Miracle-dz/UniFAR-FTUs huggingface dataset:
Babbling, chewing, and sucking: oromandibular coordination at 9 months.

PURPOSE The ontogeny of mandibular control is important for understanding the general neurophysiologic development for speech and alimentary behaviors. Prior investigations suggest that mandibular control is organized distinctively across speech and nonspeech tasks in 15-month-olds and adults and that, with development, these extant forms of motor control primarily undergo refinement and rescaling. The present investigation was designed to evaluate whether these coordinative infrastructures for alimentary behaviors and speech are evident during the earliest period of their co-occurrence. METHOD Electromyographic (EMG) signals were obtained from the mandibular muscle groups of 15 typically developing 9-month-old children during sucking, chewing, and speech. RESULTS Unlike prior investigations of 12- and 15-month-olds and adults, 9-month-olds' analyses of peak correlations among agonist and antagonist comparisons of mandibular EMG data revealed weak coupling during sucking, chewing, and babble; associated lag values for antagonist muscle groups indicated greater synchrony during alimentary behaviors and less synchrony during babble. Unlike the speech data of 15-month-olds, 9-month-olds exhibited consistent results across speech subtasks. CONCLUSION These findings were consistent with previous results in which mandibular coordination across behaviors was more variable for younger age groups, whereas the essential organization of each behavior closely reflected that seen in older infants and adults.
Collaborative Evaluation of Individual and Team Performance in Training and Match Environments Using the Coach Logic Online Platform

Sports coaches’ commonly have a limited appreciation of pedagogy (Light & Evans, 2013). Furthermore, investigations concerning coaches’ use of performance analysis for athlete learning are rare (Groom, Cushion, & Nelson, 2011). Complex Learning Theory (CLT) advocates nonlinear and sociocultural educative approaches (Light, 2013). Considering this digital age, the aim of this investigation was to examine coaches’ use of Coach Logic - an online video-based coaching platform. Seven Head Coaches (five rugby union and two field hockey) were interviewed individually whilst their coaching staff and players contributed to group interviews. Results confirmed a priori themes of active, social and interpretive as derived from CLT. Analysis of these findings established that online coaching platforms have the capacity to facilitate the active involvement of athletes in the process of performance analysis. From a social perspective, online coaching platforms have helped to develop a positive team environment and also interpersonal working. Good practice was evident relating to interpretive approaches; however, the potential for coaches to embrace more radical conceptualisations of knowledge acquisition is stark. Online coaching platforms have a place in contemporary team sport environments and can contribute to athlete learning and other important aspects of team culture and cohesion.
Heart Rate Prediction Based on Physical Activity Using Feedforwad Neural Network

The technique of combining heart rate (HR) and physical activity (PA) has been adopted in a number of research areas, such as energy expenditure measurement, autonomic nervous system assessment, sports research, etc. However, there have been few studies on the direct relationship between HR and PA. This paper proposes a HR prediction model based on the relationship between HR and PA. The predictor has the potential to be used in various areas, such as: cardiopathy research and diagnosis, heart attack warning indicator, sports capability measure and mental activity evaluation. The method has the following steps: first, the recorded HR and PA signals are preprocessed as two synchronized time sequences: HR(n) and PA(n). The inputs of the predictor are HR(n) and PA(n) in the current time step, and the output is the predicted sequence HR(n + 1) in the next time step. The feed forward neural network (FFNN) was chosen as the mathematical model of the predictor. Experiments was conducted based on the real-life signals from a healthy male. A set of 90 minute signals were collected. One half of the signal set was used to train the FFNN and the other half to validate the training. The mean absolute error of the predicted heart rate was restricted inside 5. The result shows the potential of the proposed method.
Face Recognition in the Dark

