Miracle-dz/UniFAR-FTUs huggingface dataset:
Extracting Cross-organization Emergency Response Process Models from Chinese Plans

Emergency plans are used as effective instructions of hazard emergency response and they describe the overall emergency response process in natural language. In this paper, we propose an approach to extract a BPMN process model of cross-organization emergency response from plan text. It comprises three components: model elements identification, plan text decomposition and process model generation. First, a CRF (Conditional random field) network is combined with Bi-LSTM (a bidirectional long short-term memory) network (Bi-LSTMCRF) to identify model elements. Then, plan text is decomposed into subtexts about executive departments. Finally, inner-process models of all departments are generated from these subtexts and a complete collaborative process model is integrated by message flows. In addition, a case study is introduced and the precision of model elements identification is showed to illustrate that the proposed extraction approach is feasible and available.
Preliminary evidence of early Reef Cube® enhancements to mobile benthic ecosystems

Reef Cubes® are ecological engineering units, intended to restore biodiversity while serving functions to marine engineers. Ecological engineering must be backed by quantitative scientific evidence. Here, preliminary results of the Reef Enhancement for Fisheries at Aquaculture Sites (REFAS) project are presented. 11 Clusters of Reef Cubes® were deployed in June 2021. Sampling was undertaken before and after in the Spring and Autumn of 2021, in daylight and at night-time. Guided Baited Remote Underwater Video Station (BRUVS) surveys were completed with a modified BlueROV2. All mobile benthic and demersal species were identified during a 30-minute window and quantified in terms of Max-N. Biodiversity indices, rarefaction curves and multivariate community compositions were compared at reef and non-reef sites before and after build. Average species richness, Pielou’s evenness, Simpson’s and Shannon’s indices were similar between reef and non-reef sites. Seasonal increases of total abundance and total species were found, and these were significantly greater at reef sites. There were also significant differences in the community compositions of the reef and non-reef sites that were not observed before the build. These results are significant for the implications of using Reef Cubes® and indicate that Reef Cubes® could be a potential tool for biodiversity remediation.
Speech recognition system based multi-level selective filter

In this paper, an updated source-filter based method is used for speech recognition of English speakers. The key goal of this paper is to discuss the influence of using dynamic and selective Filterbank on the efficiency of the system in terms of identifying the word being said. Dynamic structures of the bandpass filter bank are clearly able to adjust their principles in conjunction with the speech database used. In this paper, the proposed methods adopted the idea of re-select or adjusting the filter parameters values at each iteration, in accordance with the word being processed. In other words, the values of the filter components (center frequencies and bandwidths) will adjust until it gets the best set of the filter components. The genetic algorithm (GA) will be used to maintain the components of the filter and each new population of the GA represents a new set of channels for the filter bank. Results show that the efficiency of the recognition system would be improved with each new set of filter components generated by the genetic algorithm. Speech recognition, Source-filter, Filterbank, genetic algorithm, adjusted filter, Dynamic system
Dynamic Power and Rate Allocation for NOMA Based Vehicle-to-Infrastructure Communications

In this paper, a non-orthogonal multiple access (NOMA) based downlink vehicle-to-infrastructure network is considered. Particularly, we focus on the specific case of two users, one of which requires reliable road-safety-critical data transmission while the other pursues high-capacity services, with extension to multi-user scenarios. Leveraging only slow fading of channel state information, the transmit powers and target rates are jointly optimized to maximize the expected sum throughput of the capacity hungry user, with consideration of the payload delivery outage probability of the reliability sensitive user. The optimization is formulated as an unconstrained single-objective sequential decision problem via introducing a dual variable. A dynamic programming based algorithm is then designed to derive the optimal policy that maximizes the Lagrangian. Afterwards, a bisection search based method is proposed to find the optimal dual variable. The proposed scheme is shown by numerical results to be superior to the baseline methods in terms of the expected return, performance region, and objective value.
Multivariate Analysis for Multiple Network Data via Semi-Symmetric Tensor PCA

Network data are commonly collected in a variety of applications, representing either directly measured or statistically inferred connections between features of interest. In an increasing number of domains, these networks are collected over time, such as interactions between users of a social media platform on different days, or across multiple subjects, such as in multi-subject studies of brain connectivity. When analyzing multiple large networks, dimensionality reduction techniques are often used to embed networks in a more tractable low-dimensional space. To this end, we develop a framework for principal components analysis (PCA) on collections of networks via a specialized tensor decomposition we term Semi-Symmetric Tensor PCA or SS-TPCA. We derive computationally efficient algorithms for computing our proposed SS-TPCA decomposition and establish statistical efficiency of our approach under a standard low-rank signal plus noise model. Remarkably, we show that SS-TPCA achieves the same estimation accuracy as classical matrix PCA, with error proportional to the square root of the number of vertices in the network and not the number of edges as might be expected. Our framework inherits many of the strengths of classical PCA and is suitable for a wide range of unsupervised learning tasks, including identifying principal networks, isolating meaningful changepoints or outlying observations, and for characterizing the “variability network” of the most varying edges. Finally, we demonstrate the effectiveness of our proposal on simulated data and on an example from empirical legal studies. The techniques used to establish our main consistency results are surprisingly straightforward and may find use in a variety of other network analysis problems.
Many Heads but One Brain: Fusion Brain -- a Competition and a Single Multimodal Multitask Architecture

