pinecone/core-2020-05-10-deduplication huggingface dataset:
Relative-Zeta and Casimir energy for a semitransparent hyperplane
  selecting transverse modes

We study the relative zeta function for the couple of operators $A_0$ and
$A_\alpha$, where $A_0$ is the free unconstrained Laplacian in
$L^2(\mathbf{R}^d)$ ($d \geq 2$) and $A_\alpha$ is the singular perturbation of
$A_0$ associated to the presence of a delta interaction supported by a
hyperplane. In our setting the operatorial parameter $\alpha$, which is related
to the strength of the perturbation, is of the kind
$\alpha=\alpha(-\Delta_{\parallel})$, where $-\Delta_{\parallel}$ is the free
Laplacian in $L^2(\mathbf{R}^{d-1})$. Thus $\alpha$ may depend on the
components of the wave vector parallel to hyperplane; in this sense $A_\alpha$
describes a semitransparent hyperplane selecting transverse modes. As an
application we give an expression for the associated thermal Casimir energy.
Whenever $\alpha=\chi_{I}(-\Delta_{\parallel})$, where $\chi_{I}$ is the
characteristic function of an interval $I$, the thermal Casimir energy can be
explicitly computed.Comment: 21 page
Analysis of fluctuations around non linear effective dynamics

We consider the derivation of effective equations approximating the many-body
quantum dynamics of a large system of $N$ bosons in three dimensions,
interacting through a two-body potential $N^{3\beta-1}V(N^\beta x)$. For any $0
\leq \beta \leq 1$ well known results establish the trace norm convergence of
the k-particle reduced density matrices associated with the solution of the
many-body Schr\"odinger equation towards products of solutions of a
one-particle non linear Schr\"odinger equation, as $N \to \infty$. In
collaboration with C. Boccato and B. Schlein we studied fluctuations around the
approximate non linear Schr\"odinger dynamics, obtaining for all $0 < \beta <
1$ a norm approximation of the evolution of an appropriate class of data on the
Fock space.Comment: 18 pages; contribution to the Proceedings of the INdAM meeting
  "Contemporary Trends in the Mathematics of Quantum Mechanics" held in Rome in
  July 201
Load-sharing policies in parallel simulation of agent-based demographic models

Execution parallelism in agent-Based Simulation (ABS) allows to deal with complex/large-scale models. This raises the need for runtime environments able to fully exploit hardware parallelism, while jointly offering ABS-suited programming abstractions. In this paper, we target last-generation Parallel Discrete Event Simulation (PDES) platforms for multicore systems. We discuss a programming model to support both implicit (in-place access) and explicit (message passing) interactions across concurrent Logical Processes (LPs). We discuss different load-sharing policies combining event rate and implicit/explicit LPs’ interactions. We present a performance study conducted on a synthetic test case, representative of a class of agent-based models.Peer ReviewedPostprint (author's final draft
Investigations on path indexing for graph databases

Graph databases have become an increasingly popular choice for the management of the massive network data sets arising in many contemporary applications.  We investigate the effectiveness of path indexing for accelerating query processing in graph database systems, using as an exemplar the widely used open-source Neo4j graph database.  We present a novel path index design which supports efficient ordered access to paths in a graph dataset.  Our index is fully persistent and designed for external memory storage and retrieval.  We also describe a compression scheme that exploits the limited differences between consecutive keys in the index, as well as a workload-driven approach to indexing.  We demonstrate empirically the speed-ups achieved by our implementation, showing that the path index yields query run-times from 2x up to 8000x faster than Neo4j.  Empirical evaluation also shows that our scheme leads to smaller indexes than using general-purpose LZ4 compression.  The complete stand-alone implementation of our index, as well as supporting tooling such as a bulk-loader, are provided as open source for further research and development
The pure braid groups and their relatives

In this mostly survey paper, we investigate the resonance varieties, the
lower central series ranks, and the Chen ranks, as well as the residual and
formality properties of several families of braid-like groups: the pure braid
groups $P_n$, the welded pure braid groups $wP_n$, the virtual pure braid
groups $vP_n$, as well as their `upper' variants, $wP_n^+$ and $vP_n^+$. We
also discuss several natural homomorphisms between these groups, and various
ways to distinguish among the pure braid groups and their relatives.Comment: 24 pages; expanded version, to appear in the proceedings of the
  intensive research period on Perspectives in Lie theory (Pisa, 2015),
  Springer INdAM serie
Some semi-direct products with free algebras of symmetric invariants

