Miracle-dz/UniFAR-FTUs huggingface dataset:
A kind of auditory ‘primitive intelligence’ already present at birth

‘Primitive intelligence’ in audition refers to the capacity of the auditory system to adaptatively model the acoustic regularity and react neurophysiologically to violations of such regularity, thus supporting the ability to predict future auditory events. In the present study, event‐related brain potentials to pairs of tones were recorded in 11 human newborns to determine the infants' ability to extract an abstract acoustic rule, the direction of a frequency change. Most of the pairs (standard, P = 0.875) were of ascending frequency (i.e. the second tone higher than the first), while the remaining pairs (deviant, P = 0.125) were of descending frequency (the second tone being lower). Their frequencies varied among seven levels to prevent discrimination between standard and deviant pairs on the basis of absolute frequencies. We found that event‐related brain potentials to deviant pairs differed in amplitude from those to standard pairs at 50–450 ms from the onset of the second tone of a pair, indicating the infants' ability to represent the abstract rule. This finding suggests the early ontogenetic origin of ‘primitive intelligence’ in audition that eventually may form a prerequisite for later language acquisition.
Modos de Entrada no Investimento Direto no Exterior: Um Estudo da Literatura Empírica

This work is a review of quantitative empirical studies extracted from the international literature on equity entry modes used by manufacturing companies in foreign markets. The work synthesizes and evaluates the contribution of the extant literature on the topic, and identifies the cumulative knowledge from the 1990-2011 period concerning the antecedents to foreign direct investment entry-mode decisions and the impact these entry modes have on subsidiary performance, in order to offer researchers a set of suggestions to more effectively build their studies based on existing knowledge.
Noise Robust Front-end for ASR using Spectral Subtraction , Spectral Flooring and Cumulative Distribution Mapping

In this paper, a novel and noise robust front-end based on the combined application of spectral subtraction, spectral flooring and cumulative distribution mapping is proposed. Recognition experiments with the Aurora II connected digits reveal that the proposed front-end achieves an average digit accuracy of 81.46% for a model set trained from clean data and 89.54% for a model set trained from data with various noise conditions. With reference to the ETSI standard Mel-cepstral front-end, the proposed front-end obtains a relative error reduction of around 52% for the clean model set and 14% for the multi-condition model set. Moreover, it is observed that the use of a single fixed parameter to control spectral flooring is beneficial only when cumulative distribution mapping is also applied at a later stage of the frontend processing.
Semismooth Matrix-Valued Functions

Matrix-valued functions play an important role in the development of algorithms for semidefinite programming problems. This paper studies generalized differential properties of such functions related to nonsmooth-smoothing Newton methods. The first part of this paper discusses basic properties such as the generalized derivative, Rademacher's theorem, B-derivative, directional derivative, and semismoothness. The second part shows that the matrix absolute-value function, the matrix semidefinite-projection function, and the matrix projective residual function are strongly semismooth.
Curriculum‐embedded performance assessment in higher education: maximum efficiency and minimum disruption

Increasingly, institutions of higher education are required to evaluate student progress and programme effectiveness through implementation of performance assessment practices. Faculty members frequently resist performance assessment because of concerns that assessment activities will increase workloads, reduce time for scholarly activities, eliminate professional autonomy, and reduce faculty work into component parts or discrete technical competences. This paper describes how curriculum‐embedded performance assessment can be used to evaluate student and programme effectiveness without placing an undue burden on faculty. Examples of the use of curriculum‐embedded performance assessment strategies in a graduate‐level educational psychology programme are provided.
Thioredoxin System Regulation in the Central Nervous System: Experimental Models and Clinical Evidence

