nils-herrmann/clean_scirepeval huggingface dataset:
Multicriteria decision analysis in the reimbursement of new medical technologies: real world experiences from Hungary.
Venue: Value in health the journal of the International Society for Pharmacoeconomics and Outcomes Research
Year: 2014

We read with great interest the excellent article of Sussex et al.  on a pilot study of multicriteria decision analysis (MCDA) for valuing orphan medicines. MCDA seems to be more important in the past years, and many countries have tried to use it for evaluating new medical technologies [2,3] We would like to give a short overview on the application of MCDA in Hungary. Hungary can be considered an upper middle income country  We run a solidarity based health insurance system with a single payer, where the National Health Insurance Fund Administration (in Hungarian: Orszagos Egeszsegbiztositasi Penztar [OEP] is the only health care financing agency [5,6] All the hospitals with the exception of church hospitals are owned by the central government. On behalf of the central government, a governmental public agency, the National Institute for Qualityand Organizational Development in Healthcare and Medicines (in Hungarian: Gyogyszereszeti es Egeszsegugyi Minoseges Szervezetfejlesztesi Intezet [GYEMSZI] is responsible for the supervision of Hungarian hospitals [7] The Hungarian health technology assessment (HTA) Office was established in 2004, and it also belongs to GYEMSZI  Hungary adopted the directive 89/105/EEC of the Council of the European Communities on transparency for the evaluation of new drugs (2004) and medical devices (2007) applying for health insurance reimbursement  We also have a formal guideline for conducting economic evaluation of health care interventions in Hungary [11] More detailed description of the Hungarian health care system can be found elsewhere [12 17] MDCA was introduced in Hungary by a ministerial decree in 2010 for the evaluation of new hospital medical technologies applied in hospital care [18] In this term, medical technologies do include neither drugs nor medical devices. As a part of the application dossier, one must submit a formal HTA report (including a health economics analysis) clinical evaluation, clinical expert opinion, and detailed cost calculation. Detailed cost calculation should demonstrate how the new hospital medical technology complies with the special financing methods of the Hungarian hospital care. It means that beyond the cost of new technology, its additional costs (e.g. implantation surgery, hospital stay, and nursing care) must be calculated according to the diagnosis related groups such as hospital care financing [19,20] The key players of the decision making process are the health care financing agency (OEP) HTA office (within GYMESZI) Hungarian Medical College, and Scientific Council in Health Care.
Learning to Compose Words into Sentences with Reinforcement Learning
Venue: ICLR
Year: 2017

We use reinforcement learning to learn tree structured neural networks for computing representations of natural language sentences. In contrast with prior work on tree structured models in which the trees are either provided as input or predicted using supervision from explicit treebank annotations, the tree structures in this work are optimized to improve performance on a downstream task. Experiments demonstrate the benefit of learning task specific composition orders, outperforming both sequential encoders and recursive encoders based on treebank annotations. We analyze the induced trees and show that while they discover some linguistically intuitive structures (e.g. noun phrases, simple verb phrases) they are different than conventional English syntactic structures.
Graph Learning for Combinatorial Optimization: A Survey of State of the Art
Venue: Data Sci. Eng.
Year: 2021

Graphs have been widely used to represent complex data in many applications, such as e commerce, social networks, and bioinformatics. Efficient and effective analysis of graph data is important for graph based applications. However, most graph analysis tasks are combinatorial optimization (CO) problems, which are NP hard. Recent studies have focused a lot on the potential of using machine learning (ML) to solve graph based CO problems. Most recent methods follow the two stage framework. The first stage is graph representation learning, which embeds the graphs into low dimension vectors. The second stage uses machine learning to solve the CO problems using the embeddings of the graphs learned in the first stage. The works for the first stage can be classified into two categories, graph embedding methods and end to end learning methods. For graph embedding methods, the learning of the the embeddings of the graphs has its own objective, which may not rely on the CO problems to be solved. The CO problems are solved by independent downstream tasks. For end to end learning methods, the learning of the embeddings of the graphs does not have its own objective and is an intermediate step of the learning procedure of solving the CO problems. The works for the second stage can also be classified into two categories, non autoregressive methods and autoregressive methods. Non autoregressive methods predict a solution for a CO problem in one shot. A non autoregressive method predicts a matrix that denotes the probability of each node/edge being a part of a solution of the CO problem. The solution can be computed from the matrix using search heuristics such as beam search. Autoregressive methods iteratively extend a partial solution step by step. At each step, an autoregressive method predicts a node/edge conditioned to current partial solution, which is used to its extension. In this survey, we provide a thorough overview of recent studies of the graph learning based CO methods. The survey ends with several remarks on future research directions.
Climate and Capitalism: English Perceptions of Newfoundland's Natural Environment and Economic Value, 1610 1699
Year: 2017

