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Paper waste in the mockups design with regular, irregular, and amorphous patterns is a critical problem in digital printing presses. Paper waste reduction directly impacts production costs, generating business and environmental benefits. This problem can be mapped to the two-dimensional irregular bin-packing problem. In this paper, an iterated local search algorithm using a novel neighborhood structure to detect overlaps between amorphous shapes is introduced. This algorithm is used to solve the paper waste problem, modeled as one 2D irregular bin-packing problem. The experimental results show that this approach works efficiently and effectively to detect and correct the overlaps between regular, irregular, and amorphous figures.

Imperfect Channel State Information at the Transmitter (CSIT) is inevitable in modern wireless communication networks, and results in severe multi-user interference in multi-antenna Broadcast Channel (BC). While the capacity of the multi-antenna (Gaussian) BC with perfect CSIT is known and achieved by Dirty Paper Coding (DPC), the capacity and the capacity-achieving strategy of the multi-antenna BC with imperfect CSIT remain unknown. Conventional approaches therefore rely on applying communication strategies designed for perfect CSIT to the imperfect CSIT setting. In this work, we break this conventional routine and make two major contributions. First, we show that linearly precoded Rate-Splitting (RS), relying on the split of messages into common and private parts and linear precoding at the transmitter, and successive interference cancellation at the receivers, can achieve larger rate regions than DPC in multi-antenna BC with partial CSIT. Second, we propose a novel achievable scheme, denoted as Dirty Paper Coded Rate-Splitting (DPCRS), that relies on RS to split the user messages into common and private parts, and DPC to encode the private parts. We show that the rate region achieved by DPCRS in Multiple-Input Single-Output (MISO) BC with partial CSIT is enlarged beyond that of conventional DPC and that of linearly precoded RS. Gaining benefits from the capability of RS to partially decode the interference and partially treat interference as noise, DPCRS is less sensitive to CSIT inaccuracies, networks loads and user deployments compared with DPC and other existing transmission strategies. The proposed DPCRS acts as a new benchmark and the best-known achievable strategy for multi-antenna BC with partial CSIT., accepted by IEEE transactions on communications for publication

Although fabrication of colorimetric paper-based analytical devices (PADs) has drawn increasing attention recently, the signal readout method is still a crucial challenge on the way to practical exploitations. We herein introduce an integration of digital image processing with a model PAD for easing and improving the signal readout procedure. The colorimetric detection mechanism of PAD relies on in-situ induced yellowish silver nanoparticles via the interaction of silver ions, poly(vinyl alcohol), and ammonia with isoniazid. The observed color value is related with the concentration of isoniazid. The proposed algorithm is based on mathematical morphology recognition, and minimizes the errors arising from manual area selection. Besides, it allows the recognition of both circle and square shapes in 96-well plate and A4 size array designs. Since this algorithm automatically provides the blank-corrected numerical matrixes and image profiles of red, green and blue channels, further investigations such as outlier classification, construction of regression/prediction models and calculation of detection limit can be easily performed. Comparison of signal readout results of the developed algorithm with ImageJ software demonstrates significant improvements in analysis speed, reproducibility, accuracy and color values. Therefore, application of the proposed algorithm is promising as a robust technique for practical applications.

At ASIACRYPT 2018, Castryck, Lange, Martindale, Panny and Renes proposed CSIDH, which is a key-exchange protocol based on isogenies between elliptic curves, and a candidate for post-quantum cryptography. However, the implementation by Castryck et al. is not constant-time. Specifically, a part of the secret key could be recovered by the side-channel attacks. Recently, Meyer, Campos, and Reith proposed a constant-time implementation of CSIDH by introducing dummy isogenies and taking secret exponents only from intervals of non-negative integers. Their non-negative intervals make the calculation cost of their implementation of CSIDH twice that of the worst case of the standard (variable-time) implementation of CSIDH. In this paper, we propose a more efficient constant-time algorithm that takes secret exponents from intervals symmetric with respect to the zero. For using these intervals, we need to keep two torsion points on an elliptic curve and calculation for these points. We implemented our algorithm by extending the implementation in C of Meyer et al. (originally from Castryck et al.). Then our implementation achieved 152.8 million clock cycles, which is about 29.03% faster than that of Meyer et al.

Background Electrocardiograms (ECGs) are routinely recorded and stored in a variety of paper or scanned image format. Current ECG recording machines record ECG on graph paper, also it provides digitized ECG signal along with automated cardiovascular diagnosis (CVD). However, such recording machines cannot analyse preserved paper ECG records as it requires input in terms of digitized signal. Therefore, it is important to extract ECG signal from these preserved paper ECG records using digitization method. There are different paper degradations that adversely affect digitization process. The purpose of this work is to perform an image enhancement and digitization of the degraded ECG images to extract continuous ECG signal. Methods In this paper, we propose entropy-based bit plane slicing (EBPS) algorithm in which pre-processing is done using dominant color detection and local bit plane slicing. Maximum entropy based adaptive bit plane selection is applied to the pre-processed image. Discontinuous ECG correction (DECGC) is then done to produce continuous ECG signal. Results The algorithm is tested on 836 different degraded paper ECG records obtained from various diagnostic centers. After analysis with 101 known ground truth ECG signals the accuracy, sensitivity, specificity and overall F-measure of ECG is 99.42%, 99.69%, 99.81% and 99.26% respectively. The RMS error and correlation between the extracted digitized signal and ground truth for 101 cases is 0.040 and 99.89% respectively. Conclusions The EBPS method is able to remove all types of degradation in paper ECG records to generate a uniform digitized signal. Instead of manual measurement and prediction from archived paper ECG records, automated prediction (using already existing cardiovascular diagnosis software) is possible with the help of extracted digitized signal obtained using proposed digitization method, which will also help retrospective cardiovascular analysis.

Intrusion Detection System (IDS) has been an effective way to achieve higher security in detecting malicious activities for the past couple of years. Anomaly detection is an intrusion detection system. Current anomaly detection is often associated with high false alarm rates and only moderate accuracy and detection rates because it’s unable to detect all types of attacks correctly. An experiment is carried out to evaluate the performance of the different machine learning algorithms using KDD-99 Cup and NSL-KDD datasets. Results show which approach has performed better in term of accuracy, detection rate with reasonable false alarm rate.

Academic research is crucial to the development of science and technology and is an important factor that affects national strength. When writing an academic research paper, a rhetorical structure is typically used to present the paper’s ideas, but this task is quite difficult for junior researchers. To solve this problem, some studies have adopted text mining to assist with the writing, but the existing methods still require human intervention to generate sentences. Recently, due to the increasing maturity of deep learning technology and the ability to address the problem of automatic text generation, progress has been made in this area. The highly complex deep learning operations can correctly generate sequences and find correlations between sequences. When a user provides a few keywords and key sentences, the proposed algorithm can generate an introduction section for the user. The results show that the generated introduction is more coherent, clearer, and more fluent than existing summarization methods. In addition, the method proposed in this study improves the accuracy compared with traditional text extraction methods. The manuscript produced by this study has been evaluated to show that the study can produce a comprehensive introduction compared with previous studies.

