Miracle-dz/UniFAR-FTUs huggingface dataset:
Perspectives on the dynamics in loop effective black hole interior

In the loop quantum gravity context, there have been numerous proposals to quantize the reduced phase space of a black hole, and develop a classical effective description for its interior which eventually resolves the singularity. However little progress has been made towards understanding the relation between such quantum/effective minisuperspace models and what would be the spherically symmetric sector of loop quantum gravity. In particular, it is not clear whether one can extract the phenomenological predictions obtained in minisuperspace models, such as the singularity resolution and the spacetime continuation beyond the singularity, from a calculation in full loop quantum gravity. In this paper, we present an attempt in this direction in the context of Kantowski-Sachs spacetime, through the proposal of two new effective Hamiltonians for the reduced classical model. The first is derived using Thiemann classical identities for the regularized expressions, while the second is obtained as a first approximation of the expectation value of a Hamiltonian operator in loop quantum gravity in a semi-classical state peaked on the Kantowski-Sachs initial data. We then proceed with a detailed analysis of the dynamics they generate and compare them with the Hamiltonian derived in General Relativity and the common effective Hamiltonian proposed in earlier literature.
An Exploratory Analysis of the Latent Structure of Process Data via Action Sequence Autoencoder

Computer simulations have become a popular tool for assessing complex skills such as problem-solving. Log files of computer-based items record the human-computer interactive processes for each respondent in full. The response processes are very diverse, noisy, and of non-standard formats. Few generic methods have been developed to exploit the information contained in process data. In this paper we propose a method to extract latent variables from process data. The method utilizes a sequence-to-sequence autoencoder to compress response processes into standard numerical vectors. It does not require prior knowledge of the specific items and human-computer interaction patterns. The proposed method is applied to both simulated and real process data to demonstrate that the resulting latent variables extract useful information from the response processes.
A proposal for nonabelian $(0,2)$ mirrors

In this paper we give a proposal for mirrors to (0,2) supersymmetric gauged linear sigma models (GLSMs), for those (0,2) GLSMs which are deformations of (2,2) GLSMs. Specifically, we propose a construction of (0,2) mirrors for (0,2) GLSMs with E terms that are linear and diagonal, reducing to both the Hori-Vafa prescription as well as a recent (2,2) nonabelian mirrors proposal on the (2,2) locus. For the special case of abelian (0,2) GLSMs, two fo the authors have previously proposed a systematic construction, which is both simplified and generalized by the proposal here.
A Bayesian Joint Model for Spatial Point Processes with Application to Basketball Shot Chart

The success rate of a basketball shot may be higher at locations where a player makes more shots. In a marked spatial point process model, this means that the marks are dependent on the intensity of the process. We develop a Bayesian joint model of the mark and the intensity of marked spatial point process, where the intensity is incorporated in the model for the mark as a covariate. Further, we allow variable selection through the spike-slab prior. Inferences are developed with a Markov chain Monte Carlo algorithm to sample from the posterior distribution. Two Bayesian model comparison criteria, the modified Deviance Information Criterion and the modified Logarithm of the Pseudo-Marginal Likelihood, are developed to assess the fitness of different models focusing on the mark. The empirical performances of the proposed methods are examined in extensive simulation studies. We apply the proposed methods to the shot charts of four players in the NBA's 2017--2018 regular season to analyze the shot intensity in the field and the field goal percentage. The results suggest that the field goal percentages of three players are significantly positively dependent on their shot intensities, and that different players have different predictors for their field goal percentages.
Query-to-Communication Lifting Using Low-Discrepancy Gadgets

Lifting theorems are theorems that relate the query complexity of a function $f:\{0,1\}^{n}\to\{0,1\}$ to the communication complexity of the composed function $f \circ g^{n}$, for some "gadget" $g:\{0,1\}^{b}\times\{0,1\}^{b}\to\{0,1\}$. Such theorems allow transferring lower bounds from query complexity to the communication complexity, and have seen numerous applications in the recent years. In addition, such theorems can be viewed as a strong generalization of a direct-sum theorem for the gadget $g$. We prove a new lifting theorem that works for all gadgets $g$ that have logarithmic length and exponentially-small discrepancy, for both deterministic and randomized communication complexity. Thus, we significantly increase the range of gadgets for which such lifting theorems hold. Our result has two main motivations: First, allowing a larger variety of gadgets may support more applications. In particular, our work is the first to prove a randomized lifting theorem for logarithmic-size gadgets, thus improving some applications of the theorem. Second, our result can be seen as a strong generalization of a direct-sum theorem for functions with low discrepancy.
A Survey on Computational Politics