For English merchants, planters and politicians, colonizing Newfoundland required learning the limitations and opportunities afforded by the island's natural environment. The crucial period for this learning process took place from 1610, the first English effort to colonize the island, to the 1699 passing of the Act to Encourage the Trade to Newfoundland, which defined the cod fishery as the island's only viable industry. During these eighty nine years, English enterprises and policies consistently failed to meet the expectations of their backers, and new information challenged accepted ideas about Newfoundland's climate and natural resources, pressuring the supporters of those decisions to reassess the island's economic value and role as a colonial possession. This reflexive cycle of ideas, implementation and feedback shaped the creation of a series of policies and practices that guided English efforts to colonize and profit from Newfoundland by altering what industries were prioritized and how they were regulated. As a result of this process, English efforts in Newfoundland shifted away from developing an economically diverse colony with little government oversight to maximizing the fishery through government management. The challenges encountered by the English in Newfoundland, specifically how to adapt to the island's natural environment and defining what the role of a colony with a single major industry was, shaped the development of the political, social and economic institutions and questions that influence the island to this day. This dissertation's original contribution is its examination of this reflexive process and how the methods employed by policymakers, planters and merchants to learn about Newfoundland defined English colonization efforts and policymaking during the seventeenth century. This approach differs from previous studies, which have focused on how the English inhabitants in Newfoundland adapted to the island, by examining how perceptions of the island's natural environment changed in England, and how that affected business ventures and policies during the seventeenth century.
E correlator: an entropy based alert correlation system
Venue: Secur. Commun. Networks
Year: 2015

With the rapid size and complexity growth of computer networks, network supervisors are now facing a new problem, which is to analyze and manage the large amounts of security alerts that can be generated by security devices. Alert correlation systems attempt to solve this problem by finding the similarity and causality relationships between raw alerts and providing high level view of the network under surveillance. Several alert correlation methods have been proposed recently to detect known attack scenarios. This paper focuses on how to develop an intrusion alert correlation system according to the information existed in the raw alerts without using any predefined knowledge. For this purpose, first, we define the concept of alert partial entropy to find the alert clusters with the same information. Then, we represent the alert clusters by an intelligible notation called hyper alerts. The network supervisor can reduce the number of hyper alerts based on the principle of maximum entropy or by using the concept of hyper alerts partial entropy. For more visualization, we define the hyper alerts graph, which provides a global view of intrusion alerts. Our results show that the proposed entropy based alert correlation system E correlator can simplify the analysis of large number of alerts. We achieved the promising reduction ratio of 99.98% in LLS_DDOS_1.0 attack scenario in DARPA2000 dataset while the constructed hyper alerts have enough information to discover the attacker, the victim, and the attack scenario.
Strategic Asset Allocation with Climate Change v
Year: 2019

We develop a top down strategic pathway towards green investing and show that ecoinvesting should not be a puzzle or a sacrifice if investors consider the unknown impact of climate change on different asset classes. We investigate the impact of climate change over a large universe of asset classes including stocks, bonds, alternatives and a list of green assets which have low carbon emissions, in terms of their time varying risk return trade offs using a factor based vector autoregression (VAR) model with climate risk as an additional state variable. Estimation results show that green assets are, in general, resilient against temperature change, while many grey assets are negatively related to the risk of warming. When simulating a future investment opportunity set, we disentangle the climate effects from the return processes and introduce two climate scenarios: an optimistic scenario implied by the VAR estimates; and a pessimistic one based on the DICE model. We find a substantial demand for green assets under the pessimistic scenario, which verifies the hypothesis that investors with a decent awareness of climate change should find it profitable to invest in green.
Developing Professional Self Regulation of Students during Pedagogical Practice
Year: 2020

The current study aims to understand the development of students' professional self regulation during pedagogical practice. Fifty participants of this study were fourth year students of the Ternopil Volodymyr Hnatiuk National Pedagogical University, Ukraine. The following questions were considered: Has any certain level of professional self regulation been formed before practice? Will it increase during the pedagogical practice? Is it possible to influence the process of formation of future teachers' professional self regulation during the pedagogical practice by offering them specific tasks? To diagnose the formation of self regulatory mechanisms of students during pedagogical practice, we propose to use the phenomenon of readiness (namely, how to determine, characterize, and measure it) The research was carried out using the methods of questionnaires, conceptual dictionary, and expert evaluation. The level of readiness for professional self regulation in most respondents has increased from the "lower" to the "upper" limit within the Medium level. The results contribute to the conclusion that professional self regulation of a teacher as a necessary component of future professional activity is developed effectively during pedagogical practice; moreover, this process is intensified by the implementation of assignments aimed at understanding self regulatory mechanisms.
Quasi Newton methods based on ordinary differential equation approach for unconstrained nonlinear optimization
Venue: Appl. Math. Comput.
Year: 2014

