Miracle-dz/UniFAR-FTUs huggingface dataset:
N-Alkyl Carbazole Derivatives as New Tools for Alzheimer’s Disease: Preliminary Studies

Alzheimer’s disease (AD) is a progressive and age-related neurodegenerative disorder affecting brain cells and is the most common form of “dementia”, because of the cognitive detriment which takes place. Neuronal disruption represents its major feature, due to the cytosolic accumulation of amyloid β-peptide (Aβ) which leads to senile plaques formation and intracellular neurofibrillary tangles. Many studies have focused on the design and therapeutic use of new molecules able to inhibit Aβ aggregation. In this context, we evaluated the ability of two recently synthesized series of N-alkyl carbazole derivatives to increase the Aβ soluble forms, through molecular docking simulations and in vitro experiments. Our data evidenced that two carbazole derivatives, the most active, adopt distinct binding modes involving key residues for Aβ fibrillization. They exhibit a good interfering activity on Aβ aggregation in mouse (N2a) cells, stably expressing wild-type human amyloid precursor protein (APP) 695. These preliminary results are promising and we are confident that the N-alkyl carbazole derivatives may encourage next future studies needed for enlarging the knowledge about the AD disease approach.
Struggles in organizational attempts to adopt new branding logics: the case of a marketizing university

Recent research underlines that strong branded identities are created through co-creational processes in which multiple stakeholders are actively involved and brand identities are matched with cultural, political, and economic forces in society. However, there is a lack of in-depth research into how organizations attempt to adopt new branding logics. To address this research gap, we conduct a study of a university that is rebranding itself in accordance with a new market-oriented, service-dominant logic. While harmonic value co-creation between the brand and stakeholders is emphasized in an earlier literature, our study shows that attempts to adopt these logics trigger contradictory and adversarial interpretations among stakeholders about the role and identity of the focal actor vis-à-vis their own. We conclude that adopting new branding logics involves struggles and dynamics of power and resistance, which have passed unnoticed in earlier research. Resistance is not only targeted toward the brand's symbolic meanings and conducted by marginal consumer groups to enhance their own identities. Rather, it can also be targeted toward the tangible resource roles that stakeholders are expected to assume vis-à-vis the brand, and conducted by various stakeholder resistors – with the outcome of undermining and shifting the essence of the brand itself.
Automist - A Tool for Automated Instruction Set Characterization of Embedded Processors

NXP Semiconductors, Business Line Identi cation8101, Gratkorn, Mikronweg 1, Austriaulrich.neffe@nxp.comAbstract. The steadily increasing performance of mobile devices alsoimplies a rise in power consumption. To counteract this trend it is manda-tory to accomplish software power optimizations based on accurate powerconsumption models characterized for the processor. This paper presentsan environment for automated instruction set characterization based onphysical power measurements. Based on a detailed instruction set de-scription a testbench generator creates all needed test programs for acomplete characterization. Afterwards those programs are executed bythe processor and the energy consumption is measured. For an accurateenergy measurement a high performance sampling technique has beenestablished, which can be either clock or energy driven.Keywords. Software energy estimation, automated processor charac-terization, testbench generator, current measurement, clock driven sam-pling, energy driven sampling.
Inhibition and cognitive flexibility of children and adolescents with and without ASD and their parents

The purpose of the present study was to examine the inhibition and cognitive flexibility (parts of executive functioning) of children and adolescents with ASD, their parents, and controls (children and adolescents). Therefore, a sample of 43 children/adolescents with ASD, 33 children/adolescents without ASD (controls) and 46 parents of children/adolescents with ASD was used. To measure inhibition and cognitive flexibility parts of the ANT and D-Kefs were used. The shortened WISC/WAIS was used to estimate IQ, and the SRS and AQ were used to measure autistic traits in daily life. There was no difference between children/adolescents with and without ASD on inhibition or cognitive flexibility tasks. Age, however, had a significant effect on executive functioning and autistic traits in daily life. Parents of children/adolescents with ASD performed better on inhibition and cognitive flexibility tasks than their autistic children, but there was no difference between fathers and mothers on these tasks. Children with ASD had a higher verbal and total IQ than controls and the total and nonverbal IQ were significant predictors of inhibition and cognitive flexibility. The present study showed that the variables diagnosis, age and IQ play a role in the ability to inhibit responses and to switch between various conditions. Implications of these findings are discussed.
Stochastic model predictive control for LPV systems

