Miracle-dz/UniFAR-FTUs huggingface dataset:
Distribution of dendrites of descending neurons and its implications for the basic organization of the cockroach brain

To determine precisely the brain areas from which descending neurons (DNs) originate, we examined the distribution of somata and dendrites of DNs in the cockroach brain by retrogradely filling their axons from the cervical connective. At least 235 pairs of somata of DNs were stained, and most of these were grouped into 22 clusters. Their dendrites were distributed in most brain areas, including lateral and medial protocerebra, which are major termination areas of output neurons of the mushroom body, but not in the optic and antennal lobes, the mushroom body, the central complex, or the posteroventral part of the lateral horn. The last area is the termination area of major types of olfactory projection neurons from the antennal lobe, i.e., uni‐ and macroglomerular projection neurons, so these neurons have no direct connections with DNs. The distribution of axon terminals of ascending neurons overlaps with that of DN dendrites. We propose, based on these findings, that there are numerous parallel processing streams from cephalic sensory areas to thoracic locomotory centers, many of which are via premotor brain areas from which DNs originate. In addition, outputs from the mushroom body, central complex, and posteroventral part of the lateral horn converge on some of the premotor areas, presumably to modulate the activity of some sensorimotor pathways. We propose, based on our results and documented findings, that many parallel processing streams function in various forms of reflexive and relatively stereotyped behaviors, whereas indirect pathways govern some forms of experience‐dependent modification of behavior. J. Comp. Neurol. 458:158–174, 2003. © 2003 Wiley‐Liss, Inc.
Wheelchair Propulsion Biomechanics in Junior Basketball Players: A Method for the Evaluation of the Efficacy of a Specific Training Program

As participation in wheelchair sports increases, the need of quantitative assessment of biomechanical performance indicators and of sports- and population-specific training protocols has become central. The present study focuses on junior wheelchair basketball and aims at (i) proposing a method to identify biomechanical performance indicators of wheelchair propulsion using an instrumented in-field test and (ii) developing a training program specific for the considered population and assessing its efficacy using the proposed method. Twelve athletes (10 M, 2 F, age = 17.1 ± 2.7 years, years of practice = 4.5 ± 1.8) equipped with wheelchair- and wrist-mounted inertial sensors performed a 20-metre sprint test. Biomechanical parameters related to propulsion timing, progression force, and coordination were estimated from the measured accelerations and used in a regression model where the time to complete the test was set as dependent variable. Force- and coordination-related parameters accounted for 80% of the dependent variable variance. Based on these results, a training program was designed and administered for three months to six of the athletes (the others acting as control group). The biomechanical indicators proved to be effective in providing additional information about the wheelchair propulsion technique with respect to the final test outcome and demonstrated the efficacy of the developed program.
Risk factors associated with depression and suicidal ideation in a rural population

Objectives This study aimed to evaluate the risk factors associated with depression and suicidal ideation in a rural population. Methods A survey was conducted with 543 farmers from Chungcheongnam-do Province using the Center for Epidemiologic Studies Depression Scale (CES-D) for depression, Lubben Social Network Scale (LSNS) for social support, Swedish Q16 for neurotoxicity symptoms and a survey tool for farmer’s syndrome. Results After adjusting for socioeconomic factors using logistic regression analysis, poor self-rated health, low social support and neurotoxicity were positively associated with the risk of depression (odds ratio [OR], 15.96; 95% confidence interval [CI], 3.11 to 81.97; OR, 3.14; 95% CI, 1.26 to 7.82; and OR, 3.68; 95% CI, 1.08 to 12.57, respectively). The risk of suicidal ideation significantly increased with low social support, neurotoxicity and farmer’s syndrome (OR, 2.28; 95% CI, 1.18 to 4.40; OR, 6.17; 95% CI, 2.85 to 13.34; and OR, 3.70; 95% CI, 1.51 to 9.07, respectively). Conclusions Given the overall results of this study, there is a need to establish programs which can improve the health and social relationships of farmers. Also, when farmers have neurological symptoms from pesticide exposure and characteristic symptoms of farmer’s syndrome, a monitoring system for depression and suicide must be made available.Conclusions: Given the overall results of this study, there is a need to establish programs which can improve the health and social relationships of farmers. Also, when farmers have neurological symptoms from pesticide exposure and characteristic symptoms of farmer's syndrome, a monitoring system for depression and suicide must be made available.
Graph-based identification of cancer signaling pathways from published gene expression signatures using PubLiME

