Miracle-dz/UniFAR-FTUs huggingface dataset:
Topographic organization and possible function of the posterior optic tubercles in the brain of the desert locust Schistocerca gregaria

Migrating desert locusts, Schistocerca gregaria, are able to use the skylight polarization pattern for navigation. They detect polarized light with a specialized dorsal rim area in their compound eye. After multistage processing, polarization signals are transferred to the central complex, a midline‐spanning brain area involved in locomotor control. Polarization‐sensitive tangential neurons (TB‐neurons) of the protocerebral bridge, a part of the central complex, give rise to a topographic arrangement of preferred polarization angles in the bridge, suggesting that the central complex acts as an internal sky compass. TB‐neurons connect the protocerebral bridge with two adjacent brain areas, the posterior optic tubercles. To analyze the polarotopic organization of the central complex further, we investigated the number and morphologies of TB‐neurons and the presence and colocalization of three neuroactive substances in these neurons. Triple immunostaining with antisera against Diploptera punctata allatostatin (Dip‐AST), Manduca sexta allatotropin (Mas‐AT), and serotonin (5HT) raised in the same host species revealed three spatially distinct TB‐neuron clusters, each consisting of 10 neurons per hemisphere: cluster 1 and 3 showed Dip‐AST/5HT immunostaining, whereas cluster 2 showed Dip‐AST/Mas‐AT immunostaining. Five subtypes of TB‐neuron could be distinguished based on ramification patterns. Corresponding to ramification domains in the protocerebral bridge, the neurons invaded distinct but overlapping layers within the posterior optic tubercle. Similarly, neurons interconnecting the tubercles of the two hemispheres also targeted distinct layers of these neuropils. From these data we propose a neuronal circuit that may be suited to stabilize the internal sky compass in the central complex of the locust. J. Comp. Neurol. 523:1589–1607, 2015. © 2015 Wiley Periodicals, Inc.
Adaptive Neural Control of Uncertain Nonlinear Systems Using Disturbance Observer

This paper studies the problem of prescribed performance adaptive neural control for a class of uncertain multi-input and multi-output (MIMO) nonlinear systems in the presence of external disturbances and input saturation based on a disturbance observer. The system uncertainties are tackled by neural network (NN) approximation. To handle unknown disturbances, a Nussbaum disturbance observer is presented. By incorporating the disturbance observer and NNs, an adaptive prescribed performance neural control scheme is further developed. Then, the expected asymptotically convergent tracking errors between system output signals and desired signals are achieved. Numerical simulation results demonstrate the effectiveness of the proposed control scheme.
Omen+: a precise dynamic deadlock detector for multithreaded Java libraries

Designing thread-safe libraries without concurrency defects can be a challenging task. Detecting deadlocks while invoking methods in these libraries concurrently is hard due to the possible number of method invocation combinations, the object assignments to the parameters and the associated thread interleavings. In this paper, we describe the design and implementation of OMEN+ that takes a multithreaded library as the input and detects true deadlocks in a scalable manner. We achieve this by automatically synthesizing relevant multithreaded tests and analyze the associated execution traces using a precise deadlock detector. We validate the usefulness of OMEN+ by applying it on many multithreaded Java libraries and detect a number of deadlocks even in documented thread-safe libraries. The tool is available for free download at http://www.csa. iisc.ernet. in/~sss/tool/omenplus.html.
Volume based feeding versus rate based feeding in the critically ill: A UK study

Background Underfeeding in intensive care patients on enteral nutrition is commonplace and multifactorial. This can be exacerbated by interruptions caused by routine fasting for procedures and investigations. Our study aims to demonstrate that a volume based feeding protocol can overcome the barriers of underfeeding and safely increase energy and protein delivery in UK intensive care patients, potentially improving clinical outcomes. Methods In this single centre cohort study, data were collected from adult mechanically ventilated patients. We compared the standard care of rate based feeding, from an International Nutrition Survey (2014/15) to the new intervention of volume based feeding, in a mixed medical and surgical intensive care unit. The primary outcomes were the proportion of energy and protein daily targets delivered. Secondary outcomes compared the effects on gastrointestinal tolerance, glycaemic control, mortality, mechanical ventilation days, length of stay in intensive care unit and hospital. Results From a total of 82 patients (rate based feeding = 27, volume based feeding = 55), volume based feeding patients received significantly more prescribed energy (52% versus 81%; p < 0.001) and protein (40% versus 74%; p < 0.001). There was no significant difference in gastrointestinal symptoms such as gastric residual volumes (p = 0.62), glycaemic control (p = 0.94) or insulin usage (p = 0.75). Although there was an improvement in energy and protein delivery, there were no differences in mechanical ventilation days (p = 0.12), mortality (p = 0.06), length of stay in intensive care unit (p = 0.93) and hospital (p = 0.72) between the groups. Conclusion Compared to rate based feeding, volume based feeding significantly improved energy and protein provision with no adverse effects on glycaemic control or gastrointestinal tolerance, clinical outcomes were not affected.
Teacher-student Deep Clustering for Low-delay Single Channel Speech Separation

