Miracle-dz/UniFAR-FTUs huggingface dataset:
Posture Recognition with G-Sensors on Smart Phones

With the popularity of smart phones in recent years, various sensors on smart phones can be utilized to detect the movement or intention of the smart phone users. In this research, we aim at using the signals collected from the G-sensor in the smart phone to recognize the posture of the user. Signals for sit, stand, walk and run are collected to train an offline neural network as the classifier. After the neural network learns the four postures, we then implement a neural network with the learned connection weights in a smart phone app. The app can record the postures of the user for the whole day and estimate the burned calories accordingly. This app can replace the pedometer to have a more accurate estimate of calorie consumption. Details of the app are presented in this paper. The accuracy of neural networks on posture recognition with G-sensor signals is also verified by five-fold cross-validation.
The Prefrontal Cortex Activity and Psychological Effects of Viewing Forest Landscapes in Autumn Season

Recently reported research indicate that forest environments have physiological and psychological relaxing effects compared to urban environments. However, some researchers claim that the stress of the subjects from being watched by others during measurements can affect the measurement result in urban experiments conducted in the center of a street. The present study was conducted to determine whether forest environments have physiological and psychological relaxing effects, using comparison of viewing a forest area with viewing an urban area from the roof of an urban building without being watched by others. Near-infrared spectroscopy (NIRS) measurement was performed on subjects while they viewed scenery for 15 min at each experimental site (urban and forest areas). Subjective assessments were performed after the NIRS measurement was complete. Total hemoglobin and oxyhemoglobin concentrations were significantly lower in the forest area than in the urban area. For semantic differential in subjective assessments, feelings of “comfortable”, “natural”, and “soothed” were significantly higher in the forest area than in the urban area, and for profile of mood states, negative emotions were significantly lower in the forest area than in the urban area. The results of physiological and psychological measurements show that viewing the forest enabled effective relaxation.
Kernel Method Used for Multi-sensor Information Fusion in Self-Diagnostic Smart Structures

Smart structures is a multi-sensor architecture, and multi-sensor information fusion is a key technology used to realize the self-diagnosing damages function for smart structures. Due to a few of intrinsic flaws of traditional neural networks, the fusion algorithms based on kernel learning are progressing rapidly in recent years. Based on the approximately orthonormal property of wavelet kernel function, Least Square Wavelet Support Vector Machine (LS-WSVM) used for information fusion is proposed, and its tuning parameters are optimized by Genetic Algorithm (GA). Based on the feature-level fusion, LS-WSVM fusion algorithm is applied to self-diagnose damages for smart structures, and its performance is evaluated through some tests. The results show that LS-WSVM possesses the higher fusion accuracy, bitter dissemination ability than LS-SVM with Gaussian kernel function under the same conditions, and LS-WSVM can decrease the amount of computation in information fusion.
A self-organizing neural network architecture for intentional planning agents

This paper presents a model of neural network embodiment of intentions and planning mechanisms for autonomous agents. The model bridges the dichotomy of symbolic and non-symbolic representation in developing agents. Some novel techniques are introduced that enables the neural network to process and manipulate sequential and hierarchical structures of information. It is suggested that by incorporating intentional agent model which relies on explicit symbolic description with self-organizing neural networks that are good at learning and recognizing patterns, the best from both sides can be exploited. This paper demonstrates that plans can be represented as weighted connections and reasoning processes can be accommodated through multi-directional activations accross different modalities of patterns. The network seamlessly interleaves planning and learning processes towards achieving the goal. Case studies and experiments shows that the model can be used to execute, plan, and capture plans as recipes through experiences.
A new multiplierless symbol synchronization algorithm for IEEE802.11x WLANs

This paper presents an efficient multiplierless timing synchronization algorithm for OFDM symbols in WLANs following 802.11x protocols which requires using numerous multipliers to correlate the received training sequence with local long training sequence in FH (frame header). The proposed algorithm approximates and quantifies the value of the long training sequence elements to the nearest value of integer power of 2 based on the IEEE 802.11x protocols, then the multiplication can be directly simplified as a shift operation in the correlator of symbol synchronization section. The presented algorithm doesn't need to incorporate any multiplication or addition when comparing with the existing algorithms. Besides, its implementation complexity is drastically reduced by merely utilizing the logic resources in FPGA. A simulation was carried out in the Simulink platform, and the results shows that the proposed algorithm performs almost the same as the traditional technique with multiply correlators. However, it performs much better at given low SNR than the existing multiplierless correlators algorithm.
The stimulation of dendrite growth by Sema3A requires integrin engagement and focal adhesion kinase

