Reviewerly/exHarmony huggingface dataset:
Extraction of Family History Information From Clinical Notes: Deep Learning and Heuristics Approach

Background Electronic health records store large amounts of patient clinical data. Despite efforts to structure patient data, clinical notes containing rich patient information remain stored as free text, greatly limiting its exploitation. This includes family history, which is highly relevant for applications such as diagnosis and prognosis. Objective This study aims to develop automatic strategies for annotating family history information in clinical notes, focusing not only on the extraction of relevant entities such as family members and disease mentions but also on the extraction of relations between the identified entities. Methods This study extends a previous contribution for the 2019 track on family history extraction from national natural language processing clinical challenges by improving a previously developed rule-based engine, using deep learning (DL) approaches for the extraction of entities from clinical notes, and combining both approaches in a hybrid end-to-end system capable of successfully extracting family member and observation entities and the relations between those entities. Furthermore, this study analyzes the impact of factors such as the use of external resources and different types of embeddings in the performance of DL models. Results The approaches developed were evaluated in a first task regarding entity extraction and in a second task concerning relation extraction. The proposed DL approach improved observation extraction, obtaining F1 scores of 0.8688 and 0.7907 in the training and test sets, respectively. However, DL approaches have limitations in the extraction of family members. The rule-based engine was adjusted to have higher generalizing capability and achieved family member extraction F1 scores of 0.8823 and 0.8092 in the training and test sets, respectively. The resulting hybrid system obtained F1 scores of 0.8743 and 0.7979 in the training and test sets, respectively. For the second task, the original evaluator was adjusted to perform a more exact evaluation than the original one, and the hybrid system obtained F1 scores of 0.6480 and 0.5082 in the training and test sets, respectively. Conclusions We evaluated the impact of several factors on the performance of DL models, and we present an end-to-end system for extracting family history information from clinical notes, which can help in the structuring and reuse of this type of information. The final hybrid solution is provided in a publicly available code repository.
Global Trend of the 21st Century: the Deterritorization of Philosophy

Leading trend of the philosophical process is today the deterritorization of phi­losophy, that is the displacement of the focus of the philosophizing from the area of properly philosophical concepts and problems to areas of other discourses and disciplines. A more general phenomenon takes place too: the deterritorization be­comes a universal mechanism, which acts in all parts of the global system and on&nbsp;all its levels. Among many vectors of the deterritorization the following three are of prime importance for the future of the global system: the deterritorization to virtual reality, to cosmos and to eschaton. The program of the complex study of the deterritorization of philosophy is outlined and, as the first stage of this program, the analysis of the deterritorization to science is performed. We find that the development of the relationship of philosophy and science follows the&nbsp;paradigm of alternating phases of scientific onset trying to subordinate phi­losophy to science and philosophical rebuff, which restores anew the irreducible authenticity and autonomy of philosophical mind. The latest stages of this alter­nation are analyzed, starting with the onset of synergetics. We show, how scien­tific formations that came after the synergetic paradigm such as the autopoiesis and the enactivism improved gradually their epistemology and acquired form and nature of sound constructivist philosophy. Nevertheless the philosophical re­buff finds that they do not overcome essentially old scientism, which is unac­ceptable for philosophy.
Infrastructure-enhanced Multi-target Tracking Using a Multiple-model PHD Filter

Environment perception is crucial for the development of autonomous driving and advanced driver assistance systems. The cooperative perception using the infrastructure sensors can significantly expand the field of view of on-board sensors and improve the accuracy of target tracking. In this paper, we propose a hybrid vehicular perception system that incorporates both received feature-level information from infrastructure sensors and track-level data from the multi-access edge computing server (MEC-Server). An infrastructure-enhanced multiple-model probability hypothesis density is proposed to handle the feature-level data from heterogeneous infrastructure sensors. The problem of kinematic state estimation is improved with the prior information of the road environment. Furthermore, a generic communication interface between the infrastructure sensor and MEC-Server is designed, which allows the object data to have the same notion of locality through the use of a generic object state model. Simulation results show that the presented algorithm provides higher accuracy and reliability after considering the prior information of the road environment.
Computational Two-Party Correlation: A Dichotomy for Key-Agreement Protocols

