Miracle-dz/UniFAR-FTUs huggingface dataset:
DynJava: Type Safe Dynamic Code Generation in Java

Dynamic code generation is a technique that generates and executes fragments of executable code during the run-time of an program to reduce the execution time of programs. However, most of currently available dynamic code generation systems have weakness on high-level language support for describing dynamic code fragment. Especially, safety of dynamic composition of code fragments is not sufficiently supported. To solve this, we present a Java-based strongly-typed language for dynamic code generation. This language gives precise types to dynamic code fragments, to guarantee the type-safety of dynamically-composed codes.
Deriving semantic terms for images by mining the web

In this paper, we provide a novel image annotation model by mining the Web. In our approach, the concepts or words appearing in the associated text are extracted and filtered as the semantic annotations for the corresponding Web images. In order to alleviate the influence caused by the noise images, for each semantic concept, we improve Web image-word relationships using Mixture Gaussian Distribution Model. By doing so, the concepts or words relevant to any image are re-weighed by both considering their relevance to the image in term of text and in term of visual feature. In fact, all the words associated to an image are not semantically independent. We use co-occurrences between two words to describe their semantic relevance. Thus, we further use a method, called Word Promotion, to co-enhance the weights of all the words associated to a given image based on their co-occurrences. Our experiments are conducted in several ways and the results show that our annotation method can achieve a satisfactory performance.
Urban Growth Prediction: A Review of Computational Models and Human Perceptions

Human population continues to aggregate in urban centers. This inevitably increases the urban footprint with significant consequences for biodiversity, climate, and environmental resources. Urban growth prediction models have been extensively studied with the overarching goal to assist in sustainable management of urban centers. Despite the extensive body of research, these models are not frequently included in the decision making process. This review aims on bringing this gap by analyzing results from a survey investigating developer and user perceptions from the modeling and planning communities, respectively. An overview of existing models, including advantages and limitations, is also provided. A total of 156 manuscripts is identified. Analysis of aggregated statistics indicates that cellular automata are the prevailing modeling technique, present in the majority of published works. There is also a strong preference for local or regional studies, a choice possibly related to data availability. The survey found a strong recognition of the models’ potential in decision making, but also limited agreement that these models actually reach that potential in practice. Collaboration between planning and modeling communities is deemed essential for transitioning models into practice. Data availability is considered a stronger restraining factor by respondents with limited algorithmic experience, which may indicate that model input data are becoming more specialized, thus significantly limiting wide-spread applicability. This review assesses developer and user perceptions and critically discusses existing urban growth prediction models, acting as a reference for future model development. Specific guidelines are provided to facilitate transition of this relatively mature science into decision making activities
Functional Hemichannels in Astrocytes: A Novel Mechanism of Glutamate Release

Little is known about the expression and possible functions of unopposed gap junction hemichannels in the brain. Emerging evidence suggests that gap junction hemichannels can act as stand-alone functional channels in astrocytes. With immunocytochemistry, dye uptake, and HPLC measurements, we show that astrocytes in vitro express functional hemichannels that can mediate robust efflux of glutamate and aspartate. Functional hemichannels were confirmed by passage of extracellular lucifer yellow (LY) into astrocytes in nominal divalent cation-free solution (DCFS) and the ability to block this passage with gap junction blocking agents. Glutamate/aspartate release (or LY loading) in DCFS was blocked by multivalent cations (Ca2+, Ba2+, Sr2+, Mg2+, and La3+) and by gap junction blocking agents (carbenoxolone, octanol, heptanol, flufenamic acid, and 18α-glycyrrhetinic acid) with affinities close to those reported for blockade of gap junction intercellular communication. Glutamate efflux via hemichannels was also accompanied by greatly reduced glutamate uptake. Glutamate release in DCFS, however, was not significantly mediated by reversal of the glutamate transporter: release did not saturate and was not blocked by glutamate transporter blockers. Control experiments in DCFS precluded glutamate release by volume-sensitive anion channels, P2X7 purinergic receptor pores, or general purinergic receptor activation. Blocking intracellular Ca2+ mobilization by BAPTA-AM or thapsigargin did not inhibit glutamate release in DCFS. Divalent cation removal also induced glutamate release from intact CNS white matter (acutely isolated optic nerve) that was blocked by carbenoxolone, suggesting the existence of functional hemichannels in situ. Our results indicated that astrocyte hemichannels could influence CNS levels of extracellular glutamate with implications for normal and pathological brain function.
Functional cluster extraction from large spatial networks

