Recombinational repair of gaps in DNA is asymmetric in Ustilago maydis and can be explained by a migrating D-loop model.

Recombinational repair of double-stranded DNA gaps was investigated in Ustilago maydis. The experimental system was designed for analysis of repair of an autonomously replicating plasmid containing a cloned gene disabled by an internal deletion. It was discovered that crossing over rarely accompanied gap repair. The strong bias against crossing over was observed in three different genes regardless of gap size. These results indicate that gap repair in U. maydis is unlikely to proceed by the mechanism envisioned in the double-stranded break repair model of recombination, which was developed to account for recombination in Saccharomyces cerevisiae. Experiments aimed at exploring processing of DNA ends were performed to gain understanding of the mechanism responsible for the observed bias. A heterologous insert placed within a gap in the coding sequence of two different marker genes strongly inhibited repair if the DNA was cleaved at the promoter-proximal junction joining the insert and coding sequence but had little effect on repair if the DNA was cleaved at the promoter-distal junction. Gene conversion of plasmid restriction fragment length polymorphism markers engineered in sequences flanking both sides of a gap accompanied repair but was directionally biased. These results are interpreted to mean that the DNA ends flanking a gap are subject to different types of processing. A model featuring a single migrating D-loop is proposed to explain the bias in gap repair outcome based on the observed asymmetry in processing the DNA ends.

Feedback circuitry within a song-learning pathway.

The song system of birds consists of several neural pathways. One of these, the anterior forebrain pathway, is necessary for the acquisition but not for the production of learned song in zebra finches. It has been shown that the anterior forebrain pathway sequentially connects the following nuclei: the high vocal center, area X of lobus parolfactorius, the medial portion of the dorsolateral thalamic nucleus, the lateral magnocellular nucleus of anterior neostriatum (IMAN), and the robust nucleus of the archistriatum (RA). We now show in zebra finches (Taeniopygia guttata) that IMAN cells that project to RA also project to area X, forming a feedback loop within the anterior forebrain pathway. The axonal endings of the IMAN projection into area X form cohesive and distinct domains. Small injections of tracer in subregions of area X backfill a spatially restricted subset of cells in IMAN, that, in turn, send projections to RA that are arranged in horizontal layers, which may correspond to the functional representation of vocal tract muscles demonstrated by others. We infer from our data that there is a myotopic representation throughout the anterior forebrain pathway. In addition, we suggest that the parcellation of area X into smaller domains by the projection from IMAN highlights a functional architecture within X, which might correspond to units of motor control, to the representation of acoustic features of song, or both.

Double cross-validation and improved sensitivity of the rapid screen of mild traumatic brain injury

This study aimed to replicate and cross-validate the Rapid Screen of Concussion (RSC) for diagnosing mild TBI (mTBI). One hundred (81 male, 19 female) cases of mTBI and 35 (23 male and 12 female) cases of orthopaedic injuries were tested within 24 hr of injury. Double cross-validation was used to examine whether total RSC scores obtained in the cur-rent sample, generalised to one previously reported. In the new sample, mTBI patients answered fewer orientation questions, recalled fewer words on the learning trial and after a delay, judged fewer sentences in 2 min, and completed fewer symbols in the Digit Symbol Substitution Test than orthopaedic controls. The formulae and cut-offs developed on the original and new samples produced similar sensitivity and overall correct classification rates. Inclusion of the Digit Symbol Substitution Test performance of the new sample improved the sensitivity (80.2%) and specificity (82.6%) in males. It did not improve the correct classification rate in females, which was 89.5% sensitivity and 91.7% specificity before the inclusion of the Digit Symbol Substitution Test. Taken together, these results indicate that a combined score on this 12-min screen yields a measure of level of brain impairment up to 24 hr after mTBI.

Double-Wavelet Neuron Based on Analytical Activation Functions

In this paper a new double-wavelet neuron architecture obtained by modification of standard wavelet neuron, and its learning algorithm are proposed. The offered architecture allows to improve the approximation properties of wavelet neuron. Double-wavelet neuron and its learning algorithm are examined for forecasting non-stationary chaotic time series.