In this paper, we propose a hybrid ODE based quasi Newton (QN) method for unconstrained optimization problems, which combines the idea of low order implicit Runge Kutta (RK) techniques for gradient systems with the QN type updates of the Jacobian matrix such as the symmetric rank one (SR1) update. The main idea of this approach is to associate a QN matrix to approximate numerically the Jacobian matrix in the gradient system. Fundamentally this is a gradient system based on the first order optimality conditions of the optimization problem. To further extend the methods for solving large scale problems, a feature incorporated to the proposed methods is that a limited memory setting for matrix vector multiplications is used, thus avoiding the computational and storage issues when computing Jacobian information. Under suitable assumptions, global convergence of the proposed method is proved. Practical insights on the effectiveness of these approaches on a set of test functions are given by a numerical comparison with that of the limited memory BFGS algorithm (L BFGS) and conjugate gradient algorithm (CG)
Precomputing Shell Fragments for OLAP using Inverted Index Data Structure
Year: 2018

Efficient methods to generate data cubes for On Line Analytical Processing or OLAP are required for query processing and data analysis. OLAP involves multidimensional analysis of data and as well as selectively extracting and viewing data from different perspectives or points of view. In OLAP, a complex query can lead to many scans of the base relational database, leading to poor performance. This research paper provides an algorithm for the data cube generation suitable for OLAP systems in a fast way. The OLAP cube structure, based on aggregation operations and capable of fast retrieval of data, is extensively explored. The inverted index data structure, which is a mapping from content to index of the said content in any indexed data storage system, is used as an efficient tool for shell fragment computation. A study of efficiency and trade offs involved in terms of processing complexity and storage space when compared to full cube computation are also provided here. Keywords OLAP, data cube, cube shell, shell fragmentation, inverted index data structure, multidimensional analysis
Decentralized Deep Learning Using Momentum Accelerated Consensus
Venue: ICASSP 2021 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2021

We consider the problem of decentralized deep learning where multiple agents collaborate to learn from a distributed dataset. While several decentralized deep learning approaches exist, the majority consider a central parameter server topology for aggregating the model parameters from the agents. However, such a topology may be inapplicable in networked systems such as ad hoc mobile networks, field robotics, and power network systems where direct communication with the central parameter server may be inefficient. In this context, we propose and analyze a novel decentralized deep learning algorithm where the agents interact over a fixed communication topology (without a central server) Our algorithm is based on the heavy ball acceleration method used in gradient based optimization. We propose a novel consensus protocol where each agent shares with its neighbors its model parameters and gradient momentum values during the optimization process. We consider nonconvex objective functions and theoretically analyze our algorithm's performance. We present several empirical comparisons with competing decentralized learning methods to demonstrate the efficacy of our approach under different communication topologies.
Using Word2Vec to process big text data
Venue: 2015 IEEE International Conference on Big Data (Big Data)
Year: 2015

Big data is a broad data set that has been used in many fields. To process huge data set is a time consuming work, not only due to its big volume of data size, but also because data type and structure can be different and complex. Currently, many data mining and machine learning technique are being applied to deal with big data problem; some of them can construct a good learning algorithm in terms of lots of training example. However, considering the data dimension, it will be more efficient if learning algorithm is capable of selecting useful features or decreasing the feature dimension. Word2Vec, proposed and supported by Google, is not an individual algorithm, but it consists of two learning models, Continuous Bag of Words (CBOW) and Skip gram. By feeding text data into one of learning models, Word2Vec outputs word vectors that can be represented as a large piece of text or even the entire article. In our work, we first training the data via Word2Vec model and evaluated the word similarity. In addition, we clustering the similar words together and use the generated clusters to fit into a new data dimension so that the data dimension is decreased.
Multi functional stretchable and flexible sensor array to determine the location, shape, and pressure: Application in a smart robot
Year: 2018

