Miracle-dz/UniFAR-FTUs huggingface dataset:
Politecnico di Torino Porto Institutional Repository [ Proceeding ] A multi-measure nearest neighbor algorithm for time series classification

In this paper, we have evaluated some techniques for the time series classification problem. Many distance measures have been proposed as an alternative to the Euclidean Distance in the Nearest Neighbor Classifier. To verify the assumption that the combination of various similarity measures may produce a more accurate classifier, we have proposed an algorithm to combine several measures based on weights. We have carried out a set of experiments to verify the hypothesis that the new algorithm is better than the classical ones. Our results show an improvement over the well-established Nearest-Neighbor with DTW (Dynamic Time Warping), but in general, they were obtained combining few measures in each problem used in the experimental evaluation.
Generative probabilistic models of goal-directed users in task-oriented dialogs

A longstanding objective of human-computer interaction research is to develop better dialog systems for end users. The subset of user modelling research specifically, aims to provide dialog researchers with models of user behaviour to aid with the design and improvement of dialog systems. Where dialog systems are commercially deployed, they are often to be used by a vast number of users, where sub-optimal performance could lead to an immediate financial loss for the service provider, and even user alienation. Thus, there is a strong incentive to make dialog systems as functional as possible immediately, and crucially prior to their release to the public. Models of user behaviour fill this gap, by simulating the role of human users in the lab, without the losses associated with sub-optimal system performance. User models can also tremendously aid design decisions, by serving as tools for exploratory analysis of real user behaviour, prior to designing dialog software. User modelling is the central problem of this thesis. We focus on a particular kind of dialogs termed task-oriented dialogs (those centred around solving an explicit task) because they represent the frontier of current dialog research and commercial deployment. Users taking part in these dialogs behave according to a set of user goals, which specify what they wish to accomplish from the interaction, and tend to exhibit variability of behaviour given the same set of goals. Our objective is to capture and reproduce (at the semantic utterance level) the range of behaviour that users exhibit while being consistent with their goals. We approach the problem as an instance of generative probabilistic modelling, with explicit user goals, and induced entirely from data. We argue that doing so has numerous practical and theoretical benefits over previous approaches to user modelling which have either lacked a model of user goals, or have been not been driven by real dialog data. A principal problem with user modelling development thus far has been the difficulty in evaluation. We demonstrate how treating user models as probabilistic models alleviates some of these problems through the ability to leverage a whole raft of techniques and insights from machine learning for evaluation. We demonstrate the efficacy of our approach by applying it to two different kinds of task-oriented dialog domains, which exhibit two different sub-problems encountered in real dialog corpora. The first are informational (or slot-filling) domains, specifically those concerning flight and bus route information. In slot-filling domains, user goals take categorical values which allow multiple surface realisations, and are corrupted by
OPTIMIZED COLLISION DETECTION FOR FLEXIBLE OBJECT IN A LARGE ENVIRONMENT

We present a scheme for collision detection of flexible models. This scheme relies on a multiresolution technique of: 1) object location tracking to decrease time complexity, 2) the combination of bounding boxes (AABBs) and a hash grid function, and 3) computation of distance domain to determine collision with a contact surface. Experiments show that this approach can efficiently detect collisions in a large set of flexible objects.
Association of physical fitness and fatness with cognitive function in women with fibromyalgia