Previous research has established thermal infrared imagery of faces as a valid biometric and has shown high recognition performance in a wide range of scenarios. However, all these results have been obtained using eye locations that were either manually marked, or automatically detected in a coregistered visible image, making the realistic use of thermal infrared imagery alone impossible. In this paper we present the results of an eye detector on thermal infrared imagery and we analyze its impact on recognition performance. Our experiments show that although eyes cannot be detected as reliably in thermal images as in visible ones, some face recognition algorithms can still achieve adequate performance.
Impact of wave refraction due to environmental conditions on image processing quality in high resolution SAR

SAR signal processing is based on the range evolution of the different scatterers during the illumination time. The range is estimated using the time delay between the emission of the pulse and the reception of the echo. During processing, it is assumed that the wave travels through an homogeneous medium with uniform dielectric characteristics and that the optical path corresponds to the straight line between sensor and scatterer. In reality, waves travel through different media, like clouds or forest canopies which have a dielectric constant greater than 1 due to their water content. Refraction through those different media will lead to a longer optical path, combined with a smaller wave velocity. Both effects are then responsible for an overestimation of the target range, which is not constant during the whole illumination time, causing a degradation of the image quality. This paper analyzes the effect of the refraction of radar waves in the SAR signal processing.
Robust transceiver based on worst-case SINR optimization for MIMO interfering broadcast channels with imperfect channel knowledge

In this paper, we propose a robust transceiver design for MIMO interfering broadcast channels taking imperfect channel knowledge at each base station into consideration. By considering the worst-case transceiver design which maximizes the minimum signal-to-interference-plus-noise ratio (SINR), the proposed design can provide better sum rate and worst user rate performance compared to the conventional schemes. From simulation results, we show the resilience of the proposed design against imperfect channel knowledge.
Study on support vector machine based quality prediction of complex mechatronic systems

Aimed to build a health monitoring scheme of complex mechatronic systems, a quality prediction on Tennessee Eastman (TE) process based on support vector machine (SVM) is proposed in this paper after a brief investigation on the regression ability of SVM. The SVM model is builded using the datasets generated by TE process simulation platform. Furthermore the prediction precision of SVM is testified using the simulation of TE process compared with other prediction methods. It indicates from the results that SVM is more beneficial according to the root mean square errors (RMSE) between the actual and the predicted data.
Stability Analysis for k-wise Intersecting Families

We consider the following generalization of the seminal Erdős–Ko–Rado theorem, due to Frankl. For some $k\geq 2$, let $\mathcal{F}$ be a $k$-wise intersecting family of $r$-subsets of an $n$ element set $X$, i.e. for any $F_1,\ldots,F_k\in \mathcal{F}$, $\cap_{i=1}^k F_i\neq \emptyset$. If $r\leq \dfrac{(k-1)n}{k}$, then $|\mathcal{F}|\leq {n-1 \choose r-1}$. We prove a stability version of this theorem, analogous to similar results of Dinur-Friedgut, Keevash-Mubayi and others for the EKR theorem. The technique we use is a generalization of Katona's circle method, initially employed by Keevash, which uses expansion properties of a particular Cayley graph of the symmetric group.
Theme: the Nature of Learning and Knowledge

In the paper it is shown that learning can be understood by way of two metaphors, acquisition and participation. In the acquisition metaphor, learning is understood as individual acquisition of knowledge whereas the participation metaphor understands learning as the social processes of participation. Inspired by American Pragmatism I propose a 'third way', which encompasses both learning as acquisition and as participation by way of the concepts of inquiry and experience as well as an understanding of the relation between individuals and environment as that of a transaction, i.e. a continual and mutual constitution of both. Further, I suggest an understanding of organizational life and work as social worlds as a 'third way' encompassing both a systemic and a collective understanding of organizations.
Using heterogeneous linguistic knowledge in local coherence identification for information retrieval

