Miracle-dz/UniFAR-FTUs huggingface dataset:
maars: Tidy Inference under the 'Models as Approximations' Framework in R

Linear regression using ordinary least squares (OLS) is a critical part of every statistician’s toolkit. In R, this is elegantly implemented via lm() and its related functions. However, the statistical inference output from this suite of functions is based on the assumption that the model is well specified. This assumption is often unrealistic and at best satisfied approximately. In the statistics and econometrics literature, this has long been recognized and a large body of work provides inference for OLS under more practical assumptions. This can be seen as model-free inference. In this paper, we introduce our package maars (“models as approximations”) that aims at bringing research on model-free inference to R via a comprehensive workflow. The maars package differs from other packages that also implement variance estimation, such as sandwich, in three key ways. First, all functions in maars follow a consistent grammar and return output in tidy format, with minimal deviation from the typical lm() workflow. Second, maars contains several tools for inference including empirical, multiplier, residual bootstrap, and subsampling, for easy comparison. Third, maars is developed with pedagogy in mind. For this, most of its functions explicitly return the assumptions under which the output is valid. This key innovation makes maars useful in teaching inference under misspecification and also a powerful tool for applied researchers. We hope our default feature of explicitly presenting assumptions will become a de facto standard for most statistical modeling in R.
SODA10M: Towards Large-Scale Object Detection Benchmark for Autonomous Driving

Aiming at facilitating a real-world, ever-evolving and scalable autonomous driving system, we present a large-scale benchmark for standardizing the evaluation of different self-supervised and semi-supervised approaches by learning from raw data, which is the ﬁrst and largest benchmark to date. Existing autonomous driving systems heavily rely on ‘perfect’ visual perception models (e.g., detection) trained using extensive annotated data to ensure the safety. However, it is unrealistic to elaborately label instances of all scenarios and circumstances (e.g., night, extreme weather, cities) when deploying a robust autonomous driving system. Motivated by recent powerful advances of self-supervised and semi-supervised learning, a promising direction is to learn a robust detection model by collaboratively exploiting large-scale unlabeled data and few labeled data. Existing dataset (e.g., KITTI, Waymo) either provides only a small amount of data or covers limited domains with full annotation, hindering the exploration of large-scale pre-trained models. Here, we release a Large-S cale O bject D etection benchmark for A utonomous driving, named as SODA10M , containing 10 million unlabeled images and 20K images labeled with 6 representative object categories. To improve diversity, the images are collected every ten seconds per frame within 32 different cities under different weather conditions, periods and location scenes. We provide extensive experiments and deep analyses of existing supervised state-of-the-art detection models, popular self-supervised and semi-supervised approaches, and some insights about how to develop future models. We show that SODA10M can serve as a promising pre-training dataset for different self-supervised learning methods, which gives superior performance when ﬁnetuning autonomous driving downstream tasks. This benchmark will be used to hold the ICCV2021 SSLAD challenge. The data and more up-to-date information have been released at https://soda-2d.
Automated Deepfake Detection

In this paper, we propose to utilize Automated Machine Learning to adaptively search a neural architecture for deepfake detection. This is the first time to employ automated machine learning for deepfake detection. Based on our explored search space, our proposed method achieves competitive prediction accuracy compared to previous methods. To improve the generalizability of our method, especially when training data and testing data are manipulated by different methods, we propose a simple yet effective strategy in our network learning process: making it to estimate potential manipulation regions besides predicting the real/fake labels. Unlike previous works manually design neural networks, our method can relieve us from the high labor cost in network construction. More than that, compared to previous works, our method depends much less on prior knowledge, e.g., which manipulation method is utilized or where exactly the fake image is manipulated. Extensive experimental results on two benchmark datasets demonstrate the effectiveness of our proposed method for deepfake detection.
Effective Pre-Silicon Verification of Processor Cores by Breaking the Bounds of Symbolic Quick Error Detection

