Miracle-dz/UniFAR-FTUs huggingface dataset:
Lower Intent to Comply with COVID-19 Public Health Recommendations Correlates to Higher Disease Burden in Following 30 Days

Efforts to forecast coronavirus disease 2019 (COVID-19) case burden have been largely unsuccessful. Correlating responses from a national, demographically representative sample of US adults (N = 10,650) with subsequent COVID-19 infection rates, this article reports that for every 3% increase in intent to comply with public health recommendations, the authors estimate a 9% reduction in new COVID-19 cases during the subsequent 30 days. Objectives We sought to determine whether self-reported intent to comply with public health recommendations correlates with future coronavirus disease 2019 (COVID-19) disease burden. Methods A cross-sectional, online survey of US adults, recruited by snowball sampling, from April 9 to July 12, 2020. Primary measurements were participant survey responses about their intent to comply with public health recommendations. Each participant’s intent to comply was compared with his or her local COVID-19 case trajectory, measured as the 7-day rolling median percentage change in COVID-19 confirmed cases within participants’ 3-digit ZIP code area, using public county-level data, 30 days after participants completed the survey. Results After applying raking techniques, the 10,650-participant sample was representative of US adults with respect to age, sex, race, and ethnicity. Intent to comply varied significantly by state and sex. Lower reported intent to comply was associated with higher COVID-19 case increases during the following 30 days. For every 3% increase in intent to comply with public health recommendations, which could be achieved by improving average compliance by a single point for a single item, we estimate a 9% reduction in new COVID-19 cases during the subsequent 30 days. Conclusions Self-reported intent to comply with public health recommendations may be used to predict COVID-19 disease burden. Measuring compliance intention offers an inexpensive, readily available method of predicting disease burden that can also identify populations most in need of public health education aimed at behavior change.
Effects of Urban Sprawl on the Vulnerability to a Significant Tornado Impact in Northeastern Illinois

A sprawling U.S. population continues to spread into the fringes of urban development placing both populations and property in areas that were once largely unoccupied. Population tallies, housing unit totals, and housing values for 1990 and 2000 are examined to determine the extent to which this growth has affected the tornado hazard in northeastern Illinois. The growing town of Plainfield, Ill. , located southwest of Chicago, is examined to determine how vulnerability to a tornado impact has changed in the town since an F5 tornado struck the community in 1990. The population and housing data indicate an increase of 8,629 persons and 3,058 housing units affected if the tornado were to have occurred in 2000 rather than 1990. Estimations of housing value affected by the Plainfield tornado indicate a 50% increase in 2000 compared with 1990 values. In addition to studying the impacts on Plainfield, four other scenarios are examined in suburban Chicago counties using the 1990 Plainfield tornado as a model for a potentially devastating strike. The large increase in total value of homes affected for each scenario highlights the overall increase in wealth throughout the study area, specifically along the urban fringe of development. The physical vulnerability throughout the study area has increased with the rise in population, but the most socially vulnerable areas appear to remain in the older urban centers.
Pulse induced half rotational resonance with angular dependent total enhancement of multi-dimensional solid-state NMR correlation spectra

We demonstrate a new resonance condition that obeys the relation ∆δ = nνR/2, where ∆δ is the chemical shift difference between two homonuclear-coupled spins, νR is the magic-angle spinning speed and n is an integer. This ‘half rotational resonance’ recoupling condition is obtained by the application of rotor-synchronous 1H pulses when protons are dipolar-coupled to the homonuclear spins. We suggest a new experimental scheme entitled ‘pulse induced resonance with angular dependent total enhancement’ (PIRATE) that can enhance protondriven spin diffusion by the application of a single 1H pulse every rotor period. This scheme can be utilised to obtain homonuclear correlations in multidimensional solid state NMR experiments. Experimental evidence is demonstrated on the two carbon spins of glycine and is supported by numerical simulations.
PokeBNN: A Binary Pursuit of Lightweight Accuracy