Let $\mathfrak g$ be a complex reductive Lie algebra and $V$ the underling
vector space of a finite-dimensional representation of $\mathfrak g$. Then one
can consider a new Lie algebra $\mathfrak q=\mathfrak g{\ltimes} V$, which is a
semi-direct product of $\mathfrak g$ and an Abelian ideal $V$. We outline
several results on the algebra $\mathbb C[\mathfrak q^*]^{\mathfrak q}$ of
symmetries invariants of $\mathfrak q$ and describe all semi-direct products
related to the defining representation of $\mathfrak{sl}_n$ with $\mathbb
C[\mathfrak q^*]^{\mathfrak q}$ being a free algebra.Comment: minor correction
EMOOCs 2017: European MOOCs Stakeholders Summit

Producción CientíficaMOOCs can bring together thousands of students with different background in the same course. This fact presents a good opportunity for instructors to design and enact collaborative activities that may involve groups.
However, MOOCs present some drawbacks that can hinder the completion of this kind of activities such as the high drop-out rates, the self-paced character or the resistance of the students to abandon their comfort zone. The use of gamification in MOOC contexts has shown potential benefits to enhance students' engagement, encourage students to interact with others and promote activity submission, which can be useful for increasing the completion of such collaborative activities. This paper shows the gamification design, and the first outcomes, of a MOOC that involves gamified and collaborative activities. To carry this out, the course was configured with 15 badges that students can achieve when performing social and optional actions for completing the courseMinisterio de Economía, Industria y Competitividad (Project TIN2014-53199-C3-2- R)Junta de Castilla y León (programa de apoyo a proyectos de investigación - Ref. VA082U16
To change or not to change? That's the question... On MOOC-success, Barriers and Their Implications

This explorative study aimed to get an understanding of MOOC-success as seen from the perspective of the MOOC-taker and the types of barriers which might stand in the way of this success. Data of two MOOCs was used to illustrate MOOC-success from two perspectives and barriers encountered. Following the currently used approach to identify educational success, the success rate of MOOC-II was 5,6%. The success rates from the perspective of the MOOC-taker was 70%. In addition, data of MOOC-I and II showed that the encountered barriers were mainly non-MOOC-related. Workplace issues and lack of time were most frequently indicated. For MOOC-designers’ decision making regarding redesign of a MOOC after evaluation, it is valuable to have insight in these matters to prevent unnecessary design interventionsThis work is financed via a grant by the Dutch National Initiative for Education Research (NRO)/The Netherlands Organisation for Scientific Research (NWO) and the Dutch Ministry of Education, Culture and Science under the grant nr. 405-15-705 (SOONER/http://sooner.nu)
Risk Stratification of Lung Nodules Using 3D CNN-Based Multi-task
  Learning

Risk stratification of lung nodules is a task of primary importance in lung
cancer diagnosis. Any improvement in robust and accurate nodule
characterization can assist in identifying cancer stage, prognosis, and
improving treatment planning. In this study, we propose a 3D Convolutional
Neural Network (CNN) based nodule characterization strategy. With a completely
3D approach, we utilize the volumetric information from a CT scan which would
be otherwise lost in the conventional 2D CNN based approaches. In order to
address the need for a large amount for training data for CNN, we resort to
transfer learning to obtain highly discriminative features. Moreover, we also
acquire the task dependent feature representation for six high-level nodule
attributes and fuse this complementary information via a Multi-task learning
(MTL) framework. Finally, we propose to incorporate potential disagreement
among radiologists while scoring different nodule attributes in a graph
regularized sparse multi-task learning. We evaluated our proposed approach on
one of the largest publicly available lung nodule datasets comprising 1018
scans and obtained state-of-the-art results in regressing the malignancy
scores.Comment: Accepted for publication at Information Processing in Medical Imaging
  (IPMI) 201
On the Compactness, Efficiency, and Representation of 3D Convolutional
  Networks: Brain Parcellation as a Pretext Task

Deep convolutional neural networks are powerful tools for learning visual
representations from images. However, designing efficient deep architectures to
analyse volumetric medical images remains challenging. This work investigates
efficient and flexible elements of modern convolutional networks such as
dilated convolution and residual connection. With these essential building
blocks, we propose a high-resolution, compact convolutional network for
volumetric image segmentation. To illustrate its efficiency of learning 3D
representation from large-scale image data, the proposed network is validated
with the challenging task of parcellating 155 neuroanatomical structures from
brain MR images. Our experiments show that the proposed network architecture
compares favourably with state-of-the-art volumetric segmentation networks
while being an order of magnitude more compact. We consider the brain
parcellation task as a pretext task for volumetric image segmentation; our
trained network potentially provides a good starting point for transfer
learning. Additionally, we show the feasibility of voxel-level uncertainty
estimation using a sampling approximation through dropout.Comment: Paper accepted at IPMI 201
Avoiding over-detection : towards combined object detection and counting.

