Miracle-dz/UniFAR-FTUs huggingface dataset:
A Latency and Coverage Optimized Data Collection Scheme for Smart Cities Based on Vehicular Ad-Hoc Networks

Using mobile vehicles as “data mules” to collect data generated by a huge number of sensing devices that are widely spread across smart city is considered to be an economical and effective way of obtaining data about smart cities. However, currently most research focuses on the feasibility of the proposed methods instead of their final performance. In this paper, a latency and coverage optimized data collection (LCODC) scheme is proposed to collect data on smart cities through opportunistic routing. Compared with other schemes, the efficiency of data collection is improved since the data flow in LCODC scheme consists of not only vehicle to device transmission (V2D), but also vehicle to vehicle transmission (V2V). Besides, through data mining on patterns hidden in the smart city, waste and redundancy in the utilization of public resources are mitigated, leading to the easy implementation of our scheme. In detail, no extra supporting device is needed in the LCODC scheme to facilitate data transmission. A large-scale and real-world dataset on Beijing is used to evaluate the LCODC scheme. Results indicate that with very limited costs, the LCODC scheme enables the average latency to decrease from several hours to around 12 min with respect to schemes where V2V transmission is disabled while the coverage rate is able to reach over 30%.
Learning to Gather without Communication

A standard belief on emerging collective behavior is that it emerges from simple individual rules. Most of the mathematical research on such collective behavior starts from imperative individual rules, like always go to the center. But how could an (optimal) individual rule emerge during a short period within the group lifetime, especially if communication is not available. We argue that such rules can actually emerge in a group in a short span of time via collective (multi-agent) reinforcement learning, i.e learning via rewards and punishments. We consider the gathering problem: several agents (social animals, swarming robots.) must gather around a same position, which is not determined in advance. They must do so without communication on their planned decision, just by looking at the position of other agents. We present the first experimental evidence that a gathering behavior can be learned without communication in a partially observable environment. The learned behavior has the same properties as a self-stabilizing distributed algorithm, as processes can gather from any initial state (and thus tolerate any transient failure). Besides, we show that it is possible to tolerate the brutal loss of up to 90\% of agents without significant impact on the behavior.
Spectral Flow Cytometry

Interest in measuring the complete fluorescence spectra of individual cells in flow can be traced to the earliest days of flow cytometry. Recent advances in detectors, optics, and computation have made it possible to make full spectral measurements in the sub‐millisecond time frame in which flow cytometry measurements typically occur. This opens up new possibilities for applying spectroscopy to the analysis of individual cells. This unit reviews historical and contemporary approaches to spectral flow cytometry, as well as instrument design, calibration, and data analysis for spectral flow cytometry applications. Curr. Protoc. Cytom. 63:1.27. 1‐1.27. 13. © 2013 by John Wiley & Sons, Inc.
A Real-Time Robust Method to Detect BeiDou GEO/IGSO Orbital Maneuvers

The frequent maneuvering of BeiDou Geostationary Orbit (GEO) and Inclined Geosynchronous Orbit (IGSO) satellites affects the availability of real-time orbit, and decreases the accuracy and performance of positioning, navigation and time (PNT) services. BeiDou satellite maneuver information cannot be obtained by common users. BeiDou broadcast ephemeris is the only indicator of the health status of satellites, which are broadcast on an hourly basis, easily leading to ineffective observations. Sometimes, identification errors of satellite abnormity also appear in the broadcast ephemeris. This study presents a real-time robust detection method for a satellite orbital maneuver with high frequency and high reliability. By using the broadcast ephemeris and pseudo-range observations, the time discrimination factor and the satellite identification factor were defined and used for the real-time detection of start time and the pseudo-random noise code (PRN) of satellites was used for orbital maneuvers. Data from a Multi-GNSS Experiment (MGEX) was collected and analyzed. The results show that the start time and the PRN of the satellite orbital maneuver could be detected accurately in real time. In addition, abnormal start times and satellite abnormities caused by non-maneuver factors also could be detected using the proposed method. The new method not only improves the utilization of observations for users with the data effective for about 92 min, but also promotes the reliability of real-time PNT services.
Bovine Colostrum for Preterm Infants in the First Days of Life: A Randomized Controlled Pilot Trial

