Miracle-dz/UniFAR-FTUs huggingface dataset:
Personalized Medication Response Prediction for Attention-Deficit Hyperactivity Disorder: Learning in the Model Space vs. Learning in the Data Space

Attention-Deficit Hyperactive Disorder (ADHD) is one of the most common mental health disorders amongst school-aged children with an estimated prevalence of 5% in the global population (American Psychiatric Association, 2013). Stimulants, particularly methylphenidate (MPH), are the first-line option in the treatment of ADHD (Reeves and Schweitzer, 2004; Dopheide and Pliszka, 2009) and are prescribed to an increasing number of children and adolescents in the US and the UK every year (Safer et al., 1996; McCarthy et al., 2009), though recent studies suggest that this is tailing off, e.g., Holden et al. (2013). Around 70% of children demonstrate a clinically significant treatment response to stimulant medication (Spencer et al., 1996; Schachter et al., 2001; Swanson et al., 2001; Barbaresi et al., 2006). However, it is unclear which patient characteristics may moderate treatment effectiveness. As such, most existing research has focused on investigating univariate or multivariate correlations between a set of patient characteristics and the treatment outcome, with respect to dosage of one or several types of medication. The results of such studies are often contradictory and inconclusive due to a combination of small sample sizes, low-quality data, or a lack of available information on covariates. In this paper, feature extraction techniques such as latent trait analysis were applied to reduce the dimension of on a large dataset of patient characteristics, including the responses to symptom-based questionnaires, developmental health factors, demographic variables such as age and gender, and socioeconomic factors such as parental income. We introduce a Bayesian modeling approach in a “learning in the model space” framework that combines existing knowledge in the literature on factors that may potentially affect treatment response, with constraints imposed by a treatment response model. The model is personalized such that the variability among subjects is accounted for by a set of subject-specific parameters. For remission classification, this approach compares favorably with conventional methods such as support vector machines and mixed effect models on a range of performance measures. For instance, the proposed approach achieved an area under receiver operator characteristic curve of 82–84%, compared to 75–77% obtained from conventional regression or machine learning (“learning in the data space”) methods.
A Humanoid Muscle Robot Torso with Biologically Inspired Construction

Human-like robots combine the optimized biological morphology and functionality of the real human with the mechanical constraints and limitations in the creation as well as possible. This will be a trade-off at all t imes. The best biological solution of a detail is often simple in the functio n but too complex for the technical analogue. The t echnical materials are often missing what make a one-to-one copy impossible. The combination of biology and robots leads to s moother and compliant movement, which is more pleasant for us as people. Biologically inspired robots embody no ri gid movement, which are made possible by special joints, or actua tors, which give way, and can both actively and pas sively, adapt stiffness in different situations. This paper present the humanoid muscle robot torso called “Zwei-Arm-Roboter” ZAR5 in human-like proportions and functionality, which is fully actuated by artificia l air muscles. The first section gives a short intr oduction as to how Bionik engineers think in terms of compliant machines and whose technical realisation. The second sectio n looks briefly at mechanical aspects, limitations and constraints and furthermore describes the human-like anthropomorphic five-finger hand. This section also comprises a short view of t he used fluidic muscle actuators of the company FESTO 1 . Section 3 describes the electronic components and the decentr alized control architecture, which fulfils the requ irements on an evolvable control. The last section concludes the pap er.
Beyond Output Voting: Detecting Compromised Replicas Using HMM-Based Behavioral Distance

Many host-based anomaly detection techniques have been proposed to detect code-injection attacks on servers. The vast majority, however, are susceptible to "mimicry" attacks in which the injected code masquerades as the original server software, including returning the correct service responses, while conducting its attack. "Behavioral distance," by which two diverse replicas processing the same inputs are continually monitored to detect divergence in their low-level (system-call) behaviors and hence potentially the compromise of one of them, has been proposed for detecting mimicry attacks. In this paper, we present a novel approach to behavioral distance measurement using a new type of hidden Markov model, and present an architecture realizing this new approach. We evaluate the detection capability of this approach using synthetic workloads and recorded workloads of production Web and game servers, and show that it detects intrusions with substantially greater accuracy than a prior proposal on measuring behavioral distance. We also detail the design and implementation of a new architecture, which takes advantage of virtualization to measure behavioral distance. We apply our architecture to implement intrusion-tolerant Web and game servers, and through trace-driven simulations demonstrate that it experiences moderate performance costs even when thresholds are set to detect stealthy mimicry attacks.
GPU-Accelerated Data Mining with Swarm Intelligence