Optimization of Top-1 ImageNet promotes enormous networks that may be impractical in inference settings. Binary neural networks (BNNs) have the potential to significantly lower the compute intensity but existing models suffer from low quality. To overcome this deficiency, we propose Poke- Conv, a binary convolution block which improves quality of BNNs by techniques such as adding multiple residual paths, and tuning the activation function. We apply it to ResNet-50 and optimize ResNet's initial convolutional layer which is hard to binarize. We name the resulting network family PokeBNN11Poke/pnki/is pronounced similarly to pocket. PokeConv, PokeBNN, and Pokemon are abbreviations of Pocket Convolution, Pocket Binary Neural Network, and Pocket Monster, respectively. These techniques are chosen to yield favorable improvements in both top-1 accuracy and the network's cost. In order to enable joint optimization of the cost together with accuracy, we define arithmetic computation effort (ACE), a hardware- and energy-inspired cost metric for quantized and binarized networks. We also identify a need to optimize an under-explored hyper-parameter controlling the binarization gradient approximation. We establish a new, strong state-of-the-art (SOTA) on top-1 accuracy together with commonly-used CPU64 cost, ACE cost and network size metrics. ReActNet-Adam [33], the previous SOTA in BNNs, achieved a 70.5% top-1 accuracy with 7.9 ACE. A small variant of PokeBNN achieves 70.5% top-1 with 2.6 ACE, more than 3x reduction in cost; a larger PokeBNN achieves 75.6% top-1 with 7.8 ACE, more than 5% improvement in accuracy without increasing the cost. PokeBNN implementation in JAX/Flax [6, 18] and re-production instructions are open sourced.22Source code and reproduction instructions are available in AQT repos-itory: github.com/google/aqt.
Scaling Shared-Memory Data Structures as Distributed Global-View Data Structures in the Partitioned Global Address Space model

The Partitioned Global Address Space (PGAS), a memory model in which the global address space is explicitly partitioned across compute nodes in a cluster, strives to bridge the gap between shared-memory and distributed-memory programming. To further bridge this gap, there has been an adoption of global-view distributed data structures, such as 'global arrays' or 'distributed arrays'. This work demonstrates how shared-memory data structures can be modified to scale in distributed memory. Presented in this work is the Distributed Interlocked Hash Table (DIHT), a global-view distributed map data structure inpired by the Interlocked Hash Table (IHT). At 64 nodes with 44 cores per node, DIHT provides upto 110x the performance of the Chapel standard-library HashedDist.
Convergence of GANs Training: A Game and Stochastic Control Methodology

Training of generative adversarial networks (GANs) is known for its difﬁculty to converge. This paper ﬁrst conﬁrms analytically one of the culprits behind this convergence issue: the lack of convexity in GANs objective functions, hence the well-posedness problem of GANs models. Then, it proposes a stochastic control approach for hyper-parameters tuning in GANs training. In particular, it presents an optimal solution for adaptive learning rate which depends on the convexity of the objective function, and builds a precise relation between improper choices of learning rate and explosion in GANs training. Finally, empirical studies demonstrate that training algorithms incorporating this selection methodology outperform standard ones.
Uniqueness for the q-state antiferromagnetic Potts model on the regular tree

We present an elementary proof for the uniqueness regime of general q-state antiferromagnetic Potts model on the d-ary tree. The key observation is a positive association property of its boundary condition. We also obtain the exact exponential decay rate in all of the subcritical regime, and power law decay rate at the critical temperature. MSC 2010: 05C99, 60K35, 82B20.
NER-BERT: A Pre-trained Model for Low-Resource Entity Tagging