This paper considers a stochastic model predictive control of linear parameter-varying (LPV) systems described by affine parameter dependent state-space representations with additive stochastic uncertainties and probabilistic state constraints. In computing the prediction dynamics for LPV systems, the scheduling signal is given a stochastic description during the prediction horizon, which aims to overcome the shortcomings of the existing approaches where the scheduling signal is assumed to be constant or allowed to vary in a convex set. The above representation leads to LPV system dynamics consisting of additive and multiplicative uncertain stochastic terms up to second order. The prediction dynamics are reposed in an augmented form, which facilitates the feasibility of probabilistic constraints and closed-loop stability in the presence of stochastic uncertainties.
A case of exceptional reading accuracy in a child with Down syndrome: Underlying skills and the relation to reading comprehension

We report on a case of a girl with Down syndrome (DS), K.S. , whose reading accuracy is exceptional. This ability is associated with robust phonological skills and relative strengths in visual and verbal short-term memory, articulation, and speech fluency. Although her reading comprehension is age appropriate when it comes to the retention of literal information, K.S. has some difficulties in using knowledge-based inferences in reading comprehension. Reading comprehension in that sense is at a level commensurate with her oral language skills. Her reading performance parallels that of children with reading comprehension difficulties who do not have DS. This reading profile is in contrast with claims that individuals with DS mainly use sight-word strategies in reading and shows that the phonological pathway can be highly proficient in a child with DS. However, even in a case such as K.S. where reading accuracy is good, functional literacy is constrained by limited comprehension skills.
Practicality-Based Probabilistic Roadmaps Method

Probabilistic roadmap methods (PRMs) are a commonly used approach to path planning problems in a high-dimensional search space. Although PRMs can often find a solution to solving the path finding problem the solutions are often not practical in that they can cause the device to flail around or to pass very close to obstacles in the environment. This paper presents a variant of PRMs that addresses the practicality problem of the paths found by the planner. A simple and general sample adjustment method is developed, which adjusts the randomly generated nodes that make up the PRM within their local neighborhood to satisfy soft constraints required by the problem. The resulting roadmap can then be used to generate more practical paths. The approach is general and can be adapted to path planning problems with different practical requirements.
Analytical Analysis of Magnetic Field and Force Calculation in a Slotless-Type Permanent Magnet Linear Synchronous Machine; Verification with Numerical Analysis

This paper presents an analytical analysis of magnetic field in a slotless-type permanent magnet linear synchronous machine (PMLSM). For the verification, the numerical analysis (finite element method (FEM)) of magnetic field is accomplished and comparison between analytical and numerical solution of magnetic field is presented. Both analytical and numerical solutions are obtained by 2D analysis via magnetic vector potential, respectively. The paper also presents the comparison between analytical and numerical (FEM) solution of static thrust and normal force using Maxwell's stress tensor method. The verifications show that good agreement between analytical and numerical solution of magnetic field, static thrust and normal force is obtained.
Integrating Translations from Multiple Sources within the PANGLOSS Mark III Machine Translation System

Since MT systems, whatever translation method they employ, do not reach an optimum output on free text; each method handles some problems better than others. The PANGLOSS Mark III system is an MT environment that uses the best results from a variety of independent MT systems or engines working simultaneously within a single framework on the same text. This paper describes the method used to combine the outputs of the engines into a single text
Searching Similar (Sub)Sentences for Example-Based Machine Translation

