Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhancing PIO Element Detection in Medical Text Using Contextualized Embedding

In this paper, we presented an improved methodology to extract PIO elements, from abstracts of medical papers, that reduces ambiguity. The proposed technique was used to build a dataset of PIO elements that we call PICONET. We further proposed a model of PIO elements classification using state of the art BERT embedding. In addition, we investigated a contextualized embedding, BioBERT, trained on medical corpora. It has been found that using the BioBERT embedding improved the classification accuracy, outperforming the BERT-based model. This result reinforces the idea of the importance of embedding contextualization in subsequent classification tasks in this specific context.Furthermore, to enhance the accuracy of the model, we have investigated an ensemble method based on the LGBM algorithm. We trained the LGBM model, with the above models as base learners, to learn a linear combination of the predicted probabilities for the 3 classes with the TF-IDF score and the QIEF that optimizes the classification. The results indicate that these text features were good features to consider in order to boost the deeply contextualized classification model. We compared the performance of the classifier when using the features with one of the base learners and the case where we combine the base learners along with the features. We obtained the highest score in terms of AUC when we combine the base learners.The present work resulted in the creation of a PIO element dataset, PICONET, and a classification tool. These constitute and important component of our system of automatic mining of medical abstracts. We intend to extend the dataset to full medical articles. The model will be modified to take into account the higher complexity of full text data and more efficient features for model boosting will be investigated.
Non-malleable Coding for Arbitrary Varying Channels

Non-malleable codes protect against an adversary who can tamper with the coded message by using a tampering function in a specified function family, guaranteeing that the tampering result will only depend on the chosen function and not the coded message. The codes have been motivated for providing protection against tampering with hardware that stores the secret cryptographic keys, and have found significant attention in cryptography. Traditional Shannon model of communication systems assumes the communication channel is perfectly known to the transmitter and the receiver. Arbitrary Varying Channels (AVCs) remove this assumption and have been used to model adversarially controlled channels. Transmission over these channels has been originally studied with the goal of recovering the sent message, and more recently with the goal of detecting tampering with the sent messages. In this paper we introduce non-malleability as the protection goal of message transmission over these channels, and study binary (discrete memoryless) AVCs where possible tampering is modelled by the set of channel states. Our main result is that non-malleability for these channels is achievable at a rate asymptotically approaching 1. We also consider the setting of an AVC with a special state $s^{*}$, and the additional requirement that the message must be recoverable if $s^{*}$ is applied to all the transmitted bits. We give the outline of a message encoding scheme that in addition to non-malleability, can provide recovery for all $s^{*}$ channel.
Making a Sieve Random: Improved Semi-Streaming Algorithm for Submodular Maximization under a Cardinality Constraint

In this paper we consider the problem of maximizing a non-negative submodular function subject to a cardinality constraint in the data stream model. Previously, the best known algorithm for this problem was a $5.828$-approximation semi-streaming algorithm based on a local search technique (Feldman et al., 2018). For the special case of this problem in which the objective function is also monotone, the state-of-the-art semi-streaming algorithm is an algorithm known as Sieve-Streaming, which is based on a different technique (Badanidiyuru, 2014). Adapting the technique of Sieve-Streaming to non-monotone objective functions has turned out to be a challenging task, which has so far prevented an improvement over the local search based $5.828$-approximation. In this work, we overcome the above challenge, and manage to adapt Sieve-Streaming to non-monotone objective functions by introducing a "just right" amount of randomness into it. Consequently, we get a semi-streaming polynomial time $4.282$-approximation algorithm for non-monotone objectives. Moreover, if one allows our algorithm to run in super-polynomial time, then its approximation ratio can be further improved to $3 + \varepsilon$.
Classification of Thoughts into Wheelchair Control Commands using Neural Network

This paper presents the use of neural network classification thought- based commands for wheelchair control. The advantage is to assist the locked in people who are not able to use physical interfaces like joysticks or buttons. Electroencephalogram (EEG) was used to discriminate motor imagery mental tasks, such as imagination of left hand, right hand, both hands and both feet. The four task classifications were mapped into a wheelchair movement, such as forward, left, right, and backward. The motor imagery command data were collected from seven participants. A wireless device (Enobio) with 8 dry electrode recording was used. Discrete wavelet transform and Multiyear perceptron were utilized for signal processing. The results show that the proposed system provides a good classification performance with an average classification rate of 81.046%.
Burnside rings for Real 2-representation theory: The linear theory