Named entity recognition (NER) models generally perform poorly when large training datasets are unavailable for low-resource domains. Recently, pre-training a large-scale language model has become a promising direction for coping with the data scarcity issue. However, the underlying discrepancies between the language modeling and NER task could limit the models' performance, and pre-training for the NER task has rarely been studied since the collected NER datasets are generally small or large but with low quality. In this paper, we construct a massive NER corpus with a relatively high quality, and we pre-train a NER-BERT model based on the created dataset. Experimental results show that our pre-trained model can significantly outperform BERT as well as other strong baselines in low-resource scenarios across nine diverse domains. Moreover, a visualization of entity representations further indicates the effectiveness of NER-BERT for categorizing a variety of entities.
Mars: new insights and unresolved questions

Mars exploration motivates the search for extraterrestrial life, the development of space technologies, and the design of human missions and habitations. Here, we seek new insights and pose unresolved questions relating to the natural history of Mars, habitability, robotic and human exploration, planetary protection, and the impacts on human society. Key observations and findings include: – high escape rates of early Mars' atmosphere, including loss of water, impact present-day habitability; – putative fossils on Mars will likely be ambiguous biomarkers for life; – microbial contamination resulting from human habitation is unavoidable; and – based on Mars' current planetary protection category, robotic payload(s) should characterize the local martian environment for any life-forms prior to human habitation. Some of the outstanding questions are: – which interpretation of the hemispheric dichotomy of the planet is correct; – to what degree did deep-penetrating faults transport subsurface liquids to Mars' surface; – in what abundance are carbonates formed by atmospheric processes; – what properties of martian meteorites could be used to constrain their source locations; – the origin(s) of organic macromolecules; – was/is Mars inhabited; – how can missions designed to uncover microbial activity in the subsurface eliminate potential false positives caused by microbial contaminants from Earth; – how can we ensure that humans and microbes form a stable and benign biosphere; and – should humans relate to putative extraterrestrial life from a biocentric viewpoint (preservation of all biology), or anthropocentric viewpoint of expanding habitation of space? Studies of Mars' evolution can shed light on the habitability of extrasolar planets. In addition, Mars exploration can drive future policy developments and confirm (or put into question) the feasibility and/or extent of human habitability of space.
Minimax Analysis for Inverse Risk in Nonparametric Planer Invertible Regression

We study a minimax risk of estimating inverse functions on a plane, while keeping an estimator is also invertible. Learning invertibility from data and exploiting an invertible estimator are used in many domains, such as statistics, econometrics, and machine learning. Although the consistency and universality of invertible estimators have been well investigated, analysis of the efficiency of these methods is still under development. In this study, we study a minimax risk for estimating invertible bi-Lipschitz functions on a square in a $2$-dimensional plane. We first introduce two types of $L^2$-risks to evaluate an estimator which preserves invertibility. Then, we derive lower and upper rates for minimax values for the risks associated with inverse functions. For the derivation, we exploit a representation of invertible functions using level-sets. Specifically, to obtain the upper rate, we develop an estimator asymptotically almost everywhere invertible, whose risk attains the derived minimax lower rate up to logarithmic factors. The derived minimax rate corresponds to that of the non-invertible bi-Lipschitz function, which shows that the invertibility does not reduce the complexity of the estimation problem in terms of the rate. % the minimax rate, similar to other shape constraints.
"Vironment": An Art of Wearable Social Distancing

"Vironment"is a series of art pieces, social commentary, technology, etc., based on wearable health technologies of social-distancing, culminating in a social-distancing device that takes the familiar world of security and surveillance technologies that surround us and re-situates it on the body of the wearer (technologies that become part of us). This piece also introduces a conceptual framework for (1) the sensing of the self together with (2) sensing of others and (3) sensing of the environment around us.
TXOP sharing with Coordinated Spatial Reuse in Multi-AP Cooperative IEEE 802.11be WLANs

