Miracle-dz/UniFAR-FTUs huggingface dataset:
Are neuronal networks that vicious ? Or only their models ?

We present a mathematical analysis of a network with integrate and fire neurons, taking into account the realistic fact that the spike time is only known within some \textit{finite} precision. This leads us to propose a model where spikes are effective at times multiple of a characteristic time scale $\delta$, where $\delta$ can be mathematically arbitrary small. We make a complete mathematical characterization of the model-dynamics for conductance based integrate and fire models. We obtain the following results. The asymptotic dynamics is composed by finitely many periodic orbits, whose number and period can be arbitrary large and diverge in a region of the parameters space, traditionally called the ``edge of chaos'', a notion mathematically well defined in the present paper. Furthermore, except at the edge of chaos, there is a one-to-one correspondence between the membrane potential trajectories and the spikes raster plot. This shows that the neural code is entirely ``in the spikes'' in this case. As a key tool, we introduce an order parameter, easy to compute numerically, and closely related to a natural notion of entropy providing a relevant characterization of the computational capabilities of the network. This allows us to compare the computational capabilities of leaky and integrate and fire models and conductance based models. The present study considers networks with constant input, and without time-dependent plasticity, but the framework has been designed for both extensions.
Opinion Sentence Search Engine on Open-Domain Blog

We have introduced a search engine that can extract opinion sentences relevant to an open-domain query from Japanese blog pages. The engine identifies opinions based not only on positive or negative measurements but also on neutral opinions, requests, advice, and thoughts. To retrieve a number of opinion sentences that a user could reasonably be expected to read, we attempted to extract only explicitly stated writer's opinions at the sentence-level and to exclude quoted or implicational opinions. In our search engine, opinion sentences are identified based on features such as opinion clue expressions, and then, the relevance to the query of each identified opinion sentence is checked. The experimental results for various topics, obtained by comparing the output of the proposed opinion search engine with that of human judgments as to whether the sentences were opinions, showed that the proposed engine has promise as a practical application.
Real-time data fusion on stabilizing camera pose estimation output for vision-based road navigation

This paper presents a novel framework of vision-based road navigation system, which superimposes virtual 3D navigation indicators and traffic signs onto the real road scene in an Augmented Reality (AR) space. To properly align objects in the real and virtual world, it is essential to keep tracking camera’s exact 3D position and orientation, which is well known as the Registration Problem. Traditional vision based or inertial sensor based solutions are mostly designed for well-structured environment, which is however unavailable for outdoor uncontrolled road navigation applications. This paper proposed a hybrid system that combines vision, GPS and 3D inertial gyroscope technologies to stabilize the camera pose estimation output. The fusion approach is based on our PMM (parameterized model matching) algorithm, in which the road shape model is derived from the digital map referring to GPS absolute road position, and matches with road features extracted from the real image. Inertial data estimates the initial possible motion, and also serves as relative tolerance to stable the pose output. The algorithms proposed in this paper are validated with the experimental results of real road tests under different road conditions.
T-duality of large N QCD

We argue that non-supersymmetric large N QCD compactified on T^2 exhibits properties characteristic of an SL(2,Z) T-duality. The kahler structure on which this SL(2,Z) acts is given by $m/N + i\Lambda^2 A$, where A is the area of the torus, m is the 't Hooft magnetic flux on the torus, and $\Lambda^2$ is the QCD string tension.
Compassion Fatigue: The Experiences of Teachers Working with Students with Exceptionalities

The purpose of this study was to explore the social-emotional experience of teachers working with students with exceptionalities in the general education classroom. Individual interviews were conducted with five participants with in-service teaching experience. While these teachers highlighted experiencing burnout, the breadth and depth of their experiences with respect to psychological distress were more profoundly situated within compassion fatigue. Considering the root of burnout as being related to workload conditions and that of compassion fatigue being rooted in social-emotional relationships with students, it is possible that compassion fatigue and burnout emerge along parallel trajectories of psychological distress in teachers.
Power talk in DC micro grids: Constellation design and error probability performance

Power talk is a novel concept for communication among units in a Micro Grid (MG), where information is sent by using power electronics as modems and the common bus of the MG as a communication medium. The technique is implemented by modifying the droop control parameters from the primary control level. In this paper, we consider power talk in a DC MG and introduce a channel model based on Thevenin equivalent. The result is a channel whose state that can be estimated by both the transmitter and the receiver. Using this model, we present design of symbol constellations of arbitrary order and analyze the error probability performance. Finally, we also show how to design adaptive modulation in the proposed communication framework, which leads to significant performance benefits.
TA-DRD: A Three-step Automatic Duplicate Record Detection

