Miracle-dz/UniFAR-FTUs huggingface dataset:
Obstacle detection and navigation planning for autonomous micro aerial vehicles

Obstacle detection and real-time planning of collision-free trajectories are key for the fully autonomous operation of micro aerial vehicles in restricted environments. In this paper, we propose a complete system with a multimodal sensor setup for omnidirectional obstacle perception consisting of a 3D laser scanner, two stereo camera pairs, and ultrasonic distance sensors. Detected obstacles are aggregated in egocentric local multiresolution grid maps. We generate trajectories in a multi-layered approach: from mission planning to global and local trajectory planning, to reactive obstacle avoidance. We evaluate our approach in simulation and with the real autonomous micro aerial vehicle.
Performance Study of a Class-E Power Amplifier With Tuned Series-Parallel Resonance Network

This paper reports on a modified class-E amplifier with a tuned series-parallel resonance network. This configuration introduces two additional resonance frequencies. The design equations required to determine the optimum operations are analyzed in detail by introducing three additional boundary conditions. compared to the conventional class-E amplifier, the numerical results show that the improvements on load resistance, maximum transistor current, power output capability, and maximum frequency can be obtained by properly selecting the resonance frequencies of the tuned network. Comparisons between simulations and measurements of an experimental circuit of the new configuration are drawn to validate the feasibility. The drain efficiency of 81.77%, power-added efficiency of 79.58%, and output power of 32.71 dBm (1.866 W) at 250 MHz are measured.
Vanadate inhibits the ATPase activity and DNA binding capability of bacterial MutS. A structural model for the vanadate-MutS interaction at the Walker A motif.

MutS, a member of the ABC ATPases superfamily, is a mismatch DNA-binding protein constituent of the DNA post-replicative mismatch repair system (MMRS). In this work, it is shown that the ATPase activity of Pseudomonas aeruginosa and Escherichia coli MutS is inhibited by ortho- and decavanadate. Structural comparison of the region involved in the ATP binding of E.coli MutS with the corresponding region of other ABC ATPases inhibited by vanadate, including the myosin- orthovanadate-Mg complex, showed that they are highly similar. From these results it is proposed that the orthovanadate inhibition of MutS ATPase can take place by a similar mechanism to that described for other ATPases. Docking of decavanadate on the ATP-binding region of MutS showed that the energetically more favorable interaction of this compound would take place with the complex MutS- ADP-Mg, suggesting that the inhibitory effect could be produced by a steric impediment of the protein ATP/ADP exchange. Besides the effect observed on the ATPase activity, vanadate also affects the DNA-binding capability of the protein, and partially inhibits the oligomerization of MutS and the temperature-induced inactivation of the protein. From the results obtained, and considering that vanadate is an intracellular trace component, this compound could be considered as a new modulator of the MMRS.
Evaluation of Cellular Antioxidant and Antiproliferative Activities of Five Main Phyllanthus Emblica L. Cultivars in China

The cell-based antioxidant activity assay as more biological relevant assay was considered to be more accurate to predict antioxidant activity in vivo than chemical activity assays. In the present study, the five main Phyllanthus emblica L. cultivars in China were subjected for cellular antioxidant activity based on HepG2 cells as well as antiproliferative activity. Total phenolics, total flavonoids and oxygen radical absorbance capacity were also measured. The results showed that Qingyougan, Binggan and Boligan (832±100, 774±52 and 704±28 μmol of quercetin equivalents/100 g) had higher cellular antioxidant activity than Tianyougan and Yougan (553±50 and 457±24 μmol of quercetin equivalents/100 g) in phosphate buffered saline wash protocol whereas, Boligan (3735±217 μmol of quercetin equivalents/100 g) had the highest cellular antioxidant activity and Tianyougan (2025±171 μmol of quercetin equivalents/100 g) had the lowest cellular antioxidant activity in no phosphate buffered saline wash protocol. The highest and lowest antiproliferative activities were observed in Binggan and Tianyougan (median effective dose: 6.95±0.11 and 14.03±0.10 mg/ml), respectively. The significant correlation was only observed between total flavonoids and cellular antioxidant activity from no phosphate buffered saline wash protocol (R2 =0.908, P<0.05), and total flavonoids and antiproliferative activity (R2 =0.887, P<0.05), suggesting the major contribution of flavonoids to the bioactivities of emblica. Overall, the data obtained revealed that different Phyllanthus emblica L. cultivars had strong cellular antioxidant and antiproliferative activities, thus should be recommended to increase consumption for health.
Multi-modal integration of dynamic audiovisual patterns for an interactive reinforcement learning scenario

