Miracle-dz/UniFAR-FTUs huggingface dataset:
Convex integration and phenomenologies in turbulence

In this review article we discuss a number of recent results concerning wild weak solutions of the incompressible Euler and Navier-Stokes equations. These results build on the groundbreaking works of De Lellis and Szekelyhidi Jr., who extended Nash's fundamental ideas on $C^1$ flexible isometric embeddings, into the realm of fluid dynamics. These techniques, which go under the umbrella name convex integration, have fundamental analogies the phenomenological theories of hydrodynamic turbulence. Mathematical problems arising in turbulence (such as the Onsager conjecture) have not only sparked new interest in convex integration, but certain experimentally observed features of turbulent flows (such as intermittency) have also informed new convex integration constructions. First, we give an elementary construction of nonconservative $C^{0+}_{x,t}$ weak solutions of the Euler equations, first proven by De Lellis-Szekelyhidi Jr. Second, we present Isett's recent resolution of the flexible side of the Onsager conjecture. Here, we in fact follow the joint work of De Lellis-Szekelyhidi Jr. and the authors of this paper, in which weak solutions of the Euler equations in the regularity class $C^{\frac 13-}_{x,t}$ are constructed, attaining any energy profile. Third, we give a concise proof of the authors' recent result, which proves the existence of infinitely many weak solutions of the Navier-Stokes in the regularity class $C^0_t L^{2+}_x \cap C^0_t W^{1,1+}_x$. We conclude the article by mentioning a number of open problems at the intersection of convex integration and hydrodynamic turbulence.
The Impact of Climate Change on Heart Failure: A Narrative Review Study

Context: Climate change as a global threat has affected all aspects of society, including health. This study aimed to identify the role of climate change factors on cardiovascular diseases. Evidence Acquisition: The current narrative review study searched the databases including EMBASE, PubMed, Science Direct, Ovid, Cochrane Library, Magiran, and SID using Persian and English keywords such as ‘climate change’, ‘cold wave’, ‘heat wave’, ‘temperature decrease’, ‘temperature rise’, ‘heart disease’, ‘heart attack’, and ‘heart failure’ from the beginning of 2000 to the end of 2019. Results: Previous studies have shown that physical factors affect health and contribute to conditions such as cardiovascular disease and even death. Additionally, a relationship can be found between temperature decrease and heart diseases in the literature, while recent studies have shown that temperature rise is directly related to heart diseases. Conclusions: Due to increased greenhouse gas emissions and the increase in temperature, the world is witnessing a massive climate change. Considering the increase in the incidence and mortality rates due to heart diseases and the role of climate change in this increase, health policymakers need to focus on setting guidelines for reducing the complications of these diseases in high-risk groups based on the relevant studies.
Crossing the Uncanny Valley? Understanding Affinity, Trustworthiness, and Preference for More Realistic Virtual Humans in Immersive Environments

Developers have long strived to create virtual avatars that are more realistic because they are believed to be preferred over less realistic avatars; however, an “Uncanny Valley” exists in which avatars that are almost but not quite realistic trigger aversion. We used a field study to investigate whether users had different affinity, trustworthiness, and preferences for avatars with two levels of realism, one photo-realistic and one a cartoon caricature. We collected survey data and conducted one-on-one interviews with SIGGRAPH conference attendees who watched a live interview carried out utilizing two avatars, either on a large screen 2D video display or via 3D VR headsets. 18 sessions were conducted over four days, with the same person animating the photo realistic avatar but with different individuals animating the caricature avatars. Participants rated the photo-realistic avatar more trustworthy, had more affinity for it, and preferred it as a virtual agent. Participants who observed the interview through VR headsets had even stronger affinity for the photorealistic avatar and stronger preferences for it as a virtual agent. Interviews further surprisingly suggested that our ability to cross the Uncanny Valley may depend on who controls the avatar, a human or a
Incorporating Context and Location Into Social Media Analysis: A Scalable, Cloud-Based Approach for More Powerful Data Science

