pinecone/core-2020-05-10-deduplication huggingface dataset:
On the logarithmic probability that a random integral ideal is $\mathscr
  A$-free

This extends a theorem of Davenport and Erd\"os on sequences of rational
integers to sequences of integral ideals in arbitrary number fields $K$. More
precisely, we introduce a logarithmic density for sets of integral ideals in
$K$ and provide a formula for the logarithmic density of the set of so-called
$\mathscr A$-free ideals, i.e. integral ideals that are not multiples of any
ideal from a fixed set $\mathscr A$.Comment: 9 pages, to appear in S. Ferenczi, J. Ku{\l}aga-Przymus and M.
  Lema\'nczyk (eds.), Chowla's conjecture: from the Liouville function to the
  M\"obius function, Lecture Notes in Math., Springe
On the well-posedness of SPDEs with singular drift in divergence form

We prove existence and uniqueness of strong solutions for a class of
second-order stochastic PDEs with multiplicative Wiener noise and drift of the
form $\operatorname{div} \gamma(\nabla \cdot)$, where $\gamma$ is a maximal
monotone graph in $\mathbb{R}^n \times \mathbb{R}^n$ obtained as the
subdifferential of a convex function satisfying very mild assumptions on its
behavior at infinity. The well-posedness result complements the corresponding
one in our recent work arXiv:1612.08260 where, under the additional assumption
that $\gamma$ is single-valued, a solution with better integrability and
regularity properties is constructed. The proof given here, however, is
self-contained.Comment: 11 page
Examples of renormalized SDEs

We demonstrate two examples of stochastic processes whose lifts to geometric
rough paths require a renormalisation procedure to obtain convergence in rough
path topologies. Our first example involves a physical Brownian motion subject
to a magnetic force which dominates over the friction forces in the small mass
limit. Our second example involves a lead-lag process of discretised fractional
Brownian motion with Hurst parameter $H \in (1/4,1/2)$, in which the stochastic
area captures the quadratic variation of the process. In both examples, a
renormalisation of the second iterated integral is needed to ensure convergence
of the processes, and we comment on how this procedure mimics negative
renormalisation arising in the study of singular SPDEs and regularity
structures.Comment: 13 page
From non-symmetric particle systems to non-linear PDEs on fractals

We present new results and challenges in obtaining hydrodynamic limits for
non-symmetric (weakly asymmetric) particle systems (exclusion processes on
pre-fractal graphs) converging to a non-linear heat equation. We discuss a
joint density-current law of large numbers and a corresponding large deviations
principle.Comment: v2: 10 pages, 1 figure. To appear in the proceedings for the 2016
  conference "Stochastic Partial Differential Equations & Related Fields" in
  honor of Michael R\"ockner's 60th birthday, Bielefel
Bell Correlations and the Common Future

Reichenbach's principle states that in a causal structure, correlations of
classical information can stem from a common cause in the common past or a
direct influence from one of the events in correlation to the other. The
difficulty of explaining Bell correlations through a mechanism in that spirit
can be read as questioning either the principle or even its basis: causality.
In the former case, the principle can be replaced by its quantum version,
accepting as a common cause an entangled state, leaving the phenomenon as
mysterious as ever on the classical level (on which, after all, it occurs). If,
more radically, the causal structure is questioned in principle, closed
space-time curves may become possible that, as is argued in the present note,
can give rise to non-local correlations if to-be-correlated pieces of classical
information meet in the common future --- which they need to if the correlation
is to be detected in the first place. The result is a view resembling Brassard
and Raymond-Robichaud's parallel-lives variant of Hermann's and Everett's
relative-state formalism, avoiding "multiple realities."Comment: 8 pages, 5 figure
Replacing Nothing with Something Special: Contextuality-by-Default and
  Dummy Measurements