ABSTRACT This study assessed the association of fitness and fatness with cognitive function in women with fibromyalgia, and the independent influence of their single components on cognitive tasks. A total of 468 women with fibromyalgia were included. Speed of information processing and working memory (Paced Auditory Serial Addition Task), as well as immediate and delayed recall, verbal learning and delayed recognition (Rey Auditory Verbal Learning Test) were assessed. Aerobic fitness, muscle strength, flexibility and motor agility were assessed with the Senior Fitness Test battery. Body mass index, percent body fat, fat-mass index and waist circumference were measured. Aerobic fitness was associated with attention and working memory (all, p < 0.05). All fitness components were generally associated with delayed recall, verbal learning and delayed recognition (all, p < 0.05). Aerobic fitness showed the most powerful association with attention, working memory, delayed recall and verbal learning, while motor agility was the most powerful indicator of delayed recognition. None of the fatness parameters were associated with any of the outcomes (all, p > 0.05). Our results suggest that fitness, but not fatness, is associated with cognitive function in women with fibromyalgia. Aerobic fitness appears to be the most powerful fitness component regarding the cognitive tasks evaluated.
British Thoracic Society guideline for non-CF bronchiectasis

The diagnosis, investigation and particularly management of bronchiectasis has been largely empirical and the subject of relatively few controlled clinical trials. There are no clear guidelines, although an Australian position statement has been published concerning bronchiectasis in children. The purposes of these guidelines were therefore threefold: (1) to identify relevant studies in noncystic fibrosis (CF) bronchiectasis; (2) to provide guidelines on management based on published studies where possible or a consensus view; and (3) to identify gaps in our knowledge and identify areas for future study. SUMMARY OF RECOMMENDATIONS Section 2: Background and causes Congenital defects of large airways < Congenital defects should be considered in all patients with bronchiectasis. [D] Foreign bodies and aspiration < Gastric aspiration should be considered as a cause in all patients. [D] What is the current relevance of previous severe lower respiratory tract infections to patients with
Robust epsilon visibility

Analytic visibility algorithms, for example methods which compute a subdivided mesh to represent shadows, are notoriously unrobust and hard to use in practice. We present a new method based on a generalized definition of extremal stabbing lines, which are the extremities of shadow boundaries. We treat scenes containing multiple edges or vertices in degenerate configurations, (e.g., collinear or coplanar). We introduce a robust ε method to determine whether each generalized extremal stabbing line is blocked, or is touched by these scene elements, and thus added to the line's generators. We develop robust blocker predicates for polygons which are smaller than ε. For larger ε values, small shadow features merge and eventually disappear. We can thus robustly connect generalized extremal stabbing lines in degenerate scenes to form shadow boundaries. We show that our approach is consistent, and that shadow boundary connectivity is preserved when features merge. We have implemented our algorithm, and show that we can robustly compute analytic shadow boundaries to the precision of our chosen ε threshold for non-trivial models, containing numerous degeneracies.
Role of Neuraminidase in Influenza A(H7N9) Virus Receptor Binding

ABSTRACT Influenza A(H7N9) viruses have caused a large number of zoonotic infections since their emergence in 2013. They remain a public health concern due to the repeated high levels of infection with these viruses and their perceived pandemic potential. A major factor that determines influenza A virus fitness and therefore transmissibility is the interaction of the surface glycoproteins hemagglutinin (HA) and neuraminidase (NA) with the cell surface receptor sialic acid. Typically, the HA is responsible for binding to the sialic acid to allow virus internalization and the NA is a sialidase responsible for cleaving sialic acid to aid virus spread and release. N9 NA has previously been shown to have receptor binding properties mediated by a sialic acid binding site, termed the hemadsorption (Hb) site, which is discrete from the enzymatically active sialidase site. This study investigated the N9 NA from a zoonotic H7N9 virus strain in order to determine its possible role in virus receptor binding. We demonstrate that this N9 NA has an active Hb site which binds to sialic acid, which enhances overall virus binding to sialic acid receptor analogues. We also show that the N9 NA can also contribute to receptor binding due to unusual kinetic characteristics of the sialidase site which specifically enhance binding to human-like α2,6-linked sialic acid receptors. IMPORTANCE The interaction of influenza A virus glycoproteins with cell surface receptors is a major determinant of infectivity and therefore transmissibility. Understanding these interactions is important for understanding which factors are necessary to determine pandemic potential. Influenza A viruses generally mediate binding to cell surface sialic acid receptors via the hemagglutinin (HA) glycoprotein, with the neuraminidase (NA) glycoprotein being responsible for cleaving the receptor to allow virus release. Previous studies showed that the NA proteins of the N9 subtype can bind sialic acid via a separate binding site distinct from the sialidase active site. This study demonstrates for purified protein and virus that the NA of the zoonotic H7N9 viruses has a binding capacity via both the secondary binding site and unusual kinetic properties of the sialidase site which promote receptor binding via this site and which enhance binding to human-like receptors. This could have implications for understanding human-to-human transmission of these viruses.
Transfer of momentum from different arm segments to a light movable target during a straight punch thrown by expert boxers