Objectives: Mother's own milk (MM) is the best nutrition for newborn preterm infants, but is often limited in supply just after birth. Pasteurized human donor milk (DM), and especially preterm infant formula (IF) are less optimal diets than MM. We hypothesized that it is feasible to use bovine colostrum (BC), the first milk from cows, as a supplement to MM, during the first weeks after preterm birth. Methods: In an open-label, randomized, controlled pilot safety trial, supplementation of MM with BC was compared with DM supplementation (Danish unit) or IF supplementation (Chinese unit). If MM was limited or lacking, BC, DM or IF were given according to local feeding guidelines during the first 14 days of life. Results: Forty infants were included and randomized in Denmark and in China, with gestational ages 29.9 ± 0.4 and 31.1 ± 0.2 weeks, respectively. Infants supplemented with BC received more enteral protein (P < 0.05) and tended to reach full enteral feeding earlier (China only). Eight infants fed BC showed a temporary elevation in plasma tyrosine on day 7, versus 2 infants in the DM/IF groups. There were no differences between diet groups in feeding intolerance or clinical adverse events. Conclusions: Our results indicate that it is feasible to use BC as a supplement to MM during the first weeks of life to increase enteral protein intake in preterm infants. Plasma tyrosine levels may be a good marker for excessive protein intake. A larger randomized trial is required to test the safety and possible short- and long-term clinical benefits of BC supplementation during the first weeks of life for preterm infants.
Click here Terms of use : Click here Face salient points and eyes tracking for robust drowsiness detection

Robotica / Volume 30 / Issue 05 / September 2012, pp 731 741 DOI: 10.1017/S0263574711000749, Published online: 07 September 2011 Link to this article: http://journals.cambridge. org/abstract_S0263574711000749 How to cite this article: J. Jimenez-Pinto and M. Torres-Torriti (2012). Face salient points and eyes tracking for robust drowsiness detection. Robotica, 30, pp 731-741 doi:10.1017/S0263574711000749 Request Permissions : Click here
Generalized pooling pyramid with hierarchical dictionary sparse coding for event and object recognition

Feature coding and vector pooling are essential for image recognition in bag-of-visual-words (BoW) method. Encoding the low-level feature to rich one and pooling it without any information loss are very challenging works. In this paper, generalized pooling pyramid with hierarchical dictionary sparse coding is introduced to get rich sparse codes and alleviate the information loss in the phase of pooling. It includes two modules: First, with the low-level feature, hierarchical dictionary is learned for sparse coding to generate the hierarchical sparse representation. Second, in the phase of vector pooling, we present generalized pooling pyramid by utilizing the probabilistic function to model the statistical distribution of sparse codes. In the generalized pooling pyramid, the Fisher vectors which are computed with Gaussian Mixture (GMM) in different levels, are fused to represent the images. The performance of our method outperforms state-of-the-art performance in a large number of image categorization experiments on the event dataset (UIUC-Sport dataset) and the object recognition dataset (Caltech101 dataset).
Simple parameters estimation and precise over-voltage simulation in long cable connected drives

Over-voltage at the motor side when supplied by Voltage Source Inverter (VSI) using long cables could cause winding failure and it is known to be source of electromagnetic disturbances, both radiated and conducted. In this paper, the over-voltage prediction for such drives in industrial environment has been investigated. Minor run time execution and precise simulations are the main paper targets. A novel cable model has been considered for motor over-voltage and converter over-current estimation. Two different measurement methodologies for the model parameters have been introduced too. The first methodology is accurate and could measure both the proposed parameters needed by the application and the filter parameters needed to mitigate these problems. The second is simpler and could only estimate the filters parameters. Comparison between experimental and simulation results are presented in the paper to confirm the proposed methods.
EXPERIENCE OF BIODEGRADATION FOR THE DISPOSAL OF WASTE MACHINE TOOL CUTTING FLUID