We address a problem of extracting functionally similar regions in urban streets regarded as spatial networks. Such characteristics of regions will play important roles for developing and planning city promotion, travel tours and so on, as well as understanding and improving the usage of urban streets. In order to analyze such functionally similar regions, we propose an acceleration method of the FCE (functional cluster extraction) algorithm equipped with the lazy evaluation and pivot pruning techniques, which enables to efficiently deal with several large-scale networks. In our experiments using urban streets of six cities, we show that our proposed method achieved a reasonably high acceleration performance. Then, we show that functional cluster produced by our method are useful for understanding the properties of areas in a series of visualization results.
Hierarchical QoS management for time sensitive applications

The use of real time techniques in new application fields such as multimedia computing, has extended classical algorithms to more dynamic environments, introducing the problem of controlling and adapting the quality of service provided by an application. The authors investigate the possibility of integrating application-dependent adaptation strategies with reservation techniques for handling multimedia real time applications. We show how a global adaptive reservation mechanism can be combined with a local application-level adaptation, obtaining a hierarchical management scheme. The need for the two forms of adaptation (application dependent and global) and the effectiveness of the proposed hierarchical scheme are shown by a set of experiments on multimedia applications. All the experiments have been performed by running the real applications on a real time kernel.
Morphological number counts and redshift distributions to I = 25 from the Hubble Deep Fields: constraints on cosmological models from early‐type galaxies

ABSTRA C T We combine magnitude and photometric redshift data on galaxies in the Hubble Deep Fields with morphological classifications in order to separate out the distributions for early-type galaxies. The updated morphological galaxy number counts down to Ia 25 and the corresponding redshift distributions are used as joint constraints on cosmological models; in particular, on the values of the density parameter V0 and the normalized cosmological constant l 0. We find that an Einstein‐de Sitter universe with simple passive evolution gives an excellent fit to the counts and redshift data at all magnitudes. An open, low-V0 model with no net evolution (and conservation of the number of ellipticals), which fits the counts equally well, is somewhat less successful, predicting slightly lower mean redshifts and, more significantly, the lack of a high-z tail. A number-conserving model with a dominant contribution from l 0, on the other hand, is far less successful, predicting a much narrower distribution than is seen. More complex models are obviously possible, but we conclude that if large-scale transmutation between types does not occur, then the lambda-dominated models provide a very poor fit to the current data.
Information and Performance Landscapes

When no knowledge is available on the structure of the fitness landscape, effectively the fitness function is a black box. Search in this scenario is not well understood. In this paper we introduce a new theoretical approach to study search algorithms in this scenario. This is based on a new definition of the concept of landscape – the information landscape – which allows us to evaluate the quantity of information embedded in a landscape. We derive a linear approximation to the performance of an algorithm based on this notion. This leads to the definition of the concept of performance landscape, which allows us to evaluate the quality of the information in a landscape with respect to an algorithm’s search behaviour. We show that these two landscapes and their simple interaction allow us to better understand the general behaviour of search algorithms in the black box scenario. We also derive a new simple measure of problem difficulty. In experiments with a large variety of test problems and different versions of genetic algorithm we provide strong evidence supporting this framework.
An approach to analyze suicidal tendency in blogs and tweets using Sentiment Analysis

Accepted: 20/Aug/2018, Online: 31/Aug/2018 Abstract— Sentiment Analysis(SA) or Opinion Mining is done to find the opinion of the users and customers to review and analyse their opinions on various products and services. It is one of the major tasks in business these days for knowing demands of customers. In this paper a sentiment analysis method for analyzing the suicidal tendencies in blogs and tweets is proposed. The proposed method uses concepts like Bag of words ,Part of Speech and Natural Language Processing for analzing the text.In life sometimes such a situation arises where the person finds himself trapped and suicide only seems to be the ultimate respite from all problems. Such a person may not share his mental condition verbally with anyone but may share it through tweets and messages. This paper is an approach to reach out a helping hand to such people by analyzing such tendencies in their messages. The proposed method uses Python Language module Textblob for performing different analysis task on the text.
Volatility Forecasting II : Stochastic Volatility Models and Empirical Evidence

ln(σt) = α + φ(ln(σt−1)− α) + ηt so that ln(σt) is an AR(1) process, where φ is a parameter that represents how quickly volatility gets pulled toward its mean, α. If ηt is normally distributed with mean 0 and variance σ η, then ln(σt) is normally distributed, and σt therefore has a lognormal distribution. To get the unconditional mean and variance of ln(σt), E[ln(σt)] = α + φ(E[ln(σt−1)]− α) + E[ηt] and since ln(σt) is a stationary process, E[ln(σt)] = α .
Cascaded backstepping control of a Duocopter including disturbance compensation by unscented Kalman filtering

