Miracle-dz/UniFAR-FTUs huggingface dataset:
Predictability of serum vancomycin concentrations using the kinetic estimated glomerular filtration rate formula for critically ill patients .

OBJECTIVE To evaluate the predictability of serum vancomycin concentrations of critically ill patients using the new kinetic estimated glomerular filtration rate (KeGFR) and other three established eGFR formulae. MATERIALS AND METHODS We calculated serum vancomycin concentrations using software provided by the manufacturer of vancomycin. RESULTS Data were collected from 122 hospitalized adults. The mean error and mean absolute error of KeGFR were 0.81 µg/mL and 4.46 µg/mL, respectively. The root mean squared prediction error of KeGFR was the highest (6.67 µg/mL) among the four formulae. CONCLUSION The predictability of KeGFR formula did not show good accuracy in critically ill patients.
Quantum signaling to the past using P-CTCS

Closed Timelike Curves (CTCs) are intriguing relativistic objects that allow for time travel to the past and can be used as computational resources. In Deutschian Closed Timelike Curves (D-CTCs), due to the monogamy of entanglement, non-local correlations between entangled states are destroyed. In contrast, for Postselected Closed Timelike Curves (P-CTCs), a second variant of CTCs, the non-local correlations are preserved. P-CTCs can be harnessed for the signaling of non-orthogonal states to the past without a disruption of causality. In this paper, we take up signaling to the past and show a method of sending four non-orthogonal states to the past using P-CTCs. After constructing our signaling protocol, we study the causality violations that our protocol results in and put forward two consistency relations to prevent them.
Wide Range Depth Estimation from Binocular Light Field Camera

Light ﬁeld camera have been developed to capture spatial and angular information of rays. But limited by the structure of micro-lens, it acts as a short-baseline multi-view camera. Foreground objects have accurate depth map in light ﬁeld while depth information is missing in the background. To avoid such a drawback in the existing light ﬁeld depth estimation system, we propose a binocular light ﬁeld camera and introduce long-baseline stereo matching in it. The system can estimate wide range depth of scene by merging complementary depth map of far scene into depth map from light ﬁeld camera. We ﬁrstly estimate an relative depth map from light ﬁeld and stereo matching respectively, and present calibration methods that normalize both depth maps to the real depth space. Then we model depth fusion problem as Markov random ﬁeld which can be solved by graph cuts efﬁciently. Experiments show that our system have a wider depth sensing ability than either single light ﬁeld camera or traditional binocular camera.
Achieving Accompanying Beampattern Peak for High-Speed Users Via Frequency Diverse Array

In this paper, we consider how to maintain the communication quality for high-speed users in array transmission. Due to high user speed, the array transmission angle changes quickly. As a consequence, the phase shifters (beamformers) of traditional phase arrays need to be updated frequently to aim at the user, thus yielding high implementation cost. To alleviate this, we propose a novel frequency diverse array (FDA) approach, which intentionally introduces some frequency offsets across the array antennas to activate an angle-range-time dependent beampattern; i.e., the FDA beampattern peak automatically moves in space. This motivates us to carefully design FDA parameters such that the beampattern peak accompanies the quickly-moving users. To this end, we maximize the average beampattern gain along some given user trace by optimizing the frequency offsets. The block successive upper-bound minimization (BSUM) method is applied to obtain a stationary solution to this non-convex problem. Compared with phase array beamforming, the FDA approach maintains service quality for high-speed users by updating frequency offsets less frequently, thus reducing the implementation cost remarkably.
Exemplar-Supported Generative Reproduction for Class Incremental Learning

Incremental learning with deep neural networks often suffers from catastrophic forgetting, where newly learned patterns may completely erase the previous knowledge. A remedy is to review the old data (i.e. rehearsal) occasionally like humans to prevent forgetting. While recent approaches focus on storing historical data or the generator of old classes for rehearsal, we argue that they cannot fully and reliably represent old classes. In this paper, we propose a novel class incremental learning method called Exemplar-Supported Generative Reproduction (ESGR) that can better reconstruct memory of old classes and mitigate catastrophic forgetting. Speciﬁcally, we use Generative Adversarial Networks (GANs) to model the underlying distributions of old classes and select additional real exemplars as anchors to support the learned distribution. When learning from new class samples, synthesized data generated by GANs and real exemplars stored in the memory for old classes can be jointly reviewed to mitigate catastrophic forgetting. By conducting experiments on CIFAR-100 and ImageNet-Dogs, we prove that our method has superior performance against state-of-the-arts.
Attention as a Perspective for Learning Tempo-invariant Audio Queries