Abstract The aim of this study was to determine the relationship between the reductions in momentum of punching arm segments and the impulse of the impact force when boxers throw a punch at a movable target with a mass almost equal to that of the human head. Nine male expert collegiate boxers threw a rear-hand straight punch at the target with their full effort. The reductions in momentum of the upper arm, forearm and fist plus glove of the punching arm during impact and the impulse were determined using a motion capture system and an accelerometer attached to the target. The reduction in momentum of the punching arm explained approximately 95% of the impulse: 40%, 35% and 20% for the upper arm, forearm and fist plus glove, respectively. The Pearson correlation coefficient between the peak and impulse of the impact force was 0.902. These results suggest that for boxers increasing the momentum of the punching arm rather than that of the other body segments immediately before the impact is effective at increasing the impulse of the punch into the face of an opponent.
A rare cryptic translation product is presented by Kb major histocompatibility complex class I molecule to alloreactive T cells

The identity of allogeneic peptide/major histocompatibility complex (MHC) complexes that elicit vigorous T cell responses has remained an interesting problem for both practical and theoretical reasons. Although a few abundant MHC class I-bound peptides have been purified and sequenced, identifying the unique T cell-stimulating peptides from among the thousands of existing peptides is still a very difficult undertaking. In this report, we identified the antigenic peptide that is recognized by an alloreactive bm1 anti-B6 T cell clone using a novel genetic strategy that is based upon measurement of T cell receptor occupancy in single T cells. Using lacZ-inducible T cells as a probe, we screened a splenic cDNA library in transiently transfected antigen- presenting cells (APCs) and isolated a cDNA clone that allowed expression of the appropriate peptide/Kb MHC complex in APC. The antigenic octapeptide (SVVEFSSL) exactly matched the consensus Kb MHC motif, but was surprisingly encoded by a non-ATG defined translation reading frame. Furthermore, the abundance of the naturally processed analog in untransfected cells was estimated to be <10 copies per cell. These results illustrate a novel strategy for identifying T cell- stimulating antigens in general and directly show that alloreactive T cells can respond to rather rare peptide/MHC complexes. These results also suggest that the total pool of processed peptides expressed on the APC surface may include those generated by cryptic translation of normally expressed transcripts.
Generating abstraction networks using semantic similarity measure of ontology concepts

External knowledge sources are commonly used in processing large amounts of data. Large external knowledge sources, such as ontologies, often contain hundreds of thousands of concepts and relationships, making comprehension and navigation difficult. Abstraction networks enhance the usability and comprehensibility of these resources by providing a higher level of abstraction. In this paper, we develop a methodology to generate an ontology's abstraction network based on the semantic similarity measure of concepts. We first score the level of similarity between concepts based on shared hierarchical and semantic relationships. We then partition the original ontology into groups based on the concept pairs' similarity in relation to a user set threshold. We apply our methodology on the SNOMED CT Specimen hierarchy to generate three levels of abstraction.
Distance-Based k^m-Anonymization of Trajectory Data