Duplicate record detection is a key step in Deep Web data integration, but the existing approaches do not adapt to its large-scale nature. In this paper, a three-step automatic approach is proposed for duplicate record detection in Deep Web. It firstly uses cluster ensemble to select initial training instance. Then it utilizes tri-training classification to con- struct classification model. Finally, it uses evidence theory to combine the results of multiple classification models to con- struct the domain-level duplicate record detection model which can be used for large-scale duplicate record detection in the same domain. Experimental results show that the proposed approach is better than previous work and and the domain- level duplicate record detection model can get high performance.
A Rain Pixel Recovery Algorithm for Videos With Highly Dynamic Scenes

Rain removal is a very useful and important technique in applications such as security surveillance and movie editing. Several rain removal algorithms have been proposed these years, where photometric, chromatic, and probabilistic properties of the rain have been exploited to detect and remove the rainy effect. Current methods generally work well with light rain and relatively static scenes, when dealing with heavier rainfall in dynamic scenes, these methods give very poor visual results. The proposed algorithm is based on motion segmentation of dynamic scene. After applying photometric and chromatic constraints for rain detection, rain removal filters are applied on pixels such that their dynamic property as well as motion occlusion clue are considered; both spatial and temporal informations are then adaptively exploited during rain pixel recovery. Results show that the proposed algorithm has a much better performance for rainy scenes with large motion than existing algorithms.
Contours and Constraints of an Autism Genetic Database. Scientific, Social and Digital Species of Biovalue

Abstract : This paper examines the scientific, social and digital processes that shape multiple forms of biovalue evident in the development, participation and use of the Simons Simplex Collection (SSC), the largest autism genetic databases in North America. Based on interviews with SSC study participants and investigators, as well as a content analysis of a range of SSC materials, this empirical study makes visible the various contours of biovalue that are entangled between scientists who use this data for autism research, families who donate their blood and medical information to gain access to needed resources, and digital networks of exchange that make hybrid connections between and among these emergent biosocial communities. By examining the production of and interactions between scientific, social and digital forms of biovalue this paper highlights the constraints embedded within this heterogeneous assemblage to offer a critical account of the limits of the SSC and subsequent knowledge production. I contend that while the multi-dimensionality of biovalue built into the fabric of the SSC structure creates various contours of biovalue, it structurally constrains the types of production and knowledge flows that are allowed to be conceived and generated. Keywords : autism; genetic database; biovalue; biosociality; digital networks.
Multi Relational Data Mining Classification Processions – A Survey

This paper commences with the introduction of multi relational data mining, which is an area widely explored nowadays because of its fruitfulness across a wide spectrum of applications. Data mining algorithms look for patterns in data. While most existing data mining approaches look for patterns in a single data table(propositionalisation), multi-relational data mining(MRDM) approaches look for patterns that involve multiple tables (relations) from a relational database. Three popular pattern finding techniques classification, clustering and association have been explored to a reasonable level of depth. Classification is an important task in data mining and machine learning, which has been studied extensively because of its usefulness. As converting data from multiple relations into single flat relation usually causes many problems, development of classification across multiple database relations becomes important. It then provides an insight into various classification methods including ILP(Inductive Logic Programming), Relational database, emerging patterns and associative approaches. Their characteristics and comparisons in detail have also been provided.
GPGPU Accelerated Texture-Based Radiosity

Radiosity is a popular global illumination algorithm capable of achieving photorealistic rendering results. However, its use in interactive environments is limited by its computational complexity. This paper presents a GPGPU-based implementation of the gathering radiosity approach using texture-based discretisation and the OpenCL framework. Hemicubes are rendered to a texture array and processed by OpenCL kernels in parallel to compute the output radiance of the patches. Results show that even with the high synchronisation overhead of the OpenGLOpenCL interoperability, the proposed method is an order of magnitude faster than a CPU-based implementation, and that it approaches interactive speeds. Investigation of the influence of different parameters shows that an increase in hemicube size results in a linear increase in computation time, while an increase in the number of layers in the texture array dimensions results in a logarithmic decrease in computation time.
Cerebral Blood Volume ASPECTS Is the Best Predictor of Clinical Outcome in Acute Ischemic Stroke: A Retrospective, Combined Semi-Quantitative and Quantitative Assessment