The object of contextuality analysis is a set of random variables each of
which is uniquely labeled by a content and a context. In the measurement
terminology, the content is that which the random variable measures, whereas
the context describes the conditions under which this content is measured (in
particular, the set of other contents being measured "together" with this one).
Such a set of random variables is deemed noncontextual or contextual depending
on whether the distributions of the context-sharing random variables are or are
not compatible with certain distributions imposed on the content-sharing random
variables. In the traditional approaches, contextuality is either restricted to
only consistently-connected systems (those in which any two content-sharing
random variables have the same distribution) or else all
inconsistently-connected systems (those not having this property) are
considered contextual. In the Contextuality-by-Default theory, an
inconsistently connected system may or may not be contextual. There are several
arguments for this understanding of contextuality, and this note adds one more.
It is related to the fact that generally not each content is measured in each
context, so there are "empty" content-context pairs. It is convenient to treat
each of these empty pairs as containing a dummy random variable, one that does
not change the degree of contextuality in a system. These dummy random
variables are deterministic ones, attaining a single value with probability 1.
The replacement of absent random variables with deterministic ones, however,
can only be made if one allows for inconsistently-connected systems.
  KEYWORDS: contextuality, dummy measurements, inconsistent connectedness,
random variables.Comment: 3 pp, To be published by In A. Khrennikov & T. Bourama (Eds) Quantum
  Foundations, Probability and Information. Springe
External observer reflections on QBism

In this short review I present my personal reflections on QBism. I have no
intrinsic sympathy neither to QBism nor to subjective interpretation of
probability in general. However, I have been following development of QBism
from its very beginning, observing its evolution and success, sometimes with
big surprise. Therefore my reflections on QBism can be treated as "external
observer" reflections. I hope that my view on this interpretation of quantum
mechanics (QM) has some degree of objectivity. It may be useful for researchers
who are interested in quantum foundations, but do not belong to the
QBism-community, because I tried to analyze essentials of QBism critically
(i.e., not just emphasizing its advantages, as in a typical QBist publication).
QBists, too, may be interested in comments of an external observer who
monitored development of this approach to QM during the last 16 years. The
second part of the paper is devoted to interpretations of probability,
objective versus subjective, and views of Kolmogorov, von Mises, and de
Finetti. Finally, de Finetti's approach to methodology of science is presented
and compared with QBism.Comment: polishing text, better expression
At Home in the Hospital and Hospitalized at Home: Exploring Experiences of Cancer Care Environments

Contemporary cancer care takes place within a healthcare system catering for a highly mobile demographic. This study aimed to better understand the experience of patients within the cancer care environment (CCE) and the role of spatial aspects therein. We explore the effectiveness of photovoice in discussing this experience over time and the extent to which image production helps emphasize the role of spatial aspects. Three patients were interviewed over the course of six weeks. Experiences of the CCE turned out to change over time and across space as repeated travel to the hospital and transitions within the hospital resulted in new impressions and routines. Participants describe the dynamic and linked makeup of the CCE, suggesting a concatenation of places over time. The photovoice method blurs the boundary between researcher and participant, allowing features of the CCE to come to the fore that would otherwise not be considered. Over time, the hospital becomes a second home to some, facilitating more than medical consultations and treatments only. A particular challenge for hospital design is therefore to improve the initial experience. Simultaneously, the home environment becomes a place of medical care at a distance. Caution is required when transforming the home into such a place as patients can feel insecure and distant from the watchful eye of the specialists.status: publishe
Improving pool design: Interviewing physically impaired architects