The rapid increase in the aging population prompts the development of wearable devices and sophisticated robots. With their ability to collect complex information about their surroundings via e skins, robots could perform more dynamic and variable tasks such as rescue missions or caring for the elderly. In this paper, we present a new concept of utilizing a very simple, highly flexible and stretchable capacitor sensor array, that can be attached on the surface of a retractable robot hand to realize three functions: determining the location, shape, and pressure of an object. This adaptive sensing system is accomplished using capacitors connected by aligned carbon nanotube (CNT) films constructed on an elastomer dielectric material, which can reduce the requirement on the accuracy of the machine vision system. This study has a very broad application in the manufacture of intelligent software robots.
Enhanced Performance of Ultrafiltration Membrane with Powdered Zeolite Addition for Secondary Effluent Treatment
Year: 2013

The research on ultrafiltration membrane assisted by powdered zeolite for the treatment of secondary effluent from a municipal wastewater treatment plant was studied. The results show that membrane fouling rate is reduced by pre coating the ultrafiltration membrane with powdered zeolite, and the treatment performance of secondary effluent is enhanced. UV vis, three dimensional excitation emission matrix (3D EEM) fluorescence spectra and scanning electron microscopy (SEM) images for ultrafiltration were also discussed.
Somatic multicellularity as a satisficing solution to the prediction error minimization problem
Venue: Communicative integrative biology
Year: 2019

Adaptive success in the biosphere requires the dynamic ability to adjust physiological, transcriptional, and behavioral responses to environmental conditions. From chemical networks to organisms to whole communities, biological entities at all levels of organization seek to optimize their predictive power. Here, we argue that this fundamental drive provides a novel perspective on the origin of multicellularity. One way for unicellular organisms to minimize surprise with respect to external inputs is to be surrounded by reproductively disabled, i.e. somatic copies of themselves highly predictable agents which in effect reduce uncertainty in their microenvironments. We show that the transition to multicellularity can be modeled as a phase transition driven by environmental threats. We present modeling results showing how multicellular bodies can arise if non reproductive somatic cells protect their reproductive parents from environmental lethality. We discuss how a somatic body can be interpreted as a Markov blanket around one or more reproductive cells, and how the transition to somatic multicellularity can be represented as a transition from exposure of reproductive cells to a high uncertainty environment to their protection from environmental uncertainty by this Markov blanket. This is, effectively, a transition by the Markov blanket from transparency to opacity for the variational free energy of the environment. We suggest that the ability to arrest the cell cycle of daughter cells and redirect their resource utilization from division to environmental threat amelioration is the key innovation of obligate multicellular eukaryotes, that the nervous system evolved to exercise this control over long distances, and that cancer is an escape by somatic cells from the control of reproductive cells. Our quantitative model illustrates the evolutionary dynamics of this system, provides a novel hypothesis for the origin of multicellular animal bodies, and suggests a fundamental link between the architectures of complex organisms and information processing in proto cognitive cellular agents.
Clopidogrel versus Aspirin after Dual Antiplatelet Therapy in Acute Myocardial Infarction Patients Undergoing Drug Eluting Stenting
Venue: Korean circulation journal
Year: 2019

There is a paucity of data regarding the benefit of clopidogrel monotherapy after dual antiplatelet therapy (DAPT) in patients treated with drug eluting stents (DES) This study compared outcome between clopidogrel versus aspirin as monotherapy after DES for acute myocardial infarction (MI) Methods From Korea Acute Myocardial Infarction Registry National Institute of Health database, 1,819 patients treated with DES who were switched to monotherapy with clopidogrel (n=534) or aspirin (n=1,285) after uneventful 12 month DAPT were analyzed. The primary endpoint was net adverse clinical events (NACE) defined as a composite of death from any cause, MI, repeat percutaneous coronary intervention (PCI) stent thrombosis, ischemic stroke, or major bleeding during the period from 12 to 24 months.  After adjustment using inverse probability of treatment weighting, patients who received clopidogrel, compared with those treated with aspirin, had a similar incidence of NACE (0.7% and 0.7% hazard ratio, 1.06; 95% confidence interval, 0.31 3.60; p=0.923) The 2 groups had similar rates of death from any cause (0.1% in each group, p=0.789) MI (0.3% and 0.1% respectively; p=0.226) repeat PCI (0.1% and 0.3% respectively; p=0.548) stent thrombosis (0.1% and 0% respectively; p=0.121) major bleeding (0.2% in each group, p=0.974) and major adverse cardiovascular and cerebrovascular events (0.5% in each group, p=0.924)  Monotherapy with clopidogrel, compared to aspirin, after DAPT showed similar clinical outcomes in patients with acute MI treated with DES.