Learning and Exploiting Camera Geometry for Computer Vision

This work explores the use of statistical methods in describing and estimating camera poses, as well as the information feedback loop between camera pose and object detection. Surging development in robotics and computer vision has pushed the need for algorithms that infer, understand, and utilize information about the position and orientation of the sensor platforms when observing and/or interacting with their environment.

The first contribution of this thesis is the development of a set of statistical tools for representing and estimating the uncertainty in object poses. A distribution for representing the joint uncertainty over multiple object positions and orientations is described, called the mirrored normal-Bingham distribution. This distribution generalizes both the normal distribution in Euclidean space, and the Bingham distribution on the unit hypersphere. It is shown to inherit many of the convenient properties of these special cases: it is the maximum-entropy distribution with fixed second moment, and there is a generalized Laplace approximation whose result is the mirrored normal-Bingham distribution. This distribution and approximation method are demonstrated by deriving the analytical approximation to the wrapped-normal distribution. Further, it is shown how these tools can be used to represent the uncertainty in the result of a bundle adjustment problem.

Another application of these methods is illustrated as part of a novel camera pose estimation algorithm based on object detections. The autocalibration task is formulated as a bundle adjustment problem using prior distributions over the 3D points to enforce the objects' structure and their relationship with the scene geometry. This framework is very flexible and enables the use of off-the-shelf computational tools to solve specialized autocalibration problems. Its performance is evaluated using a pedestrian detector to provide head and foot location observations, and it proves much faster and potentially more accurate than existing methods.

Finally, the information feedback loop between object detection and camera pose estimation is closed by utilizing camera pose information to improve object detection in scenarios with significant perspective warping. Methods are presented that allow the inverse perspective mapping traditionally applied to images to be applied instead to features computed from those images. For the special case of HOG-like features, which are used by many modern object detection systems, these methods are shown to provide substantial performance benefits over unadapted detectors while achieving real-time frame rates, orders of magnitude faster than comparable image warping methods.

The statistical tools and algorithms presented here are especially promising for mobile cameras, providing the ability to autocalibrate and adapt to the camera pose in real time. In addition, these methods have wide-ranging potential applications in diverse areas of computer vision, robotics, and imaging.

Education and self-regulation of learning for gifted pupils: Systemic design and development.

Gifted pupils differ from their age-mates with respect to development potential, actual competencies, self-regulatory capabilities, and learning styles in one or more domains of competence. The question is how to design and develop education that fits and further supports such characteristics and competencies of gifted pupils. Analysis of various types of educational interventions for gifted pupils reflects positive cognitive or intellectual effects and differentiated social comparison or group-related effects on these pupils. Systemic preventive combination of such interventions could make these more effective and sustainable. The systemic design is characterised by three conditional dimensions: differentiation of learning materials and procedures, integration by and use of ICT support, and strategies to improve development and learning. The relationships to diagnostic, instructional, managerial, and systemic learning aspects are expressed in guidelines to develop or transform education. The guidelines imply the facilitation of learning arrangements that provide flexible self-regulation for gifted pupils. A three-year pilot in Dutch nursery and primary school is conducted to develop and implement the design in collaboration with teachers. The results constitute prototypes of structured competence domains and supportive software. These support the screening of entry characteristics of all four-year old pupils and assignment of adequate play and learning processes and activities throughout the school career. Gifted and other pupils are supported to work at their actual achievement or competency levels since their start in nursery school, in self-regulated learning arrangements either in or out of class. Each pupil can choose other pupils to collaborate with in small groups, at self-chosen tasks or activities, while being coached by the teacher. Formative evaluation of the school development process shows that the systemic prevention guidelines seem to improve learning and social progress of gifted pupils, including their self-regulation. Further development and implementation steps are discussed.

