Reviewerly/exHarmony huggingface dataset:
The Blind Men and the Elephant: Towards an Organization of Epistemic Contexts

In the last two decades of knowledge organization (KO) research, there has been an increasing interest in the context-dependent nature of human knowledge. Contextualism maintains that knowledge is not available in a neutral and objective way, but is always interwoven with the process of knowledge production and the prerequisites of the knower. As a first step towards a systematic organization of epistemic contexts, the concept of knowledge will be considered in its ontological (WHAT) and epistemological (WHO) including methodological (HOW) dimensions. In current KO research, however, either the contextualism is not fully implemented (classification-as-ontology) or the ambition for a context-transcending universal KOS seems to have been abandoned (classification-as-epistemology). Based on a combined ontology and epistemology it will be argued that a phenomena-based approach to KO as stipulated by the León Manifesto, for example, requires a revision of the underlying phenomenon concept as a relation between the known object (WHAT) and the knowing subject (WHO), which is constituted by the application of specific methods (HOW). While traditional subject indexing of documents often relies on the organizing principle “levels of being” (WHAT), for a future context indexing, two novel principles are proposed, namely “levels of knowing” (WHO) and “integral methodological pluralism” (HOW).
PISCES: Optimizing Multi-Job Application Execution in MapReduce

Nowadays, many MapReduce applications consist of groups of jobs with dependencies among each other, such as iterative machine learning applications and large database queries. Unfortunately, the MapReduce framework is not optimized for these multi-job applications. It does not explore the execution overlapping opportunities among jobs and can only schedule jobs independently. These issues significantly inflate the application execution time. This paper presents Pipeline Improvement Support with Critical chain Estimation Scheduling (PISCES), a critical chain optimization (a critical chain refers to a series of jobs which will make the application run longer if any one of them is delayed), to provide better support for multi-job applications. PISCES extends the existing MapReduce framework to allow scheduling for multiple jobs with dependencies by dynamically building up a job dependency DAG for current running jobs according to their input and output directories. Then using the dependency DAG, it provides an innovative mechanism to facilitate the data pipelining between the output phase (map phase in the Map-Only job or reduce phase in the Map-Reduce job) of an upstream job and the map phase of a downstream job. This offers a new execution overlapping between dependent jobs in MapReduce which effectively reduces the application runtime. Moreover, PISCES proposes a novel critical chain job scheduling model based on the accurate critical chain estimation. Experiments show that PISCES can increase the degree of system parallelism by up to 68 percent and improve the execution speed of applications by up to 52 percent.
Accelerated gradient sliding for structured convex optimization

Our main goal in this paper is to show that one can skip gradient computations for gradient descent type methods applied to certain structured convex programming (CP) problems. To this end, we first present an accelerated gradient sliding (AGS) method for minimizing the summation of two smooth convex functions with different Lipschitz constants. We show that the AGS method can skip the gradient computation for one of these smooth components without slowing down the overall optimal rate of convergence. This result is much sharper than the classic black-box CP complexity results especially when the difference between the two Lipschitz constants associated with these components is large. We then consider an important class of bilinear saddle point problem whose objective function is given by the summation of a smooth component and a nonsmooth one with a bilinear saddle point structure. Using the aforementioned AGS method for smooth composite optimization and Nesterov’s smoothing technique, we show that one only needs $${{\mathcal{O}}}(1/\sqrt{\varepsilon })$$ gradient computations for the smooth component while still preserving the optimal $${{\mathcal{O}}}(1/\varepsilon )$$ overall iteration complexity for solving these saddle point problems. We demonstrate that even more significant savings on gradient computations can be obtained for strongly convex smooth and bilinear saddle point problems.
An Adaptive Efficiency-Fairness Meta-Scheduler for Data-Intensive Computing

In data-intensive cluster computing platforms such as Hadoop YARN, efficiency and fairness are two important factors for system design and optimizations. Previous studies are either for efficiency or for fairness solely, without considering the tradeoff between efficiency and fairness. Recent studies observe that there is a tradeoff between efficiency and fairness because of resource contention between users/jobs. By leveraging the existing schedulers, a meta-scheduler is able to dynamically choose one of them for job/task scheduling at runtime. In this paper, we propose a meta-scheduler called FLEX to realize the tradeoff between system efficiency and fairness in Hadoop YARN. FLEX combines multiple existing schedulers into a single aggregated view without any modification on the original schedulers. Equipped with these candidate schedulers, FLEX utilizes machine learning approach to adaptively choose the most proper scheduler according to the characteristic of current running workload and user-defined Service Level Agreement (SLA). We implement FLEX in Hadoop YARN. We conduct experiments with real deployment in a local cluster and perform simulation studies with production traces. Experimental results show that the FLEX outperforms the state-of-the-art approach in two aspects: 1) Given a predefined threshold on the fairness loss, the FLEX reduces the makespan by up to 22 and 24 percent in real deployment and the large-scale simulation, respectively; 2) Given the predefined threshold on the makespan reduction, the FLEX reduces the fairness loss by up to 75 and 73 percent in real deployment and the large-scale simulation, respectively.
Nonlinear Interactions and Non-classical Light