The rate and direction of axon and dendrite growth depend on multiple guidance signals and growth factors. Semaphorin 3A (Sema3A) acts as a repellent for axons and attractant for dendrites. Here, we show that the requirement for integrin engagement distinguishes the response of axons and dendrites to Sema3A in hippocampal neurons. Sema3A promotes the extension of hippocampal dendrites by a pathway that requires focal adhesion kinase (FAK). The stimulation of dendrite growth and FAK phosphorylation by Sema3A depend on integrin engagement. Unlike their function as a target of Sema3A during the collapse of axonal growth cones, integrins facilitate the stimulation of dendrite extension. Conditional inactivation of the genes encoding β1 integrin or FAK blocks the growth-promoting effect of Sema3A but not the collapse of axonal growth cones. Our results demonstrate that different pathways mediate the stimulation of dendrite growth and the collapse of axonal growth cones by Sema3A.
Emotional speech analysis in mediation and court environments

When people communicate, their states of mind are coupled with the explicit content of the messages being transmitted. The implicit information conveyed by mental states is essential to correctly understand and frame the communication messages. In mediation, professional mediators include empathy as a fundamental skill when dealing with the relational and emotional aspects of a case. In court environments, emotion analysis intendsto point out stress or fear as indicators of the truthfulness of certain asserts. In commercial environments, such as call-centers, automatic emotional analysis through speech is focused to detect deception or frustration. Computational analysis of emotions focuses on gathering information from speech, facial expressions, body poses and movements to predict emotional states. Specifically, speech analysis has been reported as a valuable procedure for emotional state recognition. While some studies focus on the analysis of speech features to classify emotional states, others concentrate on determining the optimal classification performance. In this paper we analyze current approaches to computational analysis of emotions through speech and consider the replication of their techniques and findings in the domains of mediation and legal multimedia.
A distributed in-situ analysis method for large-scale scientific data

Recently, a massive amount of data is generated in a wide range of scientific applications such as NASA's satellite, the large hadron collider, and large synoptic survey telescope. Most of scientific data follows the array model, and there are various kinds of standard array formats such as HDF, NetCDF, MDSplus, and ROOT. SciDB is the most well-known DBMS that stores the array-based scientific data and processes queries on it. SciDB is a distributed DBMS, and so, is scalable in terms of query performance. However, it has a severe drawback that takes a huge amount of time for loading a massive amount of scientific data into DBMS. That is, it is not scalable in terms of data loading. To overcome that problem, we propose a distributed in-situ analysis method that allows processing queries on raw scientific data in a distributed manner without explicit data loading. In detail, we propose the in-situ scan operator that scans necessary data of the array format and passes it to upper operators of the pipeline of a query plan. It also performs repartitioning during in-situ scanning, which is required for correct query results. Through experiments using real datasets, we have shown that the SciDB system using our method significantly outperforms the original SciDB system by orders of magnitude in terms of the performance of the first query.
Exposure to benzene and childhood leukaemia: a pilot case-control study

Objectives Main purpose To evaluate the feasibility of a measurement-based assessment of benzene exposure in case-control studies of paediatric cancer; Additional aims To identify the sources of exposure variability; to assess the performance of two benzene biomarkers; to verify the occurrence of participation bias; to check whether exposures to benzene and to 50 Hz magnetic fields were correlated, and might exert reciprocal confounding effects. Design Pilot case-control study of childhood leukaemia and exposure to benzene assessed by repeated seasonal weekly measurements in breathing zone air samples and outside the children's dwellings, with concurrent determinations of cotinine, t-t-muconic acid (MA) and sulfo-phenylmercapturic acid (S-PMA) in urine. Participants 108 cases and 194 controls were eligible for inclusion. Results Full-participation was obtained from 46 cases and 60 controls, with low dropout rates before four repeats (11% and 17%); an additional 23 cases and 80 controls allowed the collection of outdoor air samples only. The average benzene concentration in personal and outdoor air samples was 3 μg/m3 (SD 1.45) and 2.7 μg/m3 (SD 1.41), respectively. Personal exposure was strongly influenced by outdoor benzene concentrations, higher in the cold seasons than in warm seasons, and not affected by gender, age, area of residence or caseness. Urinary excretion of S-PMA and personal benzene exposure were well correlated. Outdoor benzene levels were lower among participant controls compared with non-participants, but did not differ between participant and non-participant cases; the direction of the bias was found to depend on the cut-point chosen to distinguish exposed and unexposed. Exposures to benzene and extremely low-frequency magnetic fields were positively correlated. Conclusions Repeated individual measurements are needed to account for the seasonal variability in benzene exposure, and they have the additional advantage of increasing the study power. Measurement-based assessment of benzene exposure in studies of childhood leukaemia appears feasible, although it is financially and logistically demanding.
Intercellular Calcium Signaling in Astrocytes via ATP Release through Connexin Hemichannels*