Gene expression technology has become a routine application in many laboratories and has provided large amounts of gene expression signatures that have been identified in a variety of cancer types. Interpretation of gene expression signatures would profit from the availability of a procedure capable of assigning differentially regulated genes or entire gene signatures to defined cancer signaling pathways. Here we describe a graph-based approach that identifies cancer signaling pathways from published gene expression signatures. Published gene expression signatures are collected in a database (PubLiME: Published Lists of Microarray Experiments) enabled for cross-platform gene annotation. Significant co-occurrence modules composed of up to 10 genes in different gene expression signatures are identified. Significantly co-occurring genes are linked by an edge in an undirected graph. Edge-betweenness and k-clique clustering combined with graph modularity as a quality measure are used to identify communities in the resulting graph. The identified communities consist of cell cycle, apoptosis, phosphorylation cascade, extra cellular matrix, interferon and immune response regulators as well as communities of unknown function. The genes constituting different communities are characterized by common genomic features and strongly enriched cis-regulatory modules in their upstream regulatory regions that are consistent with pathway assignment of those genes.
Scanned monocular sonar and the doorway problem

A sonar system is presented that relies on scanning a single ultrasonic transducer and measuring echo amplitude and arrival times. Bearing angles to targets are estimated far more accurately than the transducer beamwidth as obtained with conventional sonar rings based on the Polaroid ranging module. A Gaussian beam characteristic is fitted using least squares to the amplitudes of corresponding echoes in the scan to obtain an estimate of the bearing to specular targets. As an illustration of the information gain over conventional sonar rings, the sensor approach is used on a mobile robot to find, traverse and map doorways reliably and with minimal algorithmic effort. This is compared with other work that claims the problem is difficult to solve using a conventional sonar ring of 24 Polaroid ranging modules.
SiFTO: An Empirical Method for Fitting SN Ia Light Curves

We present SiFTO, a new empirical method for modeling Type Ia supernova (SN Ia) light curves by manipulating a spectral template. We make use of high-redshift SN data when training the model, allowing us to extend it bluer than rest-frame U. This increases the utility of our high-redshift SN observations by allowing us to use more of the available data. We find that when the shape of the light curve is described using a stretch prescription, applying the same stretch at all wavelengths is not an adequate description. SiFTO therefore uses a generalization of stretch which applies different stretch factors as a function of both the wavelength of the observed filter and the stretch in the rest-frame B band. We compare SiFTO to other published light-curve models by applying them to the same set of SN photometry, and demonstrate that SiFTO and SALT2 perform better than the alternatives when judged by the scatter around the best-fit luminosity distance relationship. We further demonstrate that when SiFTO and SALT2 are trained on the same data set the cosmological results agree.
POSITIVITY OF THE GREEN FUNCTIONS FOR HIGHER ORDER ORDINARY DIFFERENTIAL EQUATIONS

We consider the equation n X k=0 ak(t)x (n k) (t) = 0, t 0, where a0(t) 1, ak(t) (k = 1,.,n) are real bounded functions. Assuming that all the roots of the polynomial z n +a1(t)z n 1 +···+an(t) (t 0) are real, we derive positivity conditions for the Green function for the Cauchy problem. We also establish a lower estimate for the Green function and a comparison theorem for solutions.
Empatica E3 — A wearable wireless multi-sensor device for real-time computerized biofeedback and data acquisition

The Empatica E3 is a wearable wireless multisensor device for real-time computerized biofeedback and data acquisition. The E3 has four embedded sensors: photoplethysmograph (PPG), electrodermal activity (EDA), 3-axis accelerometer, and temperature. It is small, light and comfortable and it is suitable for almost all real-life applications. The E3 operates both in streaming mode for real-time data processing using a Bluetooth low energy interface and in recording mode using its internal flash memory. With E3, it is possible to conduct research outside of the lab by acquiring continuous data for ambulatory situations in a comfortable and non-distracting way.
Risk and Exchange Rates

This paper develops an explicitly stochastic new open economy macroeconomics' model, which can potentially be used to explore the qualitative and quantitative welfare differences between alternative exchange rate regimes. A crucial feature is that we do not simplify by assuming certainty equivalence for producer price setting behavior. Our framework also provides a sticky-price alternative to Lucas's (1982) exchage rate risk premium model. We show that the level risk premium' in the exchage rate is potentially quite large and may be an important missing fundamental in empirical exchange rate equations. As a byproduct analysis also suggests an intriguing possible explanation of the forward premium puzzle.
Stress Sensitizes the Brain: Increased Processing of Unpleasant Pictures after Exposure to Acute Stress

