Miracle-dz/UniFAR-FTUs huggingface dataset:
Relation extraction between the clinical entities based on the shortest dependency path based LSTM

Owing to the exponential rise in the electronic medical records, information extraction in this domain is becoming an important area of research in recent years. Relation extraction between the medical concepts such as medical problem, treatment, and test etc. is also one of the most important tasks in this area. In this paper, we present an efficient relation extraction system based on the shortest dependency path (SDP) generated from the dependency parsed tree of the sentence. Instead of relying on many handcrafted features and the whole sequence of tokens present in a sentence, our system relies only on the SDP between the target entities. For every pair of entities, the system takes only the words in the SDP, their dependency labels, Part-of-Speech information and the types of the entities as the input. We develop a dependency parser for extracting dependency information. We perform our experiments on the benchmark i2b2 dataset for clinical relation extraction challenge 2010. Experimental results show that our system outperforms the existing systems.
Two decades of genetically encoded biosensors based on Förster resonance energy transfer.

Two decades have passed since the development of the first calcium indicator based on the green fluorescent protein (GFP) and the principle of Förster resonance energy transfer (FRET). During this period, researchers have advanced many novel ideas for the improvement of such genetically encoded FRET biosensors, which have allowed them to expand their targets from small molecules to signaling proteins and physicochemical properties. Although the merits of "genetically encoded" FRET biosensors became clear once various cell lines were established and several transgenic organisms were generated, the road to these developments was not necessarily a smooth one. Moreover, even today the development of new FRET biosensors remains a very labor-intensive, trial-and-error process. Therefore, at this junction, it may be worthwhile to summarize the progress of the FRET biosensor and discuss the future direction of its development and application.Key words: FRET, biosensor, fluorescent protein.
Anti-tumor immunity induced by ectopic expression of viral antigens is transient and limited by immune escape

ABSTRACT Immunotherapeutic treatments in head and neck cancer clinical trials include cancer vaccines targeting foreign viral antigens or mutational neoantigens derived from cancer-expressed proteins. Anti-tumor immune responses place cancer cells under selective pressure to lose or downregulate target antigens; therefore, vaccination against virus- or host- “driver” oncogenes are proposed as a strategy to overcome immune escape. Herein, we demonstrate the impact of immunogenic viral antigens on anti-tumor response and immune editing in MOC2-E6E7, a syngeneic murine oral cancer cell line expressing HPV-16 E6 and E7 oncoproteins. Using orthotopic syngeneic models, we observed in vivo tumor growth kinetics of MOC2-E6E7 is delayed in immunocompetent mice compared to parental MOC2 tumors. In contrast, tumor growth remained similar in Rag1-/- mice lacking adaptive immunity. MOC2-E6E7 tumors demonstrated an “inflamed” or immune-activated tumor microenvironment and greater infiltration of CD8+ T cells compared to MOC2. By real-time PCR, we detected downregulation of E6 and E7 genes in MOC2-E6E7 tumors only in immunocompetent mice, suggesting the loss of ectopic viral antigen expression due to immune editing. We then assessed the efficacy of a biomaterials-based mesoporous silica rod (MSR) cancer vaccine targeting HPV-16 E7 in our model. Vaccination induced robust infiltration of antigen-specific CD8+ T cells, which led to tumor growth delay and modestly prolonged survival in MOC2-E6E7 tumors. Increased efficacy was seen in a separate head and neck cancer tumor model, mEER, which obligately expresses E7 antigen. Collectively, our data highlight the need for both immunogenicity and ‘driver’ status of target antigens to be considered in cancer vaccine design.
Limb Versus Precordial ECG Leads as Improved Predictors of Electrical Cardioversion Outcome in Persistent Atrial Fibrillation

Electrical cardioversion (ECV) is an effective and low-cost rhythm control strategy for persistent atrial fibrillation (AF). Because of its limited mid- and long-term success rates, prediction of early failure could avoid patients with reduced chance to maintain sinus rhythm (SR). To this end and due to its proximity to the right atrium, several indices characterizing atrial activity have been proposed based on lead V1. However, information from other leads has been discarded to date. Hence, this work studies how effective some common indices computed over the whole set of 12 standard ECG leads are in predicting ECVout-come. Precisely, amplitude, dominant frequency, and sample entropy were computed from the fibrillatory (f -) waves extracted for each one of 12 standard leads acquired before ECV from 58 patients in persistent AF. The classification between the patients who relapsed to AF and maintained sinus rhythm after a follow-up of 4 weeks achieved by these parameters was better from limb lead II than from V1, thus reporting improvements about 6 and 12%. As a consequence, characterization of f-waves from the more accessible limb lead II has proven to be the best choice to improve AF ECV outcome prediction from the ECG.
Fiducia: A Personalized Food Recommender System for Zomato