We present a novel approach to pre-silicon verification of processor designs. The purpose of pre-silicon verification is to find logic bugs in a design at an early stage and thus avoid time- and cost-intensive post-silicon debugging. Our approach relies on symbolic quick error detection (Symbolic QED, or SQED). SQED is targeted at finding logic bugs in a symbolic representation of a design by combining bounded model checking (BMC) with QED tests. QED tests are powerful in generating short sequences of instructions (traces) that trigger bugs. We extend an existing SQED approach with symbolic starting states. This way, we enable the BMC tool to select starting states arbitrarily when generating a trace. To avoid false positives, (e.g., traces starting in unreachable states that may not be-have in accordance with the processor instruction-set architecture), we define constraints to restrict the set of possible starting states. We demonstrate that these constraints, togeth-er with reasonable assumptions about the system behavior, allow us to avoid false positives. Using our approach, we discovered previously unknown bugs in open-source RISC-V processor cores that existing methods cannot detect. Moreover, our novel approach out-performs existing ones in the detection of bugs having long traces and in the detection of hardware Trojans, i.e., unauthorized modifications of a design.
Design criteria of flexible capacitive pressure sensors using DIY-techniques and household materials

The flexible capacitive pressure sensors are one of the most essential and famous devices with vast applications in automobile, aerospace, marine, healthcare, wearables, consumer, and portable electronics. The fabrication of pressure sensors in a cleanroom is expensive and time-consuming; however, the sensitivity, linearity, and other performance factors of those pressure sensors are exceptional. Moreover, sometimes we require sensors that are not expensive and can be fabricated rapidly where the other performance factors do not need to be highly remarkable. In this modern era, household materials and DIY (Do-it-yourself) techniques are quite helpful, highly utilized. They are recommended to fabricate low-cost sensors and healthcare devices for personalized medicine and low-cost consumer electronics. Different flexible capacitive pressure sensors are presented and experimentally characterized for acoustic and air-pressure monitoring in this thesis. The design criteria of a cantilever-based capacitive pressure sensor are discussed. The three different designs are analysed with aspect ratios of 1.5, 1.0, and 0.67. The sensor with an aspect ratio of 0.67 shows maximum sensitivity (mechanical and electrical), better response time, and the 1st and 2nd mode of resonant frequencies is comparatively less than the other two. The cantilever designs are susceptible to slight pressure; therefore, the diaphragm-based normal mode capacitive pressure sensor is introduced in the second chapter, which defines the design criteria of diaphragm shapes. The five different diaphragms analysed are circular, elliptical, pentagon, square, and rectangular shapes. The circular capacitive pressure sensor shows maximum sensitivity, however, maximum non-linear response.
One-to-many Approach for Improving Super-Resolution

Recently, there has been discussions on the ill-posed nature of super-resolution that multiple possible reconstructions exist for a given low-resolution image. Using normalizing flows, SRflow[23] achieves state-of-the-art perceptual quality by learning the distribution of the output instead of a deterministic output to one estimate. In this paper, we adapt the concepts of SRFlow to improve GAN-based super-resolution by properly implementing the one-to-many property. We modify the generator to estimate a distribution as a mapping from random noise. We improve the content loss that hampers the perceptual training objectives. We also propose additional training techniques to further enhance the perceptual quality of generated images. Using our proposed methods, we were able to improve the performance of ESRGAN[1] in x4 perceptual SR and achieve the state-of-the-art LPIPS score in x16 perceptual extreme SR by applying our methods to RFB-ESRGAN[21].
Active and Dynamic Beam Tracking UnderStochastic Mobility

