Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal Testing Strategies to Monitor COVID-19 Traced Contacts.

The quarantine of identified close contacts has been vital to reducing transmission rates and averting secondary infection risk before symptom onset and by asymptomatic cases. The effectiveness of this contact tracing strategy to mitigate transmission is sensitive to the adherence to quarantines, which may be lower for longer quarantine periods or in vaccinated populations (where perceptions of risk are reduced). This study develops a simulation model to evaluate contact tracing strategies based on the sequential testing of identified contacts after exposure as an alternative to quarantines, in which contacts are isolated only after confirmation by a positive test. The analysis considers different number and types of tests (PCR and lateral flow antigen tests (LFA)) to identify the cost-effective testing policies that minimize the expected infecting days post-exposure considering different levels of testing capacity. This analysis suggests that even a limited number of tests can be effective at reducing secondary infection risk: two LFA tests (with optimal timing) avert infectiousness at a level that is comparable to 14-day quarantine with 90% adherence; adding a third test (PCR or LFA) reaches the efficiency of a 95% quarantine adherence. These results are robust to the exposure dates of the contact, which suggests that simple testing rules can be effective for improving contact tracing in settings where strict quarantine adherence is difficult to implement.
Controlling Segregation in Social Network Dynamics as an Edge Formation Game

This paper studies controlling segregation in social networks via exogenous incentives. We construct an edge formation game on a directed graph. A user (node) chooses the probability with which it forms an inter- or intra- community edge based on a utility function that reflects the tradeoff between homophily (preference to connect with individuals that belong to the same group) and the preference to obtain an exogenous incentive. Decisions made by the users to connect with each other determine the evolution of the social network. We explore an algorithmic recommendation mechanism where the exogenous incentive in the utility function is based on weak ties which incentivizes users to connect across communities and mitigates the segregation. This setting leads to a submodular game with a unique Nash equilibrium. In numerical simulations, we explore how the proposed model can be useful in controlling segregation and echo chambers in social networks under various settings.
Effect Of Slope Angle On The Runout Evolution of Granular Column Collapse for Varying Initial Volumes

In nature, submarine slope failures usually carry thousands of cubic-meters of sediments across extremely long distances and cause tsunamis and damages to offshore structures. This paper uses the granular column collapse experiment to investigate the effect of slope angle on the runout behavior of submarine granular landslides for different initial volumes. A two-dimensional coupled lattice Boltzman and discrete element method (LBM-DEM) approach is adopted for numerically modeling the granular column collapse. Columns with four different slope angles and six different volumes are modelled under both dry and submerged conditions. The effects of hydrodynamic interactions, including the generation of excess pore pressures, hydroplaning, and drag forces and formation of turbulent vortices, are used to explain the difference in the runout behavior of the submerged columns compared to the dry columns. The results show that at any given slope angle, there is a threshold volume above which the submerged columns have a larger final runout compared to their dry counterpart, and this threshold volume decreases with slope angle.
A scoring framework for tiered warnings and multicategorical forecasts based on fixed risk measures

The use of tiered warnings and multicategorical forecasts are ubiquitous in meteorological operations. Here, a flexible family of scoring functions is presented for evaluating the performance of ordered multicategorical forecasts. Each score has a risk parameter α$$ \alpha $$ , selected for the specific use case, so that it is consistent with a forecast directive based on the fixed threshold probability 1−α$$ 1-\alpha $$ (equivalently, a fixed α$$ \alpha $$ ‐quantile mapping). Each score also has use‐case specific weights so that forecasters who accurately discriminate between categorical thresholds are rewarded in proportion to the weight for that threshold. A variation is presented where the penalty assigned to near misses or close false alarms is discounted, which again is consistent with directives based on fixed risk measures. The scores presented provide an alternative to many performance measures currently in use, whose optimal threshold probabilities for forecasting an event typically vary with each forecast case, and in the case of equitable scores are based around sample base rates rather than risk measures suitable for users.
Abiotic molecular oxygen production—Ionic pathway from sulfur dioxide

Molecular oxygen, O2, is vital to life on Earth and possibly also on exoplanets. Although the biogenic processes leading to its accumulation in Earth’s atmosphere are well understood, its abiotic origin is still not fully established. Here, we report combined experimental and theoretical evidence for electronic state–selective production of O2 from SO2, a chemical constituent of many planetary atmospheres and one that played an important part on Earth in the Great Oxidation Event. The O2 production involves dissociative double ionization of SO2 leading to efficient formation of the O2+ ion, which can be converted to abiotic O2 by electron neutralization or by charge exchange. This formation process may contribute substantially to the abundance of O2 and related ions in planetary atmospheres, such as the Jovian moons Io, Europa, and Ganymede. We suggest that this sort of ionic pathway for the formation of abiotic O2 involving multiply charged molecular ion decomposition may also exist for other atmospheric and planetary molecules.
On the Significance of Question Encoder Sequence Model in the Out-of-Distribution Performance in Visual Question Answering