This paper is a fundamental study of the Real [Formula: see text]-representation theory of [Formula: see text]-groups. It also contains many new results in the ordinary (non-Real) case. Our framework relies on a [Formula: see text]-equivariant Morita bicategory, where a novel construction of induction is introduced. We identify the Grothendieck ring of Real [Formula: see text]-representations as a Real variant of the Burnside ring of the fundamental group of the [Formula: see text]-group and study the Real categorical character theory. This paper unifies two previous lines of inquiry, the approach to [Formula: see text]-representation theory via Morita theory and Burnside rings, initiated by the first author and Wendland, and the Real [Formula: see text]-representation theory of [Formula: see text]-groups, as studied by the second author.
Preference-based Energy Exchange in a Network of Microgrids

Peer-to-peer (P2P) energy trade may disrupt conventional electricity markets in the future for a network of multiple microgrids and entitle an individual microgrid (MG) to trade energy with alternative options instead of the main grid. In this paper, driven by the P2P electricity maket, a preference mechanism is added to the conventional energy management model of a network of microgrids, and a distributed method of alternating direction method of multipliers (ADMM) is adopted for its implementation. The effect of the preference mechanism on the customized power flow in the network is further studied for significant case studies. Results show that the customized power flows on the network of interconnected MGs can be achieved with different preference values locally chosen or imposed by the system operator.
Discovering and Understanding the Security Hazards in the Interactions between IoT Devices, Mobile Apps, and Clouds on Smart Home Platforms

A smart home connects tens of home devices to the Internet, where an IoT cloud runs various home automation applications. While bringing unprecedented convenience and accessibility, it also introduces various security hazards to users. Prior research studied smart home security from several aspects. However, we found that the complexity of the interactions among the participating entities (i.e., devices, IoT clouds, and mobile apps) has not yet been systematically investigated. In this work, we conducted an in-depth analysis of five widely-used smart home platforms. Combining firmware analysis, network traffic interception, and blackbox testing, we reverse-engineered the details of the interactions among the participating entities. Based on the details, we inferred three legitimate state transition diagrams for the three entities, respectively. Using these state machines as a reference model, we identified a set of unexpected state transitions. To confirm and trigger the unexpected state transitions, we implemented a set of phantom devices to mimic a real device. By instructing the phantom devices to intervene in the normal entity-entity interactions, we have discovered several new vulnerabilities and a spectrum of attacks against real-world smart home platforms.
Optimized energy utilization in small and large commercial loads and residential areas

In smart grid, the demand side management (DSM) techniques need to be designed to process a large number of controllable loads of several types. In this paper, we proposed a framework to study the demand side management in smart grid which contains a variety of loads in two service areas, one with multiple residential households, and one bus with commercial customers. Specifically, each household may have renewable generation as well as interruptible and uninterruptible appliances to make individual scheduling to optimize the electric energy cost by making the best time of the electricity usage according to the day ahead forecast of electricity prices. A high load bus represents a commercial area employed to demonstrate the impact of high load at any bus on voltage profile, power loss, and load flow condition, and to show the performance of the proposed DSM for large number of appliance. Using the developed simulation model, we examine the performance of the proposed DSM and study their impact on the distribution network operation and renewable generation, overall voltage deviation, real power loss, and possible problems such as reverse power flows, voltage rise have examined and compared, these problems can easily be seen at the commercial load bus.
The Complexity of Helly-B1 EPG Graph Recognition

Golumbic, Lipshteyn and Stern defined in 2009 the class of EPG graphs, as the intersection graph of edge paths on a grid. An EPG graph $G$ is a graph that admits a representation where its vertices correspond to paths in a grid $Q$, such that two vertices of $G$ are adjacent if and only if their corresponding paths in $Q$ have a common edge. If the paths in the representation have at most $k$ changes of direction (bends), we say that it is a $B_k$-EPG representation. A collection $C$ of sets satisfies the Helly property when every sub-collection of $C$ that is pairwise intersecting has at least one common element. In this paper we show that the problem of recognizing $B_k$-EPG graphs $G=(V,E)$ whose edge-intersections of paths in a grid satisfy the Helly property, so-called Helly-$B_k$ EPG graphs, is in $\mathcal{NP}$, for every $k$ bounded by a polynomial of $|V(G)|$. In addition, we show that recognizing Helly-$B_1$ EPG graphs is $NP$-complete, and it remains $NP$-complete even when restricted to 2-apex and 3-degenerate graphs.
A Lot of Progress, With More to Be Done: A Response to NIH Pathways to Prevention Report “Research Gaps for Long‐Term Drug Therapies for Osteoporotic Fracture Prevention”

