pinecone/core-2020-05-10-deduplication huggingface dataset:
Cost-Sensitive Reference Pair Encoding for Multi-Label Learning

Label space expansion for multi-label classification (MLC) is a methodology
that encodes the original label vectors to higher dimensional codes before
training and decodes the predicted codes back to the label vectors during
testing. The methodology has been demonstrated to improve the performance of
MLC algorithms when coupled with off-the-shelf error-correcting codes for
encoding and decoding. Nevertheless, such a coding scheme can be complicated to
implement, and cannot easily satisfy a common application need of
cost-sensitive MLC---adapting to different evaluation criteria of interest. In
this work, we show that a simpler coding scheme based on the concept of a
reference pair of label vectors achieves cost-sensitivity more naturally. In
particular, our proposed cost-sensitive reference pair encoding (CSRPE)
algorithm contains cluster-based encoding, weight-based training and
voting-based decoding steps, all utilizing the cost information. Furthermore,
we leverage the cost information embedded in the code space of CSRPE to propose
a novel active learning algorithm for cost-sensitive MLC. Extensive
experimental results verify that CSRPE performs better than state-of-the-art
algorithms across different MLC criteria. The results also demonstrate that the
CSRPE-backed active learning algorithm is superior to existing algorithms for
active MLC, and further justify the usefulness of CSRPE.Comment: Accepted in 22nd Pacific-Asia Conference on Knowledge Discovery and
  Data Mining (PAKDD), 201
Using metric space indexing for complete and efficient record linkage

Record linkage is the process of identifying records that refer to the same real-world entities, in situations where entity identifiers are unavailable. Records are linked on the basis of similarity between common attributes, with every pair being classified as a link or non-link depending on their degree of similarity. Record linkage is usually performed in a three-step process: first groups of similar candidate records are identified using indexing, pairs within the same group are then compared in more detail, and finally classified. Even state-of-the-art indexing techniques, such as Locality Sensitive Hashing, have potential drawbacks. They may fail to group together some true matching records with high similarity. Conversely, they may group records with low similarity, leading to high computational overhead. We propose using metric space indexing to perform complete record linkage, which results in a parameter-free record linkage process combining indexing, comparison and classification into a single step delivering complete and efficient record linkage. Our experimental evaluation on real-world datasets from several domains shows that linkage using metric space indexing can yield better quality than current indexing techniques, with similar execution cost, without the need for domain knowledge or trial and error to configure the process.Postprin
The forward kinematics of doubly-planar Gough-Stewart platforms and the position analysis of strips of tetrahedra

The final publication is available at link.springer.comA strip of tetrahedra is a tetrahedron-tetrahedron truss where any tetrahedron has two neighbors except those in the extremes which have only one. The problem of finding all the possible lengths for an edge in the strip compatible with a given distance imposed between the strip end-points has been revealed of relevance due to the large number of possible applications. In this paper, this is applied to solve the forward kinematics of 6-6 Gough-Stewart platforms with planar base and moving platform, a problem which is known to have up to 40 solutions (20 if we do not consider mirror configurations with respect to the base as different solutions).Peer ReviewedPostprint (author's final draft
Wellbeing economics : the capabilities approach to prosperity.

Economists have long sought to maximise economic growth, believing this to be their best contribution to improving human welfare. That approach is not sustainable in the face of ongoing issues such as global climate change, environmental damage, rising inequality and enduring poverty. Alternatives must be found.\ud
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This open access book addresses that challenge. It sets out a wellbeing economics framework that directly addresses fundamental issues affecting wellbeing outcomes. Drawing inspiration from the capabilities approach of Nobel Prize winner Amartya Sen, the book demonstrates how persons can enhance prosperity through their own actions and through collaboration with others.\ud
\ud
The book examines national public policy, but its analysis also focuses on choices made by individuals, households, families, civil society, local government and the global community. It therefore offers important insights for anyone concerned with improving personal wellbeing and community prosperity
YOUMARES 8 – Oceans Across Boundaries: Learning from each other: Proceedings of the 2017 conference for YOUng MARine RESearchers in Kiel, Germany\ud

This open access book presents the proceedings volume of the YOUMARES 8 conference, which took place in Kiel, Germany, in September 2017, supported by the German Association for Marine Sciences (DGM). The YOUMARES conference series is entirely bottom-up organized by and for YOUng MARine RESearchers. Qualified early career scientists moderated the scientific sessions during the conference and provided literature reviews on aspects of their research field. These reviews and the presenters’ conference abstracts are compiled here. Thus, this book discusses highly topical fields of marine research and aims to act as a source of knowledge and inspiration for further reading and research
Biodiversity and the Functioning of Ecosystems in the Age of Global Change: Integrating Knowledge Across Scales