Swarm intelligence describes the ability of groups of social animals and insects to exhibit highly organized and complex problem-solving behaviors that allow the group as a whole to accomplish tasks which are beyond the capabilities of any one of the constituent individuals. This natural phenomenon is the inspiration for swarm intelligence systems, a class of algorithms that utilizes the emergent patterns of swarms to solve computational problems. Recently, there have been a number of publications regarding the application of swarm intelligence to various data mining problems, yet very few consider multi-threaded, let alone GPU-based implementations. In this paper we adopt the General-Purpose GPU parallel computing model and show how it can be leveraged to increase the accuracy and efficiency of two types of swarm intelligence algorithms for data mining. To illustrate the efficacy of GPU computing for swarm intelligence, we present two swarm intelligence data mining algorithms implemented with CUDA for execution on a GPU device. These algorithms are: (1) AntMinerGPU, an ant colony optimization algorithm for rule-based classification, and (2) ClusterFlockGPU, a bird-flocking algorithm for data clustering. Our results indicate that the AntMinerGPU algorithm is markedly faster than the sequential algorithm on which it is based, and is able to produce classification rules which are competitive with those generated by traditional methods. Additionally, we show that ClusterFlockGPU is competitive with other swarm intelligence and traditional clustering methods, and is not affected by the dimensionality of the data being clustered making it theoretically well-suited for high-dimensional problems.
Using a class abstraction technique to predict faults in OO classes: a case study through six releases of the Eclipse JDT

In this paper, we propose an innovative suite of metrics based on a class abstraction that uses a taxonomy for OO classes (CAT) to capture aspects of software complexity through combinations of class characteristics. We empirically validate their ability to predict fault prone classes using fault data for six versions of the Java-based open-source Eclipse Integrated Development Environment. We conclude that this proposed CAT metric suite, even though it treats classes in groups rather than individually, is as effective as the traditional Chidamber and Kemerer metrics in identifying fault-prone classes.
Theoretical examination of stress fields in Pb(Zr0.5Ti0.5)O3

Abstract In this paper, we develop a rigorous formulation of the local stress field. This approach can be used in conjunction with any first-principles method to study stress fields in complex bonded systems. In particular we investigate the induced stress fields resulting from the homogeneous deformations of tetragonal PbTiO3 and rhombohedral PbZrO3. As an extension of these findings we also compute the induced stress fields resulting from homogeneous deformation of the (100) and (111) orderings of Pb(Zr0.5Ti0.5)O3. The stress-field responses in these four materials are compared and their piezoelectric responses are discussed.
The remediation of nosferatu: exploring transmedia experiences

In this paper we present The Remediation of Nosferatu, a location based augmented reality horror adventure. Using the theory of fictional universe elements, we work with diverse material from Nosferatu's horror genre and vampire themes as a case study. In this interdisciplinary research we intertwine traditional storytelling and scriptwriting skills with interaction design methods. For the game setting, we create hybrid spaces merging the fictional universe and the physical environment into one pervasive experience, centering around a variety of augmented reality activities played out at sunset. Focusing on the phenomenological world of 21 participants, we analyse triangulated data by distinguishing between a range of more "open" and "closed" styles of interactions. Our study illustrates how Speculative Play may enable non-linear storytelling elements within a transmedia fictional universe. We believe our approach can be more generally useful for designing future rich, enjoyable and meaningful transmedia experiences.
An Efficient Explanation of Individual Classifications using Game Theory

We present a general method for explaining individual predictions of classification models. The method is based on fundamental concepts from coalitional game theory and predictions are explained with contributions of individual feature values. We overcome the method's initial exponential time complexity with a sampling-based approximation. In the experimental part of the paper we use the developed method on models generated by several well-known machine learning algorithms on both synthetic and real-world data sets. The results demonstrate that the method is efficient and that the explanations are intuitive and useful.
Murine mCLCA6 Is an Integral Apical Membrane Protein of Non-goblet Cell Enterocytes and Co-localizes With the Cystic Fibrosis Transmembrane Conductance Regulator

The CLCA family of proteins consists of a growing number of structurally and functionally diverse members with distinct expression patterns in different tissues. Several CLCA homologs have been implicated in diseases with secretory dysfunctions in the respiratory and intestinal tracts. Here we present biochemical protein characterization and details on the cellular and subcellular expression pattern of the murine mCLCA6 using specific antibodies directed against the amino- and carboxy-terminal cleavage products of mCLCA6. Computational and biochemical characterizations revealed protein processing and structural elements shared with hCLCA2 including anchorage in the apical cell membrane by a transmembrane domain in the carboxy-terminal subunit. A systematic light- and electron-microscopic immunolocalization found mCLCA6 to be associated with the microvilli of non-goblet cell enterocytes in the murine small and large intestine but in no other tissues. The expression pattern was confirmed by quantitative RT-PCR following laser-capture microdissection of relevant tissues. Confocal laser scanning microscopy colocalized the mCLCA6 protein with the cystic fibrosis transmembrane conductance regulator CFTR at the apical surface of colonic crypt cells. Together with previously published functional data, the results support a direct or indirect role of mCLCA6 in transepithelial anion conductance in the mouse intestine.
Improvement of speech summarization using prosodic information