IEEE 802.11be networks (aka Wi-Fi 7) will have to cope with new bandwidth-hungry and low-latency services such as eXtended Reality and multi-party cloud gaming. With this goal in mind, transmit opportunity (TXOP) sharing between coordinated access points (APs) may contribute to alleviating inter-AP contention, hence increasing the overall network throughput. This paper evaluates two coordinated TXOP sharing strategies: coordinated time division multiple access (c-TDMA) and coordinated-TDMA with spatial reuse (c-TDMA/SR). We show that, while c-TDMA alone does not result in any significant improvement in terms of the WLAN throughput, it lays the groundwork to implement coordinated SR (c-SR) techniques. To evaluate the performance of c-TDMA/SR, we propose a fair scheduler able to select the best subset of parallel transmissions in WLAN deployments, as well as the appropriate power levels to be used by APs and stations (STAs), leading to maximum performance. The results obtained for c-TDMA/SR show significant throughput gains compared with c-TDMA, with values higher than 140% in 90% of the considered scenarios.
SpaceEdit: Learning a Unified Editing Space for Open-Domain Image Editing

Recently, large pretrained models (e.g., BERT, StyleGAN, CLIP) have shown great knowledge transfer and generalization capability on various downstream tasks within their domains. Inspired by these efforts, in this paper we propose a unified model for open-domain image editing focusing on color and tone adjustment of open-domain images while keeping their original content and structure. Our model learns a unified editing space that is more semantic, intuitive, and easy to manipulate than the operation space (e.g., contrast, brightness, color curve) used in many existing photo editing softwares. Our model belongs to the image-to-image translation framework which consists of an image encoder and decoder, and is trained on pairs of before- and after-images to produce multimodal outputs. We show that by inverting image pairs into latent codes of the learned editing space, our model can be leveraged for various downstream editing tasks such as language-guided image editing, personalized editing, editing-style clustering, retrieval, etc. We extensively study the unique properties of the editing space in experiments and demonstrate superior performance on the aforementioned tasks.
Connected Components for Infinite Graph Streams: Theory and Practice

Motivated by the properties of unending real-world cybersecurity streams, we present a new graph streaming model: XStream. We maintain a streaming graph and its connected components at single-edge granularity. In cybersecurity graph applications, input streams typically consist of edge insertions; individual deletions are not explicit. Analysts maintain as much history as possible and will trigger customized bulk deletions when necessary Despite a variety of dynamic graph processing systems and some canonical literature on theoretical sliding-window graph streaming, XStream is the first model explicitly designed to accommodate this usage model. Users can provide Boolean predicates to define bulk deletions. Edge arrivals are expected to occur continuously and must always be handled. XStream is implemented via a ring of finite-memory processors. We give algorithms to maintain connected components on the input stream, answer queries about connectivity, and to perform bulk deletion. The system requires bandwidth for internal messages that is some constant factor greater than the stream arrival rate. We prove a relationship among four quantities: the proportion of query downtime allowed, the proportion of edges that survive an aging event, the proportion of duplicated edges, and the bandwidth expansion factor. In addition to presenting the theory behind XStream, we present computational results for a single-threaded prototype implementation. Stream ingestion rates are bounded by computer architecture. We determine this bound for XStream inter-process message-passing rates in Intel TBB applications on Intel Sky Lake processors: between one and five million graph edges per second. Our single-threaded prototype runs our full protocols through multiple aging events at between one half and one a million edges per second, and we give ideas for speeding this up by orders of magnitude.
Semi-Supervised Surface Anomaly Detection of Composite Wind Turbine Blades From Drone Imagery