People with a temporary or permanent physical impairment are often excluded from bathing activities, due to the difficulties of getting into and out of the water. This paper explores pool design, specifically the design of the access to the tank, which is key for a pools inclusivity. In trying to break down existing barriers between users, accessibility experts and designers, we interviewed physically impaired architects about their perception of four types of pool access often used by wheelchair users; ramps, transfer walls, transfer systems and lifts.
The interviews revealed limitations in all four types of pool access. Therefore, combining different types of access in one single pool may be of interest in finding a balance between the limitations identified. Moreover, the interviews allowed to identify another type of pool access, designed by one of the interviewees: an upper pool border connected to an underwater bed and seat, which allows for an easier exit than transfer walls and transfer systems. Another interviewee advanced the idea of a cane holder for physically and visually impaired people, which may contribute to freeing poolside floors from obstacles and reducing the risk of falls.
All these insights may contribute to make pool design more inclusive, by accommodating specific temporary or permanent needs related to the mobility of all of us.status: publishe
Automatic Brain Tumor Segmentation using Cascaded Anisotropic
  Convolutional Neural Networks

A cascade of fully convolutional neural networks is proposed to segment
multi-modal Magnetic Resonance (MR) images with brain tumor into background and
three hierarchical regions: whole tumor, tumor core and enhancing tumor core.
The cascade is designed to decompose the multi-class segmentation problem into
a sequence of three binary segmentation problems according to the subregion
hierarchy. The whole tumor is segmented in the first step and the bounding box
of the result is used for the tumor core segmentation in the second step. The
enhancing tumor core is then segmented based on the bounding box of the tumor
core segmentation result. Our networks consist of multiple layers of
anisotropic and dilated convolution filters, and they are combined with
multi-view fusion to reduce false positives. Residual connections and
multi-scale predictions are employed in these networks to boost the
segmentation performance. Experiments with BraTS 2017 validation set show that
the proposed method achieved average Dice scores of 0.7859, 0.9050, 0.8378 for
enhancing tumor core, whole tumor and tumor core, respectively. The
corresponding values for BraTS 2017 testing set were 0.7831, 0.8739, and
0.7748, respectively.Comment: 12 pages, 5 figures. MICCAI Brats Challenge 201
Generalised Wasserstein Dice Score for Imbalanced Multi-class
  Segmentation using Holistic Convolutional Networks

The Dice score is widely used for binary segmentation due to its robustness
to class imbalance. Soft generalisations of the Dice score allow it to be used
as a loss function for training convolutional neural networks (CNN). Although
CNNs trained using mean-class Dice score achieve state-of-the-art results on
multi-class segmentation, this loss function does neither take advantage of
inter-class relationships nor multi-scale information. We argue that an
improved loss function should balance misclassifications to favour predictions
that are semantically meaningful. This paper investigates these issues in the
context of multi-class brain tumour segmentation. Our contribution is
threefold. 1) We propose a semantically-informed generalisation of the Dice
score for multi-class segmentation based on the Wasserstein distance on the
probabilistic label space. 2) We propose a holistic CNN that embeds spatial
information at multiple scales with deep supervision. 3) We show that the joint
use of holistic CNNs and generalised Wasserstein Dice scores achieves
segmentations that are more semantically meaningful for brain tumour
segmentation.Comment: Accepted as an oral presentation at the MICCAI 2017 Brain Lesion
  (BrainLes) Worksho
Bilingual contexts from comparable corpora to mine for translations of collocations

Proceedings of the 17th International Conference on Intelligent Text Processing and Computational Linguistics, CICLing2016Due to the limited availability of parallel data in many languages, we propose a methodology that benefits from comparable corpora to find translation equivalents for collocations (as a specific type of difficult-to-translate multi-word expressions). Finding translations is known to be more difficult for collocations than for words. We propose a method based on bilingual context extraction and build a word (distributional) representation model drawing on these bilingual contexts (bilingual English-Spanish contexts in our case). We show that the bilingual context construction is effective for the task of translation equivalent learning and that our method outperforms a simplified distributional similarity baseline in finding translation equivalents
The quest for the Decisive Constitutional Moment (DCM)

