Miracle-dz/UniFAR-FTUs huggingface dataset:
Health care workers’ self-perceived infection risk and COVID-19 vaccine uptake: A mixed methods study

Vaccination is the most cost-effective way of preventing Coronavirus Disease 2019 (COVID-19) although there was a considerable delay in its institution in Tanzania. This study assessed health care workers’ (HCWs) self-perceived infection risk and uptake of COVID-19 vaccines. A concurrent embedded, mixed methods design was utilized to collect data among HCWs in seven Tanzanian regions. Quantitative data was collected using a validated, pre-piloted, interviewer administered questionnaire whereas in-depth interviews (IDIs) and focus group discussions (FGDs) gathered qualitative data. Descriptive analyses were performed while chi-square test and logistic regression were used to test for associations across categories. Thematic analysis was used to analyze the qualitative data. A total of 1,368 HCWs responded to the quantitative tool, 26 participated in the IDIs and 74 in FGDs. About half of the HCW (53.6%) reported to have been vaccinated and three quarters (75.5%) self-perceived to be at a high risk of acquiring COVID-19 infection. High perceived infection risk was associated with increased COVID-19 vaccine uptake (OR 1.535). Participants perceived that the nature of their work and the working environment in the health facilities increased their infection risk. Limited availability and use of personal protective equipment (PPE) was reported to elevate the perceived infection risks. Participants in the oldest age group and from low and mid-level health care facilities had higher proportions with a high-risk perception of acquiring COVID-19 infection. Only about half of the HCWs reported to be vaccinated albeit the majority recounted higher perception of risk to contracting COVID-19 due to their working environment, including limited availability and use of PPE. Efforts to address heightened perceived-risks should include improving the working environment, availability of PPE and continue updating HCWs on the benefits of COVID-19 vaccine to limit their infection risks and consequent transmission to their patients and public.
AssistSR: Task-oriented Video Segment Retrieval for Personal AI Assistant

It is still a pipe dream that personal AI assistants on the phone and AR glasses can assist our daily life in addressing our questions like ``how to adjust the date for this watch? '' and ``how to set its heating duration? (while pointing at an oven)''. The queries used in conventional tasks (i.e. Video Question Answering, Video Retrieval, Moment Localization) are often factoid and based on pure text. In contrast, we present a new task called Task-oriented Question-driven Video Segment Retrieval (TQVSR). Each of our questions is an image-box-text query that focuses on affordance of items in our daily life and expects relevant answer segments to be retrieved from a corpus of instructional video-transcript segments. To support the study of this TQVSR task, we construct a new dataset called AssistSR. We design novel guidelines to create high-quality samples. This dataset contains 3.2k multimodal questions on 1.6k video segments from instructional videos on diverse daily-used items. To address TQVSR, we develop a simple yet effective model called Dual Multimodal Encoders (DME) that significantly outperforms several baseline methods while still having large room for improvement in the future. Moreover, we present detailed ablation analyses. Code and data are available at \url{https://github.com/StanLei52/TQVSR}.
On the influence of gravity on the propagation of light, with some comments on the A. Einstein`s 1911 Paper (1911-2022), Light Interaction With Gravity (The Mistake Made In 1911)

This is a review with some comments on the A. Einstein`s 1911 paper, which he published as one of his many attempts of the general theory of relativity. The main point of the idea is to propose a new approach about light and its motion as well as study the assumption made by A. Einstein concerning the possibility of the variation of the speed of light in presence of gravitational field.
Trustworthiness of Laser-Induced Breakdown Spectroscopy Predictions via Simulation-based Synthetic Data Augmentation and Multitask Learning

Laser-induced breakdown spectroscopy is a versatile technique that can be used to quickly measure the concentration of elements in ambient air. We tackle the issues of performance and trustworthiness of the statistical model used for predictions. We propose a method for improving the performance and trustworthiness of statistical models for LIBS. Our method uses deep convolutional multitask learning architectures to predict the concentration of the analyte and additional information as auxiliary outputs. We also introduce a simulation-based data augmentation process to synthesize more training samples. The secondary predictions from the model are used to characterize, quantify and validate its trustworthiness, taking advantage of the mutual dependencies of the weights of the neural networks. As a consequence, these output can be used to successfully detect anomalies, such as changes in the experimental conditions, and out-of-distribution samples. Results on different types of materials show that the proposed method improves the robustness and trueness of the predictions.
Deep Span Representations for Named Entity Recognition