The publication of trajectory data opens up new directions in studying human behavior, but it is challenging to perform in a privacy-preserving way. This is mainly because, the identities of individuals, whose movement is recorded in the data, can be disclosed, even after removing identifying information. Existing works to anonymize trajectory data offer privacy, but at a high data utility cost. This is because, they either do not produce truthful data, which is important in many applications, or are limited in their privacy specification component. This paper proposes an approach that overcomes these shortcomings by adapting km-anonymity to trajectory data and by using distance-based generalization. We also develop an effective and efficient anonymization algorithm, which is based on the apriori principle. Our experiments verify that this algorithm preserves data utility well, and it is fast and scalable.
Selection of optimal DNA oligos for gene expression arrays

MOTIVATION High density DNA oligo microarrays are widely used in biomedical research. Selection of optimal DNA oligos that are deposited on the microarrays is critical. Based on sequence information and hybridization free energy, we developed a new algorithm to select optimal short (20-25 bases) or long (50 or 70 bases) oligos from genes or open reading frames (ORFs) and predict their hybridization behavior. Having optimized probes for each gene is valuable for two reasons. By minimizing background hybridization they provide more accurate determinations of true expression levels. Having optimum probes minimizes the number of probes needed per gene, thereby decreasing the cost of each microarray, raising the number of genes on each chip and increasing its usage. RESULTS In this paper we describe algorithms to optimize the selection of specific probes for each gene in an entire genome. The criteria for truly optimum probes are easily stated but they are not computable at all levels currently. We have developed an heuristic approach that is efficiently computable at all levels and should provide a good approximation to the true optimum set. We have run the program on the complete genomes for several model organisms and deposited the results in a database that is available on-line (http://ural.wustl.edu/~lif/probe.pl). AVAILABILITY The program is available upon request.
The Basics of Bioethics

Chapter 1: A Map of the Terrain of Ethics Chapter 2: The Hippocratic Oath and Its Challengers: A Brief History Chapter 3: Defining Death, Abortion, and Animal Welfare: The Basis of Moral Standing Chapter 4: Problems in Benefiting and Avoiding Harm to the Patient Chapter 5: The Ethics of Respect for Persons: Lying, Cheating, and Breaking Promises and Why Physicians Have Considered Them Ethical Chapter 6: The Principle of Avoiding of Killing Chapter 7: Death and Dying: The Incompetent Patient Chapter 8: Social Ethics of Medicine: Allocation of Resources, Transplantation, and Human Subjects Research Chapter 9: Human Control of Life: Genetics, Birth Technologies and Modifying Human Nature Chapter 10: Resolving Conflicts Among Principles Chapter 11: The Virtues in Bioethics Appendices: Hippocratic Oath Principles of Medical Ethics (2001))of the American Medical Association Universal Declaration on Bioethics and Human Rights (2005)
Neuro-Fuzzy Inverse Forward Models

Internal cognitive models are useful methods for the implementation of motor control (1). The approach can be applied more generally to any intelligent adaptive control problem where the dynamics of the system (plant) is unknown and/or changing. In particular, paired inverse-forward models have been shown successfully to control complex motor tasks using robotic manipulators (1). One way this is accomplished is by training a pair of multi-layered neural networks to learn both the forward model of the plant dynamics and an inverse controller model simultaneously using an extension of the backpropagation algorithm. This paper explores a variation of the traditional multi-layered network used for teaching an inverse-forward model pair (IFMP). We investigate the use of a simple fuzzy-neural system for implementing both models.
Estimation of vital signs in bed from a single unobtrusive mechanical sensor: Algorithms and real-life evaluation

A single contact-less mechanical sensor is exploited for estimating three vital signs during sleep, namely, the heart rate, the breathing rate and an activity index related to the body movements. Robust estimations are achieved over epochs of 30 seconds. The data processing is performed with standard DSP techniques leading to an integrated solution for dealing with body motion artifacts. The algorithms are described and evaluated over a one-hundred night corpus collected from real-life recordings of healthy subjects and sleep-laboratory patients. Results show that the average error of the heart rate is of 1.25 beat per minute compared to the reference values from an ECG and the coverage of the mechanically derived estimates is 83%.
The Impact of Burnout on the Intention to Quit among Professional Nurses in the Free State Region — a National Crisis?