Existing object detection frameworks in the deep learning field generally over-detect objects, and use non-maximum suppression (NMS) to filter out excess detections, leaving one bounding box per object. This works well so long as the ground-truth bounding boxes do not overlap heavily, as would be the case with objects that partially occlude each other, or are packed densely together. In these cases it would be beneficial, and more elegant, to have a fully end-to-end system that outputs the correct number of objects without requiring a separate NMS stage. In this paper we discuss the challenges involved in solving this problem, and demonstrate preliminary results from a prototype system
Renewing climate planning locally to attend the 11th Sustainable development goal in the tropics

In the last seven years, tropical cities with a climate plan have tripled compared to the previous seven years. According to the 11th United Nations' Sustainable Development Goal, climate planning should significantly increase by 2030. The Sendai framework for disaster risk reduction (2015) and the New urban agenda signed in Quito (2016) indicate how to achieve this goal through analysis, categories of plans and specific measures. This chapter identifies the main obstacles to the significant increase in tropical human settlements with a climate plan and the possible solutions. First of all, the distribution and trend at 2030 of tropical human settlements are ascertained. Then local access to information on damage, hazard, exposure, vulnerability and risk, and the consideration of these aspects in the national guides to local climate planning are verified. Lastly, the categories of plans and climate measures recommended by the United Nations are compared with those that are most common today, using a database of 401 climate plans for 338 tropical cities relating to 41 countries. The chapter highlights the fact that the prescription for treating tropical cities affected by climate change has been prepared without an accurate diagnosis. Significantly increasing climate planning must consider that small-medium human settlements in the Tropics will prevail at least until 2030. And most effort will be required from Developing and Least Developed Countries. The recommendations of the United Nations concerning the preliminary analyses ignore the fact that local authorities usually do not have access to the necessary information
Relevance and quality of climate planning for large and medium-sized cities of the Tropics

In the last seven years, the number of plans with climate measures for tropical cities has increased 2.3 times compared to the previous seven years as a result of the initiatives of central and local governments, multi-bilateral development aid and development banks. The plans matter in achieving the 11th United Nations' Sustainable development goal. Therefore, the objective of this chapter is to ascertain the relevance and quality of climate planning in large and medium-sized cities in the Tropics. The chapter proposes and applies the QCPI-Quality of Climate Plans Index, consisting of 10 indicators (characterization of climate, number, quantification, relevance, potential impact, cost, funding sources, timetable and responsibility of measures, implementation monitoring and reporting). It is revealed that 338 tropical cities currently have a local development, emergency, master, mitigation, adaptation, risk reduction plan or a resilience or smart city strategy. These tools were unquestionably more common in large cities, especially in OCDE and BRICS countries, while they were rare in Developing Countries. Local development plans (Municipal development, general, comprehensive) were the most common in medium-sized cities, along with those with the lowest quality, while stand-alone strategies and plans (resilience, mitigation, sustainable, adaptation), applied mostly in big cities, present much higher quality
Local and scientific knowledge integration for multi-risk assessment in rural Niger

In the rural Tropics, the participatory risk assessment, based on local knowledge only, is very widespread. This practice is appropriate for hazard identification and for raising the awareness of local communities in relation to the importance of risk reduction, but it is still imprecise in determining risk level, ranking and treatment in a context of climate change, activities in which technical knowledge is unavoidable. Integration of local and technical-scientific knowledge within the framework of an encoded risk assessment method (ISO 31010), could favour more effective decision making with regard to risk reduction. The aim of this chapter is to verify the applicability of a multi-risk local assessment-MLA which combines local knowledge (participatory workshop, transect walk, hazard and resource mapping, disaster historical profile) and scientific knowledge (climate downscaling modelling, hazard probability and scenarios, potential damages, residual risk). The test is carried out in two villages of the Western Niger, particu-larly exposed to flooding and agricultural drought. The risk (hazard probability * potential damages) is identified, analysed (level of risk) and evaluated (residual risk, adaptation measures compared with potential damage costs). The MLA is feasible. The two villages, while bordering on one another, have a different risk ranking. Depending on the village, the risk treatment could reduce the risk level to 17% and to 41% of the current risk, with costs equating to 34% and 28% of the respective potential damages
Renewing climate planning locally in the tropics: Conclusions