Robots in domestic environments are receiving more attention, especially in scenarios where they should interact with parent-like trainers for dynamically acquiring and refining knowledge. A prominent paradigm for dynamically learning new tasks has been reinforcement learning. However, due to excessive time needed for the learning process, a promising extension has been made by incorporating an external parent-like trainer into the learning cycle in order to scaffold and speed up the apprenticeship using advice about what actions should be performed for achieving a goal. In interactive reinforcement learning, different uni-modal control interfaces have been proposed that are often quite limited and do not take into account multiple sensor modalities. In this paper, we propose the integration of audiovisual patterns to provide advice to the agent using multi-modal information. In our approach, advice can be given using either speech, gestures, or a combination of both. We introduce a neural network-based approach to integrate multi-modal information from uni-modal modules based on their confidence. Results show that multi-modal integration leads to a better performance of interactive reinforcement learning with the robot being able to learn faster with greater rewards compared to uni-modal scenarios.
Design and control of a clutch for a minimally-actuated biped based on the passive-dynamic simple walker

Passive-dynamic walking robots are remarkable mechanical devices capable of maintaining dynamically stable walking gaits with no actuation or control. These systems, however, depend on ideal environmental conditions for stability. Robustness and control capabilities are increased with actuation, but so is the power consumption. Such actuated robots are designed to minimize the actuation requirement by exploiting the system natural dynamics system, but still need actuation to compensate for energy dissipated by friction and collision events, as well as for more control capabilities. A simple clutch mechanism is developed for such systems to allow intermittent control of otherwise passive joints, allowing controllers to exploit the passive or actuated control when desired. The clutch is tested on a hip actuated simple 3D walker to evaluate the performance capabilities of clutched control. Preliminary tests of several control strategies suggest the clutched actuation may provide good performance at a higher efficiency compared to fully actuated systems. This paper describes the development of the clutch device and the hip-actuated biped on with which the clutch is tested, and evaluates the performance of intermittent clutch-control for several control strategies. Thesis Supervisor: Russell L. Tedrake Title: Assistant Professor, Department of Electrical Engineering and Computer Science
Pixel-based Bayesian classification for meningioma brain tumor detection using post contrast T1-weighted magnetic resonance image

This paper introduces Bayesian approach for automated delineation of meningioma brain tumor using post contrast T1 weighted magnetic resonance image. The proposed framework follows the basis of pixel based classification, combination of two stages; feature extraction followed by learning and classification of pixels into desired classes. Both intensity and texture features are extracted. Thereafter, the pixels corresponding to tumor and non tumor region are classified using feature based Bayesian learning. The performance of the proposed methodology is evaluated. The experimental results show its superiority over linear discriminant analysis (LDA), decision tree (DT), and support vector machine (SVM) classifiers.
Hiding Encrypted Message in the Features of Images

Summary This paper proposes a novel least significant bit embedding algorithm for hiding encrypted messages in nonadjacent and random pixel locations in edges of images. It first encrypts the secret message, and detects edges in the coverimage. Message bits are then, embedded in the least significant bits and random locations of the edge pixels. It ensures that the eavedroppers will not have any suspicion that message bits are hidden in the image and standard steganography detection methods can not estimate the length of the secret message correctly.
Effects of progressive oxygen depletion on sediment diagenesis and fluxes: A model for the lower St. Lawrence River Estuary

