A new wireless sensor platform with camera

Abstractthere are several platforms of wireless sensor networks such as micaz, mica2, etc. Each of them has specific characteristics. But the complexity of novel applications requires new characteristics, which more and more new designs of wireless sensor networks are needed. In this paper, the design of a sensor named Lacuna is proposed, which is a new sensor network platform implementing reliable detecting by taking real-time pictures. The paper presents a simplified model of wireless sensor networks (WSN) which is composed of the Lacuna sensors using IEEE 802.15.4 wireless technology. This model has been tested for many times and the model experimental results show that this system can run stably, reliably and efficiently. Stability, reliability, and efficiency are important because they make the operation robust to temporary disconnections or high packet loss. Due to the stability, reliability, and efficiency, the WSN transmits large amounts of continuous stable picture data messages to notebook when one of the nodes finishes taking a picture

Using machine learning for automated de-identification and clinical coding of free text data in electronic medical records

The widespread adoption of Electronic Medical Records (EMRs) in hospitals continues to increase the amount of patient data that are digitally stored. Although the primary use of the EMR is to support patient care by making all relevant information accessible, governments and health organisations are looking for ways to unleash the potential of these data for secondary purposes, including clinical research, disease surveillance and automation of healthcare processes and workflows. EMRs include large quantities of free text documents that contain valuable information. The greatest challenges in using the free text data in EMRs include the removal of personally identifiable information and the extraction of relevant information for specific tasks such as clinical coding. Machine learning-based automated approaches can potentially address these challenges. This thesis aims to explore and improve the performance of machine learning models for automated de-identification and clinical coding of free text data in EMRs, as captured in hospital discharge summaries, and facilitate the applications of these approaches in real-world use cases. It does so by 1) implementing an end-to-end de-identification framework using an ensemble of deep learning models; 2) developing a web-based system for de-identification of free text (DEFT) with an interactive learning loop; 3) proposing and implementing a hierarchical label-wise attention transformer model (HiLAT) for explainable International Classification of Diseases (ICD) coding; and 4) investigating the use of extreme multi-label long text transformer-based models for automated ICD coding. The key findings include: 1) An end-to-end framework using an ensemble of deep learning base-models achieved excellent performance on the de-identification task. 2) A new web-based de-identification software system (DEFT) can be readily and easily adopted by data custodians and researchers to perform de-identification of free text in EMRs. 3) A novel domain-specific transformer-based model (HiLAT) achieved state-of-the-art (SOTA) results for predicting ICD codes on a Medical Information Mart for Intensive Care (MIMIC-III) dataset comprising the discharge summaries (n=12,808) that are coded with at least one of the most 50 frequent diagnosis and procedure codes. In addition, the label-wise attention scores for the tokens in the discharge summary presented a potential explainability tool for checking the face validity of ICD code predictions. 4) An optimised transformer-based model, PLM-ICD, achieved the latest SOTA results for ICD coding on all the discharge summaries of the MIMIC-III dataset (n=59,652). The segmentation method, which split the long text consecutively into multiple small chunks, addressed the problem of applying transformer-based models to long text datasets. However, using transformer-based models on extremely large label sets needs further research. These findings demonstrate that the de-identification and clinical coding tasks can benefit from the application of machine learning approaches, present practical tools for implementing these approaches, and highlight priorities for further research

De-identifying Hospital Discharge Summaries: An End-to-End Framework using Ensemble of Deep Learning Models