The recently-proposed deep clustering algorithm introduced significant advances in monaural speaker-independent multi-speaker speech separation. Deep clustering operates on magnitude spectro-grams using bidirectional recurrent networks and K-means clustering, both of which require offline operation, i.e., algorithm latency is longer than utterance length. This paper evaluates architectures for reduced latency deep clustering by combining: (1) block processing to efficiently propagate the memory encoded by the recurrent network, and (2) teacher-student learning, where low-latency models learn from an offline teacher. Compared to our best performing offline model, we only lose 0.3 dB SDR at a latency of 1.2 seconds and 0.7 dB SDR at a latency of 0.6 seconds on the publicly available wsj0-2mix dataset. Moreover, by providing a detailed analysis of the failure cases for our low-latency speech separation models, we show that the cause of this performance gap is related to frame-level permutation errors, where the network fails to accurately track speaker identity throughout an utterance.
Penalized Clustering of Large-Scale Functional Data With Multiple Covariates

In this article we propose a penalized clustering method for large-scale data with multiple covariates through a functional data approach. In our proposed method, responses and covariates are linked together through nonparametric multivariate functions (fixed effects), which have great flexibility in modeling various function features, such as jump points, branching, and periodicity. Functional ANOVA is used to further decompose multivariate functions in a reproducing kernel Hilbert space and provide associated notions of main effect and interaction. Parsimonious random effects are used to capture various correlation structures. The mixed-effects models are nested under a general mixture model in which the heterogeneity of functional data is characterized. We propose a penalized Henderson's likelihood approach for model fitting and design a rejection-controlled EM algorithm for the estimation. Our method selects smoothing parameters through generalized cross-validation. Furthermore, Bayesian confidence intervals are used to measure the clustering uncertainty. Simulation studies and real-data examples are presented to investigate the empirical performance of the proposed method. Open-source code is available in the R package MFDA.
Privacy Preserving Collaborative Filtering with k-Anonymity through Microaggregation

Collaborative Filtering (CF) is a recommender system which is becoming increasingly relevant for the industry. Current research focuses on Privacy Preserving Collaborative Filtering (PPCF), whose aim is to solve the privacy issues raised by the systematic collection of private information. In this paper, we propose a new micro aggregation-based PPCF method that distorts data to provide k-anonymity, whilst simultaneously making accurate recommendations. Experimental results demonstrate that the proposed method perturbs data more efficiently than the well-known and widely used distortion method based on Gaussian noise addition.
Performance of visually guided tasks using simulated prosthetic vision and saliency-based cues

Objective. The objective of this paper is to evaluate the benefits provided by a saliency-based cueing algorithm to normally sighted volunteers performing mobility and search tasks using simulated prosthetic vision. Approach. Human subjects performed mobility and search tasks using simulated prosthetic vision. A saliency algorithm based on primate vision was used to detect regions of interest (ROI) in an image. Subjects were cued to look toward the directions of these ROI using visual cues superimposed on the simulated prosthetic vision. Mobility tasks required the subjects to navigate through a corridor, avoid obstacles and locate a target at the end of the course. Two search task experiments involved finding objects on a tabletop under different conditions. Subjects were required to perform tasks with and without any help from cues. Results. Head movements, time to task completion and number of errors were all significantly reduced in search tasks when subjects used the cueing algorithm. For the mobility task, head movements and number of contacts with objects were significantly reduced when subjects used cues, whereas time was significantly reduced when no cues were used. The most significant benefit from cues appears to be in search tasks and when navigating unfamiliar environments. Significance. The results from the study show that visually impaired people and retinal prosthesis implantees may benefit from computer vision algorithms that detect important objects in their environment, particularly when they are in a new environment.
Optimal H∞ controller with a novel control architecture in the HELVIS mini-HEV EDS

HELVIS (Hybrid Electric Vehicle In Low Scale) has shown to be a very efficient platform in the research of HEVs (Hybrid Electric Vehicles) at our institution, as a mean to introduce students of all degrees to the technical universe that surrounds the HEV subject. In this work, a novel approach on the design of controllers to the EDS (Electronic Differential System) problem is presented. The synthesis of a reliable optimal H∞ robust controller to the EDS of HELVIS autonomous mini-HEV using the γ-iteration algorithm is outlined. A new control architecture to fully meet the H∞ controller high ability to reject noises is also proposed. Furthermore, the proposed architecture is a result of an well defined and distributed blocks of functionalities which makes the system suitable for low scale embedded applications. Main aspects, as robustness to noises and performance at a vast range of frequencies are illustrated. At the end, experimental results are evaluated, after embedding the entire HELVIS ED system in a dSpace™ 1103 interface board.
Penetration of GSK1322322 into Epithelial Lining Fluid and Alveolar Macrophages as Determined by Bronchoalveolar Lavage