The reactive oxygen species produced continuously during oxidative metabolism are generated at very high rates in the brain. Therefore, defending against oxidative stress is an essential task within the brain. An important cellular system against oxidative stress is the thioredoxin system (TS). TS is composed of thioredoxin, thioredoxin reductase, and NADPH. This review focuses on the evidence gathered in recent investigations into the central nervous system, specifically the different brain regions in which the TS is expressed. Furthermore, we address the conditions that modulate the thioredoxin system in both, animal models and the postmortem brains of human patients associated with the most common neurodegenerative disorders, in which the thioredoxin system could play an important part.
Short Circuit Localization in the LHC Main Dipole Coils by Means of Room Temperature Magnetic Measurements

During the construction of the LHC main dipoles, 12 cases of short circuits between the cables of the superconducting coils have been detected. Some of them appeared only under the press, making impossible their localization after disassembly. In this paper we describe a method to locate electrical shorts through the use of room temperature magnetic measurements. An example case is discussed in detail to illustrate the features of the approach, and a statistic of the cases met during the production of more that 70% of the dipole total quantity is presented
Disturbance and Friction Compensations in Hard Disk Drives Using Neural Networks

In this paper, we show that by using two adaptive neural networks (NNs), each of which is tailored for a specific task, the tracking performance of the hard-disk-drive (HDD) actuator can be significantly improved. The first NN utilizes accelerometer signal to detect external vibrations and compensates for its effect on HDD position via feedforward action. The second NN is designed to compensate for pivot friction. The appealing advantage of the NN compensators is that the design does not involve any information on the plant, sensor, disturbance dynamics, and friction model. The stability of the proposed scheme is analyzed by the Lyapunov criterion. Experimental results show that the tracking performance of the HDDs can be improved significantly with the use of the NN compensators as compared to the case without compensation.
Dynamics of Homogeneous Moment Neuronal Networks with ContextUnits

After discussions of a few relevant patents of neural networks, a model description of moment neuronal networks with context units is given by introducing intra-layer inputs. The dynamics of homogeneous networks derived from the intra-inputs are explored, including the input-output relationships and the stability of such network. It is shown how the spontaneous activity is propagated across the homogeneous feed-forward networks with context units. Due to a more biologically reasonable context unit, such network offers a significant advantage over the recent moment neuronal networks in that it can enhance or weaken the dynamics of network by the adjustment of the parameters from context unit, based on those from network itself, and it can lead to some unexpectedly dynamic properties. In this paper we highlight the key and sophisticated role played by the context unit in dynamics of such network.
Smart City Services over a Future Internet Platform Based on Internet of Things and Cloud: The Smart Parking Case

Enhancing the effectiveness of city services and assisting on a more sustainable development of cities are two of the crucial drivers of the smart city concept. This paper portrays a field trial that leverages an internet of things (IoT) platform intended for bringing value to existing and future smart city infrastructures. The paper highlights how IoT creates the basis permitting integration of current vertical city services into an all-encompassing system, which opens new horizons for the progress of the effectiveness and sustainability of our cities. Additionally, the paper describes a field trial on provisioning of real time data about available parking places both indoor and outdoor. The trial has been carried out at Santander’s (Spain) downtown area. The trial takes advantage of both available open data sets as well as of a large-scale IoT infrastructure. The trial is a showcase on how added-value services can be created on top of the proposed architecture.
EAP COURSE DESIGN WITHIN A CONTEXT OF INSTITUTIONAL CHANGE AND CROSS-DISCIPLINARY COLLABORATION: FACTORS SHAPING THE CREATING OF 'WRITING FOR COMMERCE'

Many reports of needs analysis and curriculum design of EAP courses focus largely on the immediate pedagogic context and ensuing decision making and materials design processes of the course designers. This paper explores the process of curriculum design from the perspectives of both debates and developments within the field of language and literacy education, and the impact of international, national and institutional shifts in higher education on one course design process within one South African university. The paper explores the realities of institutional and disciplinary histories and changes that impacted on the design of an EAP course for a linguistically, culturally and racially diverse group of first-year commerce students. The intricacies of creating such a course as an inter-disciplinary school, rather than departmental, project are explored and briefly evaluated. The key principles underpinning the course design are explained. The paper concludes with consideration of why the collaborative inter-disciplinary project has faded, although the course has continued successfully.
Structuration Theory in Urban Analysis: 1. Theoretical Exegesis