Translation is a repetitive activity. The attempt to automate such a difficult task has been a long-term scientific dream; in the past years research in this field has acquired a growing interest, making some forms of Machine Translation (MT) a reality. Among the several types of approaches in MT, one of the most promising paradigms is MAHT and, in particular, example-Based Machine Translation (EBMT). An EBMT system translates by analogy, using past translations to translate other, similar sourcelanguage sentences into the target language. The basic premise is that, if a previously translated sentence occurs again, the same translation is likely to be correct. In this paper, we propose a solution based on a purely syntactic approach for searching similar sentences and parts of them in an EBMT system; the underlying similarity measure is based on the similarity between sequence of terms such that the sentences most close to a given one are those who maintain most of the original form and contents. The system efficiently retrieves and ranks the most similar sentences available and, when no useful suggestion exists, it proceeds with the retrieval of similar parts. We opted for a design that would require minimal changes to existing databases and whose similarity measure and search algorithms are completely independent from the involved languages. This work has been developed as a joint work with LOGOS S.p.A. , a worldwide leader in multilingual document translation.
Organizing and evaluating publish/subscribe systems with scopes

Modern applications and services need to process and respond to an ever growing number of internal and external events. In principle, publish/subscribe middleware would be the ideal platform for these event-driven applications, which range from the gathering of simple sensor data in ubiquitous environments up to the correlation and processing of complex events in business workflows. Most publish/subscribe middleware, however, offers no or just little support to adequately structure and organize network, services, and applications making it hard to deploy and adapt them in complex dynamic infrastructures. In this paper, we refine and concretize a scope concept for publish/subscribe systems to give application developers and system administrators powerful and effective means to organize these systems by controlling the visibility of events, subscriptions, and advertisements. Besides presenting the scope concept and discussing the integration into a middleware, our main contribution is a comprehensive evaluation based on the first publish/subscribe middleware prototype with integrated scoping. The evaluation analyzes the behavior a scoped publish/subscribe system in various settings and quantifies its benefits.
Temperature dependent actuation voltage for longer MEMS switch lifetime

A temperature dependent actuation voltage has been proposed to minimize the dielectric charging effect in micro-electromechanical system (MEMS) switch, leading to an improved switch lifetime. Mathematical models have been utilized to model the pull-in voltage variation throughout a range of thermal condition and simulate dielectric charging in the RF MEMS switch, enabling the analysis of charge built-up at the switch dielectric layer and substrate at different ambient temperature condition. The proposed temperature dependent actuation voltage has shown to reduce the dielectric charging effect of the RF MEMS switch as it minimize the applied actuation voltage to the MEMS switch during its long continuous operation.
Context-GMM: Incremental learning of sparse priors for Gaussian mixture regression

Gaussian Mixture Models have been widely used in robotic control and in sensory anticipation applications. A mixture model is learnt from demonstrations and later used to infer the most likely control signals, or is also used as a forward model to predict the change in sensory signals over time. However, such models often are too big to be tractable in real-time applications. In this paper we introduce the Context-GMM, a method to learn sparse priors over the mixture components. Such priors are stable over large amounts of time and provide a way of selecting very small subsets of mixture components without significant loss in accuracy and with huge computational savings.
Classifying mental states using prefrontal EEG and SVM for use in Brain-Computer Interface systems

Brain computer interface (BCI) technology provides a method of communication and control for people with severe motor disabilities. This study aims at investigating the potential of classifying mental states using pre-frontal Electroencephalogram (EEG) with a support vector machine (SVM) classifier for implementation of a BCI system. Singlechannel (Fp1-Fp2) EEG data was recorded from 5 righthanded subjects, participating in 7 different mental tasks. EEG frequency components were analyzed with SVM in MATLAB. The results show that SVM was able to classify the imagined hand movement activity vs. resting activity with above 85% accuracy in 3 out of 5 subjects. Testing on the offline BCI system also shows that pre-frontal EEG is a possible candidate for a two-choice BCI implementation.
Bayesian Learning of a Language Model from Continuous Speech