Astrocytes are capable of widespread intercellular communication via propagated increases in intracellular Ca2+ concentration. We have used patch clamp, dye flux, ATP assay, and Ca2+ imaging techniques to show that one mechanism for this intercellular Ca2+ signaling in astrocytes is the release of ATP through connexin channels (“hemichannels”) in individual cells. Astrocytes showed low Ca2+-activated whole-cell currents consistent with connexin hemichannel currents that were inhibited by the connexin channel inhibitor flufenamic acid (FFA). Astrocytes also showed molecular weight-specific influx and release of dyes, consistent with flux through connexin hemichannels. Transmembrane dye flux evoked by mechanical stimulation was potentiated by low Ca2+ and was inhibited by FFA and Gd3+. Mechanical stimulation also evoked release of ATP that was potentiated by low Ca2+ and inhibited by FFA and Gd3+. Similar whole-cell currents, transmembrane dye flux, and ATP release were observed in C6 glioma cells expressing connexin43 but were not observed in parent C6 cells. The connexin hemichannel activator quinine evoked ATP release and Ca2+ signaling in astrocytes and in C6 cells expressing connexin43. The propagation of intercellular Ca2+ waves in astrocytes was also potentiated by quinine and inhibited by FFA and Gd3+. Release of ATP through connexin hemichannels represents a novel signaling pathway for intercellular communication in astrocytes and other non-excitable cells.
Distributed Tracking of Multi-Agent Systems with High-Order Stochastic Nonlinear Dynamics

Abstract The distributed tracking problem is investigated for a class of multi-agent systems with high-order stochastic nonlinear dynamics where the subsystem of each agent is driven by inherent nonlinear drift and diffusion terms. For the case where the graph topology is directed and the leader is the neighbor of only a small portion of followers, a new distributed integrator backstepping design method is proposed, and distributed tracking control laws are designed to ensure that the closed-loop system has an almost surely unique solution on [0, ∞), all the states of the closed-loop system are bounded in probability, and the tracking errors can be tuned to arbitrarily small with a tunable exponential converge rate. The efficiency of the tracking controller is demonstrated by a simulation example.
Sibutramine Effects on Central Mechanisms Regulating Energy Homeostasis

During the last 50 years the global pandemic of obesity and associated life-threatening co-morbidities strongly promoted the development of anti-obesity pharmacotherapy. Sibutramine is an anti-obesity drug that in conjunction with lifestyle modifications reduces food intake and body weight. This may result from several effects: inhibition of presynaptic reuptake of monoaminergic neurotransmitters in the central nervous system, thereby suppressing appetite, induction of an increase in anorexigenic and a decrease in orexigenic neuropeptide secretion, induction of an increase in energy expenditure, and induction of peripheral sympathomimetic effects. The effects of sibutramine on anabolic and catabolic signals that regulate energy homeostasis in the hypothalamus are not completely understood. So, the aim of this review is to summarize the central mechanisms of action of sibutramine, responsible for its weight and food intake reducing potential. Despite being a useful drug in obesity treatment, awareness about the loss of long-term effectiveness and detrimental side effects of sibutramine has recently emerged. As a consequence, new drugs that produce safer and more persistent weight loss are currently undergoing clinical trials.
Anti-Stress, Behavioural and Magnetoencephalography Effects of an l-Theanine-Based Nutrient Drink: A Randomised, Double-Blind, Placebo-Controlled, Crossover Trial