This is the first part of a two-part investigation of the utility of structuration theory in empirical analysis of the urban built environment. An exegesis of Giddens's structuration theory is presented (section 2), and its geographical applications are assessed (section 3). A reworking of the structuration model is subsequently developed (section 4), and the difficulties of applying it to empirical problems are discussed (section 5). Finally, in section 6, a model for analyzing the structuration of urban space is proposed. This model is subsequently employed in a companion paper.
Literature Review: Overlapping genetic association in developmental language disorder and autism; Grey matter in high functioning autism and Asperger's syndrome; Brain activation in self‐other face discrimination

The authors followed up a previous finding of rare variants in the FOXP2 transcription factor gene causing monogenic speech and language deficits. Given that FOXP2 variants contributed rarely to developmental language disorder, the investigators sought to study the targets of this transcription factor. Transcription factors contribute to turning other genes on or off by binding to DNA of the other genes. A previous study had performed an unbiased genome-wide experiment to identify targets of FOXP2. The authors chose to follow one of the targets, the contactin-binding protein 2 gene (CNTNAP2) because it was downregulated by FOXP2 in a part of the developing human cortex closely related to language function. The set of studies used to identify targets of FOXP2 and to validate the effect of FOXP2 on CNTNAP2 were elegant and worth detailed reading of this article and supporting articles. In addition, the methods to test association of genetic variants in CNTNAP2 to developmental language disorder are outstanding for studying how to perform such analyses. The investigators had set the stage for this study by years of effort in collection of a set of families as part of the Specific Language Impairment Consortium. Specific language impairment was defined by past or current language skills 1.5 standard deviations or more below the normative men on the Clinical Evaluation of Language Fundamentals—Revised test. Non-verbal IQ of 80, clinical diagnosis of an ASD or other medical or developmental problem that could affect language acquisition (e.g. hearing impairment) were exclusionary. A set of genetic variants were selected to test for genetic association between CNTNAP2 and the trait of nonsenseword repletion. A strong association was found between this trait and CNTNAP2. The authors note that the same alleles associated with this feature of specific language impairment were also associated with the age at first words were acquired in an independent study of ASD [Alarcon et al., 2008]. The authors note that the findings are compatible with a shared genetic susceptibility of language impairment in specific language impairment and ASD. This provides support for the model that ASD represents a combination of dimensions of developmental impairment, but such a model will require further explication and testing.
Heterogeneity of Functional Properties of Clone 66 Murine Breast Cancer Cells Expressing Various Stem Cell Phenotypes

Introduction Breast cancer grows, metastasizes and relapses from rare, therapy resistant cells with a stem cell phenotype (cancer stem cells/CSCs). However, there is a lack of studies comparing the functions of CSCs isolated using different phenotypes in order to determine if CSCs are homogeneous or heterogeneous. Methods Cells with various stem cell phenotypes were isolated by sorting from Clone 66 murine breast cancer cells that grow orthotopically in immune intact syngeneic mice. These populations were compared by in vitro functional assays for proliferation, growth, sphere and colony formation; and in vivo limiting dilution analysis of tumorigenesis. Results The proportion of cells expressing CD44highCD24low/neg, side population (SP) cells, ALDH1+, CD49fhigh, CD133high, and CD34high differed, suggesting heterogeneity. Differences in frequency and size of tumor spheres from these populations were observed. Higher rates of proliferation of non-SP, ALDH1+, CD34low, and CD49fhigh suggested properties of transit amplifying cells. Colony formation was higher from ALDH1− and non-SP cells than ALDH1+ and SP cells suggesting a progenitor phenotype. The frequency of clonal colonies that grew in agar varied and was differentially altered by the presence of Matrigel™. In vivo, fewer cells with a stem cell phenotype were needed for tumor formation than “non-stem” cells. Fewer SP cells were needed to form tumors than ALDH1+ cells suggesting further heterogeneities of cells with stem phenotypes. Different levels of cytokines/chemokines were produced by Clone 66 with RANTES being the highest. Whether the heterogeneity reflects soluble factor production remains to be determined. Conclusions These data demonstrate that Clone 66 murine breast cancer cells that express stem cell phenotypes are heterogeneous and exhibit different functional properties, and this may also be the case for human breast cancer stem cells.
Benchmark synthesis for architecture and compiler exploration