A generalization of the problem of writing on dirty paper is considered in which one transmitter sends a common message to multiple receivers. Each receiver experiences on its link an additive interference (in addition to the additive noise), which is known noncausally to the transmitter but not to any of the receivers. Applications range from wireless multi-antenna multicasting to robust dirty paper coding. We develop results for memoryless channels in Gaussian and binary special cases. In most cases, we observe that the availability of side information at the transmitter increases capacity relative to systems without such side information, and that the lack of side information at the receivers decreases capacity relative to systems with such side information. For the noiseless binary case, we establish the capacity when there are two receivers. When there are many receivers, we show that the transmitter side information provides a vanishingly small benefit. When the interference is large and independent across the users, we show that time sharing is optimal. For the Gaussian case we present a coding scheme and establish its optimality in the high signal-to-interference-plus-noise limit when there are two receivers. When the interference is large and independent across users we show that time-sharing is again optimal. Connections to the problem of robust dirty paper coding are also discussed., Comment: To appear, IT. Trans. on Information Theory. Modified to incorporate reviewer's comments. Also includes some additional results for the Gaussian case

Reversible computing is a version of computing in which the computational system to a degree is reversible. An essential circumstance for reversibility of a computational version is that the relation of the mapping states of transition features to their successors have to always be one-to-one. Reversible logic has emerged as an change design technique to the conventional logic, ensuing in decrease energy consumption and lesser circuit area. In this paper, a review of an efficient architecture of the reversible FIR filter structure is presented. For reaching low electricity, reversible good judgment mode of operation is implemented inside the design. Vicinity overhead is the tradeoff inside the proposed layout. From the synthesis consequences, the proposed low electricity FIR filter architecture offers power saving when as compared to the conventional layout. The vicinity overhead is for the proposed structure.

Recycling wastewater of the pulping and paper-making industry are widely considered for clean production, which heavily rely on the timely and accurate monitoring in paper-making wastewater treatment processes. A novel predicting model based on genetic-deep belief network algorithm was proposed to improve the predictive accuracy and reliability for process monitoring. Considering the deep belief networks (DBN) as a deep learning model is aiming to describe the relationship among variables in a complex process modeling, genetic algorithm (GA) was employed to reduce the input variables dimensionality, simplify the network structure and overcome the dynamic characteristic difficulties of process data in monitoring. Compared with DBN and back propagation neural network (BPNN), the GA-DBN effectively achieved a better predictive accuracy than other tests models in complex wastewater treatment processes. The value of the coefficient of determination of GA-DBN model is increased by 1.71–1.86% and 5.21–9.32%, respectively. The GA-DBN model can be structured with fewer variables or samples to describe the complex paper-making wastewater treatment process, obtaining the better model performance and predictive accuracy.

In this paper, we study a multiple access monostatic backscatter communication (BackCom) system, where one reader talks to multiple backscatter nodes (BNs). We propose to use nonorthogonal multiple access (NOMA) to improve the efficiency of BackCom system. Specifically, the reader uses the feedback signal during the training phase to separate the multiple BNs into two groups. The reader then randomly pairs the BNs from these two groups to implement NOMA. We propose to set the reflection coefficients for these two groups differently to further distinguish the reflected signal power for the paired BNs. To characterize the network performance, we derive an analytical expression for the success rate which shows the percentage of bits that can be successfully decoded in one time slot. We also present the design guideline of the reflection coefficients for the two groups to maximize the system performance. Our numerical results show that increasing the readers transmit power has little impact on the system performance when the channel condition on the transmission link is less severe. Instead, the proper choice of the reflection coefficients for the two groups of BNs can greatly enhance the BackCom system.

In recent years, ultra-broadband communication using densification of millimeter-wave (mmWave) smallcells has attracted attention owing to its ability to accommodate increasing mobile data traffic. To make full use of mmWave access, we need to deploy ultra-broadband backhauling lines such as 10Gbps Ethernet everywhere at the expense of very high installation costs. The mmWave Edge Cloud system proposed in this paper is a solution to this problem. Based on achievable user context information e.g. mobility and traffic demand, user data can be transmitted and stored in storage (prefetching) installed at the smallcell site in advance. By using this, mmWave access can bring out sufficient performance even with low-capacity backhaul. For the analysis, we have developed a new mobility model, traffic model, and prefetching algorithm. Numerical results showed that the proposed method with 1Gbps-backhaul and prefetching can achieve 95% of the ideal system rate while reduce the average access latency by a factor of 67% as compared to the case without prefetching.

With today’s eye-tracking technologies, it is possible to analyze cognitive processes such as reading. There are many studies on that topic. We have studied some of these studies that analyze eye movements recording during reading by various machine learning algorithms.

Traditional data mining algorithms are exceptional at seeing patterns in data that humans cannot, but are often confused by details that are obvious to the organic eye. Algorithms that include humans “in-the-loop” have proved beneficial for accuracy by allowing a user to provide direction in these situations, but the slowness of human interactions causes execution times to increase exponentially. Thus, we seek to formalize frameworks that include humans “over-the-loop”, giving the user an option to intervene when they deem it necessary while not having user feedback be an execution requirement. With this strategy, we hope to increase the accuracy of solutions with minimal losses in execution time. This paper describes our vision of this strategy and associated problems.

Interference pre-cancellation as in the “writing onto dirty paper” channel crucially depends on the transmitter having exact knowledge of the way in which input and channel state combine to produce the channel output. The presence of even a small amount of uncertainty in such knowledge, gravely hampers the ability of the encoder to pre-code its transmissions against the channel state. This is particularly disappointing as it implies that interference pre-coding in practical systems is effective only when the channel estimates have very high precision, a condition which is generally unattainable in wireless environments. In this paper we show that state decoding, instead of state pre-cancellation, can be approximately optimal for a channel with discrete states when only partial channel knowledge is available. More specifically, we consider a variation of the “writing onto dirty paper” channel in which a discrete-valued state sequence is multiplied by a fast fading process and derive conditions on the fading distribution for which state decoding closely approaches capacity. This channel model is a special case of the Gelf'and-Pinsker channel and our results show an instance of this problem in which state decoding is approximately optimal.

Most works covering the topic of transfer learning propose an algorithm to solve a given domain adaptation problem, then test the algorithm using real-world datasets. A test with a real-world dataset represents a single transfer learning test condition, which partially measures an algorithm's performance. Previous research has placed little emphasis on developing a comprehensive and uniform test for transfer learning algorithms. With this in mind, a test framework is proposed, comprising of distortion profiles which define a comprehensive test suite. The unique contribution of this paper is the definition of a test framework that measures a more complete profile of a transfer learning algorithm's capability, facilitating the identification of relative poor and good performance areas. As a proof of concept, the test framework is used to test a homogeneous transfer learning algorithm. The test framework will be the basis for a number of future applications.