Computational Politics is the study of computational methods to analyze and moderate users’ behaviors related to political activities such as election campaign persuasion, political affiliation, and opinion mining. With the rapid development and ease of access to the Internet, Information Communication Technologies (ICT) have given rise to massive numbers of users joining online communities and the digitization of political practices such as debates. These communities and digitized data contain both explicit and latent information about users and their behaviors related to politics and social movements. For researchers, it is essential to utilize data from these sources to develop and design systems that not only provide solutions to computational politics but also help other businesses, such as marketers, to increase users’ participation and interactions. In this survey, we attempt to categorize main areas in computational politics and summarize the prominent studies in one place to better understand computational politics across different and multidimensional platforms. e.g., online social networks, online forums, and political debates. We then conclude this study by highlighting future research directions, opportunities, and challenges.
Multitask and Transfer Learning for Autotuning Exascale Applications

Multitask learning and transfer learning have proven to be useful in the field of machine learning when additional knowledge is available to help a prediction task. We aim at deriving methods following these paradigms for use in autotuning, where the goal is to find the optimal performance parameters of an application treated as a black-box function. We show comparative results with state-of-the-art autotuning techniques. For instance, we observe an average $1.5x$ improvement of the application runtime compared to the OpenTuner and HpBandSter autotuners. We explain how our approaches can be more suitable than some state-of-the-art autotuners for the tuning of any application in general and of expensive exascale applications in particular.
Anchor Tasks: Inexpensive, Shared, and Aligned Tasks for Domain Adaptation

We introduce a novel domain adaptation formulation from synthetic dataset (source domain) to real dataset (target domain) for the category of tasks with per-pixel predictions. The annotations of these tasks are relatively hard to acquire in the real world, such as single-view depth estimation or surface normal estimation. Our key idea is to introduce anchor tasks, whose annotations are (1) less expensive to acquire than the main task, such as facial landmarks and semantic segmentations; and (2) shared in availability for both synthetic and real datasets so that it serves as "anchor" between tasks; and finally (3) aligned spatially with main task annotations on a per-pixel basis so that it also serves as spatial anchor between tasks' outputs. To further utilize spatial alignment between the anchor and main tasks, we introduce a novel freeze approach that freezes the final layers of our network after training on the source domain so that spatial and contextual relationship between tasks are maintained when adapting on the target domain. We evaluate our methods on two pairs of datasets, performing surface normal estimation in indoor scenes and faces, using semantic segmentation and facial landmarks as anchor tasks separately. We show the importance of using anchor tasks in both synthetic and real domains, and that the freeze approach outperforms competing approaches, reaching results in facial images on par with the state-of-the-art system that leverages detailed facial appearance model.
Mixed High-Order Attention Network for Person Re-Identification

Attention has become more attractive in person re-identification (ReID) as it is capable of biasing the allocation of available resources towards the most informative parts of an input signal. However, state-of-the-art works concentrate only on coarse or first-order attention design, e.g. spatial and channels attention, while rarely exploring higher-order attention mechanism. We take a step towards addressing this problem. In this paper, we first propose the High-Order Attention (HOA) module to model and utilize the complex and high-order statistics information in attention mechanism, so as to capture the subtle differences among pedestrians and to produce the discriminative attention proposals. Then, rethinking person ReID as a zero-shot learning problem, we propose the Mixed High-Order Attention Network (MHN) to further enhance the discrimination and richness of attention knowledge in an explicit manner. Extensive experiments have been conducted to validate the superiority of our MHN for person ReID over a wide variety of state-of-the-art methods on three large-scale datasets, including Market-1501, DukeMTMC-ReID and CUHK03-NP. Code is available at http://www.bhchen. cn.
Sketch-Driven Regular Expression Generation from Natural Language and Examples

Abstract Recent systems for converting natural language descriptions into regular expressions (regexes) have achieved some success, but typically deal with short, formulaic text and can only produce simple regexes. Real-world regexes are complex, hard to describe with brief sentences, and sometimes require examples to fully convey the user’s intent. We present a framework for regex synthesis in this setting where both natural language (NL) and examples are available. First, a semantic parser (either grammar-based or neural) maps the natural language description into an intermediate sketch, which is an incomplete regex containing holes to denote missing components. Then a program synthesizer searches over the regex space defined by the sketch and finds a regex that is consistent with the given string examples. Our semantic parser can be trained purely from weak supervision based on correctness of the synthesized regex, or it can leverage heuristically derived sketches. We evaluate on two prior datasets (Kushman and Barzilay 2013; Locascio et al. 2016) and a real-world dataset from Stack Overflow. Our system achieves state-of-the-art performance on the prior datasets and solves 57% of the real-world dataset, which existing neural systems completely fail on.1
AI Predicts Independent Construction Safety Outcomes from Universal Attributes