Generalizing beyond the experiences has a significant role in developing practical AI systems. It has been shown that current Visual Question Answering (VQA) models are over-dependent on the language-priors (spurious correlations between question-types and their most frequent answers) from the train set and pose poor performance on Out-of-Distribution (OOD) test sets. This conduct limits their generalizability and restricts them from being utilized in real-world situations. This paper shows that the sequence model architecture used in the question-encoder has a significant role in the generalizability of VQA models. To demonstrate this, we performed a detailed analysis of various existing RNN-based and Transformer-based question-encoders, and along, we proposed a novel Graph attention network (GAT)-based question-encoder. Our study found that a better choice of sequence model in the question-encoder improves the generalizability of VQA models even without using any additional relatively complex bias-mitigation approaches.
A fast point solver for deep nonlinear function approximators

Deep kernel processes (DKPs) generalise Bayesian neural networks, but do not require us to represent either features or weights. Instead, at each hidden layer they represent and optimize a ﬂexible kernel. Here, we develop a Newton-like method for DKPs that converges in around 10 steps, exploiting matrix solvers initially developed in the control theory literature. These are many times faster the usual gradient descent approach. We generalise to arbitrary DKP architectures, by developing “kernel backprop”, and algorithms for “kernel autodiff”. While these methods currently are not Bayesian as they give point estimates and scale poorly as they are cubic in the number of datapoints, we hope they will form the basis of a new class of much more efﬁcient approaches to optimizing deep nonlinear function approximators
IQRM: real-time adaptive RFI masking for radio transient and pulsar searches

In a search for short timescale astrophysical transients in time-domain data, radio-frequency interference (RFI) causes both large quantities of false positive candidates and a significant reduction in sensitivity if not correctly mitigated. Here we propose an algorithm that infers a time-variable frequency channel mask directly from short-duration (∼1 s) data blocks: the method consists of computing a spectral statistic that correlates well with the presence of RFI, and then finding high outliers among the resulting values. For the latter task, we propose an outlier detection algorithm called Inter-Quartile Range Mitigation (IQRM), that is both non-parametric and robust to the presence of a trend in sequential data. The method requires no training and can in principle adapt to any telescope and RFI environment; its efficiency is shown on data from both the MeerKAT and Lovell 76-m radio telescopes. IQRM is fast enough to be used in a streaming search and has been integrated into the MeerTRAP real-time transient search pipeline. Open-source python and C++ implementations are also provided.
Why and How Governments Should Monitor AI Development

In this paper we outline a proposal for improving the governance of artificial intelligence (AI) by investing in government capacity to systematically measure and monitor the capabilities and impacts of AI systems. If adopted, this would give governments greater information about the AI ecosystem, equipping them to more effectively direct AI development and deployment in the most societally and economically beneficial directions. It would also create infrastructure that could rapidly identify potential threats or harms that could occur as a consequence of changes in the AI ecosystem, such as the emergence of strategically transformative capabilities, or the deployment of harmful systems. We begin by outlining the problem which motivates this proposal: in brief, traditional governance approaches struggle to keep pace with the speed of progress in AI. We then present our proposal for addressing this problem: governments must invest in measurement and monitoring infrastructure. We discuss this proposal in detail, outlining what specific things governments could focus on measuring and monitoring, and the kinds of benefits this would generate for policymaking. Finally, we outline some potential pilot projects and some considerations for implementing this in practice.
Re‐Analysis of the Cassini RPWS/LP Data in Titan's Ionosphere: 1. Detection of Several Electron Populations

Current models of Titan's ionosphere have difficulties in explaining the observed electron density and/or temperature. In order to get new insights, we re‐analyzed the data taken in the ionosphere of Titan by the Cassini Langmuir probe (LP), part of the Radio and Plasma Wave Science (RPWS) instrument. This is the first of two papers that present the new analysis method (current paper) and statistics on the whole data set. We suggest that between two and four electron populations are necessary to fit the data. Each population is defined by a potential, an electron density and an electron temperature and is easily visualized by a distinct peak in the second derivative of the electron current, which is physically related to the electron energy distribution function (Druyvesteyn method). The detected populations vary with solar illumination and altitude. We suggest that the four electron populations are due to photo‐ionization, magnetospheric particles, dusty plasma and electron emission from the probe boom, respectively.
Differentiable Convolution Search for Point Cloud Processing