The Constitution, as fundamental, formal law, needs some form of representation in order to gain validity. This chapter demonstrates that, apart from using symbols such as tables of the law, artists alternatively chose for an artistic device, for which the name DCM (Decisive Constitutional Moment) is put forward. This device entails the selection and depiction of a specific, pivotal moment from constitutional history. The definition of this device consists of three parts, drawing on existing theory about the decisive moment by photographer Henri Cartier-Bresson, the constitutional moment of legal scholar Bruce Ackerman, and the depiction and narration of a history and a moment in visual art, with particular attention for the relationship between the depicted moment and the moment of depiction. The chapter’s focus is on works of art from the long nineteenth century, the age of revolution, Constitution and a new idiom in art history in which artist took scenes from contemporary life as subject matter, thus combining a journalistic eyewitness account with an academic style. Looking into the main artistic media of this era and their use in DCMs allows for a better understanding of the legitimizing function of the art works under scrutiny. The chapter further develops with an elaborate study of the design entries for the 1849 competition for a constitutional monument in Brussels, Belgium, which demonstrates the variety of possible decisive constitutional moments and the challenges which artists faced when including them in their designs
Automatic Cardiac Disease Assessment on cine-MRI via Time-Series
  Segmentation and Domain Specific Features

Cardiac magnetic resonance imaging improves on diagnosis of cardiovascular
diseases by providing images at high spatiotemporal resolution. Manual
evaluation of these time-series, however, is expensive and prone to biased and
non-reproducible outcomes. In this paper, we present a method that addresses
named limitations by integrating segmentation and disease classification into a
fully automatic processing pipeline. We use an ensemble of UNet inspired
architectures for segmentation of cardiac structures such as the left and right
ventricular cavity (LVC, RVC) and the left ventricular myocardium (LVM) on each
time instance of the cardiac cycle. For the classification task, information is
extracted from the segmented time-series in form of comprehensive features
handcrafted to reflect diagnostic clinical procedures. Based on these features
we train an ensemble of heavily regularized multilayer perceptrons (MLP) and a
random forest classifier to predict the pathologic target class. We evaluated
our method on the ACDC dataset (4 pathology groups, 1 healthy group) and
achieve dice scores of 0.945 (LVC), 0.908 (RVC) and 0.905 (LVM) in a
cross-validation over the training set (100 cases) and 0.950 (LVC), 0.923 (RVC)
and 0.911 (LVM) on the test set (50 cases). We report a classification accuracy
of 94% on a training set cross-validation and 92% on the test set. Our results
underpin the potential of machine learning methods for accurate, fast and
reproducible segmentation and computer-assisted diagnosis (CAD).Comment: To appear in the STACOM 2017 proceeding
Enumerating preferred extensions : a case study of human reasoning

This paper seeks to better understand the links between human reasoning and preferred extensions as found within formal argumentation, especially in the context of uncertainty. The degree of believability of a conclusion may be associated with the number of preferred extensions in which the conclusion is credulously accepted. We are interested in whether people agree with this evaluation. A set of experiments with human participants is presented to investigate the validity of such an association. Our results show that people tend to agree with the outcome of a version of Thimm’s probabilistic semantics in purely qualitative domains as well as in domains in which conclusions express event likelihood. Furthermore, we are able to characterise this behaviour: the heuristics employed by people in understanding preferred extensions are similar to those employed in understanding probabilities.Postprin
A dynamic model of trust in dialogues

In human interactions, trust is regularly updated during a discussion. For example, if someone is caught lying, any further utterances they make will be discounted, until trust is regained. This paper seeks to model such behaviour by introducing a dialogue game which operates over several iterations, with trust updates occurring at the end of each iteration. In turn, trust changes are computed based on intuitive properties, captured through three rules. By representing agent knowledge within a preference-based argumentation framework, we demonstrate how trust can change over the course of a dialogue.Postprin
Vehicle classification based on convolutional networks applied to FM-CW
  radar signals