A key component of acute stress is a surge in vigilance that enables a prioritized processing of highly salient information to promote the organism's survival. In this study, we investigated the neural effects of acute stress on emotional picture processing. ERPs were measured during a deep encoding task, in which 40 male participants categorized 50 unpleasant and 50 neutral pictures according to arousal and valence. Before picture encoding, participants were subjected either to the Socially Evaluated Cold Pressor Test (SECPT) or to a warm water control procedure. The exposure to the SECPT resulted in increased subjective and autonomic (heart rate and blood pressure) stress responses relative to the control condition. Viewing of unpleasant relative to neutral pictures evoked enhanced late positive potentials (LPPs) over centro-parietal scalp sites around 400 msec after picture onset. Prior exposure to acute stress selectively increased the LPPs for unpleasant pictures. Moreover, the LPP magnitude for unpleasant pictures following the SECPT was positively associated with incidental free recall performance 24 hr later. The present results suggest that acute stress sensitizes the brain for increased processing of cues in the environment, particularly priming the processing of unpleasant cues. This increased processing is related to later long-term memory performance.
Convex Combination of Multiple Statistical Models With Application to VAD

This paper proposes a robust voice activity detector (VAD) based on the observation that the distribution of speech captured with far-field microphones is highly varying, depending on the noise and reverberation conditions. The proposed VAD employs a convex combination scheme comprising three statistical distributions - a Gaussian, a Laplacian, and a two-sided Gamma - to effectively model captured speech. This scheme shows increased ability to adapt to dynamic acoustic environments. The contribution of each distribution to this convex combination is automatically adjusted based on the statistical characteristics of the instantaneous audio input. To further improve the performance of the system, an adaptive threshold is introduced, while a decision-smoothing scheme caters to the intra-frame correlation of speech signals. Extensive experiments under realistic scenarios support the proposed approach of combining several models for increased adaptation and performance.
Evaluating the usefulness and the ease of use of a Web-based inspection data collection tool

Software tools can only be effective if users accept them. Investigating user acceptance requires a model explaining people's attitudes and behaviour as well as reliable and valid measurement instruments. This paper applies Davis' (1989) technology acceptance model (TAM) and its questionnaire-based measurement instrument for evaluating a newly developed Web-based inspection data collection tool (WIPS) from a user's perspective. TAM postulates that two particular user beliefs, usefulness and ease of use, predict tool usage and, thus, tool acceptance quite well. To investigate this and to assess the usefulness and ease of use of WIPS, we performed an experiment with computer science students as subjects. Our experimental results confirmed the reliability and the validity of the TAM and its questionnaire. Moreover WIPS received high ratings for both TAM determinants. These results, together with the fact that usefulness was correlated to self-predicted future usage, explain why most of our subjects preferred WIPS over paper-based forms for inspection data collection. The reliability and validity of the TAM questionnaire is not limited to inspection tools, but applies to the user acceptance evaluation of tools and techniques in general. We demonstrate this with data from an experiment we performed in an industrial setting with professional software developers to evaluate different inspection techniques.
Half Mode Substrate Integrated Waveguide 180° 3-dB Directional Couplers

In this paper, two types of half mode substrate integrated waveguide 180deg couplers are proposed, the phase-shifting structures of which are composed of metallic via fins or etched air slots. Each coupler maintains the good performance of the substrate integrated waveguide coupler with a half reduction in size and a 180deg phase difference between through and coupling ports. Prototypes working in the C-,X -, Ku-, and Ka-bands are designed and fabricated using the standard printed circuit board process. The consistency between the measured data and simulated results shows that the proposed couplers have the merits of low profile and good performance.
On decoding error exponent of Gaussian channel with noisy feedback: Nonexponential number of messages

For information transmission a discrete time channel with independent additive Gaussian noise is used. There is also feedback channel with independent additive Gaussian noise, and the transmitter observes without delay all outputs of the forward channel via that feedback channel. Transmission of nonexponential number of messages is considered and the achievable decoding error exponent for such a combination of channels is investigated. It is shown that for any finite noise in the feedback channel the achievable error exponent is better than similar error exponent of the no-feedback channel. Method of transmission/decoding used in the paper strengthens the earlier method used by authors for BSC. In particular, for small feedback noise, it allows to get the gain up to 23.6% (instead of 14.3% earlier for BSC).
In support of representational economy: Agreement in heritage Spanish

This paper investigates the morphosyntax of number and gender agreement in English-dominant heritage speakers of Spanish. Our study builds on the experimental paradigm of agreement attraction developed by Fuchs et al. (2015) and elicits responses to agreement failures to diagnose the potentially-independent contributions of number and gender features. By comparing the structuring of these agreement categories in native and heritage Spanish, we document a case of grammatical divergence: heritage speakers restructure their agreement categories, favoring fewer feature values and less structure. The principled nature of this restructuring demonstrates that the loss of agreement in heritage languages is not superficial or accidental, but rather follows from predictable structural changes.
Energy-Aware Clustering for Wireless Sensor Networks using Particle Swarm Optimization