Electronic Medical Records (EMRs) contain clinical narrative text that is of great potential value to medical researchers. However, this information is mixed with Personally Identifiable Information (PII) that presents risks to patient and clinician confidentiality. This paper presents an end-to-end de-identification framework to automatically remove PII from hospital discharge summaries. Our corpus included 600 hospital discharge summaries which were extracted from the EMRs of two principal referral hospitals in Sydney, Australia. Our end-to-end de-identification framework consists of three components: 1) Annotation: labelling of PII in the 600 hospital discharge summaries using five pre-defined categories: person, address, date of birth, identification number, phone number; 2) Modelling: training six named entity recognition (NER) deep learning base-models on balanced and imbalanced datasets; and evaluating ensembles that combine all six base-models, the three base-models with the best F1 scores and the three base-models with the best recall scores respectively, using token-level majority voting and stacking methods; and 3) De-identification: removing PII from the hospital discharge summaries. Our results showed that the ensemble model combined using the stacking Support Vector Machine (SVM) method on the three base-models with the best F1 scores achieved excellent results with a F1 score of 99.16% on the test set of our corpus. We also evaluated the robustness of our modelling component on the 2014 i2b2 de-identification dataset. Our ensemble model, which uses the token-level majority voting method on all six base-models, achieved the highest F1 score of 96.24% at strict entity matching and the highest F1 score of 98.64% at binary token-level matching compared to two state-of-the-art methods. The framework provides a robust solution to de-identifying clinical narrative text safely

Hierarchical Label-wise Attention Transformer Model for Explainable ICD Coding

International Classification of Diseases (ICD) coding plays an important role in systematically classifying morbidity and mortality data. In this study, we propose a hierarchical label-wise attention Transformer model (HiLAT) for the explainable prediction of ICD codes from clinical documents. HiLAT firstly fine-tunes a pretrained Transformer model to represent the tokens of clinical documents. We subsequently employ a two-level hierarchical label-wise attention mechanism that creates label-specific document representations. These representations are in turn used by a feed-forward neural network to predict whether a specific ICD code is assigned to the input clinical document of interest. We evaluate HiLAT using hospital discharge summaries and their corresponding ICD-9 codes from the MIMIC-III database. To investigate the performance of different types of Transformer models, we develop ClinicalplusXLNet, which conducts continual pretraining from XLNet-Base using all the MIMIC-III clinical notes. The experiment results show that the F1 scores of the HiLAT+ClinicalplusXLNet outperform the previous state-of-the-art models for the top-50 most frequent ICD-9 codes from MIMIC-III. Visualisations of attention weights present a potential explainability tool for checking the face validity of ICD code predictions

WindMill: A Parameterized and Pluggable CGRA Implemented by DIAG Design Flow

With the cross-fertilization of applications and the ever-increasing scale of models, the efficiency and productivity of hardware computing architectures have become inadequate. This inadequacy further exacerbates issues in design flexibility, design complexity, development cycle, and development costs (4-d problems) in divergent scenarios. To address these challenges, this paper proposed a flexible design flow called DIAG based on plugin techniques. The proposed flow guides hardware development through four layers: definition(D), implementation(I), application(A), and generation(G). Furthermore, a versatile CGRA generator called WindMill is implemented, allowing for agile generation of customized hardware accelerators based on specific application demands. Applications and algorithm tasks from three aspects is experimented. In the case of reinforcement learning algorithm, a significant performance improvement of $2.3\times$ compared to GPU is achieved.Comment: 7 pages, 10 figure

Reinforcement Learning Agents acquire Flocking and Symbiotic Behaviour in Simulated Ecosystems

In nature, group behaviours such as flocking as well as cross-species symbiotic partnerships are observed in vastly different forms and circumstances. We hypothesize that such strategies can arise in response to generic predator-prey pressures in a spatial environment with range-limited sensation and action. We evaluate whether these forms of coordination can emerge by independent multi-agent reinforcement learning in simple multiple-species ecosystems. In contrast to prior work, we avoid hand-crafted shaping rewards, specific actions, or dynamics that would directly encourage coordination across agents. Instead we test whether coordination emerges as a consequence of adaptation without encouraging these specific forms of coordination, which only has indirect benefit. Our simulated ecosystems consist of a generic food chain involving three trophic levels: apex predator, mid-level predator, and prey. We conduct experiments on two different platforms, a 3D physics engine with tens of agents as well as in a 2D grid world with up to thousands. The results clearly confirm our hypothesis and show substantial coordination both within and across species. To obtain these results, we leverage and adapt recent advances in deep reinforcement learning within an ecosystem training protocol featuring homogeneous groups of independent agents from different species (sets of policies), acting in many different random combinations in parallel habitats. The policies utilize neural network architectures that are invariant to agent individuality but not type (species) and that generalize across varying numbers of observed other agents. While the emergence of complexity in artificial ecosystems have long been studied in the artificial life community, the focus has been more on individual complexity and genetic algorithms or explicit modelling, and less on group complexity and reinforcement learning emphasized in this article. Unlike what the name and intuition suggests, reinforcement learning adapts over evolutionary history rather than a life-time and is here addressing the sequential optimization of fitness that is usually approached by genetic algorithms in the artificial life community. We utilize a shift from procedures to objectives, allowing us to bring new powerful machinery to bare, and we see emergence of complex behaviour from a sequence of simple optimization problems