The dramatic decline of biodiversity worldwide has raised a general concern on the impacts this process could have for the well-being of humanity. Human societies strongly depend on the benefits provided by natural ecosystems, which are the result of biogeochemical processes governed by species activities and their interaction with abiotic compartments. After decades of experimental research on the biodiversity-functioning relationship, a relative agreement has been reached on the mechanisms underlying the impacts that biodiversity loss can have on ecosystem processes. However, a general consensus is still missing. We suggest that the reason preventing an integration of existing knowledge is the scale discrepancy between observations on global change impacts and biodiversity-functioning experiments. The present chapter provides an overview of global change impacts on biodiversity across various ecological scales and its consequences for ecosystem functioning, highlighting what is known and where knowledge gaps still persist. Furthermore, the reader will be introduced to a set of tools that allow a multi-scale analysis of how global change drivers impact ecosystem functioning
Microplastics in Aquatic Systems – Monitoring Methods and Biological Consequences

Microplastic research started at the turn of the millennium and is of growing interest, as microplastics have the potential to affect a whole range of organisms, from the base of the food web to top predators, including humans. To date, most studies are initial assessments of microplastic abundances for a certain area, thereby generally distinguishing three different sampling matrices: water, sediment and biota samples. Those descriptive studies are important to get a first impression of the extent of the problem, but for a proper risk assessment of ecosystems and their inhabitants, analytical studies of microplastic fluxes, sources, sinks, and transportation pathways are of utmost importance. Moreover, to gain insight into the effects microplastics might have on biota, it is crucial to identify realistic environmental concentrations of microplastics. Thus, profound knowledge about the effects of microplastics on biota is still scarce. Effects can vary regarding habitat, functional group of the organism, and polymer type for example, making it difficult to find quick answers to the many open questions. In addition, microplastic research is accompanied by many methodological challenges that need to be overcome first to assess the impact of microplastics on aquatic systems. Thereby, a development of standardized operational protocols (SOPs) is a pre-requisite for comparability among studies. Since SOPs are still lacking and new methods are developed or optimized very frequently, the aim of this chapter is to point out the most crucial challenges in microplastic research and to gather the most recent promising methods used to quantify environmental concentrations of microplastics and effect studies
Can Climate Models Simulate the Observed Strong Summer Surface Cooling in the Equatorial Atlantic?

Variability in the tropical Atlantic Ocean is dominated by the seasonal cycle. A defining feature is the migration of the inter-tropical convergence zone into the northern hemisphere and the formation of a so-called cold tongue in sea surface temperatures (SSTs) in late boreal spring. Between April and August, cooling leads to a drop in SSTs of approximately 5°. The pronounced seasonal cycle in the equatorial Atlantic affects surrounding continents, and even minor deviations from it can have striking consequences for local agricultures.\ud
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Here, we report how state-of-the-art coupled global climate models (CGCMs) still struggle to simulate the observed seasonal cycle in the equatorial Atlantic, focusing on the formation of the cold tongue. We review the basic processes that establish the observed seasonal cycle in the tropical Atlantic, highlight common biases and their potential origins, and discuss how they relate to the dynamics of the real world. We also briefly discuss the implications of the equatorial Atlantic warm bias for CGCM-based reliable, socio-economically relevant seasonal predictions in the region
Model Transformation Reuse across Metamodels

Model transformations (MTs) are essential elements of model-driven engineering (MDE) solutions. MDE promotes the creation of domain-specific metamodels, but without proper reuse mechanisms, MTs need to be developed from scratch for each new metamodel. In this paper, we classify reuse approaches for MTs across different metamodels and compare a sample of specific approaches – model types, concepts, a-posteriori typing, multilevel modeling, and design patterns for MTs – with the help of a feature model developed for this purpose, as well as a common example. We discuss strengths and weaknesses of each approach, provide a reading grid used to compare their features, and identify gaps in current reuse approaches
The making and evaluation of Picts and Pixels : mixed exhibiting in the real and the unreal

Museums publicly display collections in a physical space to relay narratives and concepts to their audiences. Progressive technologies in an exhibition can bring in varying demographics and gather higher footfall for a museum as well as present digital heritage interpretation in an innovative manner. A mixed media exhibition can facilitate subjects with limited physical resources or difficult to display pieces as well as the visual landscape the objects were found within. A combination of Virtual Reality headsets, 3D digitized objects, digitally reconstructed archaeological sites alongside traditional object displays as methods of interpretation substantiate research in techniques and usability as well as challenges of recoup cost and digital literacies. This paper investigates the methodology, technology and evaluation of the mixed media exhibition Picts & Pixels presented by Culture Perth and Kinross and the Open Virtual Worlds research team at the University of St Andrews at the Perth Museum and Art Gallery in summer 2017.Postprin
Fidelity perception of 3D models on the web