The public health implications of osteoporosis are enormous but the disease remains underdiagnosed and undertreated. In October 2018, the National Institutes of Health (NIH) convened a Pathways to Prevention (P2P) Workshop entitled “Appropriate Use of Drug Therapies for Osteoporotic Fracture Prevention” designed to identify research gaps, suggest future research opportunities, and advance the field through an evidence‐based assessment. By design, the P2P report focused on “gaps” in our knowledge base. Unfortunately, however, the report did not sufficiently acknowledge the current evidence that unequivocally demonstrates the therapeutic efficacy of existing pharmacologic therapies for osteoporosis, which has the potential to exacerbate the current crises in osteoporosis diagnosis and treatment. © 2019 American Society for Bone and Mineral Research.
Recurrent neural networks for hydrodynamic imaging using a 2D-sensitive artificial lateral line

The lateral line is a mechanosensory organ found in fish and amphibians that allows them to sense and act on their near-field hydrodynamic environment. We present a 2D-sensitive artificial lateral line (ALL) comprising eight all-optical flow sensors, which we use to measure hydrodynamic velocity profiles along the sensor array in response to a moving object in its vicinity. We then use the measured velocity profiles to reconstruct the object’s location, via two types of neural networks: feed-forward and recurrent. Several implementations of feed-forward neural networks for ALL source localisation exist, while recurrent neural networks may be more appropriate for this task. The performance of a recurrent neural network (the long short-term memory, LSTM) is compared to that of a feed-forward neural network (the online-sequential extreme learning machine, OS-ELM) via localizing a 6 cm sphere moving at 13 cm s−1. Results show that, in a 62 cm 9.5 cm area of interest, the LSTM outperforms the OS-ELM with an average localisation error of 0.72 cm compared to 4.27 cm, respectively. Furthermore, the recurrent network is relatively less affected by noise, indicating that recurrent connections can be beneficial for hydrodynamic object localisation.
Spatial/spectral filters for microphone array

The spatial and spectral filtering characteristics of a microphone array can be greatly enhanced through the use of an appropriately designed spatial/spectral filter. In this paper, we consider the design of optimum, in the minimax sense, spatial/spectral filters. The optimum minimax design problem is solved using the new efficient computational technique of Jiang et al. Numerical results demonstrating the effectiveness of the method are given.
The Effect of Processing on Digestion of Legume Proteins

The domestic processing methods, soaking, cooking (traditional, microwave, pressure), and baking and the industrial processing, autoclaving, baking, and extrusion are used to improve consumption of legumes. The growing awareness of both health and sustainability turns the focus on protein (bio)availability. This paper reports the effect of these processing methods on the legume protein digestibility. Overall, the protein digestibility increases after processing by the different methods. However, since both the type of legume and the applied methods differ it cannot be concluded which specific method is best for the individual legume type.
When Can Stable AutoNRT Thresholds be Expected? A Clinical Implication When Fitting Young Children

Abstract Background: Objective measurements are important for programming cochlear implants in young children and other individuals who cannot participate in behavioral measurements. AutoNRT, the automatic method used to record responses from the auditory nerve in the Cochlear Ltd., implant system, is often used as a basis for estimating the threshold level and comfort level (C-level) for these patients. However, it has not been sufficiently established if AutoNRT measurements remain consistent over time. Purpose: This study aimed to determine if/when AutoNRT thresholds stabilize. Research Design: The study design was a longitudinal prospective study. Study Sample: AutoNRT thresholds were obtained from 52 young children and 80 adults. All subjects received the same implant (CI24RE Contour Advance). Data Collection and Analysis: AutoNRT thresholds were measured on all intracochlear electrodes during the surgery and at the initial activation. During the following year, children were measured at 1, 3, 6, and 12 months, and adults were measured at 6 and 12 months. The results were analyzed based on mean values, correlation, and absolute mean differences. Results: There were large variations for all electrodes between the intraoperative and postoperative AutoNRT thresholds of both children and adults. For children, the thresholds were considered to be stable from 1 month. The correlations obtained between the last two measurements, 6 and 12 months, for both children and adults were generally high for all electrodes. Conclusion: The present results demonstrate the importance of repeating the AutoNRT measurement postoperatively, at about 1 month after initial activation, to obtain reliable and stable thresholds for estimating the T- and C-level profiles.
An Approach to Measure Tilt Motion, Straightness and Position of Precision Linear Stage with a 3D Sinusoidal-Groove Linear Reflective Grating and Triangular Wave-Based Subdivision Method

This work presents a novel and compact method for simultaneously measuring errors in linear displacement and vertical straightness of a moving linear air-bearing stage using 3D sinusoidal-groove linear reflective grating and a novel triangular wave-based sequence signal analysis method. The new scheme is distinct from the previous studies as it considers two signals to analyze linear displacement and vertical straightness. In addition, the tilt motion of the precision linear stage could also be measured using the 3D sinusoidal-groove linear reflective grating. The proposed system is similar to a linear encoder and can make online measurements of stage errors to analyze automatic processes and also be used for real-time monitoring. The performance of the proposed method and its reliability have been verified by experiments. The experiments show that the maximum error of measured tilt angle, linear displacement, and vertical straightness error is less than 0.058°, 0.239 μm, and 0.188 μm, respectively. The maximum repeatability error on measurement of tilt angle, linear displacement, and vertical straightness error is less than ±0.189o, ±0.093 μm, and ±0.016 μm, respectively. The proposed system is suitable for error compensation in the multi-axis system and finds application in most industries.
Dominance rank predicts social network position across developmental stages in rhesus monkeys