This paper investigates the processing of Frequency Modulated-Continuos Wave
(FM-CW) radar signals for vehicle classification. In the last years deep
learning has gained interest in several scientific fields and signal processing
is not one exception. In this work we address the recognition of the vehicle
category using a Convolutional Neural Network (CNN) applied to range Doppler
signature. The developed system first transforms the 1-dimensional signal into
a 3-dimensional signal that is subsequently used as input to the CNN. When
using the trained model to predict the vehicle category we obtain good
performance.Comment: in Proceedings of 1st European Conference on Traffic Mining Applied
  to Police Activities (TRAP 2017
Methodological challenges in the transition towards online audience research

Producción CientíficaThis review of the literature published between 2005 and 2014 presents an overview of the methodological environment in which audience research is transiting towards the study of online audiences. Online audience research is a mix of long-established research rationales, methodical adaptations, new venues and convergent thinking. We discuss four interconnected, and sometimes contradictory, methodological trends that characterize this current environment: 1) the expansion of online ethnography and the continued importance of contextualization, 2) the influence of big data and an emphasis on uses, 3) the reliance on mixed methods and the convergence of different rationales of research, and 4) the ambiguous nature of online data and the ethical considerations for the conduct of research. In spite of a massive research activity, there remain gaps and underprivileged areas that call for a re-prioritization of research. In the conclusion of this paper, we offer recommendations to orient future research
The fuzzy Henstock-Kurzweil delta integral on time scales

We investigate properties of the fuzzy Henstock-Kurzweil delta integral
(shortly, FHK $\Delta$-integral) on time scales, and obtain two necessary and
sufficient conditions for FHK $\Delta$-integrability. The concept of uniformly
FHK $\Delta$-integrability is introduced. Under this concept, we obtain a
uniformly integrability convergence theorem. Finally, we prove monotone and
dominated convergence theorems for the FHK $\Delta$-integral.Comment: This is a preprint of a paper whose final and definite form will
  appear in 'Springer Proceedings in Mathematics and Statistics', ISSN:
  2194-1009. Submitted 30-July-2017; accepted 29-Nov-201
Stochastic Tools for Network Intrusion Detection

With the rapid development of Internet and the sharp increase of network
crime, network security has become very important and received a lot of
attention. We model security issues as stochastic systems. This allows us to
find weaknesses in existing security systems and propose new solutions.
Exploring the vulnerabilities of existing security tools can prevent
cyber-attacks from taking advantages of the system weaknesses. We propose a
hybrid network security scheme including intrusion detection systems (IDSs) and
honeypots scattered throughout the network. This combines the advantages of two
security technologies. A honeypot is an activity-based network security system,
which could be the logical supplement of the passive detection policies used by
IDSs. This integration forces us to balance security performance versus cost by
scheduling device activities for the proposed system. By formulating the
scheduling problem as a decentralized partially observable Markov decision
process (DEC-POMDP), decisions are made in a distributed manner at each device
without requiring centralized control. The partially observable Markov decision
process (POMDP) is a useful choice for controlling stochastic systems. As a
combination of two Markov models, POMDPs combine the strength of hidden Markov
Model (HMM) (capturing dynamics that depend on unobserved states) and that of
Markov decision process (MDP) (taking the decision aspect into account).
Decision making under uncertainty is used in many parts of business and
science.We use here for security tools.We adopt a high-quality approximation
solution for finite-space POMDPs with the average cost criterion, and their
extension to DEC-POMDPs. We show how this tool could be used to design a
network security framework.Comment: Accepted by International Symposium on Sensor Networks, Systems and
  Security (2017
Using Markov Models and Statistics to Learn, Extract, Fuse, and Detect
  Patterns in Raw Data

Many systems are partially stochastic in nature. We have derived data driven
approaches for extracting stochastic state machines (Markov models) directly
from observed data. This chapter provides an overview of our approach with
numerous practical applications. We have used this approach for inferring
shipping patterns, exploiting computer system side-channel information, and
detecting botnet activities. For contrast, we include a related data-driven
statistical inferencing approach that detects and localizes radiation sources.Comment: Accepted by 2017 International Symposium on Sensor Networks, Systems
  and Securit