Dominated by quantitative data science techniques, social media data analysis often fails to incorporate the surrounding context, conversation, and metadata that allows for more complete, accurate, and informed analysis. Here we describe the development of a scalable data collection infrastructure to interrogate massive amounts of tweets—including complete user conversations—to perform contextualized social media analysis. Additionally, we discuss the nuances of location metadata and incorporate it when available to situate the user conversations within geographic context through an interactive map. The map also spatially clusters tweets to identify important locations and movement between them, illuminating specific behavior, like evacuating before a hurricane. We share performance details, the promising results of concurrent research utilizing this infrastructure, and discuss the challenges and ethics of using context-rich datasets.
Implicit-Explicit Multistep Methods for Hyperbolic Systems With Multiscale Relaxation

We consider the development of high order space and time numerical methods based on Implicit-Explicit (IMEX) multistep time integrators for hyperbolic systems with relaxation. More specifically, we consider hyperbolic balance laws in which the convection and the source term may have very different time and space scales. As a consequence the nature of the asymptotic limit changes completely, passing from a hyperbolic to a parabolic system. From the computational point of view, standard numerical methods designed for the fluid-dynamic scaling of hyperbolic systems with relaxation present several drawbacks and typically lose efficiency in describing the parabolic limit regime. In this work, in the context of Implicit-Explicit linear multistep methods we construct high order space-time discretizations which are able to handle all the different scales and to capture the correct asymptotic behavior, independently from its nature, without time step restrictions imposed by the fast scales. Several numerical examples confirm the theoretical analysis.
Weakly Supervised Person Re-identification: Cost-effective Learning with A New Benchmark

Person re-identification (ReID) benefits greatly from the accurate annotations of existing datasets (e.g., CUHK03 \cite{li2014deepreid} and Market-1501 \cite{zheng2015scalable}), which are quite expensive because each image in these datasets has to be assigned with a proper label. In this work, we explore to ease the annotation of ReID by replacing the accurate annotation with inaccurate annotation, i.e., we group the images into bags in terms of time and assign a bag-level label for each bag. This greatly reduces the annotation effort and leads to the creation of a large-scale ReID benchmark called SYSU-30$k$. The new benchmark contains $30k$ categories of persons, which is about $20$ times larger than CUHK03 ($1.3k$ categories) and Market-1501 ($1.5k$ categories), and $30$ times larger the ImageNet ($1k$ categories). It totally sums up to 29,606,918 images. Learning a ReID model with bag-level annotation is called the weakly supervised ReID problem. To solve this problem, we introduce conditional random fields (CRFs) to capture the dependencies from all images in a bag and generate a reliable pseudo label for each person image. The pseudo label is further used to supervise the learning of the ReID model. When compared with the fully supervised ReID models, our method achieves the state-of-the-art performance on SYSU-30$k$ and other datasets. The code, dataset, and pretrained model will be available online.
Organizational support and intrapreneurial behavior: on the role of employees' intrapreneurial intention and self-efficacy

Despite the well-documented contributions of intrapreneurial behavior to organizational performance, the manifestations of the psychosocial factors at play remain poorly understood. Drawing on the theory of planned behavior, social exchange theory, and social cognitive theory, we propose that perceptions of organizational support would contribute to employees' intrapreneurial intentions and behaviors, but only insofar as employees feel confident about their intrapreneurial skills. The data were collected from 179 employees of a Canadian small and medium enterprise (SME) specializing in damage insurance. The regression analysis results indicate that the indirect effect of perceived organizational support on intrapreneurial behavior through intrapreneurial intention is moderated by intrapreneurial self-efficacy. These findings reveal that intrapreneurial self-efficacy is a boundary motivational condition for perceived organizational support to act on the intrapreneurial process so that intention can translate into behavior. The paper provides useful avenues for managers seeking to identify contextual and motivational levers to develop, sustain, and improve employee intrapreneurship.
A general framework for information extraction using dynamic span graphs