The term non-classical concerns light whose properties cannot be explained by classical electrodynamics and which requires invoking quantum principles to be understood. Its existence is a direct consequence of field quantization; its study is a source of our understanding of many quantum phenomena. Non-classical light also has properties that may be of technological significance. We start this chapter by discussing the definition ofNon-classical light non-classical light and basic examples. Then some of the most prominent applications ofNon-classical light non-classical light are reviewed. After that, as the principal part of our discourse, we review the most common sources ofNon-classical light non-classical light. We will find them surprisingly diverse, including physical systems of various sizes and complexity, ranging from single atoms to optical crystals and to semiconductor lasers. Putting all these dissimilar optical devices in the common perspective we attempt to establish a trend in the field and to foresee the new cross-disciplinary approaches and techniques of generatingNon-classical light non-classical light.
LanguageCrawl: a generic tool for building language models upon common Crawl

Abstract The exponential growth of the internet community has resulted in the production of a vast amount of unstructured data, including web pages, blogs and social media. Such a volume consisting of hundreds of billions of words is unlikely to be analyzed by humans. In this work we introduce the tool LanguageCrawl , which allows Natural Language Processing (NLP) researchers to easily build web-scale corpora using the Common Crawl Archive—an open repository of web crawl information, which contains petabytes of data. We present three use cases in the course of this work: filtering of Polish websites, the construction of n-gram corpora and the training of a continuous skipgram language model with hierarchical softmax. Each of them has been implemented within the LanguageCrawl toolkit, with the possibility to adjust specified language and n-gram ranks. This paper focuses particularly on high computing efficiency by applying highly concurrent multitasking. Our tool utilizes effective libraries and design. LanguageCrawl has been made publicly available to enrich the current set of NLP resources. We strongly believe that our work will facilitate further NLP research, especially in under-resourced languages, in which the lack of appropriately-sized corpora is a serious hindrance to applying data-intensive methods, such as deep neural networks.
Transactional Behavior Verification in Business Process as a Service Configuration

Business Process as a Service (BPaaS) is an emerging type of cloud service that offers configurable and executable business processes to clients over the Internet. As BPaaS is still in early years of research, many open issues remain. Managing the configuration of BPaaS builds on areas such as software product lines and configurable business processes. The problem has concerns to consider from several perspectives, such as the different types of variable features, constraints between configuration options, and satisfying the requirements provided by the client. In our approach, we use temporal logic templates to elicit transactional requirements from clients that the configured service must adhere to. For formalizing constraints over configuration, feature models are used. To manage all these concerns during BPaaS configuration, we develop a structured process that applies formal methods while directing clients through specifying transactional requirements and selecting configurable features. The Binary Decision Diagram (BDD) analysis is then used to verify that the selected configurable features do not violate any constraints. Finally, model checking is applied to verify the configured service against the transactional requirement set. We demonstrate the feasibility of our approach with several validation scenarios and performance evaluations.
Gaussian and sparse processes are limits of generalized Poisson processes

The theory of sparse stochastic processes offers a broad class of statistical models to study signals, far beyond the more classical class of Gaussian processes. In this framework, signals are represented as realizations of random processes that are solution of linear stochastic differential equations driven by Lévy white noises. Among these processes, generalized Poisson processes based on compound-Poisson noises admit an interpretation as random L-splines with random knots and weights. We demonstrate that every generalized Lévy process—from Gaussian to sparse—can be understood as the limit in law of a sequence of generalized Poisson processes. This enables a new conceptual understanding of sparse processes and suggests simple algorithms for the numerical generation of such objects.
LOGIC AND TOPOLOGY FOR KNOWLEDGE, KNOWABILITY, AND BELIEF

Abstract In recent work, Stalnaker proposes a logical framework in which belief is realized as a weakened form of knowledge 35. Building on Stalnaker’s core insights, and using frameworks developed in 11 and 3, we employ topological tools to refine and, we argue, improve on this analysis. The structure of topological subset spaces allows for a natural distinction between what is known and (roughly speaking) what is knowable ; we argue that the foundational axioms of Stalnaker’s system rely intuitively on both of these notions. More precisely, we argue that the plausibility of the principles Stalnaker proposes relating knowledge and belief relies on a subtle equivocation between an “evidence-in-hand” conception of knowledge and a weaker “evidence-out-there” notion of what could come to be known . Our analysis leads to a trimodal logic of knowledge, knowability, and belief interpreted in topological subset spaces in which belief is definable in terms of knowledge and knowability . We provide a sound and complete axiomatization for this logic as well as its uni-modal belief fragment. We then consider weaker logics that preserve suitable translations of Stalnaker’s postulates, yet do not allow for any reduction of belief. We propose novel topological semantics for these irreducible notions of belief, generalizing our previous semantics, and provide sound and complete axiomatizations for the corresponding logics.
What and With Whom? Identifying Topics in Twitter Through Both Interactions and Text

The overwhelming amount of information continuously flowing through the Twitter environment makes topic derivation essential. It indeed plays a valuable role in a variety of Twitter-based applications, including content recommendations, news summarization, market analysis, etc. Topic derivation methods are typically based on semantic features of tweet contents. Because tweets are short by nature, such methods suffer from data sparsity. To alleviate this problem, this paper proposes a topic derivation method that incorporates tweet text similarity and interactions measures. Besides the tweet contents, the approach takes into account several types of interactions amongst tweets: Tweets which mention the same people, replies and retweets. Topic derivation is done through a two-step matrix factorization process. We conducted a number of experiments on several Twitter datasets to reveal both the individual and integrated effects of the various features being considered. Our experimental results against TREC2014 and our self collected tweetMarch datasets demonstrate that the proposed method is able to provide more than 30 percent improvement compared to other advanced topic derivation methods.
Impossibility of cloning of quantum coherence

It is well known that it is impossible to clone an arbitrary quantum state. However, this inability does not lead directly to no cloning of quantum coherence. Here, in this article, we show that it is impossible to clone the coherence of an arbitrary quantum state. In particular, with an ancillary system as machine state, we show that it is impossible to clone the coherence of states whose coherence is greater than the coherence of the known states on which the transformations are defined. Also, we characterize the class of states for which coherence cloning will be possible for a given choice of machine. Furthermore, we find the maximum range of states whose coherence can be cloned perfectly. The impossibility proof also holds when we do not include machine states. Lastly, we generalize the impossibility of cloning of coherence in terms of dimension of the quantum state and coherence measure taken into consideration.
Parallel Self-Testing of the GHZ State with a Proof by Diagrams

Quantum self-testing addresses the following question: is it possible to verify the existence of a multipartite state even when one's measurement devices are completely untrusted? This problem has seen abundant activity in the last few years, particularly with the advent of parallel self-testing (i.e., testing several copies of a state at once), which has applications not only to quantum cryptography but also quantum computing. In this work we give the first error-tolerant parallel self-test in a three-party (rather than two-party) scenario, by showing that an arbitrary number of copies of the GHZ state can be self-tested. In order to handle the additional complexity of a three-party setting, we use a diagrammatic proof based on categorical quantum mechanics, rather than a typical symbolic proof. The diagrammatic approach allows for manipulations of the complicated tensor networks that arise in the proof, and gives a demonstration of the importance of picture-languages in quantum information.
Complying With Data Handling Requirements in Cloud Storage Systems

In past years, cloud storage systems saw an enormous rise in usage. However, despite their popularity and importance as underlying infrastructure for more complex cloud services, today’s cloud storage systems do not account for compliance with regulatory, organizational, or contractual data handling requirements by design. Since legislation increasingly responds to rising data protection and privacy concerns, complying with data handling requirements becomes a crucial property for cloud storage systems. We present <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Prada</small> , a practical approach to account for compliance with data handling requirements in key-value based cloud storage systems. To achieve this goal, <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Prada</small> introduces a transparent data handling layer, which empowers clients to request specific data handling requirements and enables operators of cloud storage systems to comply with them. We implement <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Prada</small> on top of the distributed database Cassandra and show in our evaluation that complying with data handling requirements in cloud storage systems is practical in real-world cloud deployments as used for microblogging, data sharing in the Internet of Things, and distributed email storage.
Weak measurement effect on optimal estimation with lower and upper bound on relativistic metrology

We address the quantum estimation of parameters encoded into the initial state of two modes of a Dirac field described by relatively accelerated parties. By using the quantum Fisher information (QFI), we investigate how the weak measurements performed before and after the accelerating observer, affect the optimal estimation of information encoded into the weight and phase parameters of the initial state shared between the parties. Studying the QFI, associated with weight parameter $ \vartheta $, we find that the acceleration at which the optimal estimation occurs may be controlled by weak measurements. Moreover, it is shown that the post-measurement plays the role of a quantum key for manifestation of the Unruh effect. On the other hand, investigating the phase estimation optimization and assuming that there is no control over the initial state, we show that the weak measurements may be utilized to match the optimal $ \vartheta $ to its predetermined value. Moreover, in addition to determination of a lower bound on the QFI with the local quantum uncertainty, we unveil an important upper bound on the precision of phase estimation in our relativistic scenario, given by the maximal steered coherence (MSC). We also obtain a compact expression of the MSC for general X states.
On generalized Erdős–Ginzburg–Ziv constants for <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:msubsup><mml:mrow><mml:mi mathvariant="double-struck">Z</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow><mml:mrow><mml:mi>d</mml:mi></mml:mrow></mml:msubsup></mml:math>

Let G be a finite abelian group, and r be a multiple of its exponent. The generalized Erdős–Ginzburg–Ziv constant sr(G) is the smallest integer s such that every sequence of length s over G has a zero-sum subsequence of length r. We find exact values of s2m(Z2d) for d≤2m+1. Connections to linear binary codes of maximal length and codes without a forbidden weight are discussed.
InkPlanner: Supporting Prewriting via Intelligent Visual Diagramming

Prewriting is the process of generating and organizing ideas before drafting a document. Although often overlooked by novice writers and writing tool developers, prewriting is a critical process that improves the quality of a final document. To better understand current prewriting practices, we first conducted interviews with writing learners and experts. Based on the learners' needs and experts' recommendations, we then designed and developed InkPlanner, a novel pen and touch visualization tool that allows writers to utilize visual diagramming for ideation during prewriting. InkPlanner further allows writers to sort their ideas into a logical and sequential narrative by using a novel widget - NarrativeLine. Using a NarrativeLine, InkPlanner can automatically generate a document outline to guide later drafting exercises. Inkplanner is powered by machine-generated semantic and structural suggestions that are curated from various texts. To qualitatively review the tool and understand how writers use InkPlanner for prewriting, two writing experts were interviewed and a user study was conducted with university students. The results demonstrated that InkPlanner encouraged writers to generate more diverse ideas and also enabled them to think more strategically about how to organize their ideas for later drafting.
Embedded Merge &amp; Split: Visual Adjustment of Data Grouping

Data grouping is among the most frequently used operations in data visualization. It is the process through which relevant information is gathered, simplified, and expressed in summary form. Many popular visualization tools support automatic grouping of data (e.g., dividing up a numerical variable into bins). Although grouping plays a pivotal role in supporting data exploration, further adjustment and customization of auto-generated grouping criteria is non-trivial. Such adjustments are currently performed either programmatically or through menus and dialogues which require specific parameter adjustments over several steps. In response, we introduce Embedded Merge & Split (EMS), a new interaction technique for direct adjustment of data grouping criteria. We demonstrate how the EMS technique can be designed to directly manipulate width and position in bar charts and histograms, as a means for adjustment of data grouping criteria. We also offer a set of design guidelines for supporting EMS. Finally, we present the results of two user studies, providing initial evidence that EMS can significantly reduce interaction time compared to WIMP-based technique and was subjectively preferred by participants.
A Cartoon-Texture Approach for JPEG/JPEG 2000 Decompression Based on TGV and Shearlet Transform

In this paper, we propose a new artifact-free variational model for JPEG/JPEG 2000 decompression based on a cartoon-texture decomposition scheme. The new infimal convolution type regularization associated with total generalized variation (TGV) and shearlet transform can reconstruct piecewise smooth images with structured textures well, due to the property of shearlet of representing the positions and orientations of singularities, which can be interpreted as the oscillation texture parts. In order to enhance the qualities of reconstructed images, we incorporate an L2 cost functional into the model, then the discretization of such functional can be easily solved by the generic proximal Primal-Dual method. Numerical experiments show that our proposed model is competitive with the learning method-Trainable Nonlinear Reaction Diffusion (TNRD) [33, 34] in term of texture preservation, and outperforms the TV-based and TGV-based variational methods.