Exploiting convolutional neural networks for point cloud processing is quite challenging, due to the inherent irregular distribution and discrete shape representation of point clouds. To address these problems, many handcrafted convolution variants have sprung up in recent years. Though with elaborate design, these variants could be far from optimal in sufficiently capturing diverse shapes formed by discrete points. In this paper, we propose PointSeaConv, i.e., a novel differential convolution search paradigm on point clouds. It can work in a purely data-driven manner and thus is capable of auto-creating a group of suitable convolutions for geometric shape modeling. We also propose a joint optimization framework for simultaneous search of internal convolution and external architecture, and introduce epsilon-greedy algorithm to alleviate the effect of discretization error. As a result, PointSeaNet, a deep network that is sufficient to capture geometric shapes at both convolution level and architecture level, can be searched out for point cloud processing. Extensive experiments strongly evidence that our proposed PointSeaNet surpasses current handcrafted deep models on challenging benchmarks across multiple tasks with remarkable margins.
Lagged couplings diagnose Markov chain Monte Carlo phylogenetic inference

Phylogenetic inference is an intractable statistical problem on a complex space. Markov chain Monte Carlo methods are the primary tool for Bayesian phylogenetic inference but it is challenging to construct efficient schemes to explore the associated posterior distribution or assess their performance. Existing approaches are unable to diagnose mixing or convergence of Markov schemes jointly across all components of a phylogenetic model. Lagged couplings of Markov chain Monte Carlo algorithms have recently been developed on simpler spaces to diagnose convergence and construct unbiased estimators. We describe a contractive coupling of Markov chains targeting a posterior distribution over a space of phylogenetic trees with branch lengths, scalar parameters and latent variables. We use these couplings to assess mixing and convergence of Markov chains jointly across all components of the phylogenetic model on trees with up to 200 leaves. Samples from our coupled chains may also be used to construct unbiased estimators.
Linked orbits of homeomorphisms of the plane and Gambaudo-Kolev Theorem

Let h : R 2 → R 2 be an orientation preserving homeomorphism of the plane. For any bounded orbit O ( x ) = { h n ( x ) : n ∈ Z } there exists a ﬁxed point x (cid:48) ∈ R 2 of h linked to O ( x ) in the sense of Gambaudo: one cannot ﬁnd a Jordan curve C ⊆ R 2 around O ( x ) , separating it from x (cid:48) , that is isotopic to h ( C ) in R 2 \ ( O ( x ) ∪ { x (cid:48) } ) .
On the Extraction of HCl and H2PtCl6 by Tributyl Phosphate: A Mode of Action Study

ABSTRACT Combining computational modeling with experimental measurements has revealed the self-assembly of nano-aggregate structures in the transfer of HCl and PtCl62– from an aqueous phase into toluene by the common industrial extractant tributyl phosphate (TBP). Molecular dynamics simulations have been coupled to analytical measurements to provide an atomistic interpretation of the mode of action of TBP under 6 M and 10 M HCl conditions. The structures conform to reverse micelles, where the Cl– or PtCl62– core is encapsulated by a hydration shell that acts as a mediating bridge to the electronegative oxygen atom in the TBP phosphate groups. For the 6 M HCl extraction model, the data support stable aggregates forming from 2–3 TBP molecules around one chloride anion if the number of water molecules encapsulating the chloride anion is no more than five; increasing the water content to 10 molecules allows a fourth TBP molecule to coordinate. For the 10 M HCl extraction model, stable structures are obtained that conform to the empirical formula (TBP.HCl.H2O)3–5. At 6 M HCl, extraction of PtCl62– is achieved by encapsulation by four TBP molecules; the data for extraction at 10 M HCl indicate larger aggregates containing multiple PtCl62– anions are likely to be forming. In all cases, the hydrated core regions of the reverse micelles are considerably exposed. The diameters of the self-assembled structures around chloride ions agree well with available literature data from small-angle neutron-scattering experiments.
DQLEL: Deep Q-Learning for Energy-Optimized LoS/NLoS UWB Node Selection