l-theanine (γ-glutamylethylamide) is an amino acid found primarily in the green tea plant. This study explored the effects of an l-theanine-based nutrient drink on mood responses to a cognitive stressor. Additional measures included an assessment of cognitive performance and resting state alpha oscillatory activity using magnetoencephalography (MEG). Thirty-four healthy adults aged 18–40 participated in this double-blind, placebo-controlled, balanced crossover study. The primary outcome measure, subjective stress response to a multitasking cognitive stressor, was significantly reduced one hour after administration of the l-theanine drink when compared to placebo. The salivary cortisol response to the stressor was reduced three hours post-dose following active treatment. No treatment-related cognitive performance changes were observed. Resting state alpha oscillatory activity was significantly greater in posterior MEG sensors after active treatment compared to placebo two hours post-dose; however, this effect was only apparent for those higher in trait anxiety. This change in resting state alpha oscillatory activity was not correlated with the change in subjective stress response or the cortisol response, suggesting further research is required to assess the functional relevance of these treatment-related changes in resting alpha activity. These findings further support the anti-stress effects of l-theanine.
RepoLike: personal repositories recommendation in social coding communities

Social coding represents a new style of software production and creation paradigm, and demands for new technologies of software reuse. Many people searching for projects package, we can provide good reuse recommendation. In this paper, we focus on an interesting research topic of recommending software repositories to social developers, which is challenging because of two points: the first is how to get the interest contexts of developers; and the second is how to rank the repository candidates for recommendation properly. We propose RepoLike, a new approach for recommending repositories to developers by predicting their interests. RepoLike explores the developers' historical development activities and the social connections with other programmers, mines the technical features of repositories and the dependencies among them, and then combines both aspects to recommend most interesting and inspiring repositories to developers. The experiment results show that our approach can surprisingly recommend closely correlated repositories to developers, and the critical test results show that the recommendation performance is strongly impacted by the interest context model.
Loss-tolerant confidential efficient authenticated broadcast scheme

In symmetric cryptography, TESLA variants are well-known valuable source authentication broadcast scheme that are secure and efficient. But TESLA, versus asymmetric approaches, do not provide confidentiality and immediate authentication, need the synchronization of parties, are not flexible due to synchronization and finally have delayed verification and overflow problem. In this paper, we have proposed a novel secure broadcast protocol that is simple, secure and efficient like TESLA variants, to overcome shortages of TESLA variants. The method provides confidentiality and has two immediate and non-immediate authentication modes. It is loss-tolerant intrinsically, which with the best of our knowledge is the first loss-tolerant confidential authenticated broadcast scheme on lossy channels, which only uses symmetric keys. We show the proposal is efficient even on resource constrained networks by conducting a real world scenario experiment.
Embedding Task-Based Neural Models into a Connectome-Based Model of the Cerebral Cortex

A number of recent efforts have used large-scale, biologically realistic, neural models to help understand the neural basis for the patterns of activity observed in both resting state and task-related functional neural imaging data. An example of the former is The Virtual Brain (TVB) software platform, which allows one to apply large-scale neural modeling in a whole brain framework. TVB provides a set of structural connectomes of the human cerebral cortex, a collection of neural processing units for each connectome node, and various forward models that can convert simulated neural activity into a variety of functional brain imaging signals. In this paper, we demonstrate how to embed a previously or newly constructed task-based large-scale neural model into the TVB platform. We tested our method on a previously constructed large-scale neural model (LSNM) of visual object processing that consisted of interconnected neural populations that represent, primary and secondary visual, inferotemporal, and prefrontal cortex. Some neural elements in the original model were “non-task-specific” (NS) neurons that served as noise generators to “task-specific” neurons that processed shapes during a delayed match-to-sample (DMS) task. We replaced the NS neurons with an anatomical TVB connectome model of the cerebral cortex comprising 998 regions of interest interconnected by white matter fiber tract weights. We embedded our LSNM of visual object processing into corresponding nodes within the TVB connectome. Reciprocal connections between TVB nodes and our task-based modules were included in this framework. We ran visual object processing simulations and showed that the TVB simulator successfully replaced the noise generation originally provided by NS neurons; i.e., the DMS tasks performed with the hybrid LSNM/TVB simulator generated equivalent neural and fMRI activity to that of the original task-based models. Additionally, we found partial agreement between the functional connectivities using the hybrid LSNM/TVB model and the original LSNM. Our framework thus presents a way to embed task-based neural models into the TVB platform, enabling a better comparison between empirical and computational data, which in turn can lead to a better understanding of how interacting neural populations give rise to human cognitive behaviors.