Let $\pi$ be an efficient two-party protocol that, given security parameter $\kappa$, both parties output single bits $X_\kappa$ and $Y_\kappa$, respectively. We are interested in how $(X_\kappa,Y_\kappa)$ “appears” to an efficient adversary that only views the transcript $T_\kappa$. We make the following contributions: (a) We develop new tools to argue about this loose notion and show (modulo some caveats) that for every such protocol $\pi$, there exists an efficient simulator such that the following holds: on input $T_\kappa$, the simulator outputs a pair $(X'_\kappa,Y'_\kappa)$ such that $(X'_\kappa,Y'_\kappa,T_\kappa)$ is (somewhat) computationally indistinguishable from $(X_\kappa,Y_\kappa,T_\kappa)$. (b) We use these tools to prove the following dichotomy theorem: every such protocol $\pi$ is either uncorrelated---it is (somewhat) indistinguishable from an efficient protocol whose parties interact to produce $T_\kappa$, but then choose their outputs independently from some product distribution (that is determined in poly-time from $T_\kappa$), or the protocol implies a key-agreement protocol (for infinitely many $\kappa$'s). Uncorrelated protocols are uninteresting from a cryptographic viewpoint, as the correlation between outputs is (computationally) trivial. Our dichotomy shows that every protocol is either completely uninteresting or implies key-agreement. (c) We use the above dichotomy to make progress on open problems on minimal cryptographic assumptions required for differentially private mechanisms for the XOR function. (d) A subsequent work [I. Haitner, N. Makriyannis, and E. Omri, in Theory of Cryptography Conference, Springer, Cham, Switzerland, 2018, pp. 539--562] uses the above dichotomy to makes progress on a long-standing open question regarding the complexity of fair two-party coin-flipping protocols. We also highlight the following two ideas regarding our technique: (a) The simulator algorithm is obtained by a carefully designed “competition” between efficient algorithms attempting to forecast $(X_\kappa,Y_\kappa)|_{T_\kappa=t}$. The winner is used to simulate the outputs of the protocol. (b) Our key-agreement protocol uses the simulation to reduce to an information theoretic setup and is, in some sense, a non-black-box.
The Outlook for Networks in the Third Decade of the 21st Century

Presents the introductory editorial for this issue of the publication.
Exploring Students’ Feedback in Online Assessment System Using Opinion Mining Technique

Opinion mining has been widely used in recent online reviews or feedback due to its ability to analyse text-based data. The use of this technique for analysing data from students’ feedback needs to be addressed, since most educational institutions are focusing more on questionnaires based on the Likert-scale rather than on the open-review type. To this end, there is a lack of online assessment systems that could automatically analyse open-review questionnaires. Therefore, the main aim of this study is to analyse students’ feedback in an online assessment system through the opinion mining technique, by focusing on textual form data derived from the open-review questionnaires. To achieve this aim, an opinion mining feedback system, known as the OMFeedback, was developed. The Vader Sentiment Intensity Analyser was adapted for processing students’ feedback and the lexicon based approach was used for analysing the words. In addition, the OMFeedback incorporates the capitalisation of words and emoji features to enrich the capability of the system. This newly developed system could lead to new paradigms in educational institutions for enhancing students’ learning process and for guiding them through their learning journey.
On the Hamming and Symbol-Pair Distance of Constacyclic Codes of Length $$p^s$$ over $$\mathbb {F}_{p^m}+ u\mathbb {F}_{p^m}$$

Let $$\mathcal {R}=\mathbb {F}_{p^m}+ u\mathbb {F}_{p^m}, u^2=0 $$ , be the finite commutative chain ring with unity, where p is a prime number, m is a positive integer and $$\mathbb {F}_{p^m}$$ is the finite field with $$p^m$$ elements. In this work, we give a simple and short proof of classification of all $$ \gamma $$ -constacyclic codes of length $$p^s$$ over $$\mathcal {R}$$ , that is, ideals of the ring $$\mathcal {R}[x]/\langle x^{p^{s}}- \gamma \rangle $$ , where $$ \gamma $$ is a nonzero element of the field $$\mathbb {F}_{p^m}$$ . This allows us to study the Hamming and symbol-pair distance distributions of all such codes.
Use of Machine learning to predict asthma exacerbations

<b>Background:</b> Asthma exacerbations negatively impact disease progression and can lead to hospitalizations and death. Ability to predict exacerbations may allow intervention for prevention and improve outcomes. We aimed to develop models using machine learning to predict risk of exacerbations, using Swedish patient level data. <b>Methods:</b> Data for 33,538 asthma patients were collected from electronic medical records and national registries covering healthcare contacts, diagnoses, prescriptions, lab tests, hospitalizations and socioeconomic factors, between 2000 and 2013. Machine-learning classifiers and logistic regression were used to create models to predict exacerbations within the next 15 days for two groups of adult asthma patients, either including or excluding Chronic Obstructive Pulmonary Disease (COPD). Model performance was assessed by mean cross validation score of area under precision-recall curve (AUPRC) and Area under receiver operating curve (AUROC) was used to compare performance with previous studies. <b>Results:</b> The predictors of exacerbation were comorbidity burden, time since first exacerbation, number of previous exacerbations and number of healthcare contacts due to asthma within the last year. Model validation on test data yielded an AUROC of 0.9 and AUPRC of 0.010, when COPD was included and AUROC=0.90 and AUPRC=0.007 when COPD was excluded. <b>Conclusion:</b> Our work suggests that clinically available information on patient history collected via EMRs and national registries might not suffice to form the basis for tools to predict future risk of asthma exacerbation. Supplementation with other kinds of data might be necessary to improve performance of the predictive model to develop a more clinically useful tool.
Author Profiling in Social Media with Multimodal Information