EXPLORING VIENNESE TUNING AND ITS BENEFITS FOR THE MODERN DOUBLE BASSIST

The role of the double bass in Vienna during the eighteenth century evolved significantly between 1760 and 1812. During these years, Viennese composers began to view the double bass less as an accompanimental instrument and more as a solo voice. Despite the abundance of music written for the double bass during this time, few of these compositions are regularly performed today. This dissertation serves three purposes. I explore how learning eighteenth-century Viennese compositions in the original tuning can influence modern performances of these works. Secondly, I document the arrangement of a lesser-know work for the modern tuned bass using the manuscript as the source material. Finally, by performing a variety of eighteenth-century bass works, I bring this music to the public's attention. The research for this dissertation has been presented in two forms. The recitals present both solo and chamber works from eighteenth-century Vienna. The repertoire for the three recitals was chosen so that each recital addresses one of the three purposes mentioned above. The research paper presents performance practices of the eighteenth century, challenges the modern double bassist faces when playing this literature, as well as a look into how to arrange one of these works for the modern tuned double bass. The three recitals were performed on the campus of the University of Maryland in the Leah M. Smith Hall, Gildenhorn Recital Hall and the Ulrich Recital Hall, respectively. Recordings of all three recitals can be found in the Digital Repository at the University of Maryland (DRUM).

How textbook design may influence learning with geography textbooks

This paper investigates how textbook design may influence students’ visual attention to graphics, photos and text in current geography textbooks. Eye tracking, a visual method of data collection and analysis, was utilised to precisely monitor students’ eye movements while observing geography textbook spreads. In an exploratory study utilising random sampling, the eye movements of 20 students (secondary school students 15–17 years of age and university students 20–24 years of age) were recorded. The research entities were double-page spreads of current German geography textbooks covering an identical topic, taken from five separate textbooks. A two-stage test was developed. Each participant was given the task of first looking at the entire textbook spread to determine what was being explained on the pages. In the second stage, participants solved one of the tasks from the exercise section. Overall, each participant studied five different textbook spreads and completed five set tasks. After the eye tracking study, each participant completed a questionnaire. The results may verify textbook design as one crucial factor for successful knowledge acquisition from textbooks. Based on the eye tracking documentation, learning-related challenges posed by images and complex image-text structures in textbooks are elucidated and related to educational psychology insights and findings from visual communication and textbook analysis.

Double blind test series

Braille is a communication tool in decline, in America by 80% since 1950, and in the UK to the extent that only 1% of blind people are now thought to read Braille.1, 2 There are a variety of causal factors, including the phasing out of Braille instruction due to the educational mainstreaming of blind children and the resistance to learning Braille by those who lose sight later in life.3Braille is a writing system of raised dots that allows blind people to read and write tactilely. Each Braille character comprises a cell of six potentially raised dots, two dots across and three dots down. It is designed only to communicate the message and does not convey the tonality provided by visual fonts.However, in his book Design Meets Disability, Graham Pullin, observes that: “Braille is interesting and beautiful, as abstract visual and tactile decoration, intriguing and indecipherable to the nonreader ” and continues; “…braille could be decorative for sighted people.”4I assert that the increasing abandonment of Braille frees it from its restrictive constraints, opening it to exploration and experimentation, and that this may result in Braille becoming dynamic expression for the sighted, as well as the partially sighted and blind.Printmaking is well suited for this exploration. Printmaking processes and techniques can result in prints aesthetically compelling to both senses of sight and touch. Established approaches, such as flocking, varnishes, puff-ink, embossing and die cut, combined with experiments in new techniques in laser cutting and 3D printing, create visually and texturally vibrant prints.In this paper I will detail my systematic investigation of sensually expressive printmaking concentrating on the issues surrounding Braille as a printmaking design element paying particular attention to the approaches and techniques used not only in producing its visual style but to those techniques used to keep it integrally tactile.

