Reviewerly/exHarmony huggingface dataset:
Local algorithms for distance-generalized core decomposition over large dynamic graphs

The distance-generalized core, also called ( k , h )-core, is defined as the maximal subgraph in which every vertex has at least k vertices at distance no longer than h. Compared with k -core, ( k , h )-core can identify more fine-grained subgraphs and, hence, is more useful for the applications such as network analysis and graph coloring. The state-of-the-art algorithms for ( k , h )-core decomposition are peeling algorithms, which iteratively delete the vertex with the minimum h -degree (i.e., the least number of neighbors within h hops). However, they suffer from some limitations, such as low parallelism and incapability of supporting dynamic graphs. To address these limitations, in this paper, we revisit the problem of ( k , h )-core decomposition. First, we introduce two novel concepts of pairwise h-attainability index and n-order H-index based on an insightful observation. Then, we thoroughly analyze the properties of n -order H-index and propose a parallelizable local algorithm for ( k , h )-core decomposition. Moreover, several optimizations are presented to accelerate the local algorithm. Furthermore, we extend the proposed local algorithm to address the ( k , h )-core maintenance problem for dynamic graphs. Experimental studies on real-world graphs show that, compared to the best existing solution, our proposed algorithms can reduce the ( k , h )-core decomposition time by 1--3 orders of magnitude and save the maintenance cost by 1--2 orders of magnitude.
Empirical Frequentist Coverage of Deep Learning Uncertainty Quantification Procedures

Uncertainty quantification for complex deep learning models is increasingly important as these techniques see growing use in high-stakes, real-world settings. Currently, the quality of a model’s uncertainty is evaluated using point-prediction metrics, such as the negative log-likelihood (NLL), expected calibration error (ECE) or the Brier score on held-out data. Marginal coverage of prediction intervals or sets, a well-known concept in the statistical literature, is an intuitive alternative to these metrics but has yet to be systematically studied for many popular uncertainty quantification techniques for deep learning models. With marginal coverage and the complementary notion of the width of a prediction interval, downstream users of deployed machine learning models can better understand uncertainty quantification both on a global dataset level and on a per-sample basis. In this study, we provide the first large-scale evaluation of the empirical frequentist coverage properties of well-known uncertainty quantification techniques on a suite of regression and classification tasks. We find that, in general, some methods do achieve desirable coverage properties on in distribution samples, but that coverage is not maintained on out-of-distribution data. Our results demonstrate the failings of current uncertainty quantification techniques as dataset shift increases and reinforce coverage as an important metric in developing models for real-world applications.
Evaluating the Application of the Mental Model Mapping Tool (M-Tool)

Mental models influence how individuals think and act in relation to their external environment and have been identified as leverage points to address sustainability challenges. Given the importance of mental models, a new tool to assess mental models has been developed: the Mental Model Mapping Tool (M-Tool). M-Tool was designed to have a standardized format and to be user-friendly for low literacy populations, using pictograms and audio instructions. In this paper, we evaluate M-Tool’s application in two studies with Tanzanian fishers. In Study 1, we investigated M-tool’s convergent validity compared to standard interviewing methods ( n = 30). Study 2 investigated M-Tool’s construct validity by relating mental model complexity to participants’ education level ( n = 185), a relationship that has been well established. The findings show that (1) mental models produced with M-Tool are more complex than mental models obtained through interviewing techniques; (2) model composition is similar across the two methods; and (3) participants with higher levels of education tend to produce more complex mental models with M-Tool than participants with lower levels of education, in line with previous research. These findings suggest that M-Tool can successfully capture mental models among diverse participants. This tool offers researchers and practitioners an instrument to map and compare perceptions of (conservation) challenges across groups.
Rendering with style

For several decades, researchers have been advancing techniques for creating and rendering 3D digital faces, where a lot of the effort has gone into geometry and appearance capture, modeling and rendering techniques. This body of research work has largely focused on facial skin, with much less attention devoted to peripheral components like hair, eyes and the interior of the mouth. As a result, even with the best technology for facial capture and rendering, in most high-end productions a lot of artist time is still spent modeling the missing components and fine-tuning the rendering parameters to combine everything into photo-real digital renders. In this work we propose to combine incomplete, high-quality renderings showing only facial skin with recent methods for neural rendering of faces, in order to automatically and seamlessly create photo-realistic full-head portrait renders from captured data without the need for artist intervention. Our method begins with traditional face rendering, where the skin is rendered with the desired appearance, expression, viewpoint, and illumination. These skin renders are then projected into the latent space of a pre-trained neural network that can generate arbitrary photo-real face images (StyleGAN2). The result is a sequence of realistic face images that match the identity and appearance of the 3D character at the skin level, but is completed naturally with synthesized hair, eyes, inner mouth and surroundings. Notably, we present the first method for multi-frame consistent projection into this latent space, allowing photo-realistic rendering and preservation of the identity of the digital human over an animated performance sequence, which can depict different expressions, lighting conditions and viewpoints. Our method can be used in new face rendering pipelines and, importantly, in other deep learning applications that require large amounts of realistic training data with ground-truth 3D geometry, appearance maps, lighting, and viewpoint.
Inference from explanation.