We propose a novel scheme to learn a language model (LM) for automatic speech recognition (ASR) directly from continuous speech. In the proposed method, we first generate phoneme lattices using an acoustic model with no linguistic constraints, then perform training over these phoneme lattices, simultaneously learning both lexical units and an LM. As a statistical framework for this learning problem, we use non-parametric Bayesian statistics, which make it possible to balance the learned model's complexity (such as the size of the learned vocabulary) and expressive power, and provide a principled learning algorithm through the use of Gibbs sampling. Implementation is performed using weighted finite state transducers (WFSTs), which allow for the simple handling of lattice input. Experimental results on natural, adult-directed speech demonstrate that LMs built using only continuous speech are able to significantly reduce ASR phoneme error rates. The proposed technique of joint Bayesian learning of lexical units and an LM over lattices is shown to significantly contribute to this improvement.
Methylene chloride poisoning in a cabinet worker.

More than a million workers are at risk for methylene chloride exposure. Aerosol sprays and paint stripping may also cause significant nonoccupational exposures. After methylene chloride inhalation, significant amounts of carbon monoxide are formed in vivo as a metabolic by-product. Poisoning predominantly affects the central nervous system and results from both carboxyhemoglobin formation and direct solvent-related narcosis. In this report, we describe a case of methylene chloride intoxication probably complicated by exogenous carbon monoxide exposure. The worker's presentation of intermittent headaches was consistent with both methylene chloride intoxication and carbon monoxide poisoning. The exposures and symptoms were corroborated by elevated carboxyhemoglobin saturations and a workplace inspection that documented significant exposures to both methylene chloride and carbon monoxide. When both carbon monoxide and methylene chloride are inhaled, additional carboxyhemoglobin formation is expected. Preventive efforts should include education, air monitoring, and periodic carboxyhemoglobin determinations. Methylene chloride should never be used in enclosed or poorly ventilated areas because of the well-documented dangers of loss of consciousness and death. ImagesFigure 1
Atmospheric Chemistry and Physics Aerosol Single Scattering Albedo Retrieval in the Uv Range: an Application to Omi Satellite Validation

The aerosol Single Scattering Albedo (SSA) and Absorbing Aerosol Optical Depth (AAOD) at 320.1 nm are derived at Rome site by the comparison between Brewer and modelled spectra. The UVSPEC radiative transfer model is used to calculate the UV irradiances for different SSA values , taking into account as input data total ozone and Aerosol Optical Depth (AOD) obtained from Brewer spectral measurements. The accuracy in determining SSA depends on the aerosol amount and on Solar Zenith Angle (SZA) value: SSA uncertainty increases when AOD and SZA decrease. The monthly mean values of SSA and AAOD during the period January 2005–June 2008 are analysed, showing a monthly and seasonal variability. It is found that the SSA and AAOD averages are 0.80±0.08 and 0.056±0.028, respectively. AAOD retrievals are also used to quantify the error in the Ozone Monitoring Instrument (OMI) surface UV products due to absorbing aerosols, not included in the current OMI UV algorithm. OMI and Brewer UV irradiances at 324.1 nm and Erythemal Dose Rates (EDRs) under clear sky conditions , are compared as a function of AAOD. Three methods are considered to investigate on the applicability of an absorbing aerosol correction on OMI UV data at Rome site. Depending on the correction methodology, the bias value decreases from 18% to 2% for spectral irradiance at 324.1 nm and from 25% to 8% for EDR.
Fragile X Mental Retardation Protein Regulates Protein Expression and mRNA Translation of the Potassium Channel Kv4.2