Social network analysis is increasingly common in studying complex interactions among individuals. Across a range of primates, high‐ranking adults are generally more socially connected, which results in better fitness outcomes. However, it still remains unclear whether this relationship between social network position and dominance rank emerges in infancy and whether, in species with a social transmission of dominance rank, social network positions are driven by the presence of the mother. To fill this gap, we first explored whether dominance ranks were related to social network position, measured via eigenvector centrality, in infants, juveniles, and adults in a troop of semi‐free‐ranging rhesus macaques (Macaca mulatta). We then examined relationships between dominance rank and eigenvector centrality in a peer‐only group of yearlings who were reared with their mothers in either a rich, socially complex environment of multigenerational (MG) kin support or a unigenerational group of mothers and their infants from birth through 8 months. In Experiment 1, we found that mother's network position predicted offspring network position and that dominants across all age categories were more central in affiliative networks (social contact, social grooming, and social play). Experiment 2 showed that high‐ranking yearlings in a peer‐only group were more central only in the social contact network. Moreover, yearlings reared in a socially complex environment of MG kin support were more central. Our findings suggest that the relationship between dominance rank and social network position begins early in life, and that complex early social environments can promote later social competency. Our data add to the growing body of evidence that the presence/absence of the mother and kin influence how dominance rank affects social network position. These findings have important implications for the role of caregivers in the social status of developing primates, which ultimately ties to health and fitness outcomes.
Prevention of postsurgical lymphedema via immediate delivery of sustained‐release 9‐cis retinoic acid to the lymphedenectomy site

Previously, we have shown that 9‐cis retinoic acid (9‐cis RA) stimulates lymphangiogenesis and limits postsurgical lymphedema in animal models when administered via daily intraperitoneal injections. In this study, we investigate whether a single‐use depot 9‐cis RA drug delivery system (DDS) implanted at the site of lymphatic injury can mitigate the development of lymphedema in a clinically relevant mouse limb model.
Role of Hedgehog Signaling in Vasculature Development, Differentiation, and Maintenance

The role of Hedgehog (Hh) signaling in vascular biology has first been highlighted in embryos by Pepicelli et al. in 1998 and Rowitch et al. in 1999. Since then, the proangiogenic role of the Hh ligands has been confirmed in adults, especially under pathologic conditions. More recently, the Hh signaling has been proposed to improve vascular integrity especially at the blood–brain barrier (BBB). However, molecular and cellular mechanisms underlying the role of the Hh signaling in vascular biology remain poorly understood and conflicting results have been reported. As a matter of fact, in several settings, it is currently not clear whether Hh ligands promote vessel integrity and quiescence or destabilize vessels to promote angiogenesis. The present review relates the current knowledge regarding the role of the Hh signaling in vasculature development, maturation and maintenance, discusses the underlying proposed mechanisms and highlights controversial data which may serve as a guideline for future research. Most importantly, fully understanding such mechanisms is critical for the development of safe and efficient therapies to target the Hh signaling in both cancer and cardiovascular/cerebrovascular diseases.
Epstein–Barr Virus and Innate Immunity: Friends or Foes?

Epstein–Barr virus (EBV) successfully persists in the vast majority of adults but causes lymphoid and epithelial malignancies in a small fraction of latently infected individuals. Innate immunity is the first-line antiviral defense, which EBV has to evade in favor of its own replication and infection. EBV uses multiple strategies to perturb innate immune signaling pathways activated by Toll-like, RIG-I-like, NOD-like, and AIM2-like receptors as well as cyclic GMP-AMP synthase. EBV also counteracts interferon production and signaling, including TBK1-IRF3 and JAK-STAT pathways. However, activation of innate immunity also triggers pro-inflammatory response and proteolytic cleavage of caspases, both of which exhibit proviral activity under some circumstances. Pathogenic inflammation also contributes to EBV oncogenesis. EBV activates NFκB signaling and induces pro-inflammatory cytokines. Through differential modulation of the proviral and antiviral roles of caspases and other host factors at different stages of infection, EBV usurps cellular programs for death and inflammation to its own benefits. The outcome of EBV infection is governed by a delicate interplay between innate immunity and EBV. A better understanding of this interplay will instruct prevention and intervention of EBV-associated cancers.