Speech summarization is a technique of extracting important sentences from spoken documents. It provides us useful information to looking for the spoken documents that we want. Spoken documents contain non-linguistic information, which is mainly expressed by prosody, while written text conveys only linguistic information. This paper describes a summarization method which uses prosodic information as well as linguistic information. The linguistic information is derived from text which is transcribed by a continuous speech recognition system. In this paper, the speech summarization is defined as extraction of important sentences from transcribed text. Importance of the sentence is predicted by the prosodic parameters and the linguistic information which are combined by multiple regression analysis. Proposed methods are evaluated both on the correlation between the predicted scores of sentence importance and the preference scores by subjects and on the accuracy of extraction of important sentences. Prosodic information improved the quality of speech summary, and it is more effective when the speech is transcribed by automatic speech recognition because speech recognition errors damage linguistic information.
The multiple functions of sensory-motor representations: an introduction

The papers in this issue consider the role of sensory-motor processes and their neural structures in higher cognitive functions such as visual and motor imagery, iconic memory, temporal judgement, mental rotation, and the representation of object and action concepts. The papers draw on a range of methodologies, from computational modelling to behavioural studies of braindamaged subjects, to fMRI, TMS, and ERP studies with normal subjects, and they present various theoretical perspectives. What unites them is the explicit effort to understand how sensory-motor processes might be integrated into cognitive theory. Damage to a particular area of the brain can result in complex deficits involving different types of cognitive domains. For example, damage to Broca’s area has been variously associated with deficits in syntactic processing, morphological processing, selecting among potential responses, short-term memory, procedural memory, sequencing, spoken and written word production, verb and preposition semantics, and motor imitation among others. It is hard to imagine what common cognitive mechanism might underlie this varied set of processes. Even more dramatic differences are found in the neuroimaging literature, where it is routinely reported that the same brain area is activated by radically different tasks, which supposedly implicate distinct cognitive operations and mechanisms. For example, Price and Friston (2005 this issue) note that the left posterior lateral fusiform (PLF) area has been implicated in reading, picture categorisation, repetition priming of words and pictures, colour naming, categorisation of Heider-Simmel stimuli, tactile object recognition, and semantic and phonological decisions on auditory words. This situation seems to undermine the classical view of a one-to-one mapping between cognitive mechanism and brain region and has stimulated anew the old debate between localisationist and non-localisationist theories of the functional organisation of the brain. However, the putative many-cognitive-processes-to-onebrain-region result has also encouraged a different type of theorising from the classical view of cognition, which was based primarily on behavioural studies with normal subjects. New approaches emphasise a different way of carving cognition at its joints. For example, Price and Friston (2005 this issue) argue for the development of new theories of cognitive processing that explicitly incorporate the noted variability in braincognition mapping. In the case of the left PLF it could be proposed that its function is some sort of sensory-motor integration and that it participates in a network that gives specificity to this general function. The idea that a network approach is the best way to proceed for
Representing and retrieving structured documents using the Dempster-Shafer theory of evidence: modelling and evaluation

In this paper we report on a theoretical model of structured document indexing and retrieval based on the Dempster‐Shafer Theory of Evidence. This includes a description of our model of structured document retrieval, the representation of structured documents, the representation of individual components, how components are combined, details of the combination process, and how relevance is captured within the model. We also present a detailed account of an implementation of the model, and an evaluation scheme designed to test the effectiveness of our model. Finally we report on the details and results of a series of experiments performed to investigate the characteristics of the model.
Perceptions and experiences of financial incentives: a qualitative study of dialysis care in England

Objective The objective of the study was to understand the extent to which financial incentives such as Payment by Results and other payment mechanisms motivate kidney centres in England to change their practices. Design The study followed a qualitative design. Data collection involved 32 in-depth semistructured interviews with healthcare professionals and managers, focusing on their subjective experience of payment structures. Participants Participants were kidney healthcare professionals, clinical directors, kidney centre managers and finance managers. Healthcare commissioners from different parts of England were also interviewed. Setting Participants worked at five kidney centres from across England. The selection was based on the prevalence of home haemodialysis, ranging from low (<3%), medium (5–8%) and high (>8%) prevalence, with at least one centre in each one of these categories at the time of selection. Results While the tariff for home haemodialysis is not a clear incentive for its adoption due to uncertainty about operational costs, Commissioning for Quality and Innovation (CQUIN) targets and the Best Practice Tariff for vascular access were seen by our case study centres as a motivator to change practices. Conclusions The impact of financial incentives designed at a policy level is influenced by the understanding of cost and benefits at the local operational level. In a situation where costs are unclear, incentives which are based on the improvement of profit margins have a smaller impact than incentives which provide an additional direct payment, even if this extra financial support is relatively small.
Equivalence checking of partial designs using dependency quantified Boolean formulae