Introduction The capability of CT perfusion (CTP) Alberta Stroke Program Early CT Score (ASPECTS) to predict outcome and identify ischemia severity in acute ischemic stroke (AIS) patients is still questioned. Methods 62 patients with AIS were imaged within 8 hours of symptom onset by non-contrast CT, CT angiography and CTP scans at admission and 24 hours. CTP ASPECTS was calculated on the affected hemisphere using cerebral blood flow (CBF), cerebral blood volume (CBV) and mean transit time (MTT) maps by subtracting 1 point for any abnormalities visually detected or measured within multiple cortical circular regions of interest according to previously established thresholds. MTT-CBV ASPECTS was considered as CTP ASPECTS mismatch. Hemorrhagic transformation (HT), recanalization status and reperfusion grade at 24 hours, final infarct volume at 7 days and modified Rankin scale (mRS) at 3 months after onset were recorded. Results Semi-quantitative and quantitative CTP ASPECTS were highly correlated (p<0.00001). CBF, CBV and MTT ASPECTS were higher in patients with no HT and mRS≤2 and inversely associated with final infarct volume and mRS (p values: from p<0.05 to p<0.00001). CTP ASPECTS mismatch was slightly associated with radiological and clinical outcomes (p values: from p<0.05 to p<0.02) only if evaluated quantitatively. A CBV ASPECTS of 9 was the optimal semi-quantitative value for predicting outcome. Conclusions Our findings suggest that visual inspection of CTP ASPECTS recognizes infarct and ischemic absolute values. Semi-quantitative CBV ASPECTS, but not CTP ASPECTS mismatch, represents a strong prognostic indicator, implying that core extent is the main determinant of outcome, irrespective of penumbra size.
Psychoanalysis and theoretical backgrounds in attachement theory research

In 1969, British psychoanalyst John Bowlby founded a scientific theory, which is now known as “an attachment theory” in which he proposed that keeping physical proximity with caregiving adults represents primary mechanism of child safety regulation and survival. As the theory evolved, due to the new pespectives gained by other fields of research such as biology, neuroscience and developmental psychology, everyday experience of working psychoanalysts showed also to be very useful in explaining how the psychologic attachment develops in humans. In this process, one of the most important concepts is mentalization that can be roughly defined as process of taking views that observe the intention, which means an approach to humans as beings guided by intrinsic intents. For this process to be effective, a person must have satisfactory capacity to mentalize, which finally leads to the development of mature “self”. Ever-growing knowledge in this field of research should eventually establish theoretically sound and truly evidence-based clinical practices, working in harmony with a research literature that is sensitive to clinical discoveries and applications. In this paper, we review attachment theory in the light of this new research, with hope of applying it to psychoanalytical practice for the benefit of the patients.
THE STELLAR AND GAS KINEMATICS OF THE LITTLE THINGS DWARF IRREGULAR GALAXY NGC 1569

In order to understand the formation and evolution of Magellanic-type dwarf irregular (dIm) galaxies, one needs to understand their three-dimensional structure. We present measurements of the stellar velocity dispersion in NGC 1569, a nearby post-starburst dIm galaxy. The stellar vertical velocity dispersion, σz, coupled with the maximum rotational velocity derived from H i observations, Vmax, gives a measure of how kinematically hot the galaxy is, and, therefore, indicates its structure. We conclude that the stars in NGC 1569 are in a thick disk with a Vmax/σz = 2.4 ± 0.7. In addition to the structure, we analyze the ionized gas kinematics from O iii observations along the morphological major axis. These data show evidence for outflow from the inner starburst region and a potential expanding shell near supermassive star cluster (SSC) A. When compared to the stellar kinematics, the velocity dispersion of the stars increases in the region of SSC A supporting the hypothesis of an expanding shell. The stellar kinematics closely follow the motion of the gas. Analysis of high-resolution H i data clearly reveals the presence of an H i cloud that appears to be impacting the eastern edge of NGC 1569. Also, an ultra-dense H i cloud can be seen extending to the west of the impacting H i cloud. This dense cloud is likely the remains of a dense H i bridge that extended through what is now the central starburst area. The impacting H i cloud was the catalyst for the starburst, thus turning the dense gas into stars over a short timescale, ∼1 Gyr. We performed a careful study of the spectral energy distribution using infrared, optical, and ultraviolet photometry, producing a state-of-the-art mass model for the stellar disk. This mass modeling shows that stars dominate the gravitational potential in the inner 1 kpc. The dynamical mass of NGC 1569, derived from Vmax, shows that the disk may be dark matter deficient in the inner region, although, when compared to the expected virial mass determined from halo abundance matching techniques, the dark matter profile seems to agree with the observed mass profile at a radius of 2.2 kpc.
Improving on the Mixture of Experts Algorithm

Difficult non-linear problems can be mapped to a set of localised linear problems. A self-organising map (SOM) is used as a gating function to a localised mixture of experts classifier and is shown to find solutions equivalent to those learned by a multi-layer perceptron while retaining the simplicity and resilience of a single-layer perceptron. Modifications to the traditional softmax gate function and expert training function are shown to produce more accurate classifiers that tend not to over-fit to training data.
The Validity of d′ Measures