This paper presents a novel benchmark synthesis framework with three key features. First, it generates synthetic benchmarks in a high-level programming language (C in our case), in contrast to prior work in benchmark synthesis which generates synthetic benchmarks in assembly. Second, the synthetic benchmarks hide proprietary information from the original workloads they are built after. Hence, companies may want to distribute synthetic benchmark clones to third parties as proxies for their proprietary codes; third parties can then optimize the target system without having access to the original codes. Third, the synthetic benchmarks are shorter running than the original workloads they are modeled after, yet they are representative. In summary, the proposed framework generates small (thus quick to simulate) and representative benchmarks that can serve as proxies for other workloads without revealing proprietary information; and because the benchmarks are generated in a high-level programming language, they can be used to explore both the architecture and compiler spaces. The results obtained with our initial framework are promising. We demonstrate that we can generate synthetic proxy benchmarks for the MiBench benchmarks, and we show that they are representative across a range of machines with different instruction-set architectures, microarchitectures, and compilers and optimization levels, while being 30 times shorter running on average. We also verify using software plagiarism detection tools that the synthetic benchmark clones hide proprietary information from the original workloads.
Subsampling matrices for wavelet decompositions on body centered cubic lattices

This work derives a family of dilation matrices for the body-centered cubic (BCC) lattice, which is optimal in the sense of spectral sphere packing. While satisfying the necessary conditions for dilation, these matrices are all cube roots of an integer scalar matrix. This property offers theoretical advantages for construction of wavelet functions in addition to the practical advantages when iterating through a perfect reconstruction filter bank based on BCC downsampling. Lastly, we factor the BCC matrix into two matrices that allow us to cascade two two-channel perfect reconstruction filter banks in order to construct a four-channel perfect reconstruction filter bank based on BCC downsampling.
Impact of Distal Protection with Filter-Type Device on Long-term Outcome after Percutaneous Coronary Intervention for Acute Myocardial Infarction: Clinical Results with Filtrap®

Aim: Although distal embolization during percutaneous coronary intervention (PCI) for acute myocardial infarction (AMI) deteriorates cardiac function, whether distal protection (DP) can improve prognosis is still controversial. We investigated whether a filter-type DP device, Filtrap®, could improve long-term outcomes after PCI for AMI. Method: We studied 164 patients (130 men, mean age: 65.7 years) who underwent PCI. Patients were divided into two groups based on the use of Filtrap®. The occurrence of congestive heart failure (CHF) and major adverse cardiac events (MACE) defined as cardiac death, recurrent AMI, and target vessel revascularization were compared. Result: Between DP (n = 53, 41 men, mean age: 65.5 years) and non-DP (n = 111, 89 men, mean age: 65.8 years) groups, although there was significantly greater plaque area in the DP group than in the non-DP group, there were no significant differences in coronary reperfusion flow after PCI. Interestingly, patients with CHF in the non-DP group exhibited a higher CK level than those in the DP group. During a 2-year follow-up period, cumulative CHF was significantly lower in the DP group than in the non-DP group (log-rank p = 0.018), and there was no significant difference in the MACE rate (log-rank p = 0.238). The use of DP device could not predict MACE, but could predict CHF by multivariate analysis (odds ratio = 0.099, 95% CI: 0.02–0.42, p = 0.005). Conclusion: These results demonstrate that favorable clinical outcomes could be achieved by the filter-type DP device in AMI, particularly in patients with CHF.
A novel minimal model to describe NEFA kinetics following an intravenous glucose challenge.