Recently, wireless sensor networks have employed the concept of mobile agent to reduce energy consumption and obtain effective data gathering. Typically, in data gathering based on mobile agent, it is an important and essential step to find out the optimal itinerary planning for the mobile agent. However, single-agent itinerary planning suffers from two primary disadvantages: task delay and large size of mobile agent as the scale of the network is expanded. Thus, using multi-agent itinerary planning overcomes the drawbacks of single-agent itinerary planning. Despite the advantages of multi-agent itinerary planning, finding the optimal number of distributed mobile agents, source nodes grouping, and optimal itinerary of each mobile agent for simultaneous data gathering are still regarded as critical issues in wireless sensor network. Therefore, in this article, the existing algorithms that have been identified in the literature to address the above issues are reviewed. The review shows that most of the algorithms used one parameter to find the optimal number of mobile agents in multi-agent itinerary planning without utilizing other parameters. More importantly, the review showed that theses algorithms did not take into account the security of the data gathered by the mobile agent. Accordingly, we indicated the limitations of each proposed algorithm and new directions are provided for future research.

For a binary code, the function-call graph (FCG) reflects its capability, structure and intrinsic relations. In this work, we propose a FCG matching algorithm based on Hungarian algorithm which makes matching between graphs of large scale possible. Also, optimizations are proposed to improve the efficiency and accuracy of FCG matching algorithm. Finally, a series of experiments are conducted to show that FCG matching is an effective method and has huge application potentiality in software and security analysis.

Highly realistic visualization becomes crucial in numerous applications such as: medical imaging, simulation and entertainment systems. In this article, we describe the image/video capture system aimed for autostereoscopic multiview displays, as well as view interpolation and image restoration algorithms used to enhance the image quality. We evaluate the proposed algorithms on a head tracking based stereoscopic multiview display.

Elasticsearch is a web search tool in view of Lucene. Apache Lucene is a free and open-source data retrieval programming library. Versatile Search gives a conveyed, multitenant-fit full-content web search tool with a HTTP web interface and pattern free JSON archives. It is created in Java and has been released as open source under the terms of the Apache License. Elasticsearch can be utilized to pursuit a wide range of records. It gives adaptable hunt, has close continuous pursuit, and backings multitenancy. It is appropriated, which implies that records can be partitioned into shards and every shard can have zero or more duplicates. Every hub has one or more shards, and goes about as a facilitator to delegate operations to the right shard(s). Elasticsearch is like a wrapper on top of Lucene. In this paper a detailed description of how lucene’s scoring algorithm works and how elasticsearch uses it as “similarity algorithm”

The No-Free Lunch theorem (Wolpert and Macready 1997) provides an important limitation of global optimization algorithms. Improving an algorithm for one class of problem is likely to make it perform more poorly for other problems. This may explain the much larger number of popular global optimizat ion algorithms compared to local optimization algorithms. This means that when we propose a new or improved global optimization algorithm, it should be targeted towards a particular application or set of applications rather than tested against a fixed set of problems. For a paper describing such an algorithm for Structural and Multidisciplinary Optimization, it should target an application including solid or fluid mechanics models that is not solved well by existing algorithms. This has become difficult to demonstrate because of the large number of available global optimization algorithms. Problems where the objective function and constraints are differentiable are not likely to satisfy the above requirement. This is because gradient based local optimizers are very efficient in finding solutions for such problems even with large number of design variables. Unless the number of local optima is very high, multi-start execution of local search techniques is likely to yield a solution more efficiently than a global search algorithm. Problems with discrete or combinatorial design variables are more likely to satisfy the requirement. Finally, many global search algorithms are stochastic in nature. This means that to demonstrate their effectiveness, one must run them multiple times so as to establish the statistical distribution of the solution as function of the design parameters.

In this paper, we propose a downlink multiple-input single-output (MISO) transmission scheme, which is assisted by an intelligent reflecting surface (IRS) consisting of a large number of passive reflecting elements. In the literature, it has been proved that nonorthogonal multiple access (NOMA) can achieve the same performance as computationally complex dirty paper coding, where the quasi-degradation condition is satisfied, conditioned on the users’ channels fall in the quasi-degradation region. However, in a conventional communication scenario, it is difficult to guarantee the quasi-degradation, because the channels are determined by the propagation environments and cannot be reconfigured. To overcome this difficulty, we focus on an IRS-assisted MISO NOMA system, where the wireless channels can be effectively tuned. We optimize the beamforming vectors and the IRS phase shift matrix for minimizing transmission power. Furthermore, we propose an improved quasi-degradation condition by using IRS, which can ensure that NOMA achieves the capacity region with high possibility. For a comparison, we study zero-forcing beamforming (ZFBF) as well, where the beamforming vectors and the IRS phase shift matrix are also jointly optimized. Comparing NOMA with ZFBF, it is shown that, with the same IRS phase shift matrix and the improved quasi-degradation condition, NOMA always outperforms ZFBF. At the same time, we identify the condition under which ZFBF outperforms NOMA, which motivates the proposed hybrid NOMA transmission. Simulation results show that the proposed IRS-assisted MISO system outperforms the MISO case without IRS, and the hybrid NOMA transmission scheme always achieves better performance than orthogonal multiple access.

Atomic force microscopy (AFM) has been an important tool for nanoscale imaging and characterization with atomic and subatomic resolution. Theoretical investigations are getting highly important for the interpretation of AFM images. Researchers have used molecular simulation to examine the AFM imaging mechanism. With a recent flurry of researches applying machine learning to AFM, AFM images obtained from molecular simulation have also been used as training data. However, the simulation is incredibly time consuming. In this paper, we apply super-resolution methods, including compressed sensing and deep learning methods, to reconstruct simulated images and to reduce simulation time. Several molecular simulation energy maps under different conditions are presented to demonstrate the performance of reconstruction algorithms. Through the analysis of reconstructed results, we find that both presented algorithms could complete the reconstruction with good quality and greatly reduce simulation time. Moreover, the super-resolution methods can be used to speed up the generation of training data and vary simulation resolution for AFM machine learning.

The health status monitoring of special transformers is of great significance to ensure the secure operation of the distribution network and the power quality of special transformer users. Given this background, an operation health status monitoring algorithm of special transformers is proposed in this paper based on balanced iterative reducing and clustering using hierarchies (BIRCH) and Gaussian Cloud methods (GCM). The algorithm is composed of two parts, i.e., the offline and online parts. For the offline part, the operating indexes of special transformers are extracted based on historical operating data, and Gaussian clouds of normal operating conditions of the special transformers are determined by BIRCH clustering and Gaussian cloud methods. For the online part, Gaussian clouds of real-time operating conditions of special transformers are determined by BIRCH clustering and Gaussian cloud methods based on real-time operation data. Then, the monitoring results of operating health status are determined by the distance between the standard Gaussian clouds and the real-time Gaussian clouds of special transformers. Finally, case studies for actual special transformers are performed to verify the proposed method, and the results show that the proposed model can effectively identify the abnormal operation of the special transformer.