Supporting the current trend in the AI community, we present the AI Journey 2021 Challenge called Fusion Brain, the first competition which is targeted to make the universal architecture which could process different modalities (in this case, images, texts, and code) and solve multiple tasks for vision and language. The Fusion Brain Challenge combines the following specific tasks: Code2code Translation, Handwritten Text recognition, Zero-shot Object Detection, and Visual Question Answering. We have created datasets for each task to test the participants' submissions on it. Moreover, we have collected and made publicly available a new handwritten dataset in both English and Russian, which consists of 94,128 pairs of images and texts. We also propose a multimodal and multitask architecture - a baseline solution, in the center of which is a frozen foundation model and which has been trained in Fusion mode along with Single-task mode. The proposed Fusion approach proves to be competitive and more energy-efficient compared to the task-specific one.
A General Relationship Between Optimality Criteria and Connectivity Indices for Active Graph-SLAM

Quantifying uncertainty is a key stage in active simultaneous localization and mapping (SLAM), as it allows to identify the most informative actions to execute. However, dealing with full covariance or even Fisher information matrices (FIMs) is computationally heavy and easily becomes intractable for online systems. In this letter, we study the paradigm of active graph-SLAM formulated over the special Euclidean group SE(n), and propose a general relationship between the FIM of the system and the Laplacian matrix of the underlying pose-graph. This link makes possible to use graph connectivity indices as utility functions with optimality guarantees, since they approximate the well-known optimality criteria that stem from optimal design theory. Experimental validation demonstrates that the proposed method leads to equivalent decisions for active SLAM in a fraction of the time.
Poly(D,L-Lactic Acid) (PDLLA) Biodegradable and Biocompatible Polymer Optical Fiber

We demonstrate that commercially available poly(D,L-lactic acid) (PDLLA) is a suitable material for the fabrication of biodegradable optical fibers with a standard heat drawing process. To do so we report on the chemical and optical characterization of the material. We address the influence of the polymer processing on the molecular weight and thermal properties of the polymer following the preparation of the polymer preforms and the fiber optic drawing process. We show that cutback measurements of the first optical fibers drawn from PDLLA return an attenuation coefficient as low as 0.11 dB/cm at 772 nm, which is the lowest loss reported this far for optical fibers drawn from bio-resorbable material. We also report on the dispersion characteristics of PDLLA, and we find that the thermo-optic coefficient is in the range of −10−4°C−1. Finally, we studied the degradation of PDLLA fibers in vitro, revealing that fibers with the largest diameter of 600 μm degrade faster than those with smaller diameters of 300 and 200 μm and feature more than 84% molecular weight loss over a period of 3 months. The evolution of the optical loss of the fibers as a function of time during immersion in phosphate-buffered saline indicates that these devices are potential candidates for use in photodynamic therapy-like application scenarios.
Outage Analysis of M-BLAST Layered Symbol Detection Algorithm for Spatial Multiplexing

In this paper, we consider the outage performance of the M-BLAST symbol detection algorithm over uncoded quasistatic MIMO channels with spatial multiplexing. The M-BLAST algorithm is known to achieve a superior error performance over its predecessor V-BLAST with a signal-to-noise ratio (SNR) improvement of as large as 2 dB. We evaluate this performance improvement analytically, which has not been studied before, by deriving the outage probability expression over generalized complex binary alphabets with two transmitting and unlimited number of receiving antennas. Although the underlying successive interference cancellation (SIC) receiver introduces a high non-linearity which complicates the analysis, our analytical and simulation results are shown to have a very good match.
A Stable Impedance-Matched Ultrawideband Antenna System Mitigating Detuning Effects for Multiple Biotelemetric Applications