Cultural heritage artefacts act as a gateway helping people learn about their social traditions and history. However, preserving these artefacts faces many difficulties, including potential destruction or damage from global warming, wars and conflicts, and degradation from day-to-day use. In addition, artefacts can only be present in one place at a time, and many of them can not be exhibited due to the limited physical space of museums. The digital domain offers opportunities to capture and represent the form and texture of these artefacts and to overcome the previously mentioned constraints by allowing people to access and interact with them on multiple platforms (mobile devices, tablets and personal computers) and network regimes. Through two experiments we study the subjective perception of the fidelity of 3D models in web browsers in order to discover perceptible resolution thresholds. This helps us create models of reasonable graphical complexity that could be fetched on the biggest range of end devices. It also enables us to design systems which efficiently optimise the user experience by adapting their behaviour based upon user perception, model characteristics and digital infrastructure.Postprin
A conceptual framework for social movements analytics for national security.

Social media tools have changed our world due to the way they convey information between individuals; this has led to many social movements either starting on social media or being organised and managed through this medium. At times however, certain human-induced events can trigger Human Security Threats such as Personal Security, Health Security, Economic Security or Political Security. The aim of this paper is to propose a holistic Data Analysis Framework for examining Social Movements and detecting pernicious threats to National Security interests. As a result of this, the proposed framework focuses on three main stages of an event (Detonating Event, Warning Period and Crisis Interpretation) to provide timely additional insights, enabling policy makers, first responders, and authorities to determine the best course of action. The paper also outlines the possible computational techniques utilised to achieve in depth analysis at each stage. The robustness and effectiveness of the framework are demonstrated by dissecting Warning Period scenarios, from real-world events, where the increase of Human Security aspects were key to identifying likely threats to National Security
Multi-Resolution Fully Convolutional Neural Networks for Monaural Audio Source Separation

In deep neural networks with convolutional layers, all the\ud
neurons in each layer typically have the same size receptive fields (RFs)\ud
with the same resolution. Convolutional layers with neurons that have\ud
large RF capture global information from the input features, while layers\ud
with neurons that have small RF size capture local details with high\ud
resolution from the input features. In this work, we introduce novel deep\ud
multi-resolution fully convolutional neural networks (MR-FCN), where\ud
each layer has a range of neurons with different RF sizes to extract multi-\ud
resolution features that capture the global and local information from its\ud
input features. The proposed MR-FCN is applied to separate the singing\ud
voice from mixtures of music sources. Experimental results show that\ud
using MR-FCN improves the performance compared to feedforward deep\ud
neural networks (DNNs) and single resolution deep fully convolutional\ud
neural networks (FCNs) on the audio source separation problem
Improving reverberant speech separation with binaural cues using temporal context and convolutional neural networks

Given binaural features as input, such as interaural level difference\ud
and interaural phase difference, Deep Neural Networks (DNNs)\ud
have been recently used to localize sound sources in a mixture of speech\ud
signals and/or noise, and to create time-frequency masks for the estimation\ud
of the sound sources in reverberant rooms. Here, we explore a\ud
more advanced system, where feed-forward DNNs are replaced by Convolutional\ud
Neural Networks (CNNs). In addition, the adjacent frames\ud
of each time frame (occurring before and after this frame) are used to\ud
exploit contextual information, thus improving the localization and separation\ud
for each source. The quality of the separation results is evaluated\ud
in terms of Signal to Distortion Ratio (SDR)
Perceptual evaluation of blind source separation in object-based audio production

Object-based audio has the potential to enable multime-\ud
dia content to be tailored to individual listeners and their reproduc-\ud
tion equipment. In general, object-based production assumes that the\ud
objects|the assets comprising the scene|are free of noise and inter-\ud
ference. However, there are many applications in which signal separa-\ud
tion could be useful to an object-based audio work\ud
ow, e.g., extracting\ud
individual objects from channel-based recordings or legacy content, or\ud
recording a sound scene with a single microphone array. This paper de-\ud
scribes the application and evaluation of blind source separation (BSS)\ud
for sound recording in a hybrid channel-based and object-based workflow, in which BSS-estimated objects are mixed with the original stereo\ud
recording. A subjective experiment was conducted using simultaneously\ud
spoken speech recorded with omnidirectional microphones in a rever-\ud
berant room. Listeners mixed a BSS-extracted speech object into the\ud
scene to make the quieter talker clearer, while retaining acceptable au-\ud
dio quality, compared to the raw stereo recording. Objective evaluations\ud
show that the relative short-term objective intelligibility and speech qual-\ud
ity scores increase using BSS. Further objective evaluations are used to\ud
discuss the in\ud
uence of the BSS method on the remixing scenario; the\ud
scenario shown by human listeners to be useful in object-based audio is\ud
shown to be a worse-case scenario
A Bramble-Pasciak conjugate gradient method for discrete Stokes problems
  with lognormal random viscosity