Motivation Elementary flux modes (EFMs) are a key tool for analyzing genome-scale metabolic networks, and several methods have been proposed to compute them. Among them, those based on solving linear programming (LP) problems are known to be very efficient if the main interest lies in computing large enough sets of EFMs. Results Here, we propose a new method called EFM-Ta that boosts the efficiency rate by analyzing the information provided by the LP solver. We base our method on a further study of the final tableau of the simplex method. By performing additional elementary steps and avoiding trivial solutions consisting of two cycles, we obtain many more EFMs for each LP problem posed, improving the efficiency rate of previously proposed methods by more than one order of magnitude. Availability and implementation Software is freely available at https://github.com/biogacop/Boost_LP_EFM. Contact fguil@um.es Supplementary information Supplementary data are available at Bioinformatics online.

BackgroundIn recent years, with increases in the amount of information available and the importance of information screening, increased attention has been paid to the calculation of textual semantic similarity. In the field of medicine, electronic medical records and medical research documents have become important data resources for clinical research. Medical textual semantic similarity calculation has become an urgent problem to be solved.ObjectiveThis research aims to solve 2 problems—(1) when the size of medical data sets is small, leading to insufficient learning with understanding of the models and (2) when information is lost in the process of long-distance propagation, causing the models to be unable to grasp key information.MethodsThis paper combines a text data augmentation method and a self-ensemble ALBERT model under semisupervised learning to perform clinical textual semantic similarity calculations.ResultsCompared with the methods in the 2019 National Natural Language Processing Clinical Challenges Open Health Natural Language Processing shared task Track on Clinical Semantic Textual Similarity, our method surpasses the best result by 2 percentage points and achieves a Pearson correlation coefficient of 0.92.ConclusionsWhen the size of medical data set is small, data augmentation can increase the size of the data set and improved semisupervised learning can boost the learning efficiency of the model. Additionally, self-ensemble methods improve the model performance. Our method had excellent performance and has great potential to improve related medical problems.

Background Before major emergencies occur, the government needs to prepare various emergency supplies in advance. To do this, it should consider the coordinated storage of different types of materials while ensuring that emergency materials are not missed or superfluous. Objective This paper aims to improve the dispatch and transportation efficiency of emergency materials under a model in which the government makes full use of Internet of Things technology and artificial intelligence technology. Methods The paper established a model for emergency material preparation and dispatch based on queueing theory and further established a workflow system for emergency material preparation, dispatch, and transportation based on a Petri net, resulting in a highly efficient emergency material preparation and dispatch simulation system framework. Results A decision support platform was designed to integrate all the algorithms and principles proposed. Conclusions The resulting framework can effectively coordinate the workflow of emergency material preparation and dispatch, helping to shorten the total time of emergency material preparation, dispatch, and transportation.

Background Informed estimates claim that 80% to 99% of alarms set off in hospital units are false or clinically insignificant, representing a cacophony of sounds that do not present a real danger to patients. These false alarms can lead to an alert overload that causes a health care provider to miss important events that could be harmful or even life-threatening. As health care units become more dependent on monitoring devices for patient care purposes, the alarm fatigue issue has to be addressed as a major concern for the health care team as well as to enhance patient safety. Objective The main goal of this paper was to propose a feasible solution for the alarm fatigue problem by using an automatic reasoning mechanism to decide how to notify members of the health care team. The aim was to reduce the number of notifications sent by determining whether or not to group a set of alarms that occur over a short period of time to deliver them together, without compromising patient safety. Methods This paper describes: (1) a model for supporting reasoning algorithms that decide how to notify caregivers to avoid alarm fatigue; (2) an architecture for health systems that support patient monitoring and notification capabilities; and (3) a reasoning algorithm that specifies how to notify caregivers by deciding whether to aggregate a group of alarms to avoid alarm fatigue. Results Experiments were used to demonstrate that providing a reasoning system can reduce the notifications received by the caregivers by up to 99.3% (582/586) of the total alarms generated. Our experiments were evaluated through the use of a dataset comprising patient monitoring data and vital signs recorded during 32 surgical cases where patients underwent anesthesia at the Royal Adelaide Hospital. We present the results of our algorithm by using graphs we generated using the R language, where we show whether the algorithm decided to deliver an alarm immediately or after a delay. Conclusions The experimental results strongly suggest that this reasoning algorithm is a useful strategy for avoiding alarm fatigue. Although we evaluated our algorithm in an experimental environment, we tried to reproduce the context of a clinical environment by using real-world patient data. Our future work is to reproduce the evaluation study based on more realistic clinical conditions by increasing the number of patients, monitoring parameters, and types of alarm.

Predicting protein structure from the amino acid sequence has been a challenge with theoretical and practical significance in biophysics. Despite the recent progresses elicited by improved inter‐residue contact prediction, contact‐based structure prediction has gradually reached the performance ceiling. New methods have been proposed to predict the inter‐residue distance, but unanimously by simplifying the real‐valued distance prediction into a multiclass classification problem. Here, a lightweight regression‐based distance prediction method is shown, which adopts the generative adversarial network to capture the delicate geometric relationship between residue pairs and thus could predict the continuous, real‐valued inter‐residue distance rapidly and satisfactorily. The predicted residue distance map allows quick structure modeling by the CNS suite, and the constructed models approach the same level of quality as the other state‐of‐the‐art protein structure prediction methods when tested on CASP13 targets. Moreover, this method can be used directly for the structure prediction of membrane proteins without transfer learning., A lightweight GAN‐based protein inter‐residue distance prediction method is reported. This method is capable of capturing the delicate geometric relationship between residue pairs and thus could predict the continuous, real‐valued distance rapidly and satisfactorily. Pipelined with structure modeling software like the CNS suite, this method could construct protein structure models with the state‐of‐the‐art quality.

The chemi-thermo-mechanical pulping (CTMP) is an important process that produces fibers for paper-making, whose output fiber length distribution (FLD) directly determines the energy consumption and product quality of the subsequent papermaking production. Therefore, study on modeling and control of the output FLD is essential for improving pulp quality and saving energy in refining process. However, the output FLD of refining process has non-Gaussian stochastic distribution property, making it difficult to use conventional methods to establish the output FLD model effectively. Under the framework of stochastic distribution control theory, this paper presents a novel modeling approach for output probability density function (PDF) of fiber length in CTMP by combining with the improved wavelet neural network (WNN). In this context, the square root B-spline approximation principle is firstly adopted to extract the B-spline weights of fiber length PDF shape as the target outputs for the WNN based B-spline weights model. Secondly, a novel two-stage hybrid learning algorithm is proposed to establish the parameters system for the WNN based weighs model by defining a multi-objective evaluation index for modeling accuracy in advance. This learning algorithm integrates multi-objective NSGA II algorithm in the first stage for better initial solutions at a global scope, and gradient descent method is employed in the second stage for accurate solutions of WNN model parameters inside a local range. As a result, the final output PDF of fiber length is reconstructed by the estimated B-spline weights using the B-spline approximation principle again. Experiments using actual industrial data have demonstrated the superiority and practicability of the proposed method.