The disposal of waste cutting fluid from the machining of uranium is problematical, because the presence of oil in the cutting fluid causes difficulties for either aqueous or solid radwaste treatment. Biodegradation offers the potential to convert this material into forms amenable to disposal as low-level radioactive waste. The real bonus of biodegradation crucially depends on the degree of mineralisation achieved, because any by-product of biodegradation other than carbon dioxide will require disposal as secondary radioactive waste.
Top-Rank Enhanced Listwise Optimization for Statistical Machine Translation

Pairwise ranking methods are the basis of many widely used discriminative training approaches for structure prediction problems in natural language processing(NLP). Decomposing the problem of ranking hypotheses into pairwise comparisons enables simple and efficient solutions. However, neglecting the global ordering of the hypothesis list may hinder learning. We propose a listwise learning framework for structure prediction problems such as machine translation. Our framework directly models the entire translation list's ordering to learn parameters which may better fit the given listwise samples. Furthermore, we propose top-rank enhanced loss functions, which are more sensitive to ranking errors at higher positions. Experiments on a large-scale Chinese-English translation task show that both our listwise learning framework and top-rank enhanced listwise losses lead to significant improvements in translation quality.
Robust control of a linear actuator with nonlinear dynamic friction and unknown width input dead-zone

This investigation mainly deals with positioning operation of the electric servo system and an adaptive sliding-mode-based controller via the output feedback scheme is derived for the servo actuator in the presence of the unknown model, nonlinear input, dynamic nonlinear friction and disturbances. The designed controller based on sliding-mode approach includes a LuGre model-based friction state estimator that ensures the cancelation of the friction, and a set of model parameter estimation algorithms for the servo actuator, which are derived in the sense of Lyapunov stability theorem. In addition, in response to uncertainties and disturbances with model or not in practical applications, especially for the unknown bounded ones, the adaptive law is presented to guarantee the system's robustness. Also, the chattering problem in sliding-mode control is confronted based on the fuzzy logic. In the end, the simulation examples under the continuous trajectory commands verify the robustness and performance of the proposed control strategy.
Amplify-and-modulo for Gaussian two-way relay channel

We consider a two-way relay channel (TWRC) in which two terminals exchange messages with the help of a relay between them. The two terminals transmit messages to the relay through the Multiple Access Channel (MAC) and the relay transmits messages to the two terminals through the Broadcast Channel (BC). We assume that the MAC and the BC do not interfere with each other, and each terminal receives signals only from the relay but not the other terminal. All the nodes are assumed to be full-duplex, which means that they can transmit and receive information at the same time. A transmission scheme for the Gaussian TWRC is said to be analog-relaying if the relay does not need any codebook for encoding. The simplest analog-relaying scheme is amplify-and-forward (AF), under which the relay amplifies the received codeword and forwards the resultant codeword to the two terminals. In this paper, we propose a new analog-relaying scheme called amplify-and-modulo (AM) based on lattice operations. AM is a slight modification of AF. Under AM, the relay first amplifies the received codeword followed by reducing the power of the amplified codeword using the modulo-lattice operation, and then forwards the resultant codeword to the two terminals. After receiving the codeword transmitted by the relay, each terminal subtracts its own information before decoding. We prove an achievable rate region for AM, and obtain a necessary and sufficient condition under which AM outperforms AF. In addition, we show by graph that AM can achieve a strictly higher equal-rate than AF and another existing analog-relaying scheme together under some scenario.
Simulating the Statistics of the First Meetings Using Dynamic "Open Environments"