This paper summarizes the thesis: ”Author Profiling in Social Media with Multimodal Information.” Our solution uses a multimodal approach to extracting information from written messages and images shared by users. Previous work has shown the existence of useful information for this task in these modalities; however, our proposal goes further, demonstrating the complementarity of the modalities when merging these two sources of information. To do this, we propose to transform images to texts, and with them, to have the same framework of representation for both kinds of information, which allow to achieve their fusion. Our work explores different methods for extracting information either from the text and the images. To represent the extracted information, different distributional term representations approaches were explored in order to identify the topics addressed by the user. For this purpose, an evaluation framework was proposed in order to identify the most appropriate method for this task. The results show that the textual descriptions of the images contain useful information for the author profiling task, and that the fusion of textual information with information extracted from the images increases the accuracy of this task.
Impact of Globalization on Human Development: A Panel Data Analysis of Selected South Asian Countries

This study tends to evaluate the impact of globalization on human development in developing countries. The objective has been achieved by analyzing the data of six SAARC countries (Pakistan, India, Sri-Lanka, Bhutan, Bangladesh, and Nepal) over 20 years from 2000-2019. Afghanistan and Maldives were removed from the study because of the unavailability of data. The multiple regression model is used for estimation, which includes many economic and demographic variables. The focused independent variables are the trade openness and foreign direct investment (FDI), which are used as a proxy for globalization. Other explanatory variables are GDP growth, percentage of the population using safely managed sanitation services, infant mortality ratio, maternal mortality ratio, birth rate, death rate, and dependency ratio (% of working-age population). Hausman test has indicated that the fixed effect model is appropriate for this study. The results of fixed effect estimation indicated that FDI has positive while trade openness has a negative impact on Human Development in selected South Asian countries.
Rammer: Enabling Holistic Deep Learning Compiler Optimizations with rTasks

Performing Deep Neural Network (DNN) computation on hardware accelerators efficiently is challenging. Existing DNN frameworks and compilers often treat the DNN operators in a data flow graph (DFG) as opaque library functions and schedule them onto accelerators to be executed individually. They rely on another layer of scheduler, often implemented in hardware, to exploit the parallelism available in the operators. Such a two-layered approach incurs significant scheduling overhead and often cannot fully utilize the available hardware resources. In this paper, we propose RAMMER, a DNN compiler design that optimizes the execution of DNN workloads on massively parallel accelerators. RAMMER generates an efficient static spatio-temporal schedule for a DNN at compile time to minimize scheduling overhead. It maximizes hardware utilization by holistically exploiting parallelism through inter- and intra- operator co-scheduling. RAMMER achieves this by proposing several novel, hardware neutral, and clean abstractions for the computation tasks and the hardware accelerators. These abstractions expose a much richer scheduling space to RAMMER, which employs several heuristics to explore this space and finds efficient schedules. We implement RAMMER for multiple hardware backends such as NVIDIA GPUs, AMD GPUs, and Graphcore IPU. Experiments show RAMMER significantly outperforms state-of-the-art compilers such as TensorFlow XLA and TVM by up to 20.1×. It also outperforms TensorRT, a vendor optimized proprietary DNN inference library from NVIDIA, by up to 3.1×.
Generating Orthogonal Voronoi Treemap for Visualization of Hierarchical Data

A novel space partitioning strategy is presented for implicit hierarchy visualization. The proposed orthogonal Voronoi treemap (OVT) partitions an empty canvas into nested orthogonal rectangles, thus the generated layout is not only flexible to diversified data value, but also much tidier than the Voronoi treemap with nested polygons. To achieve this, we first introduce a new distance calculation strategy in order to generate axis-aligned segmentation among the sites. To cope with the new segmentation strategy, we then design a sweepline + skyline heuristic algorithm to partition the canvas to generate an orthogonal Voronoi treemap. Comparative analyses on the computational experiment results in terms of aspect ratio is discussed.
Percolation transition control in quantum networks

Percolation theory allows simple description of the phase transition based on the scaling properties of the network clusters with respect to a single parameter—site/bond occupation probability. How to design a network exhibiting the percolation transition for a chosen occupation probability has been an open problem. At the same time, the task to find a structurally simple network having the desired property seemed to be impossible. I suggest a model, where the combination of the classical and the quantum resources creates a percolation transition, continuous or discontinuous on demand, for any preselected occupation probability and already in the simplest possible one-dimensional network.
Design of a Solver for Multi-Agent Epistemic Planning