Current models for audio--sheet music retrieval via multimodal embedding space learning use convolutional neural networks with a fixed-size window for the input audio. Depending on the tempo of a query performance, this window captures more or less musical content, while notehead density in the score is largely tempo-independent. In this work we address this disparity with a soft attention mechanism, which allows the model to encode only those parts of an audio excerpt that are most relevant with respect to efficient query codes. Empirical results on classical piano music indicate that attention is beneficial for retrieval performance, and exhibits intuitively appealing behavior.
Playdough to Roombots: Towards a Novel Tangible User Interface for Self-reconfigurable Modular Robots

One of the main strengths of self-reconfigurable modular robots (SRMR) is their ability to shape-shift and dynamically change their morphology. In the case of our SRMR system “Roombots”, these shapes can be quite arbitrary for a great variety of tasks while the major utility is envisioned to be self-reconfigurable furniture. As such, the ideas and inspirations from users quickly need to be translated into the final Roombots shape. This involves a multitude of separate processes and - most importantly - requires an intuitive user interface. Our current approach led to the development of a tangible user interface (TUI) which involves 3D-scanning of a shape formed by modeling clay and the necessary steps to prepare the digitized model to be formed by Roombots. The system is able to generate a solution in less than two minutes for our target use as demonstrated with various examples.
An Investigation of Noise Shaping with Perceptual Weighting for Wavenet-Based Speech Generation

We propose a noise shaping method to improve the sound quality of speech signals generated by WaveNet, which is a convolutional neural network (CNN) that predicts a waveform sample sequence as a discrete symbol sequence. Speech signals generated by WaveNet often suffer from noise signals caused by the quantization error generated by representing waveform samples as discrete symbols and the prediction error of the CNN. We analyze these noise signals and show that 1) since the prediction error is much larger than the quantization error, the effect of the quantization error on the noise signals is practically negligible, and 2) noise signals tend to cause large spectral distortion in a high-frequency band. To alleviate the adverse effect of these noise signals on the generated speech signals, the proposed noise shaping method applies a perceptual weighting filter to WaveNet, making it possible to use the frequency masking properties of the human auditory system. We conducted objective and subjective evaluations to investigate the effectiveness of the proposed method and demonstrated that it significantly improved the sound quality of the generated speech signals.
Classification of Cyberbullying in Facebook Using Selenium and SVM

Cyberbullying is one of the emerging problems over the past few years especially to teenagers. Approximately 24% of teens goes online constantly, facilitated by the widespread availability of smartphones. Almost 21% of teens said the main reason they checked social media always was to make sure nobody was saying mean or bad things to them. Cyberbullying related Facebook posts were harvested by a customized web scraper tool. These harvested data were used for classification using Support Vector Machines (SVM) model. A total of 2263 data was used for training data, Facebook posts. Based on these posts, the study achieved the precision of 88% and the recall is 87%.
A Hybrid Method Combining Tab Search and Nelder-Mead Algorithms for Global Continuous Optimization Problems

A hybrid method combining tabu search (TS) and Simplex NeIder-Mead (NM) has been proposed for solving global continuous optimization problems. Similar to enhanced continuous tabu search (ECTS) method, the proposed method works in three sequential phases: diversification, semi-intensification and intensification. TS strategy just works in the diversification. NM strategy works not only in semi-intensification but also in intensification which is different from other hybrid methods where NM strategy is just applied in the third phase. In the first phase, we proposed four methods to generate neighbors. The first numerical experiment comparing the results of four methods of neighbor generation is carried out and three important conclusions are given. First, the proposed hybrid method has a good performance in solving the global continuous optimization problems. Second, the right direction of neighbors is more important than its right hierarchy for most functions. Third, the spiral method to generate neighbors has the moderate performance both in the robustness and in the calculation speed. To compare the proposed hybrid method to the best and the most recent other published hybrid methods, the second numerical experiment with spiral method to generate neighbors is carried out. The comparison shows that our hybrid method is better than other methods when the dimension of the functions is no more than 4. Moreover, although the dimension is more than 4, it is not worse than many of them.
Forward Vehicle Collision Warning Based on Quick Camera Calibration

Forward Vehicle Collision Warning (FCW) is one of the most important functions for autonomous vehicles. In this procedure, vehicle detection and distance measurement are core components, requiring accurate localization and estimation. In this paper, we propose a simple but efficient forward vehicle collision warning framework by aggregating monocular distance measurement and precise vehicle detection. In order to obtain forward vehicle distance, a quick camera calibration method which only needs three physical points to calibrate related camera parameters is utilized. As for the forward vehicle detection, a multi-scale detection algorithm that regards the result of calibration as distance priori is proposed to improve the precision. Intensive experiments are conducted in our established real scene dataset and the results have demonstrated the effectiveness of the proposed framework.
CURE: Consistent Update With Redundancy Reduction in SDN