Within commercial wind energy generation, the monitoring and predictive maintenance of wind turbine blades in-situ is a crucial task, for which remote monitoring via aerial survey from an Unmanned Aerial Vehicle (UAV) is commonplace. Turbine blades are susceptible to both operational and weather-based damage over time, reducing the energy efficiency output of turbines. In this study, we address automating the otherwise time-consuming task of both blade detection and extraction, together with fault detection within UAV-captured turbine blade inspection imagery. We propose BladeNet, an application-based, robust dual architecture to perform both unsupervised turbine blade detection and extraction, followed by super-pixel generation using the Simple Linear Iterative Clustering (SLIC) method to produce regional clusters. These clusters are then processed by a suite of semi-supervised detection methods. Our dual architecture detects surface faults of glass fibre composite material blades with high aptitude while requiring minimal prior manual image annotation. BladeNet produces an Average Precision (AP) of 0.995 across our {\O}rsted blade inspection dataset for offshore wind turbines and 0.223 across the Danish Technical University (DTU) NordTank turbine blade inspection dataset. BladeNet also obtains an AUC of 0.639 for surface anomaly detection across the {\O}rsted blade inspection dataset.
Meta Arcade: A Configurable Environment Suite for Meta-Learning

Most approaches to deep reinforcement learning (DRL) attempt to solve a single task at a time. As a result, most existing research benchmarks consist of individual games or suites of games that have common interfaces but little overlap in their perceptual features, objectives, or reward structures. To facilitate research into knowledge transfer among trained agents (e.g. via multi-task and metalearning), more environment suites that provide configurable tasks with enough commonality to be studied collectively are needed. In this paper we present Meta Arcade, a tool to easily define and configure custom 2D arcade games that share common visuals, state spaces, action spaces, game components, and scoring mechanisms. Meta Arcade differs from prior environments in that both task commonality and configurability are prioritized: entire sets of games can be constructed from common elements, and these elements are adjustable through exposed parameters. We include a suite of 24 predefined games that collectively illustrate the possibilities of this framework and discuss how these games can be configured for research applications. We provide several experiments that illustrate how Meta Arcade could be used, including single-task benchmarks of predefined games, sample curriculum-based approaches that change game parameters over a set schedule, and an exploration of transfer learning between games.
An analytical method for direct calculation of E and H-field patterns of conductor-backed coplanar waveguides

A new direct method of computing the electromagnetic field patterns surrounding the conductor-backed coplanar waveguide (CPW) structure is proposed. Analytical closed-form expressions describing the quasi-TEM field pattern in both the air and the dielectric substrate for conductor-backed CPW's are presented. This approach is based on a new technique which employs a series of inverse conformal mappings to transform a known field pattern from a rectangular structure back into the CPW structure in order to obtain its unknown field pattern directly. A computer program based on this method has demonstrated the speed at which the fields can be plotted compared to existing methods which require repetitive application. Graphical results of these field patterns are presented as a function of the CPW's geometry and dielectric substrate thickness. These held maps which have been directly drawn with true curvilinear squares enable the determination of power flow density, since the same power flows through each square. This direct method of characterizing the power flow density throughout the CPW structure could become an important design tool for the modeling of coplanar monolithic microwave integrated circuits (CMMIC's). >
A Review on Parallel Virtual Screening Softwares for High-Performance Computers

Drug discovery is the most expensive, time-demanding, and challenging project in biopharmaceutical companies which aims at the identification and optimization of lead compounds from large-sized chemical libraries. The lead compounds should have high-affinity binding and specificity for a target associated with a disease, and, in addition, they should have favorable pharmacodynamic and pharmacokinetic properties (grouped as ADMET properties). Overall, drug discovery is a multivariable optimization and can be carried out in supercomputers using a reliable scoring function which is a measure of binding affinity or inhibition potential of the drug-like compound. The major problem is that the number of compounds in the chemical spaces is huge, making the computational drug discovery very demanding. However, it is cheaper and less time-consuming when compared to experimental high-throughput screening. As the problem is to find the most stable (global) minima for numerous protein–ligand complexes (on the order of 106 to 1012), the parallel implementation of in silico virtual screening can be exploited to ensure drug discovery in affordable time. In this review, we discuss such implementations of parallelization algorithms in virtual screening programs. The nature of different scoring functions and search algorithms are discussed, together with a performance analysis of several docking softwares ported on high-performance computing architectures.