Background Patient data is considered particularly sensitive personal data. Privacy regulations strictly govern the use of patient data and restrict their exchange. However, medical research can benefit from multicentric studies in which patient data from different institutions are pooled and evaluated together. Thus, the goals of data utilization and data protection are in conflict. Secure multiparty computation (SMPC) solves this conflict because it allows direct computation on distributed proprietary data—held by different data owners—in a secure way without exchanging private data. Objective The objective of this work was to provide a proof-of-principle of secure and privacy-preserving multicentric computation by SMPC with real-patient data over the free internet. A privacy-preserving log-rank test for the Kaplan-Meier estimator was implemented and tested in both an experimental setting and a real-world setting between two university hospitals. Methods The domain of survival analysis is particularly relevant in clinical research. For the Kaplan-Meier estimator, we provided a secure version of the log-rank test. It was based on the SMPC realization SPDZ and implemented via the FRESCO framework in Java. The complexity of the algorithm was explored both for synthetic data and for real-patient data in a proof-of-principle over the internet between two clinical institutions located in Munich and Berlin, Germany. Results We obtained a functional realization of an SMPC-based log-rank evaluation. This implementation was assessed with respect to performance and scaling behavior. We showed that network latency strongly influences execution time of our solution. Furthermore, we identified a lower bound of 2 Mbit/s for the transmission rate that has to be fulfilled for unimpeded communication. In contrast, performance of the participating parties have comparatively low influence on execution speed, since the peer-side processing is parallelized and the computational time only constitutes 30% to 50% even with optimal network settings. In the real-world setting, our computation between three parties over the internet, processing 100 items each, took approximately 20 minutes. Conclusions We showed that SMPC is applicable in the medical domain. A secure version of commonly used evaluation methods for clinical studies is possible with current implementations of SMPC. Furthermore, we infer that its application is practically feasible in terms of execution time.

The incorporation of prior knowledge about the structures of biological macromolecules into the reconstruction process of cryo-EM structure determination is proposed. Using a novel algorithm inspired by regularization by denoising, it is shown how convolutional neural networks can be used within this framework to improve reconstructions from simulated data., Three-dimensional reconstruction of the electron-scattering potential of biological macromolecules from electron cryo-microscopy (cryo-EM) projection images is an ill-posed problem. The most popular cryo-EM software solutions to date rely on a regularization approach that is based on the prior assumption that the scattering potential varies smoothly over three-dimensional space. Although this approach has been hugely successful in recent years, the amount of prior knowledge that it exploits compares unfavorably with the knowledge about biological structures that has been accumulated over decades of research in structural biology. Here, a regularization framework for cryo-EM structure determination is presented that exploits prior knowledge about biological structures through a convolutional neural network that is trained on known macromolecular structures. This neural network is inserted into the iterative cryo-EM structure-determination process through an approach that is inspired by regularization by denoising. It is shown that the new regularization approach yields better reconstructions than the current state of the art for simulated data, and options to extend this work for application to experimental cryo-EM data are discussed.

Background As a common mental disease, depression seriously affects people’s physical and mental health. According to the statistics of the World Health Organization, depression is one of the main reasons for suicide and self-harm events in the world. Therefore, strengthening depression detection can effectively reduce the occurrence of suicide or self-harm events so as to save more people and families. With the development of computer technology, some researchers are trying to apply natural language processing techniques to detect people who are depressed automatically. Many existing feature engineering methods for depression detection are based on emotional characteristics, but these methods do not consider high-level emotional semantic information. The current deep learning methods for depression detection cannot accurately extract effective emotional semantic information. Objective In this paper, we propose an emotion-based attention network, including a semantic understanding network and an emotion understanding network, which can capture the high-level emotional semantic information effectively to improve the depression detection task. Methods The semantic understanding network module is used to capture the contextual semantic information. The emotion understanding network module is used to capture the emotional semantic information. There are two units in the emotion understanding network module, including a positive emotion understanding unit and a negative emotion understanding unit, which are used to capture the positive emotional information and the negative emotional information, respectively. We further proposed a dynamic fusion strategy in the emotion understanding network module to fuse the positive emotional information and the negative emotional information. Results We evaluated our method on the Reddit data set. The experimental results showed that the proposed emotion-based attention network model achieved an accuracy, precision, recall, and F-measure of 91.30%, 91.91%, 96.15%, and 93.98%, respectively, which are comparable results compared with state-of-the-art methods. Conclusions The experimental results showed that our model is competitive with the state-of-the-art models. The semantic understanding network module, the emotion understanding network module, and the dynamic fusion strategy are effective modules for depression detection. In addition, the experimental results verified that the emotional semantic information was effective in depression detection.

Background Financial codes are often used to extract diagnoses from electronic health records. This approach is prone to false positives. Alternatively, queries are constructed, but these are highly center and language specific. A tantalizing alternative is the automatic identification of patients by employing machine learning on format-free text entries. Objective The aim of this study was to develop an easily implementable workflow that builds a machine learning algorithm capable of accurately identifying patients with rheumatoid arthritis from format-free text fields in electronic health records. Methods Two electronic health record data sets were employed: Leiden (n=3000) and Erlangen (n=4771). Using a portion of the Leiden data (n=2000), we compared 6 different machine learning methods and a naïve word-matching algorithm using 10-fold cross-validation. Performances were compared using the area under the receiver operating characteristic curve (AUROC) and the area under the precision recall curve (AUPRC), and F1 score was used as the primary criterion for selecting the best method to build a classifying algorithm. We selected the optimal threshold of positive predictive value for case identification based on the output of the best method in the training data. This validation workflow was subsequently applied to a portion of the Erlangen data (n=4293). For testing, the best performing methods were applied to remaining data (Leiden n=1000; Erlangen n=478) for an unbiased evaluation. Results For the Leiden data set, the word-matching algorithm demonstrated mixed performance (AUROC 0.90; AUPRC 0.33; F1 score 0.55), and 4 methods significantly outperformed word-matching, with support vector machines performing best (AUROC 0.98; AUPRC 0.88; F1 score 0.83). Applying this support vector machine classifier to the test data resulted in a similarly high performance (F1 score 0.81; positive predictive value [PPV] 0.94), and with this method, we could identify 2873 patients with rheumatoid arthritis in less than 7 seconds out of the complete collection of 23,300 patients in the Leiden electronic health record system. For the Erlangen data set, gradient boosting performed best (AUROC 0.94; AUPRC 0.85; F1 score 0.82) in the training set, and applied to the test data, resulted once again in good results (F1 score 0.67; PPV 0.97). Conclusions We demonstrate that machine learning methods can extract the records of patients with rheumatoid arthritis from electronic health record data with high precision, allowing research on very large populations for limited costs. Our approach is language and center independent and could be applied to any type of diagnosis. We have developed our pipeline into a universally applicable and easy-to-implement workflow to equip centers with their own high-performing algorithm. This allows the creation of observational studies of unprecedented size covering different countries for low cost from already available data in electronic health record systems.