This paper significantly improves on, and finishes to validate, the approach proposed in "Application of Machine Learning to Construction Injury Prediction" (Tixier et al. 2016 [1]). Like in the original study, we use NLP to extract fundamental attributes from raw incident reports and machine learning models are trained to predict safety outcomes (here, these outcomes are injury severity, injury type, bodypart impacted, and incident type). However, in this study, safety outcomes were not extracted via NLP but are independent (human annotations), eliminating any potential source of artificial correlation between predictors and predictands. Results show that attributes are still highly predictive, confirming the validity of the original study. Other improvements brought by the current study include the use of (1) a much larger dataset, (2) two new models (XGBoost andlinear SVM), (3) model stacking, (4) a more straight forward experimental setup with more appropriate performance metrics, and (5) an analysis of per-category attribute importance scores. Finally, the injury severity outcome is well predicted, which was not the case in the original study. This is a significant advancement.
Frank-Wolfe Network: An Interpretable Deep Structure for Non-Sparse Coding

The problem of <inline-formula> <tex-math notation="LaTeX">$L_{p}$ </tex-math></inline-formula>-norm constrained coding is to convert signal into code that lies inside an <inline-formula> <tex-math notation="LaTeX">$L_{p}$ </tex-math></inline-formula>-ball and most faithfully reconstructs the signal. Previous works under the name of sparse coding considered the cases of <inline-formula> <tex-math notation="LaTeX">$L_{0}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$L_{1}$ </tex-math></inline-formula> norms. The cases with <inline-formula> <tex-math notation="LaTeX">$p>1$ </tex-math></inline-formula> values, i.e. non-sparse coding studied in this paper, remain a difficulty. We propose an interpretable deep structure namely Frank-Wolfe Network (F-W Net), whose architecture is inspired by unrolling and truncating the Frank-Wolfe algorithm for solving an <inline-formula> <tex-math notation="LaTeX">$L_{p}$ </tex-math></inline-formula>-norm constrained problem with <inline-formula> <tex-math notation="LaTeX">$p\geq 1$ </tex-math></inline-formula>. We show that the Frank-Wolfe solver for the <inline-formula> <tex-math notation="LaTeX">$L_{p}$ </tex-math></inline-formula>-norm constraint leads to a novel closed-form nonlinear unit, which is parameterized by <inline-formula> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> and termed <inline-formula> <tex-math notation="LaTeX">$pool_{p}$ </tex-math></inline-formula>. The <inline-formula> <tex-math notation="LaTeX">$pool_{p}$ </tex-math></inline-formula> unit links the conventional pooling, activation, and normalization operations, making F-W Net distinct from existing deep networks either heuristically designed or converted from projected gradient descent algorithms. We further show that the hyper-parameter <inline-formula> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> can be made learnable instead of pre-chosen in F-W Net, which gracefully solves the non-sparse coding problem even with unknown <inline-formula> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula>. We evaluate the performance of F-W Net on an extensive range of simulations as well as the task of handwritten digit recognition, where F-W Net exhibits strong learning capability. We then propose a convolutional version of F-W Net, and apply the convolutional F-W Net into image denoising and super-resolution tasks, where F-W Net all demonstrates impressive effectiveness, flexibility, and robustness.
I Want to Forget: Fine-Grained Encryption with Full Forward Secrecy in the Distributed Setting

Managing sensitive data in highly-distributed environments is gaining a lot of attention recently. Often, once data is presented to such environments, this data is persistent there. Being able to “forget” in such environments constitutes a very desired feature due to data security and privacy issues. In particular, applying the European General Data Protection Regulation (GDPR), the “Right to be Forgotten” essentially became a data owner right. In this work, we seek for cryptographic solutions that offer the possibility to willfully lose access to data in distributed environments (which can be seen equivalent to removing that data). We argue that simple encryption mechanisms do not suffice to cover all desired requirements and provide a solution that offers several strong security and privacy features. In particular, our solution achieves forward secrecy for all participants in the system (i.e., even when user keys leak), ensures strong privacy against public observers of the system (i.e., key anonymity against tracking), and enables fine-grained access control. Having those features in parallel was unknown from the cryptographic literature. We base our solution on a novel cryptographic primitive we dub Identity-Based Puncturable Encryption (IBPE) which significantly enhances previous ideas on Puncturable Encryption (PE) due to Green and Miers (IEEE S&P 2015) and Günther et al. (EUROCRYPT 2017). We argue that black-box constructions from Hierarchical Identity-Based Encryption (HIBE) do not seem to work, albeit we do know how to construct PE from HIBE. We further introduce an important feature being crucial in the setting of always-accessible and public data, namely that of key-anonymity for IBPE such that an IBPE ciphertext reveals nothing about the encryption key. We demonstrate the feasibility of our IBPE construction with a practical prototype implementation. Finally, we show that IBPE is a very versatile tool by using it to generically instantiate forward-secret IBE and forward-secret digital signatures, latter also being of importance in a distributed setting.
Unique Rabin-Williams Signature Scheme Decryption