We introduce a general framework for several information extraction tasks that share span representations using dynamically constructed span graphs. The graphs are dynamically constructed by selecting the most confident entity spans and linking these nodes with confidence-weighted relation types and coreferences. The dynamic span graph allow coreference and relation type confidences to propagate through the graph to iteratively refine the span representations. This is unlike previous multi-task frameworks for information extraction in which the only interaction between tasks is in the shared first-layer LSTM. Our framework significantly outperforms state-of-the-art on multiple information extraction tasks across multiple datasets reflecting different domains. We further observe that the span enumeration approach is good at detecting nested span entities, with significant F1 score improvement on the ACE dataset.
Singly heavy baryons with chiral partner structure in a three-flavor chiral model

We construct an effective hadronic model including single heavy baryons (SHBs) belonging to the $(\mathbf{3},\mathbf{3})$ representation under $\mbox{SU}(3)_L \times \mbox{SU}(3)_R$ symmetry, respecting the chiral symmetry and heavy-qaurk spin-flavor symmetry. When the chiral symmetry is spontaneously broken, the SHBs are divided into the baryons with negative parity of $\bar{\mathbf 3}$ representation under $\mbox{SU}(3)$ flavor symmetry which is the chiral partners to the ones with positive parity of ${\mathbf 6}$ representation. We determine the model parameters from the available experimental data for the masses and strong decay widths of $\Sigma_c^{(\ast)}$, $\Lambda_c (2595)$, $\Xi_c (2790)$, and $\Xi_c (2815)$. Then, we predict the masses and strong decay widths of other baryons including $\Xi_b$ with negative parity. We also study radiative decays of SHBs including $\Omega_c^\ast$ and $\Omega_b^\ast$ with positive parity.
A code for square permutations and convex permutominoes

In this article we consider square permutations, a natural subclass of permutations defined in terms of geometric conditions, that can also be described in terms of pattern avoiding permutations, and convex permutoninoes, a related subclass of polyominoes. While these two classes of objects arised independently in various contexts, they play a natural role in the description of certain random horizontally and vertically convex grid configurations. We propose a common approach to the enumeration of these two classes of objets that allows us to explain the known common form of their generating functions, and to derive new refined formulas and linear time random generation algorithms for these objects and the associated grid configurations.
Achieving optimal backlog in multi-processor cup games

Many problems in processor scheduling, deamortization, and buffer management can be modeled as single- and multi-processor cup games. At the beginning of the single-processor n-cup game, all cups are empty. In each step of the game, a filler distributes 1−є units of water among the cups, and then an emptier selects a cup and removes up to 1 unit of water from it. The goal of the emptier is to minimize the amount of water in the fullest cup, also known as the backlog. The greedy algorithm (i.e., empty from the fullest cup) is known to achieve backlog O(logn), and no deterministic algorithm can do better. We show that the performance of the greedy algorithm can be exponentially improved with a small amount of randomization: After each step and for any k ≥ Ω(logє−1), the emptier achieves backlog at most O(k) with probability at least 1 −O(2−2k). We call our algorithm the smoothed greedy algorithm because if follows from a smoothed analysis of the (standard) greedy algorithm. In each step of the p-processor n-cup game, the filler distributes p(1−є) unit of water among the cups, with no cup receiving more than 1−δ units of water, and then the emptier selects p cups and removes 1 unit of water from each. Proving nontrivial bounds on the backlog for the multi-processor cup game has remained open for decades. We present a simple analysis of the greedy algorithm for the multi-processor cup game, establishing a backlog of O(є−1 logn), as long as δ > 1/poly(n). Turning to randomized algorithms, we find that the backlog drops to constant. Specifically, we show that if є and δ satisfy reasonable constraints, then there exists an algorithm that bounds the backlog after a given step by 3 with probability at least 1 − O(exp(−Ω(є2 p)). We prove that our results are asymptotically optimal for constant є, in the sense that no algorithms can achieve better bounds, up to constant factors in the backlog and in p. Moreover, we prove robustness results, demonstrating that our randomized algorithms continue to behave well even when placed in bad starting states.
From Facebook to the Streets: Russian Troll Ads and Black Lives Matter Protests