In this paper, we address the issue of rule duplication during network updates in software-defined networking (SDN). In SDN, network update involves the controller in sending update packets to desired set of switches, where the update rules are installed. To ensure update consistency, old flow rules are stored until the total update procedure is complete. Higher consumption of ternary content addressable memories (TCAMs) during update increases the cost of network update and decreases the scalability of SDN. In this paper, we propose an approach for consistent update with redundancy reduction, named CURE, which reduces the TCAM usage during update. CURE prioritizes switches according to their usage pattern and schedules updates based on priority zones. The proposed approach guarantees that highly loaded switches are updated first. CURE also maintains packet-level consistency by implementing a multilevel queuing approach. In this framework, each switch in the current update region stores the incoming packets in individual device queues until the switch completes update. Therefore, after the initiation of an update, packets are processed according to new rules only. The results of performance evaluation depict that the average rule space utilization during update using CURE is 29.954% less than using the two-phase update proposed in the existing literature.
Modeling Speech Acts in Asynchronous Conversations: A Neural-CRF Approach

Participants in an asynchronous conversation (e.g., forum, e-mail) interact with each other at different times, performing certain communicative acts, called speech acts (e.g., question, request). In this article, we propose a hybrid approach to speech act recognition in asynchronous conversations. Our approach works in two main steps: a long short-term memory recurrent neural network (LSTM-RNN) first encodes each sentence separately into a task-specific distributed representation, and this is then used in a conditional random field (CRF) model to capture the conversational dependencies between sentences. The LSTM-RNN model uses pretrained word embeddings learned from a large conversational corpus and is trained to classify sentences into speech act types. The CRF model can consider arbitrary graph structures to model conversational dependencies in an asynchronous conversation. In addition, to mitigate the problem of limited annotated data in the asynchronous domains, we adapt the LSTM-RNN model to learn from synchronous conversations (e.g., meetings), using domain adversarial training of neural networks. Empirical evaluation shows the effectiveness of our approach over existing ones: (i) LSTM-RNNs provide better task-specific representations, (ii) conversational word embeddings benefit the LSTM-RNNs more than the off-the-shelf ones, (iii) adversarial training gives better domain-invariant representations, and (iv) the global CRF model improves over local models.
Calibration of Stewart-Gough Parallel Robot with Minimum Sensor and Position Control in Joint Space

The Stewart-Gough Platform (SGP) is defined as a 6-DOF parallel robot. It consists of two rigid plates; moving platform (MP) in top and base platform (BP) in bottom and also six actuating links that connect them to each other and provide up to six DOF for the MP with respect to the BP. SGP's impressive features are high load carrying capacity, low inertia, high stiffness, better repetition and precise positioning. This paper focuses on the accuracy enhancement of a six DOF SGP robot through kinematic calibration. In this method minimum number of sensors have been used for SGP kinematic calibration. This method is presented with details and is implemented on an experimental robot. The assumption of the methodology is that the orientation of MP can be measured by two inclinometers. Calibration involves of a nonlinear function optimization with 36 unknown parameters. Nonlinear least square scheme is used for the optimization. Experimental studies reveal that the proposed method is effective in enhancing the SGP accuracy. The main contribution of this work is implementation of the proposed method on an experimental robot and achievement acceptable results by position control of SGP in joint space. The robot position is controlled in the joint space; results show that calibrated robot follows command signal more accurate than before the calibration.
Segmenting Unknown 3D Objects from Real Depth Images using Mask R-CNN Trained on Synthetic Point Clouds

The ability to segment unknown objects in depth images has potential to enhance robot skills in grasping and object tracking. Recent computer vision research has demonstrated that Mask R-CNN can be trained to segment specific categories of objects in RGB images when massive hand labeled datasets are available. As generating these datasets is time-consuming, we instead train with synthetic depth images. Many robots now use depth sensors, and recent results suggest training on synthetic depth data can generalize well to the real world. We present a method for automated dataset generation and rapidly generate a training dataset of 50k depth images and 320k object masks synthetically using simulated scenes of 3D CAD models. We train a variant of Mask R-CNN on the generated dataset to perform category-agnostic instance segmentation without hand-labeled data. We evaluate the trained network, which we refer to as Synthetic Depth (SD) Mask R-CNN, on a set of real, high-resolution images of challenging, densely cluttered bins containing objects with highly-varied geometry. SD Mask R-CNN outperforms point cloud clustering baselines by an absolute 15% in Average Precision and 20% in Average Recall, and achieves performance levels similar to a Mask RCNN trained on a massive, hand-labeled RGB dataset and fine-tuned on real images from the experimental setup. The network also generalizes well to a lower-resolution depth sensor. We deploy the model in an instance-specific grasping pipeline to demonstrate its usefulness in a robotics application. Code, the synthetic training dataset, and supplementary material are available at https://bit.ly/2letCuE .
Information System Success and Knowledge Grid Integration in Facilitating Knowledge Sharing Among Big Data Community