We applied a diagenetic model to examine the effects of a decadal‐timescale progressive decrease in bottomwater oxygen concentration on sediment geochemistry and fluxes across the sediment‐water interface. The model was calibrated using geochemical data acquired over the last 30 yr in the lower St. Lawrence River Estuary (Canada), where the bottom‐water oxygen concentration has been decreasing at an average rate of 1 µmol L−1 yr−1 over the past 70 yr. Benthic fluxes calculated from the model are comparable to fluxes derived from shipboard sediment incubations. By propagating the model forward in time and allowing the oxygen concentration to decrease further at 1 µmol L−1 yr−1, we predict changes in the distributions and fluxes of iron, manganese, phosphorus, nitrogen, and sulfur. As the oxygen concentration decreases, the concentrations and distributions of reactive phases in the sediment change at an accelerating pace. Fluxes of reduced substances out of the sediment increase, reactive iron and manganese oxide phases become depleted, and the sediment becomes progressively enriched in iron sulfides. A notable exception is the efflux of phosphate, which remains invariant. At the study site, these changes are likely to become measurable within the next 20 yr. Model results are sensitive to the effects of decreasing oxygen concentrations on the benthos, the response of which we represent by two different approximations of the bioturbation and bioirrigation formulations: a gradual decrease and a threshold type decrease. The principal obstacle to reliable predictions of how sediments will respond is the lack of knowledge about the response of bioturbation and bioirrigation to decreasing oxygen in bottom waters.
The Improvement and Application of Structure Optimization Algorithm Based on Flexible Neural Trees

in this paper, the improved structural optimization Algorithm of the flexible neural tree model is employed to select the parameters for the industrial production. With the highest accuracy and the shortest time to find the important parameters affecting the production so as to provide a theoretical support for the control of fluid industry production. In the period of learning of the flexible neural tree model, the evolution generation of Algorithm is not a fixed value and the mean error rate is utilized to control the evolution generation. The flexible neural tree model’s structure and parameters are optimized by the probabilistic incremental program evolution and the simulation annealing, respectively. The process of the decomposing furnace, which is one of the most important processes of the Cement productions, is the object of this article. And it has been demonstrated that the given method is very effective.
Optimal strategies for monitoring response to antiretroviral therapy in HIV-infected adults, adolescents, children and pregnant women: a systematic review

Objective:The objective of this review was to examine different monitoring strategies (clinical, immunologic (CD4+ T cell count measurement) and virologic (viral load measurement)) to inform revision of the 2013 WHO guidelines for antiretroviral therapy (ART) in low and middle-income countries. Design:A systematic review. Methods:We searched 10 databases, reference lists of included research studies and contacted experts in an attempt to identify all relevant studies regardless of language or publication status. We included both randomized controlled trials (RCTs) and observational studies. We selected studies that examined routine clinical monitoring (CM), immunologic monitoring (IM) or virologic monitoring (VM). CM involved clinical evaluation and basic laboratory blood testing without CD4+ T cell count or viral load. Two authors independently assessed study eligibility, extracted data and graded methodological quality. Results:A total of six studies were identified, including five RCTs and one observational study. Two RCTs among adults found an increased risk of AIDS-defining illness and mortality in CM compared to CM + IM. Two studies compared CM + IM to CM + IM + VM, with one finding a mortality advantage in the CM + IM + VM group. Duration of viremia and time to switching to a second-line regimen were longer in CM + IM compared to CM + IM + VM. Only one trial was conducted in children, and showed no difference in mortality comparing CM and CM+IM. No studies specifically studied pregnant women. Conclusion:CM + IM was shown to be beneficial in terms of a combined mortality and morbidity endpoint compared to CM alone. VM was associated with shorter duration of viremia and higher rates of switching, but an impact on mortality was not consistently shown. Pooled outcome estimates were possible with comparison of only CM to CM + IM. Further HIV research on different VL monitoring strategies is required. These data support the recommendation in the 2013 WHO ART guidelines for the use of VM to confirm and diagnose ART failure, and for the use of IM + CM when VM is not available.
Evaluation of the Influence of Sulfur-Fumigated Paeoniae Radix Alba on the Quality of Si Wu Tang by Chromatographic and Chemometric Analysis

An accurate and reliable method of high-performance liquid chromatographic fingerprint combining with multi-ingredient determination was developed and validated to evaluate the influence of sulfur-fumigated Paeoniae Radix Alba on the quality and chemical constituents of Si Wu Tang. Multivariate data analysis including hierarchical cluster analysis and principal component analysis, which integrated with high-performance liquid chromatographic fingerprint and multi-ingredient determination, was employed to evaluate Si Wu Tang in a more objective and scientific way. Interestingly, in this paper, a total of 37 and 36 peaks were marked as common peaks in ten batches of Si Wu Tang containing sun-dried Paeoniae Radix Alba and ten batches of Si Wu Tang containing sulfur-fumigated Paeoniae Radix Alba, respectively, which indicated the changed fingerprint profile of Si Wu Tang when containing sulfur-fumigated herb. Furthermore, the results of simultaneous determination for multiple ingredients showed that the contents of albiflorin and paeoniflorin decreased significantly (P < 0.01) and the contents of gallic acid and Z-ligustilide decreased to some extent at the same time when Si Wu Tang contained sulfur-fumigated Paeoniae Radix Alba. Therefore, sulfur-fumigation processing may have great influence on the quality of Chinese herbal prescription.
Trisecting representational states in short-term memory

The ability to hold information briefly in mind in the absence of external stimulation forms the core of much of higher-order cognition. This ability is referred to as short-term memory (STM). However, single-term labels such as this belie the complexity of the underlying construct. Here, we review evidence that STM is an amalgamation of three qualitatively distinct states. We argue that these distinct states emerge from the combination of frontal selection mechanisms (often considered the domain of attention and cognitive control), medial temporal binding mechanisms (often considered the domain of long-term memory, LTM), and synaptic plasticity. These various contributions lead to a single representation amenable to elaborated processing (focus of attention), a limited set of active representations among which attention can be flexibly switched (direct-access region), and passive representations whose residual traces facilitate re-activation (activated LTM). We suggest that selection and binding mechanisms are typically engaged simultaneously, providing multiple forms and routes of short-term maintenance. We propose that such a framework can resolve discrepancies among recent studies that have attempted to understand the relationship between attention and STM on the one hand, and between LTM and STM on the other. We anticipate that recent advances in neuroimaging and neurophysiology will elucidate the mechanisms underlying shifts and transformations among these representational states, providing a window into the dynamic processes of higher-order cognition.
Further Insights Into the German Version of the Multidimensional Assessment of Interoceptive Awareness (MAIA)

Abstract. Interoception is defined as an iterative process that refers to receiving, accessing, appraising, and responding to body sensations. Recently, following an extensive process of development, Mehling and colleagues (2012) proposed a new instrument, the Multidimensional Assessment of Interoceptive Awareness (MAIA), which captures these different aspects of interoception with eight subscales. The aim of this study was to reexamine the dimensionality of the MAIA by applying maximum likelihood confirmatory factor analysis (ML-CFA), exploratory structural equation modeling (ESEM), and Bayesian structural equation modeling (BSEM). ML-CFA, ESEM, and BSEM were examined in a sample of 320 German adults. ML-CFA showed a poor fit to the data. ESEM yielded a better fit and contained numerous significant cross-loadings, of which one was substantial (≥ .30). The BSEM model with approximate zero informative priors yielded an excellent fit and confirmed the substantial cross-loading found in ESEM. The study demonstrates that ESEM and BSEM are flexible techniques that can be used to improve our understanding of multidimensional constructs. In addition, BSEM can be seen as less exploratory than ESEM and it might also be used to overcome potential limitations of ESEM with regard to more complex models relative to the sample size.
Matching and Selection Methods to Measure the Impact on Wages of Training Programs for Unemployed Workers : Evaluating the Effectiveness of the Mexican PROBECAT