What do we communicate with causal explanations? Upon being told, "E because C", a person might learn that C and E both occurred, and perhaps that there is a causal relationship between C and E. In fact, causal explanations systematically disclose much more than this basic information. Here, we offer a communication-theoretic account of explanation that makes specific predictions about the kinds of inferences people draw from others' explanations. We test these predictions in a case study involving the role of norms and causal structure. In Experiment 1, we demonstrate that people infer the normality of a cause from an explanation when they know the underlying causal structure. In Experiment 2, we show that people infer the causal structure from an explanation if they know the normality of the cited cause. We find these patterns both for scenarios that manipulate the statistical and prescriptive normality of events. Finally, we consider how the communicative function of explanations, as highlighted in this series of experiments, may help to elucidate the distinctive roles that normality and causal structure play in causal judgment, paving the way toward a more comprehensive account of causal explanation. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
Enhanced Electro-optic Sampling with Quantum Probes

Employing electro-optic sampling (EOS) with ultrashort probe pulses, recent experiments showed direct measurements of quantum vacuum fields and their correlations on subcycle timescales. Here, we propose a quantum-enhanced EOS where bright photon-number entangled twin beams are used to derive conditioned nonclassical probes. In the case of the quantum vacuum, this leads to a sixfold improvement in the signal-to-noise ratio over the classically probed EOS. In addition, engineering of the conditioning protocol yields a reliable way to extract higher-order moments of the quantum noise distribution and robust discrimination of the input quantum states, for instance, a vacuum and a few-photon cat state. These improvements open a viable route toward robust tomography of quantum fields in space-time, an equivalent of homodyne detection in energy-momentum space, and the possibility of precise experiments in real-space quantum electrodynamics.
Security analysis and improvements of XOR arbitrated quantum signature-based GHZ state

Recently, Zheng et al. presented a novel signature protocol with arbitrator based on XOR encryption and GHZ state. Unfortunately, their protocol lacks security. Firstly, a quantum adversary can forge their signature based on the received information. On the other hand, their protocol is vulnerable for the adversary’s repudiation attack. Then, we bring about improvements on its security, efficiency and practicality. The improved protocol not only prevents the security defects of the old protocol, but also has better computation efficiency. On the other hand, our protocol is mainly used to sign the classical messages, which are widely used in the current network.
Galaxy morphologies revealed with Subaru HSC and super-resolution techniques. I. Major merger fractions of <i>L</i>UV ∼ 3–15 L*UV dropout galaxies at <i>z</i> ∼ 4–7

Abstract We perform a super-resolution analysis of the Subaru Hyper Suprime-Cam (HSC) images to estimate the major merger fractions of z ∼ 4–7 dropout galaxies at the bright end of galaxy UV luminosity functions (LFs). Our super-resolution technique improves the spatial resolution of the ground-based HSC images, from ∼1″ to $\lesssim \!\!{0{^{\prime \prime }_{.}}1}$, which is comparable to that of the Hubble Space Telescope, allowing us to identify z ∼ 4–7 bright major mergers at a high completeness value of $\gtrsim \!\!90\%$. We apply the super-resolution technique to 6412, 16, 94, and 13 very bright dropout galaxies at z ∼ 4, 5, 6, and 7, respectively, in a UV luminosity range of LUV ∼ 3–$15\, L_{\rm UV}^*$ corresponding to −24 ≲ MUV ≲ −22. The major merger fractions are estimated to be $f_{\rm merger}\sim 10\%$–$20\%$ at z ∼ 4 and $\sim 50\%$–$70\%$ at z ∼ 5–7, which shows no fmerger difference compared to those of a control faint galaxy sample. Based on the fmerger estimates, we verify contributions of source blending effects and major mergers to the bright-end of double power-law (DPL) shape of z ∼ 4–7 galaxy UV LFs. While these two effects partly explain the DPL shape at LUV ∼ 3–$10\, L_{\rm UV}^*$, the DPL shape cannot be explained at the very bright end of $L_{\rm UV}\gtrsim 10\, L_{\rm UV}^*$, even after the AGN contribution is subtracted. The results support scenarios in which other additional mechanisms such as insignificant mass quenching and low dust obscuration contribute to the DPL shape of galaxy UV LFs.
Lattice-Based Signatures with Tight Adaptive Corruptions and More

