). Since those effects could be attributed to an indirect effect, in the present study, we evaluated in vitro if hypothalamus and BB respond to changes in FA in a way similar to that observed in vivo. In a first set of experiments, we evaluated in hypothalamus and BB exposed to increased oleic acic or octanoic acid concentrations changes in parameters related to FA metabolism, FA transport, nuclear receptors and transcription factors, reactive oxygen species (ROS) effectors, components of the KATP channel, and (in hypothalamus) neuropeptides related to food intake. In a second set of experiments, we evaluated in hypothalamus the response of those parameters to oleic acid or octanoic acid in the presence of inhibitors of fatty acid sensing components. The responses observed in vitro in hypothalamus are comparable to those previously observed in vivo and specific inhibitors counteracted in many cases the effects of FA. These results support the capacity of rainbow trout hypothalamus to directly sense changes in MCFA or LCFA levels. In BB increased concentrations of oleic acid or octanoic acid induced changes that in general were comparable to those observed in hypothalamus supporting direct FA sensing in this tissue. However, those changes were not coincident with those observed in vivo allowing us to suggest that the FA sensing capacity of BB previously characterized in vivo is influenced by other neuroendocrine systems.
Supervised texture segmentation using localized dictionary based data modelling

In this paper, we propose a supervised algorithm for texture segmentation that uses sparsity based localized data modelling. The problem addressed is to segment a given test image whose constituent textures are known a priori. Overlapping patches are extracted from the texture. Each texture is modelled by learning the patterns of the patches that constitutes training data set. For each set of training data, a set of dictionaries are learnt, contrary to the conventional practice of one dictionary for all the patches of a texture. Each dictionary is learnt to capture the local pattern in the texture data. Texture is modelled by two level pattern learning. At the first level, clustering is used to learn the macro variations in the data pattern. Subsequently, data pattern in every cluster is modelled by a sparsity based subspace learning. These are subspaces where actual texture data lie. The set of subspaces are captured by learning a dictionary. The advantage of this approach is the accurate modelling of local data patterns which a conventional single dictionary is incapable of. Simulation results validate the proposed claim by achieving higher segmentation accuracy.
No gender differences in fatigue and functional limitations due to fatigue among patients with COPD.

AIMS AND OBJECTIVES To examine gender differences in experiences of fatigue and functional limitations due to fatigue in patients with chronic obstructive pulmonary disease and a comparison group. BACKGROUND Fatigue is a major problem for patients with chronic obstructive pulmonary disease. The prevalence rate of chronic obstructive pulmonary disease in females is increasing and to our knowledge there are few studies reporting gender differences in one of their worst symptoms, fatigue and functional limitations due to fatigue. DESIGN This study has a descriptive cross-sectional design. METHODS Patients with chronic obstructive pulmonary disease (n = 345) and a comparison group of the same gender and age from the same region as the patients (n = 245) answered mailed questions about the frequency, duration and severity of fatigue and the fatigue impact scale. RESULTS High frequency (≥ 20 days/month), long duration (≥ 6 hours/day) and great severity (one of my worst symptoms) of fatigue were experienced by 51%, 54% and 44% of the male patients with chronic obstructive pulmonary disease, respectively and 60%, 55% and 51% of the female patients with chronic obstructive pulmonary disease, respectively. The figures for the males from the comparison group were 14%, 24% and 17% and for the females 30%, 42% and 32%, respectively. There were no gender differences regarding fatigue (frequency, duration and severity) or functional limitations due to fatigue in the chronic obstructive pulmonary disease patients, while the differences between males and females from the comparison group were statistically significant for frequency and severity of fatigue as well as functional limitation due to fatigue. CONCLUSION In the comparison group, there were gender differences regarding fatigue, as well as functional limitations due to fatigue. This was not observed in the chronic obstructive pulmonary disease group. RELEVANCE TO CLINICAL PRACTICE The results of this study suggest that nurses should not take for granted that female patients with chronic obstructive pulmonary disease experience more fatigue than men.
ISIS : A Trilingual Conversational System with Learning Capabilities and Combined Interaction and Delegation Dialogs

ISIS, which abbreviates Intelligent Speech for Information Systems, is a trilingual spoken dialog system (SDS) for the financial domain. It handles two dialects of Chinese (Cantonese and Putonghua), as well as English – the predominant languages in our region. The system supports spoken language queries regarding stock market information and simulated personal portfolios. Real-time information is retrieved directly from a dedicated Reuters satellite feed. ISIS provides a system test-bed for our work in multilingual speech recognition and generation, speaker authentication, language understanding and dialog modeling. Furthermore, ISIS supports our new explorations in: (i) CORBA's interoperability and scalability for SDS development; (ii) asynchronous human-computer interaction by delegation to KQML software agents; (iii) switching between online interaction and offline delegation in a single dialog thread, and (iv) automatic incorporation of newly listed stocks to expand our system’s knowledge base.