Background COVID-19 is a global pandemic that is affecting more than 200 countries worldwide. Efficient diagnosis and treatment are crucial to combat the disease. Computer-interpretable guidelines (CIGs) can aid the broad global adoption of evidence-based diagnosis and treatment knowledge. However, currently, no internationally shareable CIG exists. Objective The aim of this study was to establish a rapid CIG development and dissemination approach and apply it to develop a shareable CIG for COVID-19. Methods A 6-step rapid CIG development and dissemination approach was designed and applied. Processes, roles, and deliverable artifacts were specified in this approach to eliminate ambiguities during development of the CIG. The Guideline Definition Language (GDL) was used to capture the clinical rules. A CIG for COVID-19 was developed by translating, interpreting, annotating, extracting, and formalizing the Chinese COVID-19 diagnosis and treatment guideline. A prototype application was implemented to validate the CIG. Results We used 27 archetypes for the COVID-19 guideline. We developed 18 GDL rules to cover the diagnosis and treatment suggestion algorithms in the narrative guideline. The CIG was further translated to object data model and Drools rules to facilitate its use by people who do not employ the non-openEHR archetype. The prototype application validated the correctness of the CIG with a public data set. Both the GDL rules and Drools rules have been disseminated on GitHub. Conclusions Our rapid CIG development and dissemination approach accelerated the pace of COVID-19 CIG development. A validated COVID-19 CIG is now available to the public.

A new method is presented for healing X-ray scattering images, allowing gaps in images to be filled and extending the image beyond the borders of the original detector exposure. This method exploits symmetry analysis to reconstruct the sample structure and thereby fill-in unmeasured regions of reciprocal space based on this automatically determined symmetry model., X-ray scattering images contain numerous gaps and defects arising from detector limitations and experimental configuration. We present a method to heal X-ray scattering images, filling gaps in the data and removing defects in a physically meaningful manner. Unlike generic inpainting methods, this method is closely tuned to the expected structure of reciprocal-space data. In particular, we exploit statistical tests and symmetry analysis to identify the structure of an image; we then copy, average and interpolate measured data into gaps in a way that respects the identified structure and symmetry. Importantly, the underlying analysis methods provide useful characterization of structures present in the image, including the identification of diffuse versus sharp features, anisotropy and symmetry. The presented method leverages known characteristics of reciprocal space, enabling physically reasonable reconstruction even with large image gaps. The method will correspondingly fail for images that violate these underlying assumptions. The method assumes point symmetry and is thus applicable to small-angle X-ray scattering (SAXS) data, but only to a subset of wide-angle data. Our method succeeds in filling gaps and healing defects in experimental images, including extending data beyond the original detector borders.

The capacity of frequency-flat multiuser MIMO downlinks is achieved with nonlinear dirty paper coding (DPC). Linear processing techniques, such as block diagonalization (BD), offer relatively low computational cost with respect to DPC and can also achieve capacity in certain cases. However, BD is not easily translated to frequency-selective channels since the channel matrix has a space-time structure (unlike the space-only information in frequency-flat channels), which induces an additional inter-symbol interference (ISI) component in the received signal. In this paper, we demonstrate that BD is feasible in frequency- selective channels when the transmitted block length is sufficiently large and the number of transmit antennas is greater than or equal to the number of users. We also present three novel techniques based on BD for frequency-selective MU- MIMO systems: time-reversal-based BD (TRBD), equalized BD (EBD), and joint processing BD (JPBD). These techniques use perfect channel state information at the transmitter (and also at the receiver, in the case of JPBD) to mitigate or eliminate both ISI and inter-user interference. We demonstrate that TRBD and EBD yield zero multiplexing gain (high SNR regime) but perform well under low SNR, while JPBD has a multiplexing gain approximately equal to the number of users for a sufficiently large transmit block length. Extensive numerical simulations show that the achievable rate performance of the proposed techniques improves that of time-reversal beamforming, a common technique for frequency- selective MU-MIMO downlinks.

This paper studies the downlink of a wireless system with an M-antenna base station, K single-antenna legitimate users and a single-antenna eavesdropper. A limited feedback-based scheduling and precoding scenario is considered that employs the orthogonal random beamforming (ORBF). The sum-rate of ORBF exhibits an identical growth rate as that of dirty paper coding for a large number of users. Unfortunately, for more realistic (low to moderate) K values, ORBF yields degraded performance. It is shown that having as many active beams as the number of transmit antennas is not always optimal for sparse network, thus scheduling strategies for ORBF with beam selection is investigated. Several user-beam selection algorithms featuring different complexity levels are proposed and their performance in terms of secrecy sum-rate is assessed. Numerical results are provided to illustrate the impact of the number of active beams on the secrecy performance.

Background The COVID-19 epidemic is still spreading globally. Contact tracing is a vital strategy in epidemic emergency management; however, traditional contact tracing faces many limitations in practice. The application of digital technology provides an opportunity for local governments to trace the contacts of individuals with COVID-19 more comprehensively, efficiently, and precisely. Objective Our research aimed to provide new solutions to overcome the limitations of traditional contact tracing by introducing the organizational process, technical process, and main achievements of digital contact tracing in Hainan Province. Methods A graph database algorithm, which can efficiently process complex relational networks, was applied in Hainan Province; this algorithm relies on a governmental big data platform to analyze multisource COVID-19 epidemic data and build networks of relationships among high-risk infected individuals, the general population, vehicles, and public places to identify and trace contacts. We summarized the organizational and technical process of digital contact tracing in Hainan Province based on interviews and data analyses. Results An integrated emergency management command system and a multi-agency coordination mechanism were formed during the emergency management of the COVID-19 epidemic in Hainan Province. The collection, storage, analysis, and application of multisource epidemic data were realized based on the government’s big data platform using a centralized model. The graph database algorithm is compatible with this platform and can analyze multisource and heterogeneous big data related to the epidemic. These practices were used to quickly and accurately identify and trace 10,871 contacts among hundreds of thousands of epidemic data records; 378 closest contacts and a number of public places with high risk of infection were identified. A confirmed patient was found after quarantine measures were implemented by all contacts. Conclusions During the emergency management of the COVID-19 epidemic, Hainan Province used a graph database algorithm to trace contacts in a centralized model, which can identify infected individuals and high-risk public places more quickly and accurately. This practice can provide support to government agencies to implement precise, agile, and evidence-based emergency management measures and improve the responsiveness of the public health emergency response system. Strengthening data security, improving tracing accuracy, enabling intelligent data collection, and improving data-sharing mechanisms and technologies are directions for optimizing digital contact tracing.