We consider the problem of active and sequential beam tracking at mmWave frequencies and above. We focus on the dynamic scenario of a UAV to UAV communications where we formulate the problem to be equivalent to tracking an optimal beamforming vector along the line-of-sight path. In this setting, the resulting beam ideally points in the direction of the angle of arrival with sufficiently high resolution. Existing solutions account for predictable movements or small random movements using filtering strategies or by accounting for predictable mobility but must resort to re-estimation protocols when tracking fails due to unpredictable movements. We propose an algorithm for active learning of the AoA through evolving a Bayesian posterior probability belief which is utilized for a sequential selection of beamforming vectors. We propose an adaptive pilot allocation strategy based on a trade-off of mutual information versus spectral efficiency. Numerically, we analyze the performance of our proposed algorithm and demonstrate significant improvements over existing strategies.
Cogradient Descent for Dependable Learning

Conventional gradient descent methods compute the gradients for multiple variables through the partial derivative. Treating the coupled variables independently while ignoring the interaction, however, leads to an insufficient optimization for bilinear models. In this paper, we propose a dependable learning based on Cogradient Descent (CoGD) algorithm to address the bilinear optimization problem, providing a systematic way to coordinate the gradients of coupling variables based on a kernelized projection function. CoGD is introduced to solve bilinear problems when one variable is with sparsity constraint, as often occurs in modern learning paradigms. CoGD can also be used to decompose the association of features and weights, which further generalizes our method to better train convolutional neural networks (CNNs) and improve the model capacity. CoGD is applied in representative bilinear problems, including image reconstruction, image inpainting, network pruning and CNN training. Extensive experiments show that CoGD improves the state-of-the-arts by significant margins. Code is available at {https://github.com/bczhangbczhang/CoGD}.
Low-temperature Ising dynamics with random initializations

Glauber dynamics on spin systems are well known to suffer exponential slowdowns at low temperatures due to the emergence of multiple metastable phases, separated by narrow bottlenecks that are hard for the dynamics to cross. It is a folklore belief that if the dynamics is initialized from an appropriate random mixture of ground states, one for each phase, then convergence to the Gibbs distribution should be much faster. However, such phenomena have largely evaded rigorous analysis, as most tools in the study of Markov chain mixing times are tailored to worst-case initializations. In this paper we develop a general framework towards establishing this conjectured behavior for the Ising model. In the classical setting of the Ising model on an N-vertex torus in ℤd, our framework implies that the mixing time for the Glauber dynamics, initialized from a 1/2-1/2 mixture of the all-plus and all-minus configurations, is N1+o(1) in dimension d=2, and at most quasi-polynomial in all dimensions d≥ 3, at all temperatures below the critical one. The key innovation in our analysis is the introduction of the notion of ”weak spatial mixing within a phase”, a low-temperature adaptation of the classical concept of weak spatial mixing. We show both that this new notion is strong enough to control the mixing time from the above random initialization (by relating it to the mixing time with plus boundary condition at O(logN) scales), and that it holds at all low temperatures in all dimensions. This framework naturally extends to more general families of graphs. To illustrate this, we use the same approach to establish optimal O(NlogN) mixing for the Ising Glauber dynamics on random regular graphs at sufficiently low temperatures, when initialized from the same random mixture.
A PDE approach to centroidal tessellations of domains

We introduce a class of systems of Hamilton-Jacobi equations that characterize critical points of functionals associated to centroidal tessellations of domains, i.e. tessellations where generators and centroids coincide, such as centroidal Voronoi tessellations and centroidal power diagrams. An appropriate version of the Lloyd algorithm, combined with a Fast Marching method on unstructured grids for the HamiltonJacobi equation, allows computing the solution of the system. We propose various numerical examples to illustrate the features of the technique. AMS-Subject Classification: 65K10, 49M05, 65D99, 35F21, 49N70.
EML Online Speech Activity Detection for the Fearless Steps Challenge Phase-III