Algorithms and protocols for opportunistic, delay tolerant and wireless ad-hoc networks are designed and validated by simulating the people interactions induced by the nodes mobility. There are cases in which we are interested in simulating just the first interaction between a pair of nodes, for instance to assess the performance of a discovery or epidemic routing protocol. In this cases nodes rapidly extinguish their utility hence it is not convenient to simulate these scenarios using a fixed amount of nodes. Thus we present a novel simulation methodology that introduces the "open environment" where nodes enter, can interact through meeting with other nodes and then exit, keeping the focus on the environment rather than on what happen before and after the nodes stay in the interesting area. The proposed approach uses the statistical distribution extracted from the real traces to reproduce directly the human interaction pattern without going through the traditional random way point approach. Meetings are simulated by a time-varying graph that holds the state of the interactions in the environment, while adapting to the statistics of single node to its history. We show that even in a simple scenario, the epidemic infection, Markov memory-less models have been fairly far from the interaction scenarios that the method reproduces.
Survivability prediction of colon cancer patients using neural networks

We utilize deep neural networks to develop prediction models for patient survival and conditional survival of colon cancer. Our models are trained and validated on data obtained from the Surveillance, Epidemiology, and End Results Program. We provide an online outcome calculator for 1, 2, and 5 years survival periods. We experimented with multiple neural network structures and found that a network with five hidden layers produces the best results for these data. Moreover, the online outcome calculator provides conditional survival of 1, 2, and 5 years after surviving the mentioned survival periods. In this article, we report an approximate 0.87 area under the receiver operating characteristic curve measurements, higher than the 0.85 reported by Stojadinovic et al.
Improving Electrical Impedance Tomography Imaging of the Lung with Patient-specific 3D Models

Electrical Impedance Tomography (EIT) visualizes conductivity changes inside the thorax, which correlate with breathing and cardiac activity. While featuring high temporal resolution, no patient risks and bedside application, EIT has a very low spatial resolution, and its anatomical correspondence depends crucially on the choice of body model for image reconstruction. In contrast to the state of the art simpliﬁed or averaged 2D body models, we propose a workﬂow to generate patient-speciﬁc 3D models from Computed Tomography (CT) segmentations. This method acknowledges the 3D characteristics of EIT-induced currents in the body, while measurements are only performed in 2D. The workﬂow was designed in collaboration with medical experts such that its applicability in the clinical context becomes feasible. This is in contrast to most other works that only consider isolated algorithms and neglect the clinical demands and tasks. Our approach generates CT segmentations using another novel workﬂow based on interactive sketching, computes a tetrahedral multi-material mesh and creates a forward model with these results. The GREIT reconstruction algorithm is used to generate EIT images using the 3D model, while its parameters are tuned to the 3D properties of the mesh. We present results from two pigs, with three EIT datasets each, including mechanical ventilation, ventilation under the inﬂuence of lung injury, and ventilation-free regional perfusion analysis. We discovered three anatomical phenomena in the improved EIT images that could be visualized and explained using our workﬂow, while they caused some confusion in image interpretation using the state of the art techniques. These results, though not yet quantitatively measured, show the improved image quality and better anatomical signiﬁcance, and stress the importance of accurate body models for EIT application in clinical research and patient treatment.
Area Determination of Diabetic Foot Ulcer Images Using a Cascaded Two-Stage SVM-Based Classification

The standard chronic wound assessment method based on visual examination is potentially inaccurate and also represents a significant clinical workload. Hence, computer-based systems providing quantitative wound assessment may be valuable for accurately monitoring wound healing status, with the wound area the best suited for automated analysis. Here, we present a novel approach, using support vector machines (SVM) to determine the wound boundaries on foot ulcer images captured with an image capture box, which provides controlled lighting and range. After superpixel segmentation, a cascaded two-stage classifier operates as follows: in the first stage, a set of k binary SVM classifiers are trained and applied to different subsets of the entire training images dataset, and incorrectly classified instances are collected. In the second stage, another binary SVM classifier is trained on the incorrectly classified set. We extracted various color and texture descriptors from superpixels that are used as input for each stage in the classifier training. Specifically, color and bag-of-word representations of local dense scale invariant feature transformation features are descriptors for ruling out irrelevant regions, and color and wavelet-based features are descriptors for distinguishing healthy tissue from wound regions. Finally, the detected wound boundary is refined by applying the conditional random field method. We have implemented the wound classification on a Nexus 5 smartphone platform, except for training which was done offline. Results are compared with other classifiers and show that our approach provides high global performance rates (average sensitivity = 73.3%, specificity = 94.6%) and is sufficiently efficient for a smartphone-based image analysis.
The price of fog: a data-driven study on caching architectures in vehicular networks