We consider the partial equivalence checking problem (PEC), i. e. , checking whether a given partial implementation of a combinational circuit can (still) be extended to a complete design that is equivalent to a given full specification. To solve PEC, we give a linear transformation from PEC to the question whether a dependency quantified Boolean formula (DQBF) is satisfied. Our novel algorithm to solve DQBF based on quantifier elimination can therefore be applied to solve PEC.We also present first experimental results showing the feasibility of our approach and the inaccuracy of QBF approximations, which are usually used for deciding the PEC so far.
Design of low power VCO enabled quantizer in Continuous Time Sigma Delta ADC for signal processing application

An accurate design of low power Voltage Controlled Oscillator (VCO) enabled quantizer in Continuous Time Sigma Delta ADC in 180nm CMOS technology using Tanner EDA tools is done. The proposed architecture consists of the loop filter, VCO quantizer and the DAC in the feedback side of model. The Operational Amplifier (OPAMP) used in designed of loop filters offers 40dB gain, 70 degree phase margin and unity gain bandwidth of 79.06MHz that consuming power of 3 mW. Even order harmonics of VCO are reduced by VCO quantizer loop structures. The Higher order loop filter is designed using an active Resistance and Capacitive based integrators and VCO quantizer is implemented using 15 multiple stage ring oscillator and register of DFF which provides an added advantage of low phase noise with frequency of 100 KHz rang. Remarkable power dissipation of overall circuit is 3mW.
Random Offset Curves and Surfaces with Controllable Noise ( Extended Abstract )

We introduce a novel parametric representation called ROPN (Random Offset Parametric representation with Noise), which is useful to represent a family of shapes sharing similar complex patterns often being able to be defined with the gradient noise functions. PORN is defined by replacing a static offset distance in a general offset with a gradient noise function. For example, PORN curve Cn(t) is defined by: Cn(t) = C(t) + N(t) · Noise(x), (1)
Genome-wide analysis of mRNA translation profiles in Saccharomyces cerevisiae

We have analyzed the translational status of each mRNA in rapidly growing Saccharomyces cerevisiae. mRNAs were separated by velocity sedimentation on a sucrose gradient, and 14 fractions across the gradient were analyzed by quantitative microarray analysis, providing a profile of ribosome association with mRNAs for thousands of genes. For most genes, the majority of mRNA molecules were associated with ribosomes and presumably engaged in translation. This systematic approach enabled us to recognize genes with unusual behavior. For 43 genes, most mRNA molecules were not associated with ribosomes, suggesting that they may be translationally controlled. For 53 genes, including GCN4, CPA1, and ICY2, three genes for which translational control is known to play a key role in regulation, most mRNA molecules were associated with a single ribosome. The number of ribosomes associated with mRNAs increased with increasing length of the putative protein-coding sequence, consistent with longer transit times for ribosomes translating longer coding sequences. The density at which ribosomes were distributed on each mRNA (i.e., the number of ribosomes per unit ORF length) was well below the maximum packing density for nearly all mRNAs, consistent with initiation as the rate-limiting step in translation. Global analysis revealed an unexpected correlation: Ribosome density decreases with increasing ORF length. Models to account for this surprising observation are discussed.
LAGRANGIAN STATISTICS OF DARK HALOS IN A ΛCDM COSMOLOGY

New statistical properties of dark matter halos in Lagrangian space are presented. Tracing back the dark matter particles constituting bound halos resolved in a series of N-body simulations, we measure quantitatively the correlations of the proto-halo's inertia tensors with the local tidal tensors and investigate how the correlation strength depends on the proto-halo's sphericity, local density, and filtering scale. It is shown that the majority of the proto-halos exhibit strong correlations between the two tensors provided that the tidal field is smoothed on the proto-halo's mass scale. The correlation strength is found to increase as the proto-halo's sphericity increases, as the proto-halo's mass increases, and as the local density becomes close to the critical value, δec. It is also found that those peculiar proto-halos which exhibit exceptionally weak correlations between the two tensors tend to acquire higher specific angular momentum in Eulerian space, which is consistent with the linear tidal torque theory. In the light of our results, it is intriguing to speculate a hypothesis that the low surface brightness galaxies observed at present epoch correspond to the peculiar proto-halos with extreme low sphericity whose inertia tensors are weakly correlated with the local tidal tensors.