A prominent characteristic of the inherited intellectual impairment disease fragile X syndrome (FXS) is neuronal hyperexcitability, resulting in a variety of symptoms, such as hyperactivity, increased sensitivity to sensory stimuli, and a high incidence of epileptic seizures. These symptoms account for a significant part of the disease pattern, but the underlying molecular mechanisms of neuronal hyperexcitability in FXS remain poorly understood. FXS is caused by loss of expression of fragile X mental retardation protein (FMRP), which regulates synaptic protein synthesis and is a key player to limit signaling pathways downstream of metabotropic glutamate receptors 1/5 (mGlu1/5). Recent findings suggest that FMRP might also directly regulate voltage-gated potassium channels. Here, we show that total and plasma membrane protein levels of Kv4.2, the major potassium channel regulating hippocampal neuronal excitability, are reduced in the brain of an FXS mouse model. Antagonizing mGlu5 activity with 2-methyl-6-(phenylethynyl)-pyridine (MPEP) partially rescues reduced surface Kv4.2 levels in Fmr1 knock-out (KO) mice, suggesting that excess mGlu1/5 signal activity contributes to Kv4.2 dysregulation. As an additional mechanism, we show that FMRP is a positive regulator of Kv4.2 mRNA translation and protein expression and associates with Kv4.2 mRNA in vivo and in vitro. Our results suggest that absence of FMRP-mediated positive control of Kv4.2 mRNA translation, protein expression, and plasma membrane levels might contribute to excess neuronal excitability in Fmr1 KO mice, and thus imply a potential mechanism underlying FXS-associated epilepsy.
A Language-Independent Unsupervised Model for Morphological Segmentation

Morphological segmentation has been shown to be beneficial to a range of NLP tasks such as machine translation, speech recognition, speech synthesis and information retrieval. Recently, a number of approaches to unsupervised morphological segmentation have been proposed. This paper describes an algorithm that draws from previous approaches and combines them into a simple model for morphological segmentation that outperforms other approaches on English and German, and also yields good results on agglutinative languages such as Finnish and Turkish. We also propose a method for detecting variation within stems in an unsupervised fashion. The segmentation quality reached with the new algorithm is good enough to improve grapheme-to-phoneme conversion.
Infomax Control as a Model of Real Time Behavior: Theory and Application to the Detection of Social Contingency

I present a model of behavior for situations in which organisms react to the environment in a manner that maximizes information gain. I call the approach “Infomax control” for it combines the theory of optimal control with information maximization models of perception. The approach is not cognitivist, in that it is better described as a continuous “dance” of actions and reactions with the world, rather than a turntaking inferential process like chess-playing. The approach however is intelligent in that it produces behaviors that optimize long-term information gain. I illustrate how Infomax control can be used to understand the detection of social contingency in 10 month old infants. The results suggest that, while lacking language, by this age infants actively “ask questions” to the environment, i.e., schedule their actions in a manner that maximizes the expected information return. A real time Infomax controller was implemented on a humanoid robot to detect people using contingency information. The system worked robustly requiring little bandwidth and computational cost. This suggest that contingency is indeed a reliable source of information to detect the presence of humans and that the infant brain is likely to capitalize on it to solve this task.
Compromised Resources Allocation Model for Emergency Response

Under the constraint of resources requirement, a compromised resources allocation model for emergency response and a negotiation strategy as a fast access between resource suppliers and requesters are mainly discussed in this paper. According to the characteristics of continuous emergency problem, the mathematical model is presented to help solve the large-scale public emergency difficulties. Finally, a case study is carried on to prove the algorithm effective.
Vocabulary Insertion and the Geometry of Local Cases

The paper argues for a new approach to vocabulary insertion which allows insertion at non terminal nodes. This new Vocabulary Insertion Principle dispenses with the operation of Fusion in Distributed Morphology and allows more accurate predictions about possible portmanteau morphemes to be made. I test this new principle against data from local case morphology. I also propose a novel structure for local case affixes which is supported by evidence from implicational universals and attested/unattested portmanteau morphemes.