Speech Activity Detection (SAD), locating speech segments within an audio recording, is a main part of most speech technology applications. Robust SAD is usually more difficult in noisy conditions with varying signal-to-noise ratios (SNR). The Fearless Steps challenge has recently provided such data from the NASA Apollo-11 mission for different speech processing tasks including SAD. Most audio recordings are degraded by different kinds and levels of noise varying within and between channels. This paper describes the EML online algorithm for the most recent phase of this challenge. The proposed algorithm can be trained both in a supervised and unsupervised manner and assigns speech and non-speech labels at runtime approximately every 0.1 sec. The experimental results show a competitive accuracy on both development and evaluation datasets with a real-time factor of about 0.002 using a single CPU machine.
Urban green space use during a time of stress: A case study during the COVID‐19 pandemic in Brisbane, Australia

Abstract Spending time in nature is one potential way to cope with the negative physical and psychological health impacts from major stressful life events. In 2020, a large fraction of the global population was impacted by restrictions to contain the spread of the COVID‐19 outbreak, a period characterised by marked health risks and behavioural changes. Here we explore whether people responded to this stressor by spending more time in nature and investigate the reasons for any changes. We surveyed 1,002 people in Brisbane, Australia in 2020, to measure the change in use of green space during the restrictions period and benefits people associated with visiting them. About 36% of participants increased their urban green space use, but 26% reduced it, indicating a great deal of flux. Furthermore, 45% of the previous non‐users of urban green space began using it for the first time during the restrictions period. Older people were less likely to increase their green space use and those with a backyard were more likely to increase their use of green spaces. Participants' change in use occurred regardless of the amount of green space available in close proximity to their households. In addition, we did not find a relationship between nature‐relatedness and change in use. People's reasons for green space use shifted during the pandemic‐related restrictions period, with many emphasising improvement of personal well‐being rather than consolidating community capital. Most participants indicated an increase in the importance of the psychological and physical benefits obtained from urban green spaces. We conclude that increased urban green space use during moments of stress such as the COVID‐19 pandemic has the potential to ameliorate some of the negative effects of the stressor, but that the capacity and desire to spend more time in green space varies markedly across society. Sufficient urban green space provision for all sections of society will maximise the opportunity to employ a nature‐based coping mechanism during times of personal or community stress.
Screening of potential bio-molecules from Moringa olifera against SARS-CoV-2 main protease using computational approaches

Abstract COVID-19 caused by SARS-CoV-2 is responsible for the deaths of millions of people worldwide. It is having devastating effects on the people of all countries. In this regard, the phytochemicals of medicinal plants could be explored to prevent this disease. M. oleifera is a miracle plant with antibacterial, antiviral, and antioxidant properties because of its high content of flavonoids, glucosides and glucosinolates. Therefore, we constructed a library of 294 phytochemicals of M. oleifera and filtered it through the FAF-Drugs4. Further, molecular docking studies of filtered phytochemicals were performed with Mpro enzyme to investigate the binding interactions. Drug likeness properties, ADMET prediction were analyzed to determine the therapeutic aspect of these compounds. Based on the binding energy score of the top 4 compounds, the results indicate that Vicenin-2 has the highest binding affinity (−8.6 kcal mol−1) as compared to the reference molecule (−8.4 kcal mol−1). ADMET result reveals that all top four compounds have minimal toxic effects and good absorption. Further, 500 ns molecular dynamics simulation of the top four compounds showed that Kaempferol-3-O-rutinoside and Vitexin have good stability with Mpro. These two compounds were then subjected for MMPBSA (last 50 ns) calculation to analyze the protein-ligand stability and dynamic behavior. Kaempferol-3-O-rutinoside and Vitexin showed very good binding free energy i.e. −40.136 kJ mol−1 and −26.784 kJ mol−1, respectively. Promising outcomes from MD simulations evidence the worth of these compounds for future drug development to combat coronavirus disease. Communicated by Ramaswamy H. Sarma GRAPHICAL ABSTRACT
Catalytic function, mechanism, and application of plant acyltransferases