Introduction: Intelligent Diagnostic Assistant can be used for complicated diagnosis of skin diseases, which are among the most common causes of disability. The aim of this study was to design and implement a computerized intelligent diagnostic assistant for complicated skin diseases through C5’s Algorithm. Method: An applied-developmental study was done in 2015. Knowledge base was developed based on interviews with dermatologists through questionnaires and checklists. Knowledge representation was obtained from the train data in the database using Excel Microsoft Office. Clementine Software and C5’s Algorithms were applied to draw the decision tree. Analysis of test accuracy was performed based on rules extracted using inference chains. The rules extracted from the decision tree were entered into the CLIPS programming environment and the intelligent diagnostic assistant was designed then. Results: The rules were defined using forward chaining inference technique and were entered into Clips programming environment as RULE. The accuracy and error rates obtained in the training phase from the decision tree were 99.56% and 0.44%, respectively. The accuracy of the decision tree was 98% and the error was 2% in the test phase. Conclusion: Intelligent diagnostic assistant can be used as a reliable system with high accuracy, sensitivity, specificity, and agreement.

Methods for efficient data extraction for neutron spin-echo spectroscopy with multipixel area detectors and – at a pulsed neutron source – multiple time-of-flight wavelength bins are presented., Neutron spin-echo spectrometers with a position-sensitive detector and operating with extended time-of-flight-tagged wavelength frames are able to collect a comprehensive set of data covering a large range of wavevector and Fourier time space with only a few instrumental settings in a quasi-continuous way. Extracting all the information contained in the raw data and mapping them to a suitable physical space in the most efficient way is a challenge. This article reports algorithms employed in dedicated software, DrSpine (data reduction for spin echo), that achieves this goal and yields reliable representations of the intermediate scattering function S(Q, t) independent of the selected ‘binning’.

Background Chest computed tomography (CT) is crucial for the detection of lung cancer, and many automated CT evaluation methods have been proposed. Due to the divergent software dependencies of the reported approaches, the developed methods are rarely compared or reproduced. Objective The goal of the research was to generate reproducible machine learning modules for lung cancer detection and compare the approaches and performances of the award-winning algorithms developed in the Kaggle Data Science Bowl. Methods We obtained the source codes of all award-winning solutions of the Kaggle Data Science Bowl Challenge, where participants developed automated CT evaluation methods to detect lung cancer (training set n=1397, public test set n=198, final test set n=506). The performance of the algorithms was evaluated by the log-loss function, and the Spearman correlation coefficient of the performance in the public and final test sets was computed. Results Most solutions implemented distinct image preprocessing, segmentation, and classification modules. Variants of U-Net, VGGNet, and residual net were commonly used in nodule segmentation, and transfer learning was used in most of the classification algorithms. Substantial performance variations in the public and final test sets were observed (Spearman correlation coefficient = .39 among the top 10 teams). To ensure the reproducibility of results, we generated a Docker container for each of the top solutions. Conclusions We compared the award-winning algorithms for lung cancer detection and generated reproducible Docker images for the top solutions. Although convolutional neural networks achieved decent accuracy, there is plenty of room for improvement regarding model generalizability.

Computational fluid dynamics (CFD) is a promising tool to aid in clinical diagnoses of cardiovascular diseases. However, it uses assumptions that simplify the complexities of the real cardiovascular flow. Due to high-stakes in the clinical setting, it is critical to calculate the effect of these assumptions in the CFD simulation results. However, existing CFD validation approaches do not quantify error in the simulation results due to the CFD solver’s modeling assumptions. Instead, they directly compare CFD simulation results against validation data. Thus, to quantify the accuracy of a CFD solver, we developed a validation methodology that calculates the CFD model error (arising from modeling assumptions). Our methodology identifies independent error sources in CFD and validation experiments, and calculates the model error by parsing out other sources of error inherent in simulation and experiments. To demonstrate the method, we simulated the flow field of a patient-specific intracranial aneurysm (IA) in the commercial CFD software star-ccm+. Particle image velocimetry (PIV) provided validation datasets for the flow field on two orthogonal planes. The average model error in the star-ccm+ solver was 5.63 ± 5.49% along the intersecting validation line of the orthogonal planes. Furthermore, we demonstrated that our validation method is superior to existing validation approaches by applying three representative existing validation techniques to our CFD and experimental dataset, and comparing the validation results. Our validation methodology offers a streamlined workflow to extract the “true” accuracy of a CFD solver.

Background: Social media offers promise for communicating the risks and health effects of harmful products and behaviors to larger and hard-to-reach segments of the population. Nearly 70% of US adults use some social media. However, rigorous research across different social media is vital to establish successful evidence-based health communication strategies that meet the requirements of the evolving digital landscape and the needs of diverse populations. Objective: The aim of this study was to expand and test a software tool (Trial Promoter) to support health promotion and education research by automating aspects of the generation, distribution, and assessment of large numbers of social media health messages and user comments. Methods: The tool supports 6 functions (1) data import, (2) message generation deploying randomization techniques, (3) message distribution, (4) import and analysis of message comments, (5) collection and display of message performance data, and (6) reporting based on a predetermined data dictionary. The tool was built using 3 open-source software products: PostgreSQL, Ruby on Rails, and Semantic UI. To test the tool’s utility and reliability, we developed parameterized message templates (N=102) based upon 2 government-sponsored health education campaigns, extracted images from these campaigns and a free stock photo platform (N=315), and topic-related hashtags (N=4) from Twitter. We conducted a functional correctness analysis of the generated social media messages to assess the algorithm’s ability to produce the expected output for each input. We defined 100% correctness as use of the message template text and substitution of 3 message parameters (ie, image, hashtag, and destination URL) without any error. The percent correct was calculated to determine the probability with which the tool generates accurate messages. Results: The tool generated, distributed, and assessed 1275 social media health messages over 85 days (April 19 to July 12, 2017). It correctly used the message template text and substituted the message parameters 100% (1275/1275) of the time as verified by human reviewers and a custom algorithm using text search and attribute-matching techniques. Conclusions: A software tool can effectively support the generation, distribution, and assessment of hundreds of health promotion messages and user comments across different social media with the highest degree of functional correctness and minimal human interaction. The tool has the potential to support social media–enabled health promotion research and practice: first, by enabling the assessment of large numbers of messages to develop evidence-based health communication, and second, by providing public health organizations with a tool to increase their output of health education messages and manage user comments. We call on readers to use and develop the tool and to contribute to evidence-based communication methods in the digital age.

Background: An increasing number of doctor reviews are being generated by patients on the internet. These reviews address a diverse set of topics (features), including wait time, office staff, doctor’s skills, and bedside manners. Most previous work on automatic analysis of Web-based customer reviews assumes that (1) product features are described unambiguously by a small number of keywords, for example, battery for phones and (2) the opinion for each feature has a positive or negative sentiment. However, in the domain of doctor reviews, this setting is too restrictive: a feature such as visit duration for doctor reviews may be expressed in many ways and does not necessarily have a positive or negative sentiment. Objective: This study aimed to adapt existing and propose novel text classification methods on the domain of doctor reviews. These methods are evaluated on their accuracy to classify a diverse set of doctor review features. Methods: We first manually examined a large number of reviews to extract a set of features that are frequently mentioned in the reviews. Then we proposed a new algorithm that goes beyond bag-of-words or deep learning classification techniques by leveraging natural language processing (NLP) tools. Specifically, our algorithm automatically extracts dependency tree patterns and uses them to classify review sentences. Results: We evaluated several state-of-the-art text classification algorithms as well as our dependency tree–based classifier algorithm on a real-world doctor review dataset. We showed that methods using deep learning or NLP techniques tend to outperform traditional bag-of-words methods. In our experiments, the 2 best methods used NLP techniques; on average, our proposed classifier performed 2.19% better than an existing NLP-based method, but many of its predictions of specific opinions were incorrect. Conclusions: We conclude that it is feasible to classify doctor reviews. Automatically classifying these reviews would allow patients to easily search for doctors based on their personal preference criteria.