Robotic Sensing and Manipulation of Deformable Linear Objects with Learning-based methods

Nowadays robotic applications are widespread and most of the manipulation tasks are efficiently solved. However, Deformable-Objects (DOs) still represent a huge limitation for robots. The main difficulty in DOs manipulation is dealing with the shape and dynamics uncertainties, which prevents the use of model-based approaches (since they are excessively computationally complex) and makes sensory data difficult to interpret. This thesis reports the research activities aimed to address some applications in robotic manipulation and sensing of Deformable-Linear-Objects (DLOs), with particular focus to electric wires. In all the works, a significant effort was made in the study of an effective strategy for analyzing sensory signals with various machine learning algorithms. In the former part of the document, the main focus concerns the wire terminals, i.e. detection, grasping, and insertion. First, a pipeline that integrates vision and tactile sensing is developed, then further improvements are proposed for each module. A novel procedure is proposed to gather and label massive amounts of training images for object detection with minimal human intervention. Together with this strategy, we extend a generic object detector based on Convolutional-Neural-Networks for orientation prediction. The insertion task is also extended by developing a closed-loop control capable to guide the insertion of a longer and curved segment of wire through a hole, where the contact forces are estimated by means of a Recurrent-Neural-Network. In the latter part of the thesis, the interest shifts to the DLO shape. Robotic reshaping of a DLO is addressed by means of a sequence of pick-and-place primitives, while a decision making process driven by visual data learns the optimal grasping locations exploiting Deep Q-learning and finds the best releasing point. The success of the solution leverages on a reliable interpretation of the DLO shape. For this reason, further developments are made on the visual segmentation.

Integrated communication and learning towards autonomous mobile radio networks

The integration of distributed and ubiquitous intelligence has emerged over the last years as the mainspring of transformative advancements in mobile radio networks. As we approach the era of “mobile for intelligence”, next-generation wireless networks are poised to undergo significant and profound changes. Notably, the overarching challenge that lies ahead is the development and implementation of integrated communication and learning mechanisms that will enable the realization of autonomous mobile radio networks. The ultimate pursuit of eliminating human-in-the-loop constitutes an ambitious challenge, necessitating a meticulous delineation of the fundamental characteristics that artificial intelligence (AI) should possess to effectively achieve this objective. This challenge represents a paradigm shift in the design, deployment, and operation of wireless networks, where conventional, static configurations give way to dynamic, adaptive, and AI-native systems capable of self-optimization, self-sustainment, and learning. This thesis aims to provide a comprehensive exploration of the fundamental principles and practical approaches required to create autonomous mobile radio networks that seamlessly integrate communication and learning components. The first chapter of this thesis introduces the notion of Predictive Quality of Service (PQoS) and adaptive optimization and expands upon the challenge to achieve adaptable, reliable, and robust network performance in dynamic and ever-changing environments. The subsequent chapter delves into the revolutionary role of generative AI in shaping next-generation autonomous networks. This chapter emphasizes achieving trustworthy uncertainty-aware generation processes with the use of approximate Bayesian methods and aims to show how generative AI can improve generalization while reducing data communication costs. Finally, the thesis embarks on the topic of distributed learning over wireless networks. Distributed learning and its declinations, including multi-agent reinforcement learning systems and federated learning, have the potential to meet the scalability demands of modern data-driven applications, enabling efficient and collaborative model training across dynamic scenarios while ensuring data privacy and reducing communication overhead.

A deep learning model via thermodynamics and condensed matter physics

Deep Learning architectures give brilliant results in a large variety of fields, but a comprehensive theoretical description of their inner functioning is still lacking. In this work, we try to understand the behavior of neural networks by modelling in the frameworks of Thermodynamics and Condensed Matter Physics. We approach neural networks as in a real laboratory and we measure the frequency spectrum and the entropy of the weights of the trained model. The stochasticity of the training occupies a central role in the dynamics of the weights and makes it difficult to assimilate neural networks to simple physical systems. However, the analogy with Thermodynamics and the introduction of a well defined temperature leads us to an interesting result: if we eliminate from a CNN the "hottest" filters, the performance of the model remains the same, whereas, if we eliminate the "coldest" ones, the performance gets drastically worst. This result could be exploited in the realization of a training loop which eliminates the filters that do not contribute to loss reduction. In this way, the computational cost of the training will be lightened and more importantly this would be done by following a physical model. In any case, beside important practical applications, our analysis proves that a new and improved modeling of Deep Learning systems can pave the way to new and more efficient algorithms.

Ecological Literacy And Beyond: Problem-based Learning For Future Professionals.