This paper proposes a novel approach to automatic text segmentation without a full semantic understanding. In order to analyse the linguistic bonds and determine the degree of coherence that a text may exhibit, the tremendous diversity of textual relations in a discourse network is represented. A corpus of mutual linguistic knowledge that captures the similarity of meaning and causal relations is encoded in the discourse network, which is then subjected to a cluster algorithm. As a result, segments in the text are segregated into clusters according to their textual similarity. Topic boundaries in a text can be identified by observing the shifts of segments from one cluster to another. The experimental results show that the combination of the heterogeneous knowledge is capable of addressing the topic shifts. Comparison with a related method demonstrates that the algorithm is closely related to the topic boundaries. Given the increasing recognition of text structure in the fields of information retrieval in unpartitioned text, this approach provides a quantitative model and an efficient tool in text segmentation.
EFFICIENT UNSUPERVISED CLUSTERING THROUGH INTELLIGENT OPTIMIZATION

A novel methodology for unsupervised data clustering based on Evolutionary Computation, named “Intelligent Unsupervised Clustering” (IUC) is introduced. IUC searches for the “optimal clusters’ representatives” using Evolutionary Algorithms (EAs) and utilising a Window Density Function (WDF) as an objective function. EAs ensure that the representative is posed in a region of points of high density. IUC aims in finding a highly dense hyperrectangle around the cluster’s representative, that captures a part of cluster. Therefore, IUC uses a windowing technique and gradually enlarges a window, which is centered on the best individual generated from the EA. This process continues until the increase of the value of WDF does not change “drastically”. The whole process is repeated on the unclustered data, until all the clusters are discovered. The quality of clustering, delivered by the IUC, is compared with well-known clustering algorithms and the experimental results illustrate its efficiency and accuracy.
Co-evolution of phytoplankton C:N:P stoichiometry and the deep ocean N:P ratio

There is a long-established, remarkable correspondence between the nitrogen-tophosphorus ratio N:P 15 of deep ocean water and the “Redfield ratio” of N:P 16 required by phytoplankton. Redfield and subsequent workers have suggested that it is due to N-fixing organisms being selected when N:P 16. Models have shown this mechanism can work, but recent observations reveal that the real system is more complex. First, the C:N:P stoichiometry of phytoplankton varies with growth rate, nutrient and light limitation, species and phylum. Second, although N-fixation is sometimes P-limited and suppressed by N-addition, there is also evidence for Fe-limitation, light-limitation and P and Fe co-limitation of N-fixers. Here 10
Determination of Mitochondrial Functional State in Vital Tissues by Cytobiochemical Analysis of Peripheral Blood Lymphocytes

In the work, we offer a brief report by research results demonstrating correlations between energy status of peripheral blood lymphocytes and functional state of vital tissues mitochondria. Basing on our preliminary data, showing correlations between succinate dehydrogenase (SDH) activity in peripheral blood lymphocytes and speed or duration of pyridine nucleotides reduction in mitochondria of rat brain and liver tissues, we propose an idea of using cytobiochemical method of SDH estimation in peripheral blood lymphocytes for evaluation of tissue mitochondria status. The method could be successfully used in clinical practice for early revealing of energy metabolism disturbances that allows preventing disease development and optimizing the diseases therapy.
I STITUTO DI A NALISI DEI S ISTEMI ED I NFORMATICA “ Antonio Ruberti

The max-cut problem and the associated cut polytope on complete graphs have been extensively studied over the last 25 years. However, little research has been conducted for the cut polytope on arbitrary graphs. In this study we describe new separation and lifting procedures for the cut polytope on such graphs. These procedures exploit algorithmic and structural results known for the cut polytope on complete graphs to generate valid, and sometimes facet defining, inequalities for the cut polytope on arbitrary graphs in a cutting plane framework. We report computational results on a set of well-established benchmark problems.
Mind the gap: another look at the problem of the semantic gap in image retrieval