A cascaded control strategy for an innovative Duocopter test stand - a helicopter with two rotors combined with a guiding mechanism - is presented in this paper. The guiding mechanism consists of a rocker arm with a sliding carriage that enforces a planar workspace of the Duocopter. The Duocopter is connected to the carriage by a rotary joint and offers 3 degrees of freedom. The derived system model has similarities with a PVTOL and a planar model of a quadrocopter but involves additional terms due to the guiding mechanism. In the paper, a model-based cascaded control strategy is proposed: the outer MIMO control loop is given by the inverted system model to control the horizontal and the vertical Duocopter position with a nonlinear error dynamics derived from backstepping techniques. The rotation angle of the Duocopter is controlled in a linear inner control loop of high bandwidth. Due to uncertain system parameters and reasonable simplifications at the modelling of the test stand, the control structure is extended by an unscented Kalman filter. Thereby, an excellent tracking performance in vertical and horizontal direction can be achieved. The efficiency of the proposed control strategy is demonstrated by both simulations and experiments.
Analysis of the Aurora Large Voc Table baseli

In this paper, we analyze the results of the recent Aurora large vocabulary evaluations. Two consortia submitted proposals on speech recognition front ends for this evaluation: (1) Qualcomm, ICSI, and OGI (QIO), and (2) Motorola, France Telecom, and Alcatel (MFA). These front ends used a variety of noise reduction techniques including discriminative transforms, feature normalization, voice activity detection, and blind equalization. Participants used a common speech recognition engine to postprocess their features. In this paper, we show that the results of this evaluation were not significantly impacted by suboptimal recognition system parameter settings. Without any front end specific tuning, the MFA front end outperforms the QIO front end by 9.6% relative. With tuning, the relative performance gap increases to 15.8%. Both the mismatched microphone and additive noise evaluation conditions resulted in a significant degradation in performance for both front ends.
Collaborative data analysis with smart tangible devices

We present a tangible approach for exploring and comparing multi-dimensional data points collaboratively by combining Sifteo Cubes with glyph visualizations. Various interaction techniques like touching, shaking, moving or rotating the displays support the user in the analysis. Context dependent glyph-like visualization techniques make best use of the available screen space and cube arrangements. As a first proof of concept we apply our approach to real multi-dimensional datasets and show with a coherent use case how our techniques can facilitate the exploration and comparison of data points. Finally, further research directions are shown when combining Sifteo Cubes with glyphs and additional context information provided by multi-touch tables.
Delay-Bounded Range Queries in DHT-based Peer-to-Peer Systems

Many general range query schemes for DHT-based peer-to-peer (P2P) systems have been proposed, which do not need to modify the underlying DHTs. However, most existing works have the query delay depending on both the scale of the system and the size of the query space or the specific query, and thus cannot guarantee to return the query results in a bounded delay. In this paper, we propose Armada, an efficient general range query scheme to support single-attribute and multipleattribute range queries. Armada is the first delaybounded range query scheme over constant-degree DHTs, and can return the results for any range query within 2logN hops in a P2P system with N peers. Results of analysis and simulations show that the average delay of Armada is less than logN, and the average message cost of single-attribute range queries is about logN+2n.2 (n is the number of peers that intersect with the query). These results are very close to the lower bounds on delay and message cost of range queries over constant-degree DHTs.
Physical Activity in Patients Treated With Peritoneal Dialysis

Background: Patients with chronic diseases are known to benefit from exercise. Despite a lack of compelling evidence, patients with end-stage kidney disease treated with peritoneal dialysis are often discouraged from participating in exercise programs that include resistance training due to concerns about the development of hernias and leaks. The actual effects of physical activity with or without structured exercise programs for these patients remain unclear. The purpose of this study is to more completely define the risks and benefits of physical activity in the end-stage kidney disease population treated with peritoneal dialysis. Methods/design: We will conduct a systematic review examining the effects of physical activity on end-stage kidney disease patients treated with peritoneal dialysis. For the purposes of this review, exercise will be considered a purposive subcategory of physical activity. The primary objective is to determine if physical activity in this patient population is associated with improvements in mental health, physical functioning, fatigue and quality of life and if there is an increase in adverse outcomes. With the help of a skilled librarian, we will search MEDLINE, EMBASE, CINAHL, and Cochrane Central Register of Controlled Trials for randomized trials and observational studies. We will include adult end-stage kidney disease patients treated with peritoneal dialysis that have participated in an exercise training program or had their level of physical activity assessed directly or by self-report. The study must include an assessment of the association between physical activity and one of our primary or secondary outcomes measures. We will report study quality using the Cochrane Risk of Bias Assessment Tool for randomized controlled trials and the Newcastle–Ottawa Scale for observational studies. Quality across studies will be assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach. The results of this review may help to inform guideline development for exercise recommendations specific to this patient population. Systematic review registration: PROSPERO:CRD42016041695.
Combined gene/cell therapies provide long-term and pervasive rescue of multiple pathological symptoms in a murine model of globoid cell leukodystrophy