We construct the first tightly secure signature schemes in the multi-user setting with adaptive corruptions from lattices. In stark contrast to the previous tight constructions whose security is solely based on number-theoretic assumptions, our schemes are based on the Learning with Errors (LWE) assumption which is supposed to be post-quantum secure. The security of our scheme is independent of the numbers of users and signing queries, and it is in the non-programmable random oracle model. Our LWE-based scheme is compact, namely, its signatures contain only a constant number of lattice vectors. At the core of our construction are a new abstraction of the existing lossy identification (ID) schemes using dual-mode commitment schemes and a refinement of the framework by Diemert et al. (PKC 2021) which transforms a lossy ID scheme to a signature using sequential OR proofs. In combination, we obtain a tight generic construction of signatures from dual-mode commitments in the multi-user setting. Improving the work of Diemert et al., our new approach can be instantiated using not only the LWE assumption, but also an isogeny-based assumption. We stress that our LWE-based lossy ID scheme in the intermediate step uses a conceptually different idea than the previous lattice-based ones. Of independent interest, we formally rule out the possibility that the aforementioned “ID-to-Signature” methodology can work tightly using parallel OR proofs. In addition to the results of Fischlin et al. (EUROCRYPT 2020), our impossibility result shows a qualitative difference between both forms of OR proofs in terms of tightness.
Quantum communications in a moderate-to-strong turbulent space

Since the invention of the laser in the 60s, one of the most fundamental communication channels has been the free-space optical channel. For this type of channel, a number of effects generally need to be considered, including diffraction, refraction, atmospheric extinction, pointing errors and, most importantly, turbulence. Because of all these adverse features, the free-space optical (FSO) channel is more difficult to study than a stable fiber-based link. For the same reasons, only recently it has been possible to establish the ultimate performances achievable in quantum communications via free-space channels, together with practical rates for continuous variable (CV) quantum key distribution (QKD). Differently from previous literature, mainly focused on the regime of weak turbulence, this work considers the FSO channel in the more challenging regime of moderate-to-strong turbulence, where effects of beam widening and breaking are more important than beam wandering. This regime may occur in long-distance free-space links on the ground, in uplink to high-altitude platform systems (HAPS) and, more interestingly, in downlink from near-horizon satellites. In such a regime we rigorously investigate ultimate limits for quantum communications and show that composable keys can be extracted using CV-QKD.
Crystalline: Lowering the Cost for Developers to Collect and Organize Information for Decision Making

Developers perform online sensemaking on a daily basis, such as researching and choosing libraries and APIs. Prior research has introduced tools that help developers capture information from various sources and organize it into structures useful for subsequent decision-making. However, it remains a laborious process for developers to manually identify and clip content, maintaining its provenance and synthesizing it with other content. In this work, we introduce a new system called Crystalline that attempts to automatically collect and organize information into tabular structures as the user searches and browses the web. It leverages natural language processing to automatically group similar criteria together to reduce clutter as well as passive behavioral signals such as mouse movement and dwell time to infer what information to collect and how to visualize and prioritize it. Our user study suggests that developers are able to create comparison tables about 20% faster with a 60% reduction in operational cost without sacrificing the quality of the tables.
Self-Supervised Scene Flow Estimation With 4-D Automotive Radar

Scene flow allows autonomous vehicles to reason about the arbitrary motion of multiple independent objects which is the key to long-term mobile autonomy. While estimating the scene flow from LiDAR has progressed recently, it remains largely unknown how to estimate the scene flow from a 4-D radar - an increasingly popular automotive sensor for its robustness against adverse weather and lighting conditions. Compared with the LiDAR point clouds, radar data are drastically sparser, noisier and in much lower resolution. Annotated datasets for radar scene flow are also in absence and costly to acquire in the real world. These factors jointly pose the radar scene flow estimation as a challenging problem. This work aims to address the above challenges and estimate scene flow from 4-D radar point clouds by leveraging self-supervised learning. A robust scene flow estimation architecture and three novel losses are bespoken designed to cope with intractable radar data. Real-world experimental results validate that our method is able to robustly estimate the radar scene flow in the wild and effectively supports the downstream task of motion segmentation.
Investigating Positive and Negative Qualities of Human-in-the-Loop Optimization for Designing Interaction Techniques