This paper attempts to review and characterise the problem of the semantic gap in image retrieval and the attempts being made to bridge it. In particular, we draw from our own experience in user queries, automatic annotation and ontological techniques. The first section of the paper describes a characterisation of the semantic gap as a hierarchy between the raw media and full semantic understanding of the media's content. The second section discusses real users' queries with respect to the semantic gap. The final sections of the paper describe our own experience in attempting to bridge the semantic gap. In particular we discuss our work on auto-annotation and semantic-space models of image retrieval in order to bridge the gap from the bottom up, and the use of ontologies, which capture more semantics than keyword object labels alone, as a technique for bridging the gap from the top down.
Proliferation and TH1/TH2 Cytokine Production in Human Peripheral Blood Mononuclear Cells after Treatment with Cypermethrin and Mancozeb In Vitro

In recent times, human cell-based assays are gaining attention in assessments of immunomodulatory effects of chemicals. In the study here, the possible effects of cypermethrin and mancozeb on lymphocyte proliferation and proinflammatory (tumor necrosis factor (TNF-) α) and immunoregulatory cytokine (interferon- (IFN-) γ, interleukins (IL) 2, 4, 6, and 10) formation in vitro were investigated. Human peripheral blood mononuclear cells (PBMC) were isolated and exposed for 6 hr to noncytotoxic doses (0.45–30 µM) of cypermethrin or mancozeb in the presence of activating rat S9 fraction. Cultures were then further incubated for 48 or 72 hr in fresh medium containing phytohemagglutinin (10 µg/mL) to assess, respectively, effects on cell proliferation (BrdU-ELISA method) and cytokine formation (flow cytometric bead immunoassays). Mancozeb induced dose-dependent increases in lymphocyte proliferation, inhibition of production of TNFα and the TH2 cytokines IL-6 and IL-10, and an increase in IFNγ (TH1 cytokine) production (at least 2-fold compared to control); mancozeb also induced inhibition of IL-4 (TH2) and stimulated IL-2 (TH1) production, albeit only in dose-related manners for each. In contrast, cypermethrin exposure did not cause significant effects on proliferation or cytokine profiles. Further studies are needed to better understand the functional significance of our in vitro findings.
Is mommy talking to daddy or to me? Exploring parental estimates of child language exposure using the Multilingual Infant Language Questionnaire

ABSTRACT Language input is a key factor in bi-/multilingual research. It roots in the definition of bi-/multilingualism and influences infant cognitive development since and even before birth. The methods used to assess language exposure among bi-/multilingual infants vary across studies. This paper discusses the parental report patterns of the amount/degree of exposure to their children and provides an algorithm-based Multilingual Infant Language Questionnaire (MILQ) targeting the amount of hours and degree of exposure an infant is exposed to each language. In the MILQ, parental feedback between general language input (languages spoken in the environment an infant resides in) and direct language exposure (languages spoken directly to an infant) are differentiated. Comparing the results drawn from general and direct perspectives, parental estimates of their children’s exposure match the general but not direct language input condition. Implications of these results are discussed.
The Effect of Sample Size and Cognitive Strategy on Probability Estimation Bias

Probability estimation is an essential cognitive function in perception, motor control, and decision making. Many studies have shown that when making decisions in a stochastic operant conditioning task, people and animals behave as if they underestimatethe probability of rare events. It is commonly assumed that this behavior is a natural consequence of estimating a probability from a small sample, also known as sampling bias. The objective of this paper is to challenge this common lore. We show that in fact, probabilities estimated from a small sample can lead to behaviors that will be interpreted as underestimatingor as overestimating the probability of rare events, depending on the cognitive strategy used. Moreover, this sampling bias hypothesis makes an implausible prediction that minute differences in the values of the sample size or the underlying probability will determine whether rare events will be underweighted or overweighed. We discuss the implications of this sensitivity for the design and interpretation of experiments. Finally, we propose an alternative sequential learning model with a resetting of initial conditions for probability estimation and show that this model predicts the experimentally-observed robust underweighting of rare events.
Antidepressant Effects of Aripiprazole Augmentation for Cilostazol-Treated Mice Exposed to Chronic Mild Stress after Ischemic Stroke