Subliminal perception occurs when prime stimuli that participants claim not to be aware of nevertheless influence subsequent processing of a target. This claim, however, critically depends on correct methods to assess prime awareness. Typically, d′ (“d prime”) tasks administered after a priming task are used to establish that people are unable to discriminate between different primes. Here, we show that such d′ tasks are influenced by the nature of the target, by attentional factors, and by the delay between stimulus presentation and response. Our results suggest that the standard d′ task is not a straightforward measure of prime visibility. We discuss the implications of our findings for subliminal perception research.
Rotor Fault Diagnosis in Three Phase Induction Motors Using the Wavelet Transform

In this paper the faults diagnosis of induction machines based on the discrete wavelet transform (DWT) is detailed. The wavelet decomposition is used to extract the information from a signal over a wide range of frequencies. This analysis is performed in both time and frequency domains. The Daubechies wavelet is selected for the analysis of the stator current. Wavelet components appear to be useful for detecting different electrical faults. In this paper we will study the problem of broken rotor bars, and end-ring segment. Keywords— Fault diagnosis, Induction Machines, broken endring, broken rotor bars, Wavelet transform, Decomposition level
Control of a Dynamic Driving Simulator: Time-Variant Motion Cueing Algorithms and Prepositioning

Die vorliegende Diplomarbeit beschäftigt sich mit dem Problem der realistischen Darstellung von Bewegungshinweisen (Motion Cues) in dynamischen Simulatoren. Der Fahrsimulator des Instituts für Verkehrsführung und Fahrzeugsteuerung am Deutschen Zentrum für Luft und Raumfahrt in Braunschweig nutzt ein Hexapod Bewegungssystem, um Beschleunigungen in allen sechs Freiheitsgraden darzustellen. Das Ziel dieser Arbeit ist es, den Fahreindruck des Probanden zu verbessern. Das Modul, welches die von einer Mehrkörpersimulation des Autos gelieferten Bewegungen in Signale umsetzt, die von der Plattform dargestellt werden können, soll sich zu diesem Zweck an die aktuelle Fahrsituation anpassen. Zwei adaptive Lösungen wurden entwickelt, die zum einen zwischen statischen Motion Cueing Algorithmen wechseln und zum anderen zeitvariante Filterstrukturen nutzen. Eine der Fahrsituation angepaßte Vorpositionierung des Simulators verschiebt die Plattform innerhalb des Arbeitsraumes, um Bereiche, die laut prädiziertem Verhalten vorwiegend genutzt werden, zu vergrößern. Auf diese Weise ist es möglich, die Kapazität der Bewegungsplattform hinsichtlich Arbeitsraum, Geschwindigkeit des Verfahrens und Darstellung der Beschleunigungen voll auszuschöpfen.
Optimizing Queries in a Logic-based Information Integration System

The SINTAGMA information integration system is an infrastructure for accessing several different information sources together. Besides providing a uniform interface to the information sources (databases, web services, web sites, RDF resources, XML files), semantic integration is also needed. Semantic integration is carried out by providing a high-level model and the mappings to the models of the sources. When executing a query of the high level model, a query is transformed to a low-level query plan, which is a piece of Prolog code that answers the high-level query. This transformation is done in two phases. First, the Query Planner produces a plan as a logic formula expressing the low-level query. Next, the Query Optimizer transforms this formula to executable Prolog code and optimizes it according to structural and statistical information about the information sources. This article discusses the main ideas of the optimization algorithm and its implementation.
Trajectories of endurance activity following pulmonary rehabilitation in COPD patients

Maintenance of physical activity following pulmonary rehabilitation remains a challenge for patients with chronic obstructive pulmonary disease (COPD). The objectives of this study were to identify patterns of endurance activity after completion of pulmonary rehabilitation and to characterise people who succeed and those who have difficulty maintaining endurance activity. In a longitudinal study embedded within a randomised clinical trial, 206 individuals with COPD underwent a 3-month pulmonary rehabilitation programme. Weekly duration of endurance activity was assessed at 4, 6, 8 and 12 months after the start of rehabilitation. Trajectory modelling was used to determine the most common patterns of activity during the post-rehabilitation phase from 4–12 months. Three distinct patterns were identified, two of which indicated difficulty in maintaining endurance activity: 61 individuals reported a high activity level at 4 months (2.7 h·week−1) and stayed high; 114 individuals started at a low activity level (mean 1.0 h·week−1) and stayed low; and 31 individuals started high (3.0 h·week−1) and declined. The low activity group was characterised by more severe disease and greater respiratory impairment. The high and declined group had less severe disease and respiratory impairment, but reported greater barriers to exercise. Pulmonary rehabilitation should include interventions aimed at minimising barriers, in order to induce long-term behaviour change.