We study linear systems of equations arising from a stochastic Galerkin
finite element discretization of saddle point problems with random data and its
iterative solution. We consider the Stokes flow model with random viscosity
described by the exponential of a correlated random process and shortly discuss
the discretization framework and the representation of the emerging matrix
equation. Due to the high dimensionality and the coupling of the associated
symmetric, indefinite, linear system, we resort to iterative solvers and
problem-specific preconditioners. As a standard iterative solver for this
problem class, we consider the block diagonal preconditioned MINRES method and
further introduce the Bramble-Pasciak conjugate gradient method as a promising
alternative. This special conjugate gradient method is formulated in a
non-standard inner product with a block triangular preconditioner. From a
structural point of view, such a block triangular preconditioner enables a
better approximation of the original problem than the block diagonal one. We
derive eigenvalue estimates to assess the convergence behavior of the two
solvers with respect to relevant physical and numerical parameters and verify
our findings by the help of a numerical test case. We model Stokes flow in a
cavity driven by a moving lid and describe the viscosity by the exponential of
a truncated Karhunen-Loeve expansion. Regarding iteration counts, the
Bramble-Pasciak conjugate gradient method with block triangular preconditioner
is superior to the MINRES method with block diagonal preconditioner in the
considered example.Comment: 23 pages, 3 figures, 1 tabl
Generic Singularities of 3D Piecewise Smooth Dynamical Systems

The aim of this paper is to provide a discussion on current directions of
research involving typical singularities of 3D nonsmooth vector fields. A brief
survey of known results is presented. The main purpose of this work is to
describe the dynamical features of a fold-fold singularity in its most basic
form and to give a complete and detailed proof of its local structural
stability (or instability). In addition, classes of all topological types of a
fold-fold singularity are intrinsically characterized. Such proof essentially
follows firstly from some lines laid out by Colombo, Garc\'ia, Jeffrey,
Teixeira and others and secondly offers a rigorous mathematical treatment under
clear and crisp assumptions and solid arguments. One should to highlight that
the geometric-topological methods employed lead us to the completely
mathematical understanding of the dynamics around a T-singularity. This
approach lends itself to applications in generic bifurcation theory. It is
worth to say that such subject is still poorly understood in higher dimension.Comment: 28 pages, 12 figure
Use of DUCK Keyboard for a Daily Use

This article presents the new version of the DUCK keyboard. To evaluate this new prototype, we chose an evaluation with novice users, in two steps: A first step with a copy of sentences. Then we let them get familiarized with our system for 15 days. Finally, we re-evaluate their performance after this phase of familiarization. Results shows that, after a learning phase, the participants significantly improve their input performance with the DUCK keyboard. Text input speed and accuracy have been improved
Kernelization lower bounds for finding constant-size subgraphs.

Kernelization is an important tool in parameterized algorithmics. Given an input instance accompanied by a parameter, the goal is to compute in polynomial time an equivalent instance of the same problem such that the size of the reduced instance only depends on the parameter and not on the size of the original instance. In this paper, we provide a first conceptual study on limits of kernelization for several polynomial-time solvable problems. For instance, we consider the problem of finding a triangle with negative sum of edge weights parameterized by the maximum degree of the input graph. We prove that a linear-time computable strict kernel of truly subcubic size for this problem violates the popular APSP-conjecture
Robust Pedestrian Detection for Semi-automatic\ud
Construction of A Crowded Person\ud
Re-Identification Dataset

The problem of re-identification of people in a crowd com-\ud
monly arises in real application scenarios, yet it has received less atten-\ud
tion than it deserves. To facilitate research focusing on this problem, we\ud
have embarked on constructing a new person re-identification dataset\ud
with many instances of crowded indoor and outdoor scenes. This paper proposes a two-stage robust method for pedestrian detection in a\ud
complex crowded background to provide bounding box annotations. The\ud
first stage is to generate pedestrian proposals using Faster R-CNN and\ud
locate each pedestrian using Non-maximum Suppression (NMS). Candidates in dense proposal regions are merged to identify crowd patches.\ud
We then apply a bottom-up human pose estimation method to detect\ud
individual pedestrians in the crowd patches. The locations of all subjects are achieved based on the bounding boxes from the two stages. The\ud
identity of the detected subjects throughout each video is then automatically annotated using multiple features and spatial-temporal clues. The\ud
experimental results on a crowded pedestrians dataset demonstrate the\ud
effectiveness and efficiency of the proposed method