For years, the Mexican government has funded and administered a training program offered to unemployed workers, called PROBECAT (Programa de Becas de Capacitación para Desempleados). In this paper we examine the impact of this program on wages of a cohort of beneficiaries of the training provided by various institutions. These institutions differ in their degree of autonomy with respect to the central government, in their organizational resources and in their capacity to identify and adapt their services to the requirements of the area in which they are located. A comparison group of individuals that were eligible for the program, but did not participate in it (i.e. unemployed individuals looking for a job) was obtained from the National Survey of Urban Employment (ENEU). This is a survey applied quarterly in Mexico that uses a rotation system, with groups of individuals remaining in the survey for five consecutive quarters. In view of the non-experimental nature of this evaluation to assess what would wages of trainees have been, if they had not participated in the program, we rely on two different methods to infer counterfactual outcomes 2. One of them, the matching method, assumes that selection to participate into the program only occurred on the basis of observable characteristics of individuals; the other one corrects the self-selection biases in estimated impacts that occur when this is not assumed. 1 The author acknowledges the skilful research assistance from Gonzalo Rangel and the helpful comments from participants of the 10th International Conference on Panel Data organized by the DIW in Berlin. Previous versions of this paper also benefited form discussions with Jeff Smith, Petra Todd and Belem Trejo. 2 We know that participants were either self-selected or selected by somebody else to enter the program, whereas the comparison group members were not.
Extending the Engagement Taxonomy: Software Visualization and Collaborative Learning

As collaborative learning in general, and pair programming in particular, has become widely adopted in computer science education, so has the use of pedagogical visualization tools for facilitating collaboration. However, there is little theory on collaborative learning with visualization, and few studies on their effect on each other. We build on the concept of the engagement taxonomy and extend it to classify finer variations in the engagement that result from the use of a visualization tool. We analyze the applicability of the taxonomy to the description of the differences in the collaboration process when visualization is used. Our hypothesis is that increasing the level of engagement between learners and the visualization tool results in a higher positive impact of the visualization on the collaboration process. This article describes an empirical investigation designed to test the hypothesis. The results provide support for our extended engagement taxonomy and hypothesis by showing that the collaborative activities of the students and the engagement levels are correlated.
Firm Decision-making Structure and Compliance with Environmental Regulations: Evidence from Environmental Auditing

We explore how limits to our insight on the underlying decision-making structure of regulated entities may affect the conclusions we draw about the likely impacts of environmental policies, with a focus on environmental auditing. We examine the conditions under which a multi-facility firm chooses to adopt a standardized auditing. Our firm-level empirical analysis confirms that a uniform auditing outcome is less likely among firms (1) with a more heterogeneous portfolio of facilities, and (2) that are less likely to make decision errors. We examine the implications of adding controls for firm decision-making structure in a facility-level empirical analysis of auditing’s impact on compliance. Our results suggest that the estimated compliance impact of auditing varies depending on whether or not we include these controls.
High Resolution Simulations of the General Circulation of the Martian Atmo- sphere: Small and Medium Scale Disturbances and Dust Lifting Processes.

sphere: Small and Medium Scale Disturbances and Dust Lifting Processes. Yoshiyuki O. Takahashi, Yoshi-Yuki Hayashi, Masatsugu Odaka, and Wataru Ohfuchi, Department of Earth and Planetary Sciences, Kobe University, Japan (yot@people.kobe-u.ac.jp), Center for Planetary Sciences, Kobe University, Japan, Department of Cosmosciences, Hokkaido University, Japan, and The Earth Simulator Center, Japan Agency for Marine-Earth Science and Technology Introduction