In the Tropics, a significant increase in the number of cities provided with climate plans by 2020, as announced in the 11th Sustainable development goalof the United Nations, requires an unprecedented effort. To achieve it, we have to simplify the planning process and improve the quality of the plans. The aim of this book was to collect methods and experiences to inspire the simplification of the planning process and increase the quality of climate planning. We focused attention on the three critical phases of the planning process: analysis, decision making in planning, climate measures. Sixteen case studies from Ethiopia, Haiti, Malawi, Mexico, Niger, Senegal, Tanzania and Thailand cover automatic weather stations in remote areas, rainfall estimation gridded datasets, open data for vulnerability index to climate change, early warning systems, quality of climate plans index, multi-risk local assessment, flooding risk evaluation method, backcasting, spatial dimension in disaster risk reduction and resilience, gasification stoves, index-based insurance and vulnerability risk credit. After indicating the possible analyses, 19 recommendations were supplied to the United Nations SDGs monitoring system, the national weather services and those responsible for natural risks, to the Development banks, Official development aid and the research institutions
Tracking climate change vulnerability at municipal level in rural Haiti using open data

In least developed tropical countries, vulnerability to climate change (CC) at local scale follows an indicator-based approach and uses information gathered mainly through household surveys or focus groups. Conceived in this way, the vulnerability assessment is rarely repeatable in time, cannot be compared with those carried out in other contexts and usually has low spatial coverage. The growing availability of open source information at municipal level, routinely col-lected, now allows us to switch to vulnerability tracking (continuous, low cost, consistent with global monitoring systems). The aim of this chapter is to propose and verify the applicability of a VICC-Vulnerability Index to Climate Change on a municipal scale for Haiti. The chapter identifies open source information on na-tional, departmental and municipal scale, selects the information on a municipal scale on the basis of quality, identifies the indicators, evaluates the robustness of the index and measures it. The index consists of 10 indicators created using infor-mation relating to monthly precipitations, population density, flood prone areas, crop deficit, farmers for self-consumption, rural accessibility, local plans for CC adaptation, irrigated agriculture and cholera incidence. This information is gath-ered for the 125 mainly rural municipalities of Haiti. The description and discus-sion of the results in followed by suggestions to improve the index aimed at do-nors, local authorities and users
Blockchain transaction analysis using dominant sets

Blockchain is an emerging backbone technology behind different crypto-currencies. It can also be used for other purposes and areas. There are different scalability issues associated with blockchain. It is important to know the in depth structure of blockchain by identifying common behaviors of the transactions and the effect of these behaviors on the nodes of the network. Dominant set approach can categorize the blockchain transactions into different clusters without mentioning number of clusters in advance. The experimental evaluation of blockchain transactions shows better clustering accuracy of dominant set approach than existing method of central clustering approach
Socializing accountability for improving primary healthcare: an action research program in rural Karnataka

The Alma Ata Declaration of 1978 invoked a socialising form of accountability through which communities and health workers participated in and were jointly accountable for primary healthcare. Aside from a few experiments, by the 1990s these ideals were quickly replaced by policy prescriptions based on increasing efficiency in data quality and reporting through the introduction of health information systems. More recently, there has been a revival of interest in community participation as a mechanism for improving the poor status of primary healthcare in developing countries through the constitution of village health committees. This paper documents and reflects on nine years of research on interventions aimed at improving primary healthcare accountability in rural Karnataka. Over this period, our focus has shifted from studying how computerised health information systems can strengthen conventional accountability systems to a period of extended participatory action research aimed at socialising accountability practices at village level. The findings from this study constitute vital knowledge for reforming the primary healthcare sector through different policy measures including the design of appropriate technology-based solutions
From Longhand Writing to Word Processing: A Phenomenological Study of the Technophobe Turned Novelist

Firmly grounded on the assumption that using digital technology is an intentional, conscious and subjective experience, this study adopts a transcendental phenomenological approach to reveal the meaning of the individual experience of using digital technology. This study reports the experience of a self-described technophobe, creative woman, who, after learning how to type on a computer keyboard, used word processor software on a donated computer to write and eventually publish a novel. As result of a reflective analysis, according to the tenets of transcendental phenomenology, the essence of the lifeworld phenomenon of using digital technology revealed three interdependent experiences: imaginative, epiphanic and symbiotic. This study explains how an individual uses digital technology to fulfil her needs and achieve her goals as well as demonstrates the potential of transcendental phenomenology in information systems research
Vehicle Classification in Traffic Environments Using the Growing Neural Gas

Traffic monitoring is one of the most popular applications of automated video surveillance. Classification of the vehicles into types is important in order to provide the human traffic controllers with updated information about the characteristics of the traffic flow, which facilitates their decision making process. In this work, a video surveillance system is proposed to carry out such classification. First of all, a feature extraction process is carried out to obtain the most significant features of the detected vehicles. After that, a set of Growing Neural Gas neural networks is employed to determine their types. A qualitative and quantitative assessment of the proposal is carried out on a set of benchmark traffic video sequences, with favorable results.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech