Reviewerly/exHarmony huggingface dataset:
Task-Based Effectiveness of Interactive Contiguous Area Cartograms

Cartograms are map-based data visualizations in which the area of each map region is proportional to an associated numeric data value (e.g., population or gross domestic product).A cartogram is called contiguous if it conforms to this area principle while also keeping neighboring regions connected.Because of their distorted appearance, contiguous cartograms have been criticized as difficult to read.Some authors have suggested that cartograms may be more legible if they are accompanied by interactive features (e.g., animations, linked brushing, or infotips).We conducted an experiment to evaluate this claim.Participants had to perform visual analysis tasks with interactive and noninteractive contiguous cartograms.The task types covered various aspects of cartogram readability, ranging from elementary lookup tasks to synoptic tasks (i.e., tasks in which participants had to summarize high-level differences between two cartograms).Elementary tasks were carried out equally well with and without interactivity.Synoptic tasks, by contrast, were more difficult without interactive features.With access to interactivity, however, most participants answered even synoptic questions correctly.In a subsequent survey, participants rated the interactive features as "easy to use" and "helpful."Our study suggests that interactivity has the potential to make contiguous cartograms accessible even for those readers who are unfamiliar with interactive computer graphics or do not have a prior affinity to working with maps.Among the interactive features, animations had the strongest positive effect, so we recommend them as a minimum of interactivity when contiguous cartograms are displayed on a computer screen.
Unsupervised Assignment Flow: Label Learning on Feature Manifolds by Spatially Regularized Geometric Assignment

This paper introduces the unsupervised assignment flow that couples the assignment flow for supervised image labeling (Åström et al. in J Math Imaging Vis 58(2):211–238, 2017) with Riemannian gradient flows for label evolution on feature manifolds. The latter component of the approach encompasses extensions of state-of-the-art clustering approaches to manifold-valued data. Coupling label evolution with the spatially regularized assignment flow induces a sparsifying effect that enables to learn compact label dictionaries in an unsupervised manner. Our approach alleviates the requirement for supervised labeling to have proper labels at hand, because an initial set of labels can evolve and adapt to better values while being assigned to given data. The separation between feature and assignment manifolds enables the flexible application which is demonstrated for three scenarios with manifold-valued features. Experiments demonstrate a beneficial effect in both directions: adaptivity of labels improves image labeling, and steering label evolution by spatially regularized assignments leads to proper labels, because the assignment flow for supervised labeling is exactly used without any approximation for label learning.
Self-testing and certification using trusted quantum inputs

Device-independent certification of quantum devices is of crucial importance for the development of secure quantum information protocols. So far, the most studied scenario corresponds to a system consisting of different non-characterized devices that observers probe with classical inputs to obtain classical outputs. The certification of relevant quantum properties follows from the observation of correlations between these events that do not have a classical counterpart. In the fully device-independent scenario no assumptions are made on the devices and therefore their non-classicality follows from Bell non-locality. There exist other scenarios, known as semidevice-independent, in which assumptions are made on the devices, such as their dimension, and non-classicality is associated to the observation of other types of correlations with no classical analogue. More recently, the use of trusted quantum inputs for certification has been introduced. The goal of this work is to study the power of this formalism and describe self-testing protocols in various settings using trusted quantum inputs. We also relate these different types of self-testing to some of the most basic quantum information protocols, such as quantum teleportation. Finally, we apply our findings to quantum networks and provide methods for estimating the quality of the whole network, as well as of parts of it.
Homomorphic encryption of linear optics quantum computation on almost arbitrary states of light with asymptotically perfect security

The authors quantify how indistinguishable any pair of quantum states with constrained energy can be after they both experience a random displacement in phase space. This continuous variable analogue of a quantum one-time pad allows one to securely delegate quantum optical computation and is easy to implement with present-day technology.
Evaluation of Software Product Quality Metrics

Computing devices and associated software govern everyday life, and form the backbone of safety critical systems in banking, healthcare, automotive and other fields. Increasing system complexity, quickly evolving technologies and paradigm shifts have kept software quality research at the forefront. Standards such as ISO's 25010 express it in terms of sub-characteristics such as maintainability, reliability and security. A significant body of literature attempts to link these subcharacteristics with software metric values, with the end goal of creating a metric-based model of software product quality. However, research also identifies the most important existing barriers. Among them we mention the diversity of software application types, development platforms and languages. Additionally, unified definitions to make software metrics truly language-agnostic do not exist, and would be difficult to implement given programming language levels of variety. This is compounded by the fact that many existing studies do not detail their methodology and tooling, which precludes researchers from creating surveys to enable data analysis on a larger scale. In our paper, we propose a comprehensive study of metric values in the context of three complex, open-source applications. We align our methodology and tooling with that of existing research, and present it in detail in order to facilitate comparative evaluation. We study metric values during the entire 18-year development history of our target applications, in order to capture the longitudinal view that we found lacking in existing literature. We identify metric dependencies and check their consistency across applications and their versions. At each step, we carry out comparative evaluation with existing research and present our results.
New Lower Bounds to the Output Entropy of Multi-Mode Quantum Gaussian Channels

We prove that quantum thermal Gaussian input states minimize the output entropy of the multi-mode quantum Gaussian attenuators and amplifiers that are entanglement breaking and of the multi-mode quantum Gaussian phase contravariant channels among all the input states with an given entropy. This is the first time that this property is proven for a multi-mode channel without restrictions on the input states. A striking consequence of this result is a new lower bound on the output entropy of all the multi-mode quantum Gaussian attenuators and amplifiers in terms of the input entropy. We apply this bound to determine new upper bounds to the communication rates in two different scenarios. The first is classical communication to two receivers with the quantum degraded Gaussian broadcast channel. The second is the simultaneous classical communication, quantum communication and entanglement generation or the simultaneous public classical communication, private classical communication, and quantum key distribution with the Gaussian quantum-limited attenuator.
MULTI-VIEW STEREO WITH SEMANTIC PRIORS

Patch-based stereo is nowadays a commonly used image-based technique for dense 3D reconstruction in large scale multi-view applications. The typical steps of such a pipeline can be summarized in stereo pair selection, depth map computation, depth map refinement and, finally, fusion in order to generate a complete and accurate representation of the scene in 3D. In this study, we aim to support the standard dense 3D reconstruction of scenes as implemented in the open source library OpenMVS by using semantic priors. To this end, during the depth map fusion step, along with the depth consistency check between depth maps of neighbouring views referring to the same part of the 3D scene, we impose extra semantic constraints in order to remove possible errors and selectively obtain segmented point clouds per label, boosting automation towards this direction. I n order to reassure semantic coherence between neighbouring views, additional semantic criterions can be considered, aiming to elim inate mismatches of pixels belonging in different classes.
Towards the statistical construction of hybrid development methods

Abstract Hardly any software development process is used as prescribed by authors or standards. Regardless of company size or industry sector, a majority of project teams and companies use hybrid development methods (short: hybrid methods) that combine different development methods and practices. Even though such hybrid methods are highly individualized, a common understanding of how to systematically construct synergetic practices is missing. In this article, we make a first step towards a statistical construction procedure for hybrid methods. Grounded in 1467 data points from a large‐scale practitioner survey, we study the question: What are hybrid methods made of and how can they be systematically constructed? Our findings show that only eight methods and few practices build the core of modern software development. Using an 85% agreement level in the participants' selections, we provide examples illustrating how hybrid methods can be characterized by the practices they are made of. Furthermore, using this characterization, we develop an initial construction procedure, which allows for defining a method frame and enriching it incrementally to devise a hybrid method using ranked sets of practice.
Investigation of the contribution of virtual reality to architectural education

This study mainly examines the contribution of the virtual reality environment to architectural education. The primary aim of the study was to investigate the theoretical possibilities of VR technology in an interactive and participatory educational environment that would allow students to examine architectural components and inter-component relationships. A group of 160 volunteers participated in the study, with participants asked to design villas in both natural (non-VR) and virtual reality (VR) environments within a specific period. Designs made in both environments (VR and non-VR) were evaluated by a team of five experts (jurors). For the evaluation, jurors wore eye-tracking devices and were asked to comment on the designs in both environments. In the virtual reality environment designs, the following categories showed significant differences over the drawings in a natural environment: functionality, aesthetics, user perception of space and internal physical quality (light quality), indicating that the virtual reality designs were examined more closely by the jurors than were those in the natural environment. This study will contribute to design discipline if virtual reality systems are adopted in architecture education.
Recent Advances on Quantum Key Distribution Overcoming the Linear Secret Key Capacity Bound

Abstract A crucial goal for quantum key distribution (QKD) is to transmit unconditionally secure keys over long distances. Previous studies show that the key rate of point‐to‐point QKD is limited by a secret key rate capacity bound, and higher key rates would require quantum repeaters. In 2018, the seminal twin‐field (TF) QKD protocol is proposed to provide a remarkable solution to overcoming the linear secret key capacity bound. This article presents an up‐to‐date survey on recent developments in this area, including the security proofs of phase‐matching QKD and other TF‐QKD type protocols, the theoretical examinations of these protocols under realistic conditions, and the recent experimental demonstrations.
Transforming task representations to perform novel tasks

An important aspect of intelligence is the ability to adapt to a novel task without any direct experience (zero-shot), based on its relationship to previous tasks. Humans can exhibit this cognitive flexibility. By contrast, models that achieve superhuman performance in specific tasks often fail to adapt to even slight task alterations. To address this, we propose a general computational framework for adapting to novel tasks based on their relationship to prior tasks. We begin by learning vector representations of tasks. To adapt to new tasks, we propose meta-mappings, higher-order tasks that transform basic task representations. We demonstrate the effectiveness of this framework across a wide variety of tasks and computational paradigms, ranging from regression to image classification and reinforcement learning. We compare to both human adaptability and language-based approaches to zero-shot learning. Across these domains, meta-mapping is successful, often achieving 80-90% performance, without any data, on a novel task, even when the new task directly contradicts prior experience. We further show that meta-mapping can not only generalize to new tasks via learned relationships, but can also generalize using novel relationships unseen during training. Finally, using meta-mapping as a starting point can dramatically accelerate later learning on a new task, and reduce learning time and cumulative error substantially. Our results provide insight into a possible computational basis of intelligent adaptability and offer a possible framework for modeling cognitive flexibility and building more flexible artificial intelligence systems.
Denoising-Based Turbo Message Passing for Compressed Video Background Subtraction

In this paper, we consider the compressed video background subtraction problem that separates the background and foreground of a video from its compressed measurements. The background of a video usually lies in a low dimensional space and the foreground is usually sparse. More importantly, each video frame is a natural image that has textural patterns. By exploiting these properties, we develop a message passing algorithm termed offline denoising-based turbo message passing (DTMP). We show that these structural properties can be efficiently handled by the existing denoising techniques under the turbo message passing framework. We further extend the DTMP algorithm to the online scenario where the video data is collected in an online manner. The extension is based on the similarity/continuity between adjacent video frames. We adopt the optical flow method to refine the estimation of the foreground. We also adopt the sliding window based background estimation to reduce complexity. By exploiting the Gaussianity of messages, we develop the state evolution to characterize the per-iteration performance of offline and online DTMP. Comparing to the existing algorithms, DTMP can work at much lower compression rates, and can subtract the background successfully with a lower mean squared error and better visual quality for both offline and online compressed video background subtraction.
Analysis of trends in modern library and information infrastructure in the ongoing pandemics. (Review of international professional publications). (Part 1)

The paper reviews some international online library and information professional publications including the websites of library associations, academic research charities and foundations and research groups. The paper is divided into the following sections: pandemics and open science, coronavirus and library materials and spaces, new impetus for digitization, copyright – latest news. These topics have not been predefined but taken shape as a result of analytical work and research in the body of professional publications. Analysis of leading library experts’ discussions in social networks has also helped to reveal current trends in library and information infra-structure development. COVID-19 pandemics brought about significant changes to the life and projects of the whole library and information space. A major step to open science has been taken when academic journal publishers, leading universities, research charities have taken responsibility to work in a collaborative manner and share coronavirus infection research data and results speedily and freely. Transition to digital collections in library holdings has accelerated, collections of e-books are growing, and the effect of prior digitization projects has manifested itself. The importance of libraries as a trustworthy source of information does not fade; caution is needed when using information from preprints published on preprint servers; the increasing need of informing digital materials users of attached copyright is highlighted.
All-fiber source and sorter for multimode correlated photons

Abstract Photons occupying multiple spatial modes hold a great promise for implementing high-dimensional quantum communication. We use spontaneous four-wave mixing to generate multimode photon pairs in a few-mode fiber. We show the photons are correlated in the fiber mode basis using an all-fiber mode sorter. Our demonstration offers an essential building block for realizing high-dimensional quantum protocols based on standard, commercially available fibers, in an all-fiber configuration.
Membership Function Derivatives Transformation Approach for Stability Analysis and Stabilization Control of T–S Fuzzy Systems

In this text, a membership function derivatives (MFDs) extrema-based method is proposed to relax the conservatism both in stability analysis and synthesis problems of Takagi-Sugeno fuzzy systems. By the designed algorithm, the nonpositiveness of the MFDs extrema is conquered. For an open-loop system, based on certain information of the MFs and derivatives, a series of convex stability conditions is derived. Then, an extremum-based construction method is adopted to involve the MF information. For the shape of MFDs, a coordinate transformation algorithm is proposed to involve it in the stability conditions to achieve local stable effects. For a state-feedback control system, conditions guaranteeing the stability and robustness are listed. Finally, simulation examples and comparisons are carried out to clarify the conservatism reduction results of the raised method.
Utterance Verification-Based Dysarthric Speech Intelligibility Assessment Using Phonetic Posterior Features

In the literature, the task of dysarthric speech intelligibility assessment has been approached through development of different low-level feature representations, subspace modeling, phone confidence estimation or measurement of automatic speech recognition system accuracy. This paper proposes a novel approach where the intelligibility is estimated as the percentage of correct words uttered by a speaker with dysarthria by matching and verifying utterances of the speaker with dysarthria against control speakers' utterances in phone posterior feature space and broad phonetic posterior feature space. Experimental validation of the proposed approach on the UA-Speech database, with posterior feature estimators trained on the data from auxiliary domain and language, obtained a best Pearson's correlation coefficient (r) of 0.950 and Spearman's correlation coefficient (ρ) of 0.957. Furthermore, replacing control speakers' speech with speech synthesized by a neural text-to-speech system obtained a best r of 0.931 and p of 0.961.
Impact of correlations and heavy tails on quantum error correction

We show that space- and time-correlated single-qubit rotation errors can lead to high-weight errors in a quantum circuit when the rotation angles are drawn from heavy-tailed distributions. This leads to a breakdown of quantum error correction, yielding reduced or in some cases no protection of the encoded logical qubits. While heavy-tailed phenomena are prevalent in the natural world, there is very little research as to whether noise with these statistics exist in current quantum processing devices. Furthermore, it is an open problem to develop tomographic or noise spectroscopy protocols that could test for the existence of noise with such statistics. These results suggest the need for quantum characterization methods that can reliably detect or reject the presence of such errors together with continued first-principles studies of the origins of space- and time-correlated noise in quantum processors. If such noise does exist, physical or control-based mitigation protocols must be developed to mitigate this noise as it would severely hinder the performance of fault-tolerant quantum computers.
A Robust Thermal Infrared Vehicle and Pedestrian Detection Method in Complex Scenes

In complex scenes, it is a huge challenge to accurately detect motion-blurred, tiny, and dense objects in the thermal infrared images. To solve this problem, robust thermal infrared vehicle and pedestrian detection method is proposed in this paper. An important weight parameter β is first proposed to reconstruct the loss function of the feature selective anchor-free (FSAF) module in its online feature selection process, and the FSAF module is optimized to enhance the detection performance of motion-blurred objects. The proposal of parameter β provides an effective solution to the challenge of motion-blurred object detection. Then, the optimized anchor-free branches of the FSAF module are plugged into the YOLOv3 single-shot detector and work jointly with the anchor-based branches of the YOLOv3 detector in both training and inference, which efficiently improves the detection precision of the detector for tiny and dense objects. Experimental results show that the method proposed is superior to other typical thermal infrared vehicle and pedestrian detection algorithms due to 72.2% mean average precision (mAP).