This paper presents Fiducia, a food review system involving a pipeline which processes restaurant-related reviews obtained from Zomato (India's largest restaurant search and discovery service). Fiducia is specific to popular cafe food items and manages to identify relevant information pertaining to each item separately in the reviews. It uses a sentiment check on these pieces of text and accordingly suggests an appropriate restaurant for the particular item depending on user-item and item-item similarity. Experimental results show that the sentiment analyzer module of Fiducia achieves an accuracy of over 85% and our final recommender system achieves an RMSE of about 1.01 beating other baselines.
Effect of Ordo in Assessment of Financial and Non-Financial Information

This research focuses on investor decision making on information provided by the company. Belief-adjustment models emphasize the order of presentation of information. Order effects occur when decisions made by individuals differ after receiving evidence in a different order. In order of evidence, the characteristics of the evidence are mixed between confirmative (positive) information and unconfirmed (negative) information.The participants of this study are investors who have investment accounts. The design of the 2x4 experimental method is divided into analysis of factor 2 (presentation pattern) x 4 (information value), which aims to test that the presentation of information in step by step (SBS) will have a better impact than end of sequence (EOS). There are 8 combinations of instruments contain patterns and information values that are used as a source of stock valuation. ANOVA analysis is used for this study. The results showed that there was an effect of the pattern of information delivery in investment decision making when the SBS and EOS disclosure patterns in hypothesis 1 and hypothesis 2. The results of hypothesis 3 did not support the belief adjustment model theory.
Voltages and AC Corrosion on Metallic Tubes in Umbilical Cables Caused by Magnetic Induction From Power Cable Charging Currents

Offshore umbilical cables often include both power cables and elements such as metallic tubes and signal cables. It was concluded from a recent failure of stainless tubes that ac corrosion was the cause of the failure. This paper describes how ac corrosion can lead to tube failure when a puncture exists in the insulating tube coating. The variation in power cable charging currents along the umbilical cable causes a nonzero voltage to exist in the tubes, even though they are grounded at both ends. If the current density associated with the puncture current is sufficiently high, ac corrosion will result. The current density is calculated as a function of the cable length and the variable speed drive operating frequency for three alternative power cable designs. Comparison with measured corrosion rates shows that ac corrosion can corrode a hole in the tube in weeks or months, depending on the cable design and length, and also on the electrical operating conditions. Analytical expressions and numerical procedures are presented, which can be used to determine whether a given umbilical cable installation involves the risk of ac corrosion. It is also shown that substantial heating effects in the puncture area can result in case of excessive harmonics in the supply voltage.
MP2ML: a mixed-protocol machine learning framework for private inference

Privacy-preserving machine learning (PPML) has many applications, from medical image classification and anomaly detection to financial analysis. nGraph-HE enables data scientists to perform private inference of deep learning (DL) models trained using popular frameworks such as TensorFlow. nGraph-HE computes linear layers using the CKKS homomorphic encryption (HE) scheme. The non-polynomial activation functions, such as MaxPool and ReLU, are evaluated in the clear by the data owner who obtains the intermediate feature maps. This leaks the feature maps to the data owner from which it may be possible to deduce the DL model weights. As a result, such protocols may not be suitable for deployment, especially when the DL model is intellectual property. In this work, we present MP2ML, a machine learning framework which integrates nGraph-HE and the secure two-party computation framework ABY, to overcome the limitations of leaking the intermediate feature maps to the data owner. We introduce a novel scheme for the conversion between CKKS and secure multi-party computation to execute DL inference while maintaining the privacy of both the input data and model weights. MP2ML is compatible with popular DL frameworks such as TensorFlow that can infer pre-trained neural networks with native ReLU activations. We benchmark MP2ML on the CryptoNets network with ReLU activations, on which it achieves a throughput of 33.3 images/s and an accuracy of 98.6%. This throughput matches the previous state-of-the-art work, even though our protocol is more accurate and scalable.
Top eigenpair statistics for weighted sparse graphs

We develop a formalism to compute the statistics of the top eigenpair of weighted sparse graphs with finite mean connectivity and bounded maximal degree. Framing the problem in terms of optimisation of a quadratic form on the sphere and introducing a fictitious temperature, we employ the cavity and replica methods to find the solution in terms of self-consistent equations for auxiliary probability density functions, which can be solved by population dynamics. This derivation allows us to identify and unpack the individual contributions to the top eigenvector’s components coming from nodes of degree k. The analytical results are in perfect agreement with numerical diagonalisation of large (weighted) adjacency matrices, and are further cross-checked on the cases of random regular graphs and sparse Markov transition matrices for unbiased random walks.
Miura-ori tube metamaterial with tunable dynamic property

Structures and/or materials with engineered functionality, capable of achieving targeted mechanical responses reacting to changes in external excitation, have various potential engineering applications, e.g. aerospace, oceanographic engineering, soft robot, and several others. Yet tunable mechanical performance is normally realized through carefully designing the architecture of structures, which is usually porous, leading to the complexity of the fabrication of the structures even using the recently emerged 3D printing technique. In this study we show that origami technique can provide an alternative solution to achieving the aim by carefully stacking the classical Miura sheets into the Miura-ori tube metamaterial and tuning the geometric parameters of the origami metamaterial. By combining numerical and experimental methods, we have demonstrated that an extremely broad range of natural frequency and dynamic response of the metamaterial can be achieved. The proposed structure can be easily fabricated from a single thin sheet made of one material and simultaneously owns better mechanical properties than the Miura sheet.
Non-Halal Ingredients Detection of Food Packaging Image Using Convolutional Neural Networks

Halal food is absolute for Muslims. Unfortunately, many packaged foods have not been certified halal. One way of looking at the ingredients is listed. However, often the code of composition is unfamiliar. Current image processing and machine learning technologies can help read the image of the packaged food composition for the detection of non-Halal compositions. The development supports this and using Android smartphones. However, it is often constrained by the size of the font and position of packaged foods uneven. Deep learning one of the developments in machine learning that has a high level of recognition. It was used to detect the Halal composition of packaged food using convolutional neural networks (CNNs). Image of packaged food had been segmented each character before recognized by CNN's. The CNN models using the 250 epoch and 64 batches tested to obtain optimal parameters and configuration, including the use of an adaptive learning rate method (AdaDelta) and Stochastic Gradient Descent (SGD) configuration. The accuracy of each character is 98.08% if using SGD and 98.02% if using AdaDelta. Non-Halal detection combined character to the word and compared with the list of non-Halal compositions. The segmentation process significantly determined the accuracy. It made the overall word accuracy of only 50%, whose most significant error lied in the wrong segmentation process.
Gamification in MOOCs: Engagement Application Test in Energy Sustainability Courses

Massive open online courses (MOOCs) have triggered a sudden change in the educational scene. Its characteristics of being free, heterogeneous, multi-thematic, and fostering lifelong learning have completely changed the instructional design scene, allowing these innovations and new architectures of teaching and learning to be included. However, MOOCs have been criticized by the scientific community for their high dropout rates and low overall completion rates, which has called into question their effectiveness as a pedagogical tool. This paper analyzes how the application of gamification strategies in MOOCs on energy sustainability affects participants’ engagement and seeks to identify what types of interactive gamification media are more useful in generating interest and motivation in students. In order to do so, a mixed quasi-experimental method is used. A gamification board with challenges, badges, and leaderboards to a sample is used, and at the same time, this platform is analyzed using the integrated theoretical gamification model in e-learning environments. In the MOOCs where gamification strategies were applied, a global completion rate of 14.43% was obtained, while in those without gamification, 6.162% was obtained. Likewise, the degree of student engagement with respect to the completion rate of activities was much higher in the gamified platform (28.032%) than in the traditional design (13.252%). The results show that applying gamification strategies in MOOCs achieves a higher level of engagement and student motivation.
A MapReduce Based Tool for the Analysis and Discovery of Novel Therapeutic Targets

We present here a novel algorithm based on a MapReduce approach to facilitate the discovery of novel therapeutic targets. The proposed algorithm has been enabled to scan a set biological pathways in order to discover non-trivial (less common) routes. Such routes represent a chain of biochemical interactions among different biological actors. These actors can be represented by quite distant nodes along the devised pathway. Our approach detects nodes that are far from the initial target nodes, also showing the number of times that a given route has been found inside the selected set of biological pathways.
Contract-Based Demand Response Model for High Performance Computing Systems

High-performance computing (HPC) systems can consume substantial electricity during their operation, and consequently incur significant energy cost. Demand response is a scheme adopted by energy consumers to reduce energy use during the high load periods upon request from the power grid. In this paper, we propose a demand response model to allow both HPC operators and HPC users to jointly reduce the energy consumption. We apply the contract theory, originated from economics for studying the contractual arrangements among economic actors, and design a reward mechanism to ensure participation of both HPC operators and HPC users in the demand response program. We perform both analytical and simulation studies of our proposed approach. Our analyses show that our contract-based demand response model is both feasible (in ensuring both individual rationality and incentive compatibility) and optimal. The trace-based simulation demonstrates the practicality of the proposed approach.
Proportion Scheduler to Improve the Mismatched Locality in YARN