Nowadays many domains interested to use big data to improve their decision making, strategic planning, and productivity while communication infrastructure supports various applications for users to share their resources, ideas or experiences. However, accessing, managing, analyzing, and using the rapidly expanding big data had raised challenges especially because of dispersed and heterogeneous nature of data. This review aimed to identify the factors to influence facilitating knowledge sharing among big data community from user satisfaction aspect and the way knowledge grid effect big data sharing by Delone and Maclean model for information system success. The research analysis and results revealed three features of knowledge grid which may influence information and system quality which defined as main factors affect knowledge sharing among the community from member's aspect. It also demonstrates seven factors to measure the facilitating knowledge sharing from community member's satisfaction view and quantity of knowledge sharing. Finally, the review established an initial conceptual model for facilitating knowledge sharing among big data which in follow up research will review by domain experts.
Bayesian Generative Model Based on Color Histogram of Oriented Phase and Histogram of Oriented Optical Flow for Rare Event Detection in Crowded Scenes

In this paper, we propose a new method for rare event detection in crowded scenes using a combination of Color Histogram of Oriented Phases (CHOP) and Histogram of Oriented Optical Flow (HOOF). We propose to detect and filter spatio-temporal interest points (STIP) based on the visual saliency information of the scene. Once salient STIPs are detected, the motion and appearance information of the surrounding scene are extracted. Finally, the extracted information from normal scenes are modelled by using a Bayesian generative model (Latent Dirichlet Allocation). The rare events are detected by processing the likelihood of the current scene in regard to the obtained model. The proposed method has been tested on the publicly available UMN dataset and compared with different state-of-the-art algorithms. We have shown that our method is very competitive and provides promising results.
Simulating Software Defined Networking Using Mininet to Optimize Host Communication in a Realistic Programmable Network

In this paper, Mininet-WiFi was used to simulate a Software Defined Network to demonstrate Mininet-WiFi's ability to be used as the Software Defined Network emulator which can also be integrated to the existing network using a Network Virtualized Function. A typical organization's computer network was simulated which consisted of a website hosted on the LAMP (Linux, Apache, MySQL, PHP) virtual machine, and an F5 application delivery controller (ADC) which provided load balancing of requests sent to the web applications. A website page request was sent from the virtual stations inside Mininet-WiFi. The request was received by the application delivery controller, which then used round robin to send the request to one of the web servers on the LAMP virtual machine. The web server then returned the requested website to the requesting virtual stations using the simulated virtual network. The significance of these results is that it presents Mininet-WiFi as an emulator, which can be integrated into a real programmable networking environment offering a portable, cost effective and easily deployable testing network, which can be run on a single computer. These results are also beneficial to modern network deployments as the live network devices can also communicate with the testing environment for the data center, cloud and mobile provides.
Cardiovascular and Self-Regulatory Consequences of SES-Based Social Identity Threat

This work examined the effects of socioeconomic status (SES)-based social identity threat on cardiovascular indexes of challenge and threat and self-regulatory strength. Participants (N = 104) took an exam described as either diagnostic of intellectual ability (identity threat) or framed as a problem-solving task (control) while we recorded cardiovascular reactivity and assessed participants’ physical self-control. Under identity threat, lower SES students exhibited impaired performance, reduced self-control, and cardiovascular threat reactivity. In contrast, higher SES students under threat exhibited the reverse pattern—a boost in performance, no change in self-regulation, and cardiovascular challenge reactivity. Furthermore, while measures of general arousal (heart rate and pre-ejection period) were unrelated to performance, cardiovascular patterns of challenge and threat were significantly associated with performance under identity threat. Results provide evidence that SES-based stigma influences physiological and self-regulatory processes.
Inspiration Learning through Preferences

Current imitation learning techniques are too restrictive because they require the agent and expert to share the same action space. However, oftentimes agents that act differently from the expert can solve the task just as good. For example, a person lifting a box can be imitated by a ceiling mounted robot or a desktop-based robotic-arm. In both cases, the end goal of lifting the box is achieved, perhaps using different strategies. We denote this setup as \textit{Inspiration Learning} - knowledge transfer between agents that operate in different action spaces. Since state-action expert demonstrations can no longer be used, Inspiration learning requires novel methods to guide the agent towards the end goal. In this work, we rely on ideas of Preferential based Reinforcement Learning (PbRL) to design Advantage Actor-Critic algorithms for solving inspiration learning tasks. Unlike classic actor-critic architectures, the critic we use consists of two parts: a) a state-value estimation as in common actor-critic algorithms and b) a single step reward function derived from an expert/agent classifier. We show that our method is capable of extending the current imitation framework to new horizons. This includes continuous-to-discrete action imitation, as well as primitive-to-macro action imitation.