ABSTRACT GSK1322322 is a potent peptide deformylase inhibitor with in vitro and in vivo activity against multidrug-resistant skin and respiratory pathogens. This report provides plasma and intrapulmonary pharmacokinetics, safety, and tolerability of GSK1322322 after repeat (twice daily intravenous dosing for 4 days) dosing at 1,500 mg. Plasma samples were collected over the last 12-hour dosing interval of repeat dosing following the day 4 morning dose (the last dose). Bronchoalveolar lavage samples were collected once in each subject, either before or at 2 or 6 h after the last intravenous dose. Plasma area under the concentration-time curve (AUC0–τ) was 66.7 μg · h/ml, and maximum concentration of drug in serum (Cmax) was 25.4 μg/ml following repeat doses of intravenous GSK1322322. The time course of epithelial lining fluid (ELF) and alveolar macrophages (AM) mirrored the plasma concentration-time profile. The AUC0–τ for ELF and AM were 78.9 μg · h/ml and 169 μg · h/ml, respectively. The AUC0–τ ratios of ELF and AM to total plasma were 1.2 and 2.5, respectively. These ratios increased to 3.5 and 7.4, respectively, when unbound plasma was considered. These results are supportive of GSK1322322 as a potential antimicrobial agent for the treatment of lower respiratory tract bacterial infections caused by susceptible pathogens. (This study has been registered at ClinicalTrials.gov under registration number NCT01610388.)
Seasonal dynamics of waterbird assembly mechanisms revealed by patterns in phylogenetic and functional diversity in a subtropical wetland

Despite growing interest in using phylogenetic and functional methods to understand community assembly, few studies have examined how these methods can be used to assess seasonal variation in assembly mechanisms among migrant species. Migration can rapidly alter the relative influence of stochastic processes, species interactions, or environmental factors in shaping communities across seasons. Here, we describe seasonal dynamics in the phylogenetic and functional diversity of waterbirds in Mai Po Wetland, a subtropical region with significant and predictable temporal variation in climate and migratory bird density. Phylogenetic α diversity varied seasonally, exhibiting a clustered structure (indicative of environmental filtering) in summer, and over‐dispersed structure (indicative of biotic filtering) in winter. Phylogenetic diversity in spring and autumn exhibited a more intermediate, random structure, consistent with stochastic arrivals and departures of migrants. Functional diversity was clustered in spring but showed over‐dispersion in the other three seasons. Phylogenetic β diversity in summer and winter assemblages was characterized by two distinct groups, while spring and autumn assemblages were mixed. Our results suggest that waterbird assemblages were primarily shaped by interspecific competition in winter, while random processes tended to shape assemblages in spring and fall. Environmental factors played a more important role in summer, during periods of high heat stress. In addition, species co‐occurrence patterns were significantly more strongly related to phylogenetic similarity in winter than in summer. Our results suggest that the relative importance of assemblage mechanisms can vary seasonally in response to changing environmental conditions, suggesting that studies attempting to infer a single dominant assembly mechanism may ignore important assembly processes. Temporal shifts in assembly mechanisms may play an important role in maintaining diversity in subtropical and temperate wetlands and perhaps also in other dynamic systems.
Data Mining for Education Decision Support: A Review

Management of higher education must continue to evaluate on an ongoing basis in order to improve the quality of institutions. This will be able to do the necessary evaluation of various data, information, and knowledge of both internal and external institutions. They plan to use more efficiently the collected data, develop tools so that to collect and direct management information, in order to support managerial decision making. The collected data could be utilized to evaluate quality, perform analyses and diagnoses, evaluate dependability to the standards and practices of curricula and syllabi, and suggest alternatives in decision processes. Data minings to support decision making are well suited methods to provide decision support in the education environments, by generating and presenting relevant information and knowledge towards quality improvement of education processes. In educational domain, this information is very useful since it can be used as a base for investigating and enhancing the current educational standards and managements. In this paper, a review on data mining for academic decision support in education field is presented. The details of this paper will review on recent data mining in educational field and outlines future researches in educational data mining.
DifferCloudStor: Differentiated Quality of Service for Cloud Storage