Ecological science contributes to solving a broad range of environmental problems. However, lack of ecological literacy in practice often limits application of this knowledge. In this paper, we highlight a critical but often overlooked demand on ecological literacy: to enable professionals of various careers to apply scientific knowledge when faced with environmental problems. Current university courses on ecology often fail to persuade students that ecological science provides important tools for environmental problem solving. We propose problem-based learning to improve the understanding of ecological science and its usefulness for real-world environmental issues that professionals in careers as diverse as engineering, public health, architecture, social sciences, or management will address. Courses should set clear learning objectives for cognitive skills they expect students to acquire. Thus, professionals in different fields will be enabled to improve environmental decision-making processes and to participate effectively in multidisciplinary work groups charged with tackling environmental issues.

Two-dimensional Ultrasound And Ultrasound Elastography Imaging Of Trigger Points In Women With Myofascial Pain Syndrome Treated By Acupuncture And Electroacupuncture: A Double-blinded Randomized Controlled Pilot Study.

Chronic pain has been often associated with myofascial pain syndrome (MPS), which is determined by myofascial trigger points (MTrP). New features have been tested for MTrP diagnosis. The aim of this study was to evaluate two-dimensional ultrasonography (2D US) and ultrasound elastography (UE) images and elastograms of upper trapezius MTrP during electroacupuncture (EA) and acupuncture (AC) treatment. 24 women participated, aged between 20 and 40 years (M ± SD = 27.33 ± 5.05) with a body mass index ranging from 18.03 to 27.59 kg/m2 (22.59 ± 3.11), a regular menstrual cycle, at least one active MTrP at both right (RTPz) and left trapezius (LTPz) and local or referred pain for up to six months. Subjects were randomized into EA and AC treatment groups and the control sham AC (SHAM) group. Intensity of pain was assessed by visual analogue scale; MTrP mean area and strain ratio (SR) by 2D US and UE. A significant decrease of intensity in general, RTPz, and LTPz pain was observed in the EA group (p = 0.027; p < 0.001; p = 0.005, respectively) and in general pain in the AC group (p < 0.001). Decreased MTrP area in RTPz and LTPz were observed in AC (p < 0.001) and EA groups (RTPz, p = 0.003; LTPz, p = 0.005). Post-treatment SR in RTPz and LTPz was lower than pre-treatment in both treatment groups. 2D US and UE effectively characterized MTrP and surrounding tissue, pointing to the possibility of objective confirmation of subjective EA and AC treatment effects.

Pain Intensity And Cervical Range Of Motion In Women With Myofascial Pain Treated With Acupuncture And Electroacupuncture: A Double-blinded, Randomized Clinical Trial.

Acupuncture stimulates points on the body, influencing the perception of myofascial pain or altering physiologic functions. The aim was to evaluate the effect of electroacupuncture (EAC) and acupuncture (AC) for myofascial pain of the upper trapezius and cervical range of motion, using SHAM acupuncture as control. Sixty women presenting at least one trigger point at the upper trapezius and local or referred pain for more than six months were randomized into EAC, AC, and SHAM groups. Eight sessions were scheduled and a follow-up was conducted after 28 days. The Visual Analog Scale assessed the intensity of local and general pain. A fleximeter assessed cervical movements. Data were analyzed using paired t or Wilcoxon's tests, ANOVA or Friedman or Kruskal-Wallis tests and Pearson's correlation (α=0.05). There was reduction in general pain in the EAC and AC groups after eight sessions (P<0.001). A significant decrease in pain intensity occurred for the right trapezius in all groups and for the left trapezius in the EAC and AC groups. Intergroup comparisons showed improvement in general pain in the EAC and AC groups and in local pain intensity in the EAC group (P<0.05), which showed an increase in left rotation (P=0.049). The AC group showed increases in inclination (P=0.005) sustained until follow-up and rotation to the right (P=0.032). EAC and AC were effective in reducing the pain intensity compared with SHAM. EAC was better than AC for local pain relief. These treatments can assist in increasing cervical range of motion, albeit subtly.