Online trolling is typically studied in the IS literature as an uncoordinated, anarchic activity. Coordinated, strategic online trolling is not well understood despite its prevalence on social media. To shed light on this prevailing activity, the present study examines the proposition that coordinated online trolling is timed to leverage macro societal unrest. In testing this proposition, we analyzed the dynamics of the Russian State’s coordinated trolling campaign against the United States beginning in 2015. Using the May 2018 release of all Russian Troll Facebook advertisements, this study constructs a topic model of the content of these ads. The relationship between ad topics and the frequency of Black Lives Matter protests is examined. We argue that the frequency of Black Lives Matter protests proxies for civil unrest and divisiveness in the United States. The study finds that Russian ads related to police brutality were issued to coincide with periods of higher unrest. This work also finds that during periods of relative calm (evidenced by lower frequency of protests) Russian ads were relatively innocuous.
Linear-Time and Efficient Distributed Algorithms for List Coloring Graphs on Surfaces

In 1994, Thomassen proved that every planar graph is 5-list-colorable. In 1995, Thomassen proved that every planar graph of girth at least five is 3-list-colorable. His proofs naturally lead to quadratic-time algorithms to find such colorings. Here, we provide the first linear-time algorithms to find such colorings. For a fixed surface S, Thomassen showed in 1997 that there exists a linear-time algorithm to decide if a graph embedded in S is 5-colorable and similarly in 2003 if a graph of girth at least five embedded in S is 3-colorable. Using the theory of hyperbolic families, the author and Thomas showed such algorithms exist for list-colorings. Around the same time, Dvorak and Kawarabayashi also provided such algorithms. Moreover, they gave an algorithm to find such colorings (if they exist). Here we provide the first such algorithm which is fixed parameter tractable with genus as the parameter; indeed, we provide a linear-time algorithm to find such colorings. In 1988, Goldberg, Plotkin and Shannon provided a deterministic distributed algorithm for 7-coloring n-vertex planar graphs in O(log n) rounds. In 2018, Aboulker, Bonamy, Bousquet, and Esperet provided a deterministic distributed algorithm for 6-coloring n-vertex planar graphs in polylogarithmic rounds. Their algorithm in fact works for 6-list-coloring. They also provided a polylogarithmic algorithm for 4-list-coloring triangle-free planar graphs. Chechik and Mukhtar independently obtained such algorithms for ordinary coloring in O(log n) rounds, which is best possible in terms of running time. Here we provide the first polylogarithmic deterministic distributed algorithms for 5-coloring n-vertex planar graphs and similarly for 3-coloring planar graphs of girth at least five. Indeed, these algorithms run in O(log n) rounds, work also for list-colorings, and even work on a fixed surface (assuming such a coloring exists).
Crisis Warning Apps: Investigating the Factors Influencing Usage and Compliance with Recommendations for Action

Effectively disseminating warnings of threats such as floods, thunderstorms, or terrorist attacks is essential for saving lives in affected areas. With the widespread use of mobile devices such as smartphones, mobile warning applications (warning apps) enable the efficient transmission of warnings via pushnotifications. Use of warning apps in crisis or threat situations, however, has received little attention by researchers. Therefore, we investigate in this study the factors that affect the use of warning apps and the intention to comply with recommendations for action transmitted via such apps. We rely on prior research that studied compliance intention during campus emergencies, research on warning and risk communication, and research on technology usage. We find that risk perception, trust, and subjective norm positively influence both use of a warning app and compliance intention, whereas concerns about data security have negative effects. Our findings inform research in the context of risk communication and technology usage as well as providers of warning apps seeking to promote their apps effectively.
Computing the Mostar index in networks with applications to molecular graphs