Span-based models are one of the most straightforward methods for named entity recognition (NER). Existing span-based NER systems shallowly aggregate the token representations to span representations. However, this typically results in significant ineffectiveness for long-span entities, a coupling between the representations of overlapping spans, and ultimately a performance degradation. In this study, we propose DSpERT (Deep Span Encoder Representations from Transformers), which comprises a standard Transformer and a span Transformer. The latter uses low-layered span representations as queries, and aggregates the token representations as keys and values, layer by layer from bottom to top. Thus, DSpERT produces span representations of deep semantics. With weight initialization from pretrained language models, DSpERT achieves performance higher than or competitive with recent state-of-the-art systems on eight NER benchmarks. Experimental results verify the importance of the depth for span representations, and show that DSpERT performs particularly well on long-span entities and nested structures. Further, the deep span representations are well structured and easily separable in the feature space.
Knowledge Prompts: Injecting World Knowledge into Language Models through Soft Prompts

Soft prompts have been recently proposed as a tool for adapting large frozen language models (LMs) to new tasks. In this work, we repurpose soft prompts to the task of injecting world knowledge into LMs. We introduce a method to train soft prompts via self-supervised learning on data from knowledge bases. The resulting soft knowledge prompts (KPs) are task independent and work as an external memory of the LMs. We perform qualitative and quantitative experiments and demonstrate that: (1) KPs can effectively model the structure of the training data; (2) KPs can be used to improve the performance of LMs in different knowledge intensive tasks.
On the Performance of Gradient Tracking with Local Updates

We study the decentralized optimization problem where a network of $n$ agents seeks to minimize the average of a set of heterogeneous non-convex cost functions distributedly. State-of-the-art decentralized algorithms like Exact Diffusion and Gradient Tracking (GT) involve communicating every iteration. However, communication is expensive, resource intensive, and slow. This work analyzes a locally updated GT method (LUG T), where agents perform local recursions before interacting with their neighbors. While local updates have been shown to reduce communication overhead in practice, their theoretical influence has not been fully characterized. We show LU-GT has the same communication complexity as the Federated Learning setting but allows decentralized (symmetric) network topologies. In addition, we prove that the number of local updates does not degrade the quality of the solution achieved by LU-GT. Numerical results reveal that local updates may lead to lower communication costs in specific regimes (e.g., well-connected graphs).
Understanding HTML with Large Language Models

Large language models (LLMs) have shown exceptional performance on a variety of natural language tasks. Yet, their capabilities for HTML understanding -- i.e., parsing the raw HTML of a webpage, with applications to automation of web-based tasks, crawling, and browser-assisted retrieval -- have not been fully explored. We contribute HTML understanding models (fine-tuned LLMs) and an in-depth analysis of their capabilities under three tasks: (i) Semantic Classification of HTML elements, (ii) Description Generation for HTML inputs, and (iii) Autonomous Web Navigation of HTML pages. While previous work has developed dedicated architectures and training procedures for HTML understanding, we show that LLMs pretrained on standard natural language corpora transfer remarkably well to HTML understanding tasks. For instance, fine-tuned LLMs are 12% more accurate at semantic classification compared to models trained exclusively on the task dataset. Moreover, when fine-tuned on data from the MiniWoB benchmark, LLMs successfully complete 50% more tasks using 192x less data compared to the previous best supervised model. Out of the LLMs we evaluate, we show evidence that T5-based models are ideal due to their bidirectional encoder-decoder architecture. To promote further research on LLMs for HTML understanding, we create and open-source a large-scale HTML dataset distilled and auto-labeled from CommonCrawl.
Language Prior Is Not the Only Shortcut: A Benchmark for Shortcut Learning in VQA

Visual Question Answering (VQA) models are prone to learn the shortcut solution formed by dataset biases rather than the intended solution. To evaluate the VQA models' reasoning ability beyond shortcut learning, the VQA-CP v2 dataset introduces a distribution shift between the training and test set given a question type. In this way, the model cannot use the training set shortcut (from question type to answer) to perform well on the test set. However, VQA-CP v2 only considers one type of shortcut and thus still cannot guarantee that the model relies on the intended solution rather than a solution specific to this shortcut. To overcome this limitation, we propose a new dataset that considers varying types of shortcuts by constructing different distribution shifts in multiple OOD test sets. In addition, we overcome the three troubling practices in the use of VQA-CP v2, e.g., selecting models using OOD test sets, and further standardize OOD evaluation procedure. Our benchmark provides a more rigorous and comprehensive testbed for shortcut learning in VQA. We benchmark recent methods and find that methods specifically designed for particular shortcuts fail to simultaneously generalize to our varying OOD test sets. We also systematically study the varying shortcuts and provide several valuable findings, which may promote the exploration of shortcut learning in VQA.
3D Bayesian Variational Full Waveform Inversion