Compact GF(2) systemizer and optimized constant-time hardware sorters for Key Generation in Classic McEliece

Classic McEliece is a code-based quantum-resistant public-key scheme characterized with relative high encapsulation/decapsulation speed and small cipher- texts, with an in-depth analysis on its security. However, slow key generation with large public key size make it hard for wider applications. Based on this observation, a high-throughput key generator in hardware, is proposed to accelerate the key generation in Classic McEliece based on algorithm-hardware co-design. Meanwhile the storage overhead caused by large-size keys is also minimized. First, compact large-size GF(2) Gauss elimination is presented by adopting naive processing array, singular matrix detection-based early abort, and memory-friendly scheduling strategy. Second, an optimized constant-time hardware sorter is proposed to support regular memory accesses with less comparators and storage. Third, algorithm-level pipeline is enabled for high-throughput processing, allowing for concurrent key generation based on decoupling between data access and computation

LnCompare: gene set feature analysis for human long non-coding RNAs.

Interest in the biological roles of long noncoding RNAs (lncRNAs) has resulted in growing numbers of studies that produce large sets of candidate genes, for example, differentially expressed between two conditions. For sets of protein-coding genes, ontology and pathway analyses are powerful tools for generating new insights from statistical enrichment of gene features. Here we present the LnCompare web server, an equivalent resource for studying the properties of lncRNA gene sets. The Gene Set Feature Comparison mode tests for enrichment amongst a panel of quantitative and categorical features, spanning gene structure, evolutionary conservation, expression, subcellular localization, repetitive sequences and disease association. Moreover, in Similar Gene Identification mode, users may identify other lncRNAs by similarity across a defined range of features. Comprehensive results may be downloaded in tabular and graphical formats, in addition to the entire feature resource. LnCompare will empower researchers to extract useful hypotheses and candidates from lncRNA gene sets

Sperm Cryopreservation in Brown Bear (Ursus arctos): Preliminary Aspects

P. 9-17The development of sperm cryopreservation procedures in brown bear is the basis for establishing a specific genetic resource bank aimed at the preservation of a Cantabric brown bear population, which is seriously threatened. Several issues complicate the development of these cryopreservation procedures: lack of previous specific studies, a high incidence of urospermia and spermagglutination observed in bear ejaculates. Moreover, the availability of individuals for research from these threatened populations is problematic. In the case of the Cantabric brown bear, we have used males from other populations, but of the same species, as surrogates, to carry out a direct extrapolation of the results. Urospermia – Moreover, 70% of the ejaculates are urine contaminated and spermagglutination have a detrimental effect on post‐thawing cell quality recovery in this species. Considering the high value of these samples (autochthonous population with few individuals), a pre‐selection of the ejaculates is not a viable alternative. Preventive methods reducing the mentioned detrimental effects need to be developed. On the basis of previous data, we can suppose that bear spermatozoa resist freezing injuries well. Nevertheless, because of the scarcity of this information, it is necessary to conduct further research on bear semen freezing under field conditions. Epidydimal spermatozoa can be important for genetic resource banking of threatened populations and thus specific cryobiological protocols need to be assayed. To date, 168 brown bear ejaculates have been frozen by the ITRA‐ULE group at the University of León (Spain) in the development of methodologies for the preservation of brown bear sperm.S