Abstract Acyltransferases (ATs) are important tailoring enzymes that contribute to the diversity of natural products. They catalyze the transfer of acyl groups to the skeleton, which improves the lipid solubility, stability, and pharmacological activity of natural compounds. In recent years, a number of ATs have been isolated from plants. In this review, we have summarized 141 biochemically characterized ATs during the period July 1997 to October 2020, including their function, heterologous expression systems, and catalytic mechanisms. Their catalytic performance and application potential has been further discussed.
Comparison of the accelerating effect of graphene oxide and graphene on anaerobic transformation of bisphenol F by Pseudomonas sp. LS

ABSTRACT It was found that bisphenol F (BPF) could be anaerobically transformed to 4,4-dihydroxybenzophenone using nitrate as an electron acceptor by Pseudomonas sp. LS. However, BPF removal was a slow process under anaerobic conditions. In the present study, effects of graphene oxide (GO) and graphene on the anaerobic transformation of BPF were studied in detail. Results showed that GO (2–10 mg/L) and graphene (2–20 mg/L) could increase the anaerobic biotransformation rate of BPF. For GO-mediated system, GO was partially reduced, and then the reduced GO (rGO) as an electron mediator increased biotransformation rate of BPF. Further analysis showed that the promoting effect of 10 mg/L GO was over 1.5-fold higher compared with that of 10 mg/L graphene. BPF could be transformed using GO as an electron acceptor. GO and graphene was also used as nutrient scaffolds to promote cell growth via adsorbing proteins. Moreover, GO was a better cell growth promoter than graphene. These studies indicated that GO played more roles and exhibited a better accelerating effect on anaerobic removal of BPF compared with graphene. GRAPHICAL ABSTRACT
Reversible blood-brain barrier opening utilizing the membrane active peptide melittin in vitro and in vivo

The blood-brain barrier (BBB) tightly controls entry of molecules and cells into the brain, restricting the delivery of therapeutics. Blood-brain barrier opening (BBBO) utilizes reversible disruption of cell-cell junctions between brain microvascular endothelial cells to enable transient entry into the brain. Development of BBBO techniques has been hindered by a lack of physiological models for in vitro study. Here, we utilize an in vitro tissue-engineered microvessel model to demonstrate that melittin, a membrane active peptide present in bee venom, supports BBBO. From endothelial and neuronal viability studies, we identify the accessible concentration range for BBBO. We then use a tissue-engineered model of the human BBB to optimize dosing and elucidate the mechanism of opening. Melittin and other membrane active variants transiently increase paracellular permeability via disruption of cell-cell junctions. In mice, we demonstrate a minimum clinically effective intra-arterial dose of 3 μM·min melittin, which is reversible within one day and neurologically safe. Melittin-induced BBBO represents a novel platform for delivery of therapeutics into the brain.
Engagement of Latino immigrant men who have sex with men for HIV prevention through eHealth: preferences across social media platforms

ABSTRACT Objective eHealth has growing potential to enhance access to HIV prevention for hard to reach populations, including young Latino immigrant men who have sex with men (MSM) in the United States. We examined the feasibility and acceptability of using eHealth tools, specifically social media platforms, to facilitate HIV testing and pre-exposure prophylaxis (PrEP) uptake among this population. Design We utilized a community sensitive approach to conduct 30 in-depth interviews and five focus groups with young Latino immigrant MSM in Seattle, WA. Data were analyzed using thematic analysis with both data-driven inductive and a priori deductive approaches. Results Participants were open to receiving HIV information via social media platforms. Participants recommended that social media content be tailored with their language preferences, cultural norms, and beliefs about HIV testing and PrEP in mind. Further, participants emphasized that content avoid stigmatizing HIV or Latino MSM’s complex identities. Conclusions Results have implications for utilizing social media platforms and developing HIV prevention interventions for Latino immigrant MSM. Findings highlight that HIV prevention content should acknowledge how identities as an emerging adult, Latino, immigrant, and MSM, warrant unique consideration.