Date du colloque: 01/2009; International audience; This paper provides an overview of the application of Bayesian inference to accelerated life testing (ALT) models for the concrete case of estimation by Maximum of Aposteriori (MAP) method in the case of constant stress levels. It studies the Bayesian inference over the accelerated life model as presented in [1]. It suites, integrates and generalizes the particular cases presented in [2] and [3]. Towards the end, weighting of the prior information according to data is integrated. The paper also illustrates an experimental example.

Motivation: The prediction of biologically active compounds is of great importance for high-throughput screening (HTS) approaches in drug discovery and chemical genomics. Many computational methods in this area focus on measuring the structural similarities between chemical structures. However, traditional similarity measures are often too rigid or consider only global similarities between structures. The maximum common substructure (MCS) approach provides a more promising and flexible alternative for predicting bioactive compounds. Results: In this article, a new backtracking algorithm for MCS is proposed and compared to global similarity measurements. Our algorithm provides high flexibility in the matching process, and it is very efficient in identifying local structural similarities. To predict and cluster biologically active compounds more efficiently, the concept of basis compounds is proposed that enables researchers to easily combine the MCS-based and traditional similarity measures with modern machine learning techniques. Support vector machines (SVMs) are used to test how the MCS-based similarity measure and the basis compound vectorization method perform on two empirically tested datasets. The test results show that MCS complements the well-known atom pair descriptor-based similarity measure. By combining these two measures, our SVM-based model predicts the biological activities of chemical compounds with higher specificity and sensitivity. Contact: ycao@cs.ucr.edu Supplementary information: Supplementary data are available at Bioinformatics online.

We consider the Broadcast Channel (BC), or in other words the multi-user (MU) downlink in a cell with a base station equipped with multiple antennas and mobile terminals equipped with a single antenna (MU-MISO case). It is well-known that user selection not only leads to multi-user diversity but also to decreased suboptimality of simple beamforming (BF) techniques compared to optimal Dirty Paper Coding (DPC) approaches. User selection by exhaustive search can be simplified to greedy approaches, in which one user gets added at a time. We review DPC-style and BF-style greedy user selection (GUS), which are developed with DPC or BF transmitters in mind. We introduce a new interpretation of the BF-style user selection criterion, which corresponds to a new approximate criterion that becomes more accurate for a large user pool. This new criterion allows interpretation and comparison of the respective roles of channel strength and orthogonality in DPC-style and BF-style selection.

Background: Adequate health literacy is important for people to maintain good health and manage diseases and injuries. Educational text, either retrieved from the Internet or provided by a doctor’s office, is a popular method to communicate health-related information. Unfortunately, it is difficult to write text that is easy to understand, and existing approaches, mostly the application of readability formulas, have not convincingly been shown to reduce the difficulty of text. Objective: To develop an evidence-based writer support tool to improve perceived and actual text difficulty. To this end, we are developing and testing algorithms that automatically identify difficult sections in text and provide appropriate, easier alternatives; algorithms that effectively reduce text difficulty will be included in the support tool. This work describes the user evaluation with an independent writer of an automated simplification algorithm using term familiarity. Methods: Term familiarity indicates how easy words are for readers and is estimated using term frequencies in the Google Web Corpus. Unfamiliar words are algorithmically identified and tagged for potential replacement. Easier alternatives consisting of synonyms, hypernyms, definitions, and semantic types are extracted from WordNet, the Unified Medical Language System (UMLS), and Wiktionary and ranked for a writer to choose from to simplify the text. We conducted a controlled user study with a representative writer who used our simplification algorithm to simplify texts. We tested the impact with representative consumers. The key independent variable of our study is lexical simplification, and we measured its effect on both perceived and actual text difficulty. Participants were recruited from Amazon’s Mechanical Turk website. Perceived difficulty was measured with 1 metric, a 5-point Likert scale. Actual difficulty was measured with 3 metrics: 5 multiple-choice questions alongside each text to measure understanding, 7 multiple-choice questions without the text for learning, and 2 free recall questions for information retention. Results: Ninety-nine participants completed the study. We found strong beneficial effects on both perceived and actual difficulty. After simplification, the text was perceived as simpler ( P

Background: Medical Faculties Network (MEFANET) has established itself as the authority for setting standards for medical educators in the Czech Republic and Slovakia, 2 independent countries with similar languages that once comprised a federation and that still retain the same curricular structure for medical education. One of the basic goals of the network is to advance medical teaching and learning with the use of modern information and communication technologies. Objective: We present the education portal AKUTNE.CZ as an important part of the MEFANET’s content. Our focus is primarily on simulation-based tools for teaching and learning acute medicine issues. Methods: Three fundamental elements of the MEFANET e-publishing system are described: (1) medical disciplines linker, (2) authentication/authorization framework, and (3) multidimensional quality assessment. A new set of tools for technology-enhanced learning have been introduced recently: Sandbox (works in progress), WikiLectures (collaborative content authoring), Moodle-MEFANET (central learning management system), and Serious Games (virtual casuistics and interactive algorithms). The latest development in MEFANET is designed for indexing metadata about simulation-based learning objects, also known as electronic virtual patients or virtual clinical cases. The simulations assume the form of interactive algorithms for teaching and learning acute medicine. An anonymous questionnaire of 10 items was used to explore students’ attitudes and interests in using the interactive algorithms as part of their medical or health care studies. Data collection was conducted over 10 days in February 2013. Results: In total, 25 interactive algorithms in the Czech and English languages have been developed and published on the AKUTNE.CZ education portal to allow the users to test and improve their knowledge and skills in the field of acute medicine. In the feedback survey, 62 participants completed the online questionnaire (13.5%) from the total 460 addressed. Positive attitudes toward the interactive algorithms outnumbered negative trends. Conclusions: The peer-reviewed algorithms were used for conducting problem-based learning sessions in general medicine (first aid, anesthesiology and pain management, emergency medicine) and in nursing (emergency medicine for midwives, obstetric analgesia, and anesthesia for midwifes). The feedback from the survey suggests that the students found the interactive algorithms as effective learning tools, facilitating enhanced knowledge in the field of acute medicine. The interactive algorithms, as a software platform, are open to academic use worldwide. The existing algorithms, in the form of simulation-based learning objects, can be incorporated into any educational website (subject to the approval of the authors). [J Med Internet Res 2013;15(7):e135]