Thousands of qualified nurses leave the South African health sector on an annual basis owing to various reasons, including burnout. Research showed that demanding work circumstances could influence employees to consider whether to leave an organisation or not. The aim of this study was to determine the level of burnout among professional nurses and to explore the potential impact of burnout on the intention to quit/change. The sample consisted of 563 professional nurses representing 140 clinics located in five health districts in the Free State region. A total of 542 (97%) questionnaires were completed and returned. Participants in the study were predominantly black (83.8%), female (89.3%), day-shift working (89.8%) and full-time employed (99.8%). The measuring tools included a biographical questionnaire, Maslach's Burnout Inventory to determine the level of burnout of professional nurses, and an Intention to Quit/Change questionnaire. Analysis of variance was used to determine differences regarding burnout between different groups with varying degrees of intention to quit/change. Respondents exhibited high levels of Emotional Exhaustion, Depersonalization and average levels of Personal Accomplishment. Respondents with the highest levels of Emotional Exhaustion and Depersonalization and the lowest levels of Personal Accomplishment displayed a higher degree of intention to quit/change.
Pattern Languages in Interaction Design: Structure and Organization

Now that individual patterns for Interaction Design have started to appear, the issue of structuring collections of patterns into Pattern Languages becomes relevant, both from a theoretical and a practical perspective. In this paper, we investigate how Pattern Languages in Interaction Design can be structured in a meaningful and practical way. A top-down approach is taken where patterns for Interaction Design are organized hierarchically, from high-level design problems to low-level design problems. In addition, the usefulness of additional views and classifications for practical use are discussed.
LEOPARD: Lightweight Edge-Oriented Partitioning and Replication for Dynamic Graphs

This paper introduces a dynamic graph partitioning algorithm, designed for large, constantly changing graphs. We propose a partitioning framework that adjusts on the fly as the graph structure changes. We also introduce a replication algorithm that is tightly integrated with the partitioning algorithm, which further reduces the number of edges cut by the partitioning algorithm. Even though the proposed approach is handicapped by only taking into consideration local parts of the graph when reassigning vertices, extensive evaluation shows that the proposed approach maintains a quality partitioning over time, which is comparable at any point in time to performing a full partitioning from scratch using a state-the-art static graph partitioning algorithm such as METIS. Furthermore, when vertex replication is turned on, edge-cut can improve by an order of magnitude.
Effects of prenatal and lactation nicotine exposure on rat testicular interstitial tissue

Nicotine is largely consumed as a component of cigarettes. It induces apoptosis, interferes with endocrine function by changing the sex hormones secretion and leads to male infertility. Testosterone is produced from cholesterol by Leydig cells (LC), with the participation of testicular macrophages (MO). Thus, to investigate whether nicotine administration to pregnant and lactating rats changes cholesterol and sexual hormone levels and LC and MO populations of offspring, female rats received nicotine (2 mg/kg/day) through osmotic minipumps from the first day of pregnancy up to the end of weaning. At 1, 30, 60 and 90 days post‐partum (dpp) the plasma cholesterol and testosterone levels were obtained, as well as the biometric, histopathological and stereological testicular parameters. Nicotine reduced the body weight, cholesterol levels and lipid droplet number in foetal LC at 1 dpp. The number of apoptotic LC did not change in the offspring of nicotine group at any age studied. No alterations in the numerical densities of MO and LC occurred at 60 and 90 dpp. Hypertrophy of mature LC and increase in cholesterol and testosterone levels were noted at 90 dpp. In conclusion, nicotine when administered to rats throughout pregnancy and lactation induces morphofunctional alterations of foetal and mature LC and affects cholesterol and testosterone levels.