Globoid cell leukodystrophy (GLD) is a lysosomal storage disease caused by deficient activity of β-galactocerebrosidase (GALC). The infantile forms manifest with rapid and progressive central and peripheral demyelination, which represent a major hurdle for any treatment approach. We demonstrate here that neonatal lentiviral vector-mediated intracerebral gene therapy (IC GT) or transplantation of GALC-overexpressing neural stem cells (NSC) synergize with bone marrow transplant (BMT) providing dramatic extension of lifespan and global clinical–pathological rescue in a relevant GLD murine model. We show that timely and long-lasting delivery of functional GALC in affected tissues ensured by the exclusive complementary mode of action of the treatments underlies the outstanding benefit. In particular, the contribution of neural stem cell transplantation and IC GT during the early asymptomatic stage of the disease is instrumental to enhance long-term advantage upon BMT. We clarify the input of central nervous system, peripheral nervous system and periphery to the disease, and the relative contribution of treatments to the final therapeutic outcome, with important implications for treatment strategies to be tried in human patients. This study gives proof-of-concept of efficacy, tolerability and clinical relevance of the combined gene/cell therapies proposed here, which may constitute a feasible and effective therapeutic opportunity for children affected by GLD.
Fuzzy Model for a Computer Simulation of Bird Flocking

In the dissertation I address the question, “How to use fuzzy logic for modelling bird flocking? ” The research was led by the assumption that this complex behaviour could be much more easily described by using simple linguistic descriptions (e.g. collections of if-then rules) than by using mathematical equations. Indeed, most of the existing knowledge about the behaviour of flocks is available in the form of the observer's linguistic descriptions and explanations of the perceived behaviour that were typically obtained during field work. I begin the dissertation by presenting a formal definition of an extended Moore automaton, denoted as the animat, and continue by showing the latter can be used to formalize the most renowned existing bird flocking models. In the rest of the dissertation I employ fuzzy logic to enable the use of linguistic rules for the description of the animats’ behaviour and present the efficiency by developing a fuzzy model for the computer simulation of bird flocking, restrained to a two-dimensional universe without obstacles. A part of the dissertation is also devoted to the definition of metrics that allow a qualitative evaluation of bird flocking models. These metrics are used in the concluding part of the dissertation, where the newly introduced fuzzy model is compared, through a series of controlled experiments, with the most renowned existing ones.
Psychological and physiologiacal correlates of emotion regulation

The principal aim of this thesis is to examine how emotion regulation and mindfulness are related to cardiovascular activity and the implications of these relationships for the understanding of aggression. Studies one and two aimed to detail the relationship of mindfulness to psychopathic traits and emotion regulation. Results of these studies collectively imply that mindfulness shares certain features with primary psychopathic traits, including reduced physiological responding to aversive stimuli. Study three aimed to investigate whether slow-paced breathing, associated with an increase in vagal output and thus decrease in heart rate, may exert effects on emotion regulation. Participation in a paced breathing course improved emotion regulation and increased trait mindfulness in a sample of male offenders. Study four aimed to explore how cardiovascular activity and psychopathic traits relate to female perpetrated intimate partner violence. Increased vagal activity, was found to be positively linked to proactive aggression and partner violence. Study five aimed to extend the results of Study four to a sample of male offenders. The results showed that high vagal activity is related to low empathy and good performance on the Stroop task. Collectively, these findings have implications for the use of mindfulness based treatments and the understanding of aggression.
A New Vertical Diffusion Package with an Explicit Treatment of Entrainment Processes

Abstract This paper proposes a revised vertical diffusion package with a nonlocal turbulent mixing coefficient in the planetary boundary layer (PBL). Based on the study of Noh et al. and accumulated results of the behavior of the Hong and Pan algorithm, a revised vertical diffusion algorithm that is suitable for weather forecasting and climate prediction models is developed. The major ingredient of the revision is the inclusion of an explicit treatment of entrainment processes at the top of the PBL. The new diffusion package is called the Yonsei University PBL (YSU PBL). In a one-dimensional offline test framework, the revised scheme is found to improve several features compared with the Hong and Pan implementation. The YSU PBL increases boundary layer mixing in the thermally induced free convection regime and decreases it in the mechanically induced forced convection regime, which alleviates the well-known problems in the Medium-Range Forecast (MRF) PBL. Excessive mixing in the mixed layer in the presenc.