Wireless sensor networks (WSNs) are mainly characterized by their limited and non-replenishable energy supply. Hence, the need for energy efficient infrastructure is becoming increasingly more important since it impacts upon the network operational lifetime. Sensor node clustering is one of the techniques that can expand the lifespan of the whole network through data aggregation at the cluster head. In this paper, we present an energy-aware clustering for wireless sensor networks using particle swarm optimization (PSO) algorithm which is implemented at the base station. We define a new cost function, with the objective of simultaneously minimizing the intra-cluster distance and optimizing the energy consumption of the network. The performance of our protocol is compared with the well known cluster-based protocol developed for WSNs, LEACH (low-energy adaptive clustering hierarchy) and LEACH-C, the later being an improved version of LEACH. Simulation results demonstrate that our proposed protocol can achieve better network lifetime and data delivery at the base station over its comparatives.
The Extreme Cold Anomaly over Southeast Asia in February 2008: Roles of ISO and ENSO*

A record-breaking, long-persisting extreme cold anomaly (ECA) over Southeast Asia, accompanied by an intraseasonal convection over the Maritime Continent, is identified during the La Nina mature phase in February 2008. The cause of the ECA, in particular the role of the intraseasonal oscillation (ISO) and El Nino-Southern Oscillation (ENSO) on the ECA, is investigated by diagnosing observations and con- ducting numerical experiments. The ECA is associated with an enhanced prolonged Siberian high (SH) and a persistent northerly anomaly over Southeast Asia. In contrast to conventional cold surges, which are characterized by a synoptic time scale (less than 10 days), the northerly anomaly associated with the ECA persisted for a month or so. The onset of the northerly anomalyis concurrent with a phase change of an ISO over Sumatra. Unlike the normal ISO that continues its eastward journey, the convection associated with this ISO stationed there during all of February 2008. Numerical experiments with an anomaly atmospheric GCM suggest that the ISO heating over the Maritime Continent is responsible for initiating and maintaining the northerly anomaly. The westward progression of the La Nina is crucial for blocking the ISO. The circulation and SST anomalies associated with the La Nina moved westward at a speed of about 158 longitude per month. By early February, the suppressed convective anomaly had moved to the far western Pacific. The westward shift of the cold episode prevented the ISO from moving farther eastward. In addition to its blocking effect, the La Nina also enhanced the heating over the Maritime Continent through the anomalous Walker circulation. Therefore, it is the combined effect of the ISO and ENSO that maintained a prolonged positive heating anomaly, which resulted in a persistent northerly anomaly and thus the ECA.
The Effects of Risk Aversion on Job Matching : Can Differences in Risk Aversion Explain the Wage Gap ?

Previous research has shown that more risk averse workers are at a disadvantage when bargaining over wages. However, it has yet to be determined whether this differential treatment can be sustained in markets where equally productive workers differ in their attitudes toward risk. This paper investigates the question by examining a simple matching model in which the standard assumptions of risk neutrality and homogeneity are relaxed. It is shown that more risk averse workers have lower reservation wages, and conditional on productivity they receive lower wages. Thus, they are faced with a wage distribution which is stochastically dominated by that of less risk averse workers. As a result more risk averse workers are less likely to enter the labor force, and their unemployment rate and duration are smaller. Evidence from the psychology literature and recent empirical evidence in economics indicate that women are more risk averse than men. The results presented in this paper therefore suggest that gender differences in risk aversion may provide an alternative explanation for the observed differences in male and female labor market experiences. Contrary to most discrimination models, this model is consistent with both utility and profit maximization in the long run.
Simplify Support Vector Machines by Iterative Learning

Support vector machines (SVM) are well known to give good results on a wide variety of pattern recognition problems, but for large scale problems, the number of support vectors usually is large, which results in substantially slow classification speed. Existing study has proposed to speed the SVM classification by decreasing the number of support vectors. In this paper it is found that SVM trained with most important training points have less support vectors and equivalent accuracy to those of SVM trained with the full training set. An iterative procedure is proposed to train the simplified SVM with most important training points, and the careful preprocessing on outliers also is used to speed the iterative learning. Computational results indicate that, compared with SVM trained with full training set, proposed method can obtain simplified SVMs with much less support vectors and equivalent classification accuracy, which supports proposed method as an effective method to obtain a simplified SVM for large problems. Keywords—Simplified SVM, Support Vector Machine, Iterative Learning