Designers reportedly struggle with design optimization tasks where they are asked to find a combination of design parameters that maximizes a given set of objectives. In HCI, design optimization problems are often exceedingly complex, involving multiple objectives and expensive empirical evaluations. Model-based computational design algorithms assist designers by generating design examples during design, however they assume a model of the interaction domain. Black box methods for assistance, on the other hand, can work with any design problem. However, virtually all empirical studies of this human-in-the-loop approach have been carried out by either researchers or end-users. The question stands out if such methods can help designers in realistic tasks. In this paper, we study Bayesian optimization as an algorithmic method to guide the design optimization process. It operates by proposing to a designer which design candidate to try next, given previous observations. We report observations from a comparative study with 40 novice designers who were tasked to optimize a complex 3D touch interaction technique. The optimizer helped designers explore larger proportions of the design space and arrive at a better solution, however they reported lower agency and expressiveness. Designers guided by an optimizer reported lower mental effort but also felt less creative and less in charge of the progress. We conclude that human-in-the-loop optimization can support novice designers in cases where agency is not critical.
A digital quantum simulation of the Agassi model

A digital quantum simulation of the Agassi model from nuclear physics is proposed and analyzed. The proposal is worked out for the case with four different sites. Numerical simulations and analytical estimations are presented to illustrate the feasibility of this proposal with current technology. The proposed approach is fully scalable to a larger number of sites. The use of a quantum correlation function as a probe to explore the quantum phases by quantum simulating the time dynamics, with no need of computing the ground state, is also studied. Evidence is given showing that the amplitude of the time dynamics of a correlation function in this quantum simulation is linked to the different quantum phases of the system. This approach establishes an avenue for the digital quantum simulation of useful models in nuclear physics.
Non-Markovian Steady States of a Driven Two-Level System

We show that an open quantum system in a non-Markovian environment can reach steady states that it cannot reach in a Markovian environment. As these steady states are unique for the non-Markovian regime, they could offer a simple way of detecting non-Markovianity, as no information about the system's transient dynamics is necessary. In particular, we study a driven two-level system (TLS) in a semi-infinite waveguide. Once the waveguide has been traced out, the TLS sees an environment with a distinct memory time. The memory time enters the equations as a time delay that can be varied to compare a Markovian to a non-Markovian environment. We find that some non-Markovian states show exotic behaviors such as population inversion and steady-state coherence beyond 1/sqrt[8], neither of which is possible for a driven TLS in the Markovian regime, where the time delay is neglected. Additionally, we show how the coherence of quantum interference is affected by time delays in a driven system by extracting the effective Purcell-modified decay rate of a TLS in front of a mirror.
EvoLearner: Learning Description Logics with Evolutionary Algorithms

Classifying nodes in knowledge graphs is an important task, e.g., for predicting missing types of entities, predicting which molecules cause cancer, or predicting which drugs are promising treatment candidates. While black-box models often achieve high predictive performance, they are only post-hoc and locally explainable and do not allow the learned model to be easily enriched with domain knowledge. Towards this end, learning description logic concepts from positive and negative examples has been proposed. However, learning such concepts often takes a long time and state-of-the-art approaches provide limited support for literal data values, although they are crucial for many applications. In this paper, we propose EvoLearner—an evolutionary approach to learn concepts in , which is the attributive language with complement () paired with qualified cardinality restrictions () and data properties (). We contribute a novel initialization method for the initial population: starting from positive examples, we perform biased random walks and translate them to description logic concepts. Moreover, we improve support for data properties by maximizing information gain when deciding where to split the data. We show that our approach significantly outperforms the state of the art on the benchmarking framework SML-Bench for structured machine learning. Our ablation study confirms that this is due to our novel initialization method and support for data properties.
Blockchain-enabled beer game: a software tool for familiarizing the application of blockchain in supply chain management

Purpose This paper aims to introduce, conceptualize and demonstrate a software tool named “Blockchain-Enabled Beer Game” (BEBG) for familiarizing the application of blockchain in inventory management, one of the critical components of supply chain management. Design/methodology/approach This paper follows a methodology of design-based research and develops a software tool in the form of a role-play simulation game. The proposed game adopts the theme of the traditional beer distribution game to establish a blockchain-enabled scenario for inventory management. A decentralized application (DApp) was prototyped on the Ethereum blockchain to demonstrate the tool. Findings The proposed software tool is effective in teaching and training the application of blockchain in inventory management. While interacting with BEBG, players witness how each inventory-related transaction gets secured with blockchain. A basic understanding of the fundamentals of blockchain is a prerequisite for using this tool. BEBG is not self-explanatory, and an instructor is essential for assisting the players. Originality/value Software tools currently available to familiarize with blockchain technology cannot convey its practical applications. Addressing this gap, BEBG allows the users to experience the application of blockchain in inventory management. Academic institutions, especially business schools, can use this tool to teach the students the practical use of blockchain technology. Industries can adopt BEBG for training the employees. The research community can devise BEBG to infer the impact of blockchain in supply chain management.