YARN is a prevailing central resource management architecture, which allocates a group of resources to each application. The resource group is consistent with locality requests of tasks in applications. But each application allocates the resources in the resource group to each task according to locality capacities of resources. Different rules in two levels of resource allocations lead to mismatched localities for tasks, which hurts performances of applications. There is a lack of researches about mismatched localities for tasks in YARN. This paper designs a Proportion scheduler to improve mismatched localities without dragging applications. The locality capacities of resources becomes unified allocation rule. Resources are classified by the locality requests of tasks, so that resources in the same category are versatile for tasks with the same level of locality request. This classification decreases the mismatched probability. In addition, the improvement of mismatched localities makes compromises between different applications. Every application is assigned with proportional resources in different locality scales for improved performances. Compared to baseline schedulers, there are 2 times data-local tasks and more than 30% rack-local tasks. The Proportion decreases makespan of applications by a maximum 66.7% and network traffic by an utmost 80%.
Integrating Temporal Evolution with Cellular Automata for Simulating Land Cover Change

Simultaneously considering the spatial and temporal processes is essential for land cover simulation models. A cellular automaton (CA) usually simulates the spatial conversion of land cover through post-classification comparisons between the beginning and the end of the training period. However, such an approach does not consider the temporal evolution of land cover. As a result, a CA model fails to explain the realistic land cover change. This paper proposes a temporal-dimension-extension CA (TDE-CA) by integrating the temporal evolution of land cover with a CA. In the TDE-CA, the Breaks for Additive Season and Trend (BFAST) monitor algorithm was employed in the temporal evolution simulation module (TESM) to simulate the gradual evolution of land cover, and an optimized random forest CA (optimized RF-CA) was used to simulate the spatial conversion driven by many spatial variables. Subsequently, the Ensemble Kalman Filter (EnKF) was employed to integrate the TESM with the optimized RF-CA. The TDE-CA was then tested in the land cover simulation of Shendong mining area during the period 2005–2015. The TDE-CA was compared with a Null model, with its sub-models, and with the traditional CA models, including the Logistic-CA and the MLP-CA (Multilayer Perceptron CA) models. The results show that the TDE-CA is superior to the Null model. Furthermore, the overall accuracy and the Kappa coefficient of the TDE-CA were 79.84% and 71.61%, respectively; compared with the TESM and the optimized RF-CA, the values showed 17.14% and 4.48% improvements in the overall accuracies and 0.2167 and 0.0512 improvements in the Kappa coefficients, respectively. When compared with the Logistic-CA and the MLP-CA, we measured 8.41% and 8.25% improvements in the overall accuracies and 0.0985 and 0.0964 improvements in the Kappa coefficients. These experiments indicate that the TDE-CA not only provides an effective model for the spatiotemporal dynamical simulation of land cover, but also enhances the development of the existing simulation theory.
A Strongly Consistent Sparse k-means Clustering with Direct l1 Penalization on Variable Weights

We propose the Lasso Weighted $k$-means ($LW$-$k$-means) algorithm as a simple yet efficient sparse clustering procedure for high-dimensional data where the number of features ($p$) can be much larger compared to the number of observations ($n$). In the $LW$-$k$-means algorithm, we introduce a lasso-based penalty term, directly on the feature weights to incorporate feature selection in the framework of sparse clustering. $LW$-$k$-means does not make any distributional assumption of the given dataset and thus, induces a non-parametric method for feature selection. We also analytically investigate the convergence of the underlying optimization procedure in $LW$-$k$-means and establish the strong consistency of our algorithm. $LW$-$k$-means is tested on several real-life and synthetic datasets and through detailed experimental analysis, we find that the performance of the method is highly competitive against some state-of-the-art procedures for clustering and feature selection, not only in terms of clustering accuracy but also with respect to computational time.
Antecedents of the Intention to Disclose Personal Information on the Internet: A Review and Model Extension

In order to reap the benefits which the Internet offers, users often have to provide personal information over the Web. Data types that are frequently required by online vendors include names, mailing and e-mail addresses, telephone numbers or credit card numbers. Previous research has identified several antecedents which influence users’ decisions on providing personal details over the Web. This paper adds to the existing research by scrutinizing the concept of personal information and positing an individual's perceived risk of personal information as an antecedent of information disclosure. The results of an empirical survey show that users differentiate between various types of personal data according to the risk of privacy intrusion. Perceived risk of personal information turns out to be a stronger predictor for the intention to provide personal information online than trust in the Internet or in the online vendor.