The aim of this study was to determine the effects and underlying mechanism of aripiprazole (APZ) augmentation for cilostazol (CLS)-treated post-ischemic stroke mice that were exposed to chronic mild stress (CMS). Compared to treatment with either APZ or CLS alone, the combined treatment resulted in a greater reduction in depressive behaviors, including anhedonia, despair-like behaviors, and memory impairments. This treatment also significantly reduced atrophic changes in the striatum, cortex, and midbrain of CMS-treated ischemic mice, and inhibited neuronal cell apoptosis, particularly in the striatum and the dentate gyrus of the hippocampus. Greater proliferation of neuronal progenitor cells was also observed in the ipsilateral striatum of the mice receiving combined treatment compared to mice receiving either drug alone. Phosphorylation of the cyclic adenosine monophosphate response element binding protein (CREB) was increased in the striatum, hippocampus, and midbrain of mice receiving combined treatment compared to treatment with either drug alone, particularly in the neurons of the striatum and hippocampus, and dopaminergic neurons of the midbrain. Our results suggest that APZ may augment the antidepressant effects of CLS via co-regulation of the CREB signaling pathway, resulting in the synergistic enhancement of their neuroprotective effects.
Passive Amyloid Immunotherapy Clears Amyloid and Transiently Activates Microglia in a Transgenic Mouse Model of Amyloid Deposition

The role of microglia in the removal of amyloid deposits after systemically administered anti-Aβ antibodies remains unclear. In the current study, we injected Tg2576 APP transgenic mice weekly with an anti-Aβ antibody for 1, 2, or 3 months such that all mice were 22 months at the end of the study. In mice immunized for 3 months, we found an improvement in alternation performance in the Y maze. Histologically, we were able to detect mouse IgG bound to congophilic amyloid deposits in those mice treated with the anti-Aβ antibody but not in those treated with a control antibody. We found that Fcγ receptor expression on microglia was increased after 1 month of treatment, whereas CD45 was increased after 2 months of treatment. Associated with these microglial changes was a reduction in both diffuse and compact amyloid deposits after 2 months of treatment. Interestingly, the microglia markers were reduced to control levels after 3 months of treatment, whereas amyloid levels remained reduced. Serum Aβ levels and anti-Aβ antibody levels were elevated to similar levels at all three survival times in mice given anti-Aβ injections rather than control antibody injections. These data show that the antibody is able to enter the brain and bind to the amyloid deposits, likely opsonizing the Aβ and resulting in Fcγ receptor-mediated phagocytosis. Together with our earlier work, our data argue that all proposed mechanisms of anti-Aβ antibody-mediated amyloid removal can be simultaneously active.
Initial effects of a community-based initiative for energy saving: an experimental analysis

Can community-based behavioural intervention reduce energy use in the home? We report on initial data from an ongoing matched case and control field experiment on energy saving. Household energy use in 175 households is measured using monitoring equipment, recording electrical power consumption and temperature. Participants in treatment and control groups received improvements to the thermal insulation of their homes. A behavioural intervention in the treatment group began with a 2 hour workshop on energy saving led by a community-based environmental group. We find some evidence of reductions in electrical power over the period of analysis, compared to the estimated counterfactual, but no evidence of reduced spatial heating or baseload power. The data are consistent with a substantial effect lasting over 3 months, although this cannot be inferred with confidence because of high variance. We explore the policy implications of the finding that a relatively modest level of community intervention has potentially substantial impact on energy use.
Performance Comparison of Permanent Magnet Synchronous Motor and Induction Motor for Cooling Tower Application

The paper discusses the basic construction and types available in Permanent Magnet Synchronous Motor (PMSM) on the basis of the arrangement of the permanent magnets. The various pros and cons of each arrangement are discussed in brief. The paper elucidates the application of PMSM in industry for cooling towers, with statistical data and various practical results concerning various important parameters such as efficiency, power factor and load current. It clearly justifies and supports the efficient use of PMSM over conventional induction motors (IM) with high efficiency, with an in-depth analysis.