Seismic full-waveform inversion (FWI) provides high resolution images of the subsurface by exploiting information in the recorded seismic waveforms. This is achieved by solving a highly nonlinear and nonunique inverse problem. Bayesian inference is therefore used to quantify uncertainties in the solution. Variational inference is a method that provides probabilistic, Bayesian solutions efficiently using optimization. The method has been applied to 2D FWI problems to produce full Bayesian posterior distributions. However, due to higher dimensionality and more expensive computational cost, the performance of the method in 3D FWI problems remains unknown. We apply three variational inference methods to 3D FWI and analyse their performance. Specifically we apply automatic differential variational inference (ADVI), Stein variational gradient descent (SVGD) and stochastic SVGD (sSVGD), to a 3D FWI problem, and compare their results and computational cost. The results show that ADVI is the most computationally efficient method but systematically underestimates the uncertainty. The method can therefore be used to provide relatively rapid but approximate insights into the subsurface together with a lower bound estimate of the uncertainty. SVGD demands the highest computational cost, and still produces biased results. In contrast, by including a randomized term in the SVGD dynamics, sSVGD becomes a Markov chain Monte Carlo method and provides the most accurate results at intermediate computational cost. We thus conclude that 3D variational full-waveform inversion is practically applicable, at least in small problems, and can be used to image the Earth’s interior and to provide reasonable uncertainty estimates on those images.
Inverse set estimation and inversion of simultaneous confidence intervals

Motivated by the questions of risk assessment in climatology (temperature change in North America) and medicine (impact of statin usage and COVID-19 on hospitalized patients), we address the problem of estimating the set in the domain of a function whose image equals a predefined subset. Existing methods that construct confidence sets require strict assumptions. We generalize the estimation of such sets to dense and non-dense domains with protection against"data peeking"by proving that confidence sets of multiple levels can be simultaneously constructed with the desired confidence non-asymptotically through inverting simultaneous confidence bands. A non-parametric bootstrap algorithm and code are provided.
Enhanced tetragonality in (x)PbTiO3-(1−x)Bi(Zn1∕2Ti1∕2)O3 and related solid solution systems

The solid solutions (x)PbTiO3-(1−x)Bi(Zn1∕2Ti1∕2)O3, (x)PbTiO3-(1−x)Bi(Zn1∕2Zn1∕2)O3, and (x)PbTiO3-(1−x)Bi(Zn1∕2Sn1∕2)O3, have been examined by x-ray diffraction, dielectric measurements, and thermal analysis. Unlike most PbTiO3-based solid solutions, these systems show enhanced tetragonality with substitution for PbTiO3. In particular, the (x)PbTiO3-(1−x)Bi(Zn1∕2Ti1∕2)O3 system exhibits a high c∕a ratio of 1.11 for x=0.60. Accordingly, the Curie temperature (TC) also increases, exceeding 700°C at the same composition. It is proposed that PbTiO3-Bi(B)O3-type solid solutions which contain only highly polarizable cations on the B site are likely to exhibit similar enhancements in tetragonality and TC.
Phenotype Switching in Chemotaxis Aggregation Models Controls the Spontaneous Emergence of Large Densities

We consider a phenotype-switching chemotaxis model for aggregation, in which a chemotactic population is capable of switching back and forth between a chemotaxing state (performing chemotactic movement) and a secreting state (producing the attractant). We show that the switching rate provides a powerful mechanism for controlling the densities of spontaneously emerging aggregates. Specifically, in two- and three-dimensional settings it is shown that when both switching rates coincide and are suitably large, then the densities of both the chemotaxing and the secreting population will exceed any prescribed level at some points in the considered domain. This is complemented by two results asserting the absence of such aggregation phenomena in corresponding scenarios in which one of the switching rates remains within some bounded interval.
The Role of Coverage in Online Reinforcement Learning

Coverage conditions -- which assert that the data logging distribution adequately covers the state space -- play a fundamental role in determining the sample complexity of offline reinforcement learning. While such conditions might seem irrelevant to online reinforcement learning at first glance, we establish a new connection by showing -- somewhat surprisingly -- that the mere existence of a data distribution with good coverage can enable sample-efficient online RL. Concretely, we show that coverability -- that is, existence of a data distribution that satisfies a ubiquitous coverage condition called concentrability -- can be viewed as a structural property of the underlying MDP, and can be exploited by standard algorithms for sample-efficient exploration, even when the agent does not know said distribution. We complement this result by proving that several weaker notions of coverage, despite being sufficient for offline RL, are insufficient for online RL. We also show that existing complexity measures for online RL, including Bellman rank and Bellman-Eluder dimension, fail to optimally capture coverability, and propose a new complexity measure, the sequential extrapolation coefficient, to provide a unification.
The Small-scale Dynamo in a Multiphase Supernova-driven Medium