Vehicular users are expected to consume large amounts of data, for both entertainment and navigation purposes. This will put a strain on cellular networks, which will be able to cope with such a load only if proper caching is in place; this in turn begs the question of which caching architecture is the best-suited to deal with vehicular content consumption. In this paper, we leverage a large-scale, crowd-sourced trace to (i) characterize the vehicular traffic demand, in terms of overall magnitude and content breakup; (ii) assess how different caching approaches perform against such a real-world load; (iii) study the effect of recommendation systems and local content items. We define a price-of-fog metric, expressing the additional caching capacity to deploy when moving from traditional, centralized caching architectures to a "fog computing" approach, where caches are closer to the network edge. We find that for location-specific items, such as the ones that vehicular users are most likely to request, such a price almost disappears. Vehicular networks thus make a strong case for the adoption of mobile-edge caching, as we are able to reap the benefit thereof -- including a reduction in the distance travelled by data, within the core network -- with little or none of the associated disadvantages.
OhioState at SemEval-2018 Task 7: Exploiting Data Augmentation for Relation Classification in Scientific Papers Using Piecewise Convolutional Neural Networks

We describe our system for SemEval-2018 Shared Task on Semantic Relation Extraction and Classification in Scientific Papers where we focus on the Classification task. Our simple piecewise convolution neural encoder performs decently in an end to end manner. A simple inter-task data augmentation significantly boosts the performance of the model. Our best-performing systems stood 8th out of 20 teams on the classification task on noisy data and 12th out of 28 teams on the classification task on clean data.
A Soil Management Assessment Framework (SMAF) Evaluation of Brazilian Sugarcane Expansion on Soil Quality

The Soil Management Assessment Framework (SMAF) was developed to evaluate impacts of land use and management practices on soil quality (SQ), but its suitability for Brazilian tropical soils was unknown. We hypothesized that SMAF would be sensitive enough to detect SQ changes associated with sugarcane (Saccharum officinarum L.) expansion for ethanol production. Field studies were performed at three sites across the south-central region of Brazil, aiming to quantify the impacts of a land use change sequence (i.e., native vegetation–pasture–sugarcane) on SQ. Eight soil indicators were individually scored using SMAF curves developed primarily for North American soils and integrated into an overall Soil Quality Index (SQI) and its chemical, physical, and biological sectors. The SMAF scores were correlated with two other approaches used to assess SQ changes, soil organic C (SOC) stocks and Visual Evaluation of Soil Structure (VESS) scores. Our findings showed that the SMAF was an efficient tool for assessing land use change effects on the SQ of Brazilian tropical soils. The SMAF scoring curves developed using robust algorithms allowed proper assignment of scores for the soil chemical, physical, and biological indicators assessed. The SQI scores were significantly correlated with SOC stocks and VESS scores. Long-term transition from native vegetation to extensive pasture promoted significant decreases in soil chemical, physical, and biological indicators. Overall SQI suggested that soils under native vegetation were functioning at 87% of their potential capacity, while pasture soils were functioning at 70%. Conversions of pasture to sugarcane induced slight improvements in SQ, primarily because of improved soil fertility. Sugarcane soils are functioning at 74% of their potential capacity. Based on this study, management strategies were developed to improve SQ and the sustainability of sugarcane production in Brazil.