Recently, a bond-additive topological descriptor, named as the Mostar index, has been introduced as a measure of peripherality in networks. For a connected graph $G$, the Mostar index is defined as $Mo(G) = \sum_{e=uv \in E(G)} |n_u(e) - n_v(e)|$, where for an edge $e=uv$ we denote by $n_u(e)$ the number of vertices of $G$ that are closer to $u$ than to $v$ and by $n_v(e)$ the number of vertices of $G$ that are closer to $v$ than to $u$. In this paper, we generalize the definition of the Mostar index to weighted graphs and prove that the Mostar index of a weighted graph can be computed in terms of Mostar indices of weighted quotient graphs. As a consequence, we show that the Mostar index of a benzenoid system can be computed in sub-linear time with respect to the number of vertices. Finally, our method is applied to some benzenoid systems and to a fullerene patch.
Riemannian Normalizing Flow on Variational Wasserstein Autoencoder for Text Modeling

Recurrent Variational Autoencoder has been widely used for language modeling and text generation tasks. These models often face a difficult optimization problem, also known as KL vanishing, where the posterior easily collapses to the prior and model will ignore latent codes in generative tasks. To address this problem, we introduce an improved Variational Wasserstein Autoencoder (WAE) with Riemannian Normalizing Flow (RNF) for text modeling. The RNF transforms a latent variable into a space that respects the geometric characteristics of input space, which makes posterior impossible to collapse to the non-informative prior. The Wasserstein objective minimizes the distance between marginal distribution and the prior directly and therefore does not force the posterior to match the prior. Empirical experiments show that our model avoids KL vanishing over a range of datasets and has better performance in tasks such as language modeling, likelihood approximation, and text generation. Through a series of experiments and analysis over latent space, we show that our model learns latent distributions that respect latent space geometry and is able to generate sentences that are more diverse.
Robotic Psychology. What Do We Know about Human-Robot Interaction and What Do We Still Need to Learn?

“Robotization”, the integration of robots in human life will change human life drastically. In many situations, such as in the service sector, robots will become an integrative part of our lives. Thus, it is vital to learn from extant research on human-robot interaction (HRI). This article introduces robotic psychology that aims to bridge the gap between humans and robots by providing insights into particularities of HRI. It presents a conceptualization of robotic psychology and provides an overview of research on service-focused human-robot interaction. Theoretical concepts, relevant to understand HRI with are reviewed. Major achievements, shortcomings, and propositions for future research will be discussed.
Knowledge Management to Support Supply Chain Sustainability and Collaboration Practices

This exploratory research examined the contribution of knowledge management (KM) to sustainability and collaboration practices across a number of small to medium size Australian food and beverage exporters. A growing focus on sustainable supply chain management (SSCM) in global supply chains offers opportunities for organizations to create value and secure competitive advantage by employing strategic KM practices to support SSCM. Analysis of eight case study organizations revealed that KM contributes to SSCM across a number of areas, including strategic focus, protecting firm reputation and performance, risk management, innovation, collaboration and relationships with partners.
ASAP: Architecture Search, Anneal and Prune

Automatic methods for Neural Architecture Search (NAS) have been shown to produce state-of-the-art network models. Yet, their main drawback is the computational complexity of the search process. As some primal methods optimized over a discrete search space, thousands of days of GPU were required for convergence. A recent approach is based on constructing a differentiable search space that enables gradient-based optimization, which reduces the search time to a few days. While successful, it still includes some noncontinuous steps, e.g., the pruning of many weak connections at once. In this paper, we propose a differentiable search space that allows the annealing of architecture weights, while gradually pruning inferior operations. In this way, the search converges to a single output network in a continuous manner. Experiments on several vision datasets demonstrate the effectiveness of our method with respect to the search cost and accuracy of the achieved model. Specifically, with $0.2$ GPU search days we achieve an error rate of $1.68\%$ on CIFAR-10.