Implantable antennas are integral but challenging components of wireless biomedical implants. They are very sensitive to variations in host tissue environments and their performance is affected by coupling with circuitry and other components. To overcome these issues, a small-sized ultrawideband antenna system with stable impedance matching has been proposed. The suggested antenna has a small volume of 28.85 mm3 and mitigates detuning due to changes in implantation scenarios or coupling with circuitry and batteries of the device. Initially, the antenna and devices were designed in a homogeneous muscle box using finite element method-based Ansys HFSS. For further verification and multiple applicability, the proposed antenna within the devices was implanted into different implantation sites in a realistic human Duke model and analyzed through finite-difference time-domain-based Sim4Life. Each full package device is composed of batteries, electronics, an antenna system, and sensor packs enclosed in a biocompatible casing. The proposed antenna was fabricated, devices were printed using 3-D printing technology, and measurements are carried out through immersion in a saline solution and in a porcine heart. A wide bandwidth of ≥84% in both simulations and measurements were achieved for all possible cases.
Quantum Fields, Dark Matter, Elko Fields and Non-Standard Wigner Classes

Quantum Fields, Dark Matter, Elko Fields and Non-Standard Wigner Classes Adam Gillard In this thesis we examine the Elko field dark matter candidate, its interactions, and possible theoretical origins. We discuss important areas in which Elko Field Theory is incomplete and propose what we consider to be the most natural ways of plugging the holes in the theory. The way we propose to plug these holes enables Elko fields to interact with Standard Model gauge quanta. Any possible Elko darkness may be then due to Elko non-locality. The possible existence of Elko gauge interactions constitutes a significant result in this thesis. We also explore how Elko quantum fields might arise on the state space. We show that the Elko field is not a quantum field in the sense of Weinberg and that the Elko field violates the symmetries of the Lorentz group; another significant result altering how we think about Elko Field Theory. We also show that subgroups of the Lorentz group do not give rise to Elko fields (or their VSR counterparts) on the state space. We also examine the non-standard Wigner classes and show that in the context of our most natural ways of plugging the holes present in Elko Field Theory, Elko fields do not arise there either. We also show that in one of the non-standard Wigner classes, under certain conditions, there can exist a local massive spin-1/2 quantum field Majorana type dark matter candidate that is a well-defined quantum field in the sense of Weinberg. We give the dynamics of this new quantum field and also specify under what conditions this quantum field can exist. We finish the thesis by exploring Elko fields and their left and right-handed components in the context of the Electroweak Theory, in a more speculative way. We take the general concept of mass dimension transmutation introduced for Classical Spinor Theory by J.M. Hoff da Silva and R. da Rocha and apply it to the state space in the most natural way. We use this to derive a formula linking Dirac fields to the left-handed components of Elko fields and suggest the possibility of mass dimension transmutation being involved in electroweak interactions. Finally, we point out that although Elko fields cannot enter the Standard Model doublets, they can form their own doublets, the resulting symmetry currents of which can couple to the symmetry currents of the Standard Model.
Early Student Grade Prediction: An Empirical Study

In higher educational institutes, early grade prediction is an important area of interest as it allows instructors to improve students’ performance in their courses by providing special attention at the early stages. Machine learning techniques can be utilized for students’ grades prediction in different courses. However, the performance of these techniques is highly dependent on the quality of data that made the selection of model a challenging task. Therefore, in this paper, we evaluate different state-of-the-art machine learning techniques for university students grade prediction. Ultimately we find that Restricted Boltzmann Machines (RBM) can more accurately predict students’ grades. The predicted grades by these techniques visualize uncertainty on student learning and can be used for confidence gains, student degree planning, personalized advising, and to enable instructors to identify potential students who might need assistance in relevant courses.
Risk of repeated suicide attempt after redeeming prescriptions for antidepressants: a register-based study in Denmark

Abstract Background It remains unclear how SSRIs and other antidepressants are associated with the risk of repeated suicide attempts. We aimed to analyse the association between redeemed antidepressant prescriptions and the risk of repeated suicide attempts, hypothesising that antidepressant treatment is associated with increased risk of repeated suicide attempts. Methods The study was based on Danish register data and a validated cohort of 1842 suicide attempts. We used three Cox regression models (crude, adjusted and propensity score matched) to analyse the data; these models included both static and dynamic time-dependent factors. Results 1842 individuals attempted suicide in the study period, with a total of 210 repeated attempts. Individuals redeeming antidepressant prescriptions were more likely to repeat a suicide attempt. All crude models showed all antidepressants to be significant risk factors (HR around 1.39), whereas all adjusted models showed all antidepressants to be insignificant risk factors. Conclusion We found no significant increased risk of repeated suicide attempts in individuals redeeming a prescription for any antidepressant (or only SSRIs) when considering the individuals' baseline risk of repetition. This study is based on validated suicide attempts, register data, and strong epidemiology designs, but it still has some limitations, and the results should be replicated and confirmed in other studies.
Use of a non-medicated plaster in chronic lumbar back pain: a randomized controlled trial