For a large scale cloud cluster, providing data access which crosses through high performance computing servers, network and storage servers, and scheduling resources among multi-tenants are challenging jobs, especially with specific level of quality of service (QoS) requirements. Besides the criteria of current availability and reliability in service level agreement (SLA), throughput and latency are also critical QoS performance measurements to the tenants. Any QoS solution, unfortunately, cannot be achieved without introducing additional overhead. A good QoS design should always be cost effective. This paper proposes a cost-effective QoS infrastructure over a distributed storage system, providing work-conserving differentiated QoS for multi-tenants cloud storage. The proposed QoS infrastructure combines global service differentiation with adaptive share allocation to support differentiated QoS and achieves both service isolation among users from the same class and service differentiation among users from different classes.
Research on quaternion spline interpolation algorithm for 5-axis machining

Applications for 5-axis have increased tremendously in recent years. Machining curved surfaces using CNC software represents a challenging task. The conventional interpolation algorithms for 5-axis CNC machining are focused on studying a curved surface is approximated by a linear grid of closely spaced points, which are traversed in a linear or circular interpolation mode to machine the desired shape. However, it can not only cause machining path error, but also cause a lot of wear and tear on the machines. To overcome these problems, a research on interpolation algorithm for 5-axis tool paths generated using quaternion-quintic spherical Bézier spline for tool positioning and orientation was presented In this paper. The proposed algorithm was verified by Matlab platform. The analysis of results was also provided in this paper. This proposed algorithm is suitable for 5-axis highspeed and high-precision machining.
Singing Voice Separation: A Study on Training Data

In the recent years, singing voice separation systems showed increased performance due to the use of supervised training. The design of training datasets is known as a crucial factor in the performance of such systems. We investigate on how the characteristics of the training dataset impacts the separation performances of state-of-the-art singing voice separation algorithms. We show that the separation quality and diversity are two important and complementary assets of a good training dataset. We also provide insights on possible transforms to perform data augmentation for this task.
Communication loads on clinical staff in the emergency department

Medicare rebates are currently available for these tests. The additional cost of these new technologies should be considered in the context of the cost of diagnosis, treatment and subsequent cytological and clinical followup of biologically insignificant disease. The money saved from improved patient selection for treatment and reduced frequency and late commencement of screening would allow more resources to be allocated to enrolling women (who are currently underscreened) and to funding these new technologies.
Intrauterine environment and cognitive development in young twins

Intrauterine factors important for cognitive development, such as birth weight, chorionicity and umbilical cord characteristics were investigated. A total of 663 twin pairs completed the Wechsler Intelligence Scale for Children-Revised and scores were available for Performance, Verbal and Total Intelligence Quotient (IQ). The intrauterine factors examined were birth weight, placental weight and morphology, cord knots, cord length and cord insertion. IQ scores for the varying levels of the intrauterine markers adjusting for gender and gestational age were calculated. The heritability of IQ and the association between IQ and intrauterine environment were examined. Twins with lower birth weight and cord knots had lower IQ scores. The aetiology of IQ is largely distinct from that of birth weight and cord knots, and non-shared environment may influence the observed relationships.
Coarse-Grain MTCMOS Sleep Transistor Sizing Using Delay Budgeting

Power gating is one of the most effective techniques in reducing the standby leakage current of VLSI circuits. In this paper we introduce a new approach for sleep transistor sizing which minimizes the total sleep transistor width for a coarse-grain multi-threshold CMOS circuit assuming a given standard cell and sleep transistor placement. First, the circuit is decomposed into a set of modules, each containing the set of logic cells that are closest to a sleep transistor cell. Next given an upper bound on the overall circuit speed degradation, the global timing slack is distributed among different clusters using a delay-budgeting. The slack distribution result is then used to size the sleep transistors such that the total sleep transistor width is minimized while accounting for the parasitic resistances of the virtual ground net. Results show that the proposed sizing algorithm produces sleep transistor sizes that are 40% smaller than those produced by previous approaches.
Scheduling-Control Co-Design for WirelessHART Networks

WirelessHART is an open standard for wireless sensor-actuator networks in process monitoring and control. Recent years have seen initial success in real-world deployment of control systems over WirelessHART networks. Due to stringent bandwidth constraints in WirelessHART networks, it is important to develop a wireless transmission scheduling and control co-design approach to optimize the control performance subject to network resource constraints. This work addresses the co-design problem of determining the optimal sampling rates of multiple feedback control loops sharing a WirelessHART network. The objective is to optimize their control performance while ensuring that all end-to-end delays through the network are within their respective deadlines. We first propose a new network delay analysis that runs in pseudo-polynomial time for feedback control loops. More efficient and less pessimistic than existing analysis, the new analysis is then extended to derive a polynomial-time analysis suitable for scheduling-control codesign. We then develop a subgradiant optimization approach for the scheduling-control co-design problem for WirelessHART networks. Simulations based on both random network topologies and the real network topologies of a 78-node wireless sensor network testbed demonstrate the advantages of the new network delay analysis over a state-of-the-art analysis, and that the subgradient optimization achieves similar performance as simulated annealing, with significantly lower run time for simulated control systems over WirelessHART networks.