Dynamics of nonesterified fatty acid (NEFA) metabolism in humans requires quantification if we are to understand the etiology of such diseases as type 1 and 2 diabetes, as well as metabolic syndrome and obesity, or if we are to elucidate the mechanism of action of various interventions. We present a new compartmental model that employs the pattern of plasma glucose concentrations in healthy young adults to predict dynamic changes that occur in plasma NEFA concentrations during either a glucose-only intravenous glucose tolerance test, or an insulin-modified intravenous tolerance test, or a modified protocol during which variable-rate glucose infusions were administered to prevent plasma glucose from declining below 100 mg/dl. The model described all of the major features of NEFA response to an intravenous glucose tolerance test, including an initial latency phase, a phase during which plasma NEFA concentrations plummet to a nadir, and a rebound phase during which plasma NEFA concentrations may rise to a plateau concentration, which may be substantially higher than the initial basal NEFA concentration. This model is consistent with physiological processes and provides seven adjustable parameters that can be used to quantify NEFA production (lipolysis) and utilization (oxidation). When tested on data from the scientific literature, the range in estimated rate of lipolysis was 24-36 micromol.l(-1). min(-1) and for NEFA oxidation rate was 25-54 micromol.l(-1). min(-1). All model parameters were well identified and had coefficients of variation < 15% of their estimated values. It is concluded that this model is suitable to describe NEFA kinetics in human subjects.
System Identification with General Dynamic Neural Networks and Network Pruning

This paper presents an exact pruning algorithm with adaptive pruning interval for general dynamic neural networks (GDNN). GDNNs are artificial neural networks with internal dynamics. All layers have feedback connections with time delays to the same and to all other layers. The structure of the plant is unknown, so the identification process is started with a larger network architecture than necessary. During parameter optimization with the LevenbergMarquardt (LM) algorithm irrelevant weights of the dynamic neural network are deleted in order to find a model for the plant as simple as possible. The weights to be pruned are found by direct evaluation of the training data within a sliding time window. The influence of pruning on the identification system depends on the network architecture at pruning time and the selected weight to be deleted. As the architecture of the model is changed drastically during the identification and pruning process, it is suggested to adapt the pruning interval online. Two system identification examples show the architecture selection ability of the proposed pruning approach. Keywords—System identification, dynamic neural network, recurrent neural network, GDNN, optimization, Levenberg Marquardt, real time recurrent learning, network pruning, quasi-online learning.
Reducing Amyloid-Related Alzheimer's Disease Pathogenesis by a Small Molecule Targeting Filamin A

PTI-125 is a novel compound demonstrating a promising new approach to treating Alzheimer's disease (AD), characterized by neurodegeneration and amyloid plaque and neurofibrillary pathologies. We show that the toxic signaling of amyloid-β42 (Aβ42) by the α7-nicotinic acetylcholine receptor (α7nAChR), which results in tau phosphorylation and formation of neurofibrillary tangles, requires the recruitment of the scaffolding protein filamin A (FLNA). By binding FLNA with high affinity, PTI-125 prevents Aβ42's toxic cascade, decreasing phospho-tau and Aβ aggregates and reducing the dysfunction of α7nAChRs, NMDARs, and insulin receptors. PTI-125 prevents Aβ42 signaling by drastically reducing its affinity for α7nAChRs and can even dissociate existing Aβ42–α7nAChR complexes. Additionally, PTI-125 prevents Aβ-induced inflammatory cytokine release by blocking FLNA recruitment to toll-like receptor 4, illustrating an anti-inflammatory effect. PTI-125's broad spectrum of beneficial effects is demonstrated here in an intracerebroventricular Aβ42 infusion mouse model of AD and in human postmortem AD brain tissue.