Background and aim: The latest technology on far infrared radiations reflects the radiations emitted by the human body and induces an antalgic and anti-inflammatory effect without active ingredients. Our primary aim was to assess pain level modifications throughout the treatment period with two different types of patches, compared to a placebo. As secondary aims, we focused on addressing patients’ quality of life and range of motion changes with each patch. Methods: We assessed 54 patients with chronic lumbar back pain treated with FIT Therapy (far infrared technology) patch. Three different types of FIT Therapy patches (F4, F3, and placebo) were used according to the different power of action and patients allocated in a randomized fashion into the 3 arms of the study. Every single patient was assessed during the study using the VAS pain scale, the Roland Morris Disability Questionnaire for quality of life, and ROM for a total of 14 days. Results: Only the F4 patch group significantly reduced pain level at T14 compared to the placebo group (p<0.05). Meanwhile, F3 showed only a non-significant decrease compared to placebo (p=0.254). In terms of lifestyle improvements, both F3 and F4 recorded a decrease on the RMDQ of 4 and 6 points, respectively. Conclusions: Currently, we still need further studies with longer follow-up to consider the FIT Therapy patches F4 a valid alternative as a “non-medicated pain relief”, but it proved to have a role in alleviating painful symptoms and improving function in chronic lumbar back pain without adverse events. (www.actabiomedica.it)
Perceptions of Ontario health system leaders on single-entry models for managing the COVID-19 elective surgery backlog: an interpretive descriptive study

Background: The COVID-19 pandemic has exacerbated pre-existing challenges with respect to access to elective surgery across Canada, and a single-entry model (SEM) approach has been proposed as an equitable and efficient method to help manage the backlog. With Ontario’s recent investment in centralized surgical wait-list management, we sought to understand the views of health system leaders on the role of SEMs in managing the elective surgery backlog. Methods: We used the qualitative method of interpretive description to explore participant perspectives and identify practical strategies for policy-makers, administrators and clinical leaders. We conducted semistructured interviews with health system leaders from across Ontario on Zoom between March and June 2021. We used snowball and purposive sampling. Inclusion criteria included Ontario health care leaders, fluent in English or French, in positions relevant to managing the elective surgery backlog. Exclusion criteria were individuals who work outside Ontario, or do not hold relevant roles. Results: Our interviews with 10 health system leaders — including hospital chief executive officers, surgeons, administrators and policy experts — resulted in 5 emergent domains: perceptions of the backlog, operationalizing and financing SEMs, barriers, facilitators, and equity and patient factors. All participants emphasized the need for clinical leaders to champion SEMs and the utility of SEMs in managing wait-lists for high-volume, low-acuity, low-complexity and low-variation surgeries. Interpretation: Although SEMs are no panacea, the participants in our study stated that they believe SEMs can improve quality and reduce variability in wait times when SEMs are designed to address local needs and are implemented with buy-in from champions. Health care leaders should consider SEMs for improving surgical backlog management in their local jurisdictions.
Wireless Outdoor Network Planning for a Smart City Pilot

This paper detail the process of radio frequency network planning and design for PASMO pilot project, an open living lab for cooperative ITS and smart cities, starting from analyzing the network and users requirements, going through choosing the appropriate technologies and equipments, and finally testing and validating the feasibility of the proposed deployment plan. Other important outcome of this work is a detailed map of the data rates and signal strength for different radio technologies, supporting the design of robust locationaware communication mechanisms.
Spinal automaticity of movement control and its role in recovering function after spinal injury

ABSTRACT Introduction The significance of the spinal cord in controlling postural and locomotor functions largely reemerged in the mid-1970s under the leadership of Sten Grillner, demonstrating key phenomena of ‘central pattern generation’ and ‘fictive locomotion’ with an evolutionary perspective. These concepts raised the question of how much function can be recovered after paralysis, given the intrinsic automaticity of spinal networks in injured and uninjured states in adults. Areas covered This review explores biological mechanisms governing spinal control of movements such as posture and locomotion. We focus on concepts that have evolved from experiments performed over the past decade. Rather than a comprehensive review of the vast literature on the neural control of posture and locomotion, we focus on the various mechanisms underlying functional automaticity, and their clinical relevance. Expert opinion We propose that multiple combinations of sensory mechanoreceptors linked to proprioception that generate an infinite number of different sensory ensembles, having species-specific meaning and extensive influence in controlling posture and locomotion. These sensory ensembles are translated as a probabilistic phenomenon into highly specific but indeterminate actions. Therefore, we opine that spinal translation of these ensembles in real-time plays a central role in the automaticity of motor control in individuals with and without severe neuromotor dysfunction.