As the interest in Artificial Intelligence continues to grow it is becoming more and more important to investigate formalization and tools that allow us to exploit logic to reason about the world.In particular, given the increasing number of multi-agents systems that could benefit from techniques of automated reasoning, exploring new ways to define not only the world's status but also the agents' information is constantly growing in importance.This type of reasoning, i.e., about agents' perception of the world and also about agents' knowledge of her and others' knowledge, is referred to as epistemic reasoning.In our work we will try to formalize this concept, expressed through epistemic logic, for dynamic domains.In particular we will attempt to define a new action-based language for multi-agent epistemic planning and to implement an epistemic planner based on it.This solver should provide a tool flexible enough to be able to reason on different domains, e.g., economy, security, justice and politics, where reasoning about others' beliefs could lead to winning strategies or help in changing a group of agents' view of the world.
Quantum Speed Limits For Adiabatic Evolution, Loschmidt Echo and Beyond

One often needs to estimate how fast an evolving state of a quantum system can depart from some target state or target subspace of a Hilbert space. Such estimates are known as quantum speed limits. We derive a quantum speed limit for a general time-dependent target subspace. When the target subspace is an instantaneous invariant subspace of a time-dependent Hamiltonian, the obtained quantum speed limit bounds the adiabatic fidelity, which is a figure of merit of quantum adiabaticity. We also compare two states evolving under two different Hamiltonians and derive a bound on the Loschmidt echo.
Intelligent Recognition of Time Stamp Characters in Solar Scanned Images from Film

Prior to the availability of digital cameras, the solar observational images are typically recorded on films, and the information such as date and time were stamped in the same frames on film. It is significant to extract the time stamp information on the film so that the researchers can efficiently use the image data. This paper introduces an intelligent method for extracting time stamp information, namely, the convolutional neural network (CNN), which is an algorithm in deep learning of multilayer neural network structures and can identify time stamp character in the scanned solar images. We carry out the time stamp decoding for the digitized data from the National Solar Observatory from 1963 to 2003. The experimental results show that the method is accurate and quick for this application. We finish the time stamp information extraction for more than 7 million images with the accuracy of 98%.
Quantum correlations for anonymous metrology

We introduce the task of anonymous metrology, in which a physical parameter of an object may be determined without revealing the object's location. Alice and Bob share a correlated quantum state, with which one of them probes the object. Upon receipt of the quantum state, Charlie is then able to estimate the parameter without knowing who possesses the object. We show that quantum correlations are resources for this task when Alice and Bob do not trust the devices in their labs. The anonymous metrology protocol moreover distinguishes different kinds of quantum correlations according to the level of desired security: discord is needed when the source of states is trustworthy, otherwise entanglement is necessary.
Exploiting Higher Order Smoothness in Derivative-free Optimization and Continuous Bandits

We study the problem of zero-order optimization of a strongly convex function. The goal is to find the minimizer of the function by a sequential exploration of its values, under measurement noise. We study the impact of higher order smoothness properties of the function on the optimization error and on the cumulative regret. To solve this problem we consider a randomized approximation of the projected gradient descent algorithm. The gradient is estimated by a randomized procedure involving two function evaluations and a smoothing kernel. We derive upper bounds for this algorithm both in the constrained and unconstrained settings and prove minimax lower bounds for any sequential search method. Our results imply that the zero-order algorithm is nearly optimal in terms of sample complexity and the problem parameters. Based on this algorithm, we also propose an estimator of the minimum value of the function achieving almost sharp oracle behavior. We compare our results with the state-of-the-art, highlighting a number of key improvements.
Free Kleene algebras with domain

First we identify the free algebras of the class of algebras of binary relations equipped with the composition and domain operations. Elements of the free algebras are pointed labelled finite rooted trees. Then we extend to the analogous case when the signature includes all the Kleene algebra with domain operations; that is, we add union and reflexive transitive closure to the signature. In this second case, elements of the free algebras are 'regular' sets of the trees of the first case. As a corollary, the axioms of domain semirings provide a finite quasiequational axiomatisation of the equational theory of algebras of binary relations for the intermediate signature of composition, union, and domain. Next we note that our regular sets of trees are not closed under complement, but prove that they are closed under intersection. Finally, we prove that under relational semantics the equational validities of Kleene algebras with domain form a decidable set.
Cellular Monads from Positive GSOS Specifications

We give a leisurely introduction to our abstract framework for operational semantics based on cellular monads on transition categories. Furthermore, we relate it for the first time to an existing format, by showing that all Positive GSOS specifications generate cellular monads whose free algebras are all compositional. As a consequence, we recover the known result that bisimilarity is a congruence in the generated labelled transition system.