Magnetic fields grow quickly, even at early cosmological times, suggesting the action of a small-scale dynamo (SSD) in the interstellar medium (ISM) of galaxies. Many studies have focused on idealized, isotropic, homogeneous, turbulent driving of the SSD. Here we analyze more realistic simulations of supernova-driven turbulence to understand how it drives an SSD. We find that SSD growth rates are intermittently variable as a result of the evolving multiphase ISM structure. Rapid growth in the magnetic field typically occurs in hot gas, with the highest overall growth rates occurring when the fractional volume of hot gas is large. SSD growth rates correlate most strongly with vorticity and fluid Reynolds number, which also both correlate strongly with gas temperature. Rotational energy exceeds irrotational energy in all phases, but particularly in the hot phase while SSD growth is most rapid. Supernova rate does not significantly affect the ISM average kinetic energy density. Rather, higher temperatures associated with high supernova rates tend to increase SSD growth rates. SSD saturates with total magnetic energy density around 5% of equipartition to kinetic energy density, increasing slightly with magnetic Prandtl number. While magnetic energy density in the hot gas can exceed that of the other phases when SSD grows most rapidly, it saturates below 5% of equipartition with kinetic energy in the hot gas, while in the cold gas it attains 100%. Fast, intermittent growth of the magnetic field appears to be a characteristic behavior of supernova-driven, multiphase turbulence.
DeltaFS: Pursuing Zero Update Overhead via Metadata-Enabled Delta Compression for Log-structured File System on Mobile Devices

Data compression has been widely adopted to release mobile devices from intensive write pressure. Delta compression is particularly promising for its high compression efﬁcacy over conventional compression methods. However, this method suffers from non-trivial system overheads incurred by delta maintenance and read penalty, which prevents its applicability on mobile devices. To this end, this paper proposes DeltaFS, a metadata-enabled Delta compression on log-structured File System for mobile devices, to achieve utmost compressing efﬁ-ciency and zero hardware costs. DeltaFS smartly exploits the out-of-place updating ability of Log-structured File System (LFS) to alleviate the problems of write ampliﬁcation, which is the key bottleneck for delta compression implementation. Speciﬁcally, DeltaFS utilizes the inline area in ﬁle inodes for delta maintenance with zero hardware cost, and integrates an inline area manage strategy to improve the utilization of constrained inline area. Moreover, a complimentary delta maintenance strategy is incorporated, which selectively maintains delta chunks in the main data area to break through the limitation of constrained inline area. Experimental results show that DeltaFS substantially reduces write trafﬁcs by up to 64.8%, and improves the I/O performance by up to 37.3%. 1
Spectral Geometric Verification: Re-Ranking Point Cloud Retrieval for Metric Localization

In large-scale metric localization, an incorrect result during retrieval will lead to an incorrect pose estimate or loop closure. Re-ranking methods propose to take into account all the top retrieval candidates and re-order them to increase the likelihood of the top candidate being correct. However, state-of-the-art re-ranking methodsare inefficient when re-ranking many potential candidates due to their need for resource intensive point cloud registration between the query and each candidate. In this work, we propose an efficient spectral method for geometric verification (named SpectralGV) that does not require registration. We demonstrate how the optimal inter-cluster score of the correspondence compatibility graph of two point clouds represents a robust fitness score measuring their spatial consistency. This score takes into account the subtle geometric differences between structurally similar point clouds and therefore can be used to identify the correct candidate among potential matches retrieved by global similarity search. SpectralGV is deterministic, robust to outlier correspondences, and can be computed in parallel for all potential candidates.We conduct extensive experiments on 5 large-scale datasets to demonstrate that SpectralGV outperforms other state-of-the-art re-ranking methods and show that it consistently improves the recall and pose estimation of 3 state-of-the-art metric localization architectures while having a negligible effect on their runtime.
Time evolution of uniform sequential circuits

Simulating time evolution of generic quantum many-body systems using classical numerical approaches has an exponentially growing cost either with evolution time or with the system size. In this work, we present a polynomially scaling hybrid quantum-classical algorithm for time evolving a one-dimensional uniform system in the thermodynamic limit. This algorithm uses a layered uniform sequential quantum circuit as a variational ansatz to represent infinite translation-invariant quantum states. We show numerically that this ansatz requires a number of parameters polynomial in the simulation time for a given accuracy. Furthermore, this favourable scaling of the ansatz is maintained during our variational evolution algorithm. All steps of the hybrid optimization are designed with near-term digital quantum computers in mind. After benchmarking the evolution algorithm on a classical computer, we demonstrate the measurement of observables of this uniform state using a finite number of qubits on a cloud-based quantum processing unit. With more efficient tensor contraction schemes, this algorithm may also offer improvements as a classical numerical algorithm.