The extremely efficient Rabin-Williams signature scheme relies on decryption of a quadratic equation in order to retrieve the original message. Customarily, square roots are found using the Chinese Remainder Theorem. This can be done in polynomial time, but generally produces four options for the correct message which must be analyzed to determine the correct one. This paper resolves the problem of efficient deterministic decryption to the correct message modulo p2q by establishing conditions on the primes p and q as well as on any legitimate message. We do this using the CRT modulo pq to find four roots. We show that the correct root (initial message) is the only one of these four which is in our allowed message set (it is in fact the smallest of the four integers) and which satisfies a quadratic equation modulo p2q; no additional work is required to eliminate the others. As a result, we propose what we believe is now the most efficient version of R-W signature scheme decryption.
Developing a Better and More User-Friendly Numeracy Scale for Patients

Background: A person's ability to work with and understand numerical information (i.e., numeracy) is increasingly important in everyday health and other decision-making contexts. Several survey measures of numeracy have been developed to address this trend, including the widely used General Numeracy Scale (GNS), which is thematically focused on health decision-making and is assumed to measure a unidimensional construct of numeracy. Objective: The present research was designed to evaluate this proposed unidimensional structure of general numeracy, for which prior data have given mixed empirical support. Methods: Three samples completed the GNS, in different forms, and responses were analyzed in terms of underlying factor structure. Key Results: We show that both one-factor and four-factor models of numeracy are plausible based on the GNS (Study 1), and then develop a multiple-choice version of the GNS (i.e., the MC-GNS) that demonstrates some increased clarity in factor structure due to the consistent response format (Study 2). A further study evaluated the convergent and discriminant validity of the MC-GNS (Study 3), finding it to be as good as or better than the prior scale. Conclusions: Additionally, the MC-GNS is easier for people to take, likely to be less stressful, and easier for practitioners to score. Collectively, this research identifies a problem with the GNS measure, develops improvements to help address this problem, and in the process creates a way to more easily measure numeracy in practical settings. [HLRP: Health Literacy Research and Practice. 2019;3(3):e174–e180. ] Plain Language Summary: Numeracy is important across health contexts. Prevalent numeracy scales assumedly measure a single construct but empirical support for this is lacking. We find both one- and four-factor models are consistent with one scale and develop a revision that clarifies this structure without sacrificing validity. This revised numeracy scale is easier to administer and score, and therefore preferable in practical settings.
Impact of a nurse engagement intervention on pain, agitation and delirium assessment in a community intensive care unit

Background In 2013, the Society of Critical Care Medicine published a revised version of the ICU Pain, Agitation, and Delirium (PAD) guidelines. Immobility and sleep were subsequently added in 2018. Despite the well-established advantages of implementing these guidelines, adoption and adherence remain suboptimal. This is especially true in community settings, where PAD assessment is performed less often, and the implementation of PAD guidelines has not yet been studied. The purpose of this prospective interventional study is to evaluate the effect of a multifaceted nurse engagement intervention on PAD assessment in a community intensive care unit (ICU). Methods All patients admitted to our community ICU for over 24 hours were included. A 20-week baseline audit was performed, followed by the intervention, and a 20-week postintervention audit. The intervention consisted of a survey, focus groups and education sessions. Primary outcomes included rates of daily PAD assessment using validated tools. Results There were improvements in the number of patients with at least one assessment per day of pain (67.5% vs 59.3%, p=0.04), agitation (93.1% vs 78.7%, p<0.001) and delirium (54.2% vs 39.4%, p<0.001), and the number of patients with target Richmond Agitation-Sedation Scale ordered (63.1% vs 46.8%, p=0.002). There was a decrease in the rate of physical restraint use (10.0% vs 30.9%, p<0.001) and no change in self-extubation rate (0.9% vs 2.5%, p=0.2). Conclusion The implementation of a multifaceted nurse engagement intervention has the potential to improve rates of PAD assessment in community ICUs. Screening rates in our ICU remain suboptimal despite these improvements. We plan to implement multidisciplinary interventions targeting physicians, nurses and families to close the observed care gap.