Ultra Wide Band (UWB) has been emerged as a technology to provide reliable, accurate, and energy-efficient indoor navigation/localization systems. There are, however, several key challenges ahead for its efficient implementation including complexity of the identification/mitigation of Non Line of Sight (NLoS) links, and the limited battery life of UWB beacons, which is especially problematic in practical circumstances with certain beacons located in strategic positions. To address these challenges, we introduce an efficient node selection framework to enhance the location accuracy, without using complex NLoS mitigation methods, while maintaining a balance between the remaining battery life of UWB beacons. Referred to as the Deep Q-Learning Energy-optimized LoS/NLoS (DQLEL) UWB node selection framework, the mobile user is autonomously trained to determine the optimal set of UWB beacons to be localized based on the 2-D Time Difference of Arrival (TDoA) framework. The effectiveness of the proposed DQLEL framework is evaluated in terms of the link condition, the deviation of the remaining battery life of UWB beacons, location error, and cumulative rewards. Based on the simulation results, the proposed DQLEL framework significantly outperformed its counterparts across the aforementioned aspects.
A Battle of Network Structures: An Empirical Study of CNN, Transformer, and MLP

Convolutional neural networks (CNN) are the dominant deep neural network (DNN) architecture for computer vision. Recently, Transformer and multi-layer perceptron (MLP)-based models, such as Vision Transformer and MLP-Mixer, started to lead new trends as they showed promising results in the ImageNet classification task. In this paper, we conduct empirical studies on these DNN structures and try to understand their respective pros and cons. To ensure a fair comparison, we first develop a unified framework called SPACH which adopts separate modules for spatial and channel processing. Our experiments under the SPACH framework reveal that all structures can achieve competitive performance at a moderate scale. However, they demonstrate distinctive behaviors when the network size scales up. Based on our findings, we propose two hybrid models using convolution and Transformer modules. The resulting Hybrid-MS-S+ model achieves 83.9% top-1 accuracy with 63M parameters and 12.3G FLOPS. It is already on par with the SOTA models with sophisticated designs. The code and models are publicly available at https://github.com/microsoft/SPACH.
Searching for an Effective Defender: Benchmarking Defense against Adversarial Word Substitution

Recent studies have shown that deep neural network-based models are vulnerable to intentionally crafted adversarial examples, and various methods have been proposed to defend against adversarial word-substitution attacks for neural NLP models. However, there is a lack of systematic study on comparing different defense approaches under the same attacking setting. In this paper, we seek to fill the gap of systematic studies through comprehensive researches on understanding the behavior of neural text classifiers trained by various defense methods under representative adversarial attacks. In addition, we propose an effective method to further improve the robustness of neural text classifiers against such attacks, and achieved the highest accuracy on both clean and adversarial examples on AGNEWS and IMDB datasets by a significant margin. We hope this study could provide useful clues for future research on text adversarial defense. Codes are available at https://github.com/RockyLzy/TextDefender.
The missing link: Developing a safety case for perception components in automated driving

—Safety assurance is a central concern for the develop- ment and societal acceptance of automated driving (AD) systems. Perception is a key aspect of AD that relies heavily on Machine Learning (ML). Despite the known challenges with the safety assurance of ML-based components, proposals have recently emerged for unit-level safety cases addressing these components. Unfortunately, AD safety cases express safety requirements at the system level and these efforts are missing the critical linking argument needed to integrate safety requirements at the system level with component performance requirements at the unit level. In this paper, we propose the Integration Safety Case for Perception (ISCaP), a generic template for such a linking safety argument speciﬁcally tailored for perception components. The template takes a deductive and formal approach to deﬁne strong traceability between levels. We demonstrate the applicability of ISCaP with a detailed case study and discuss its use as a tool to support incremental development of perception components.
Histogram Equalization Of The Image

The relevance and impact of probability distributions on image processing are the subject of this study.It may be characterized as a probability distribution function of brightness for a certain area, which might be a whole picture. To generate a histogram, the probability density function of the brightness is frequently calculated by counting how many times each brightness occurs in the picture region. The brightness average is defined as the sample mean of the brightness of pixels in a certain region. The frequency is shown by the histogram. The histogram has a wide range of uses in image processing. It could, for starters, be used for picture analysis. Second, the functions of an image's brightness and contrast, as well as the final two uses of equalizing and thresholding. Normalizing a histogram is one technique to convert the intensities of discrete distributions to the probability of discrete distribution functions. The technique to equalize the histogram is to control the image's contrast by altering their intensity distribution functions. The major goal of this procedure is to give the cumulative probability function a linear trend (CDF). A method of segmentation is to divide a section of the picture into constituent areas or objects.
Opinions are Made to be Changed: Temporally Adaptive Stance Classification

Given the rapidly evolving nature of social media and people's views, word usage changes over time. Consequently, the performance of a classifier trained on old textual data can drop dramatically when tested on newer data. While research in stance classification has advanced in recent years, no effort has been invested in making these classifiers have persistent performance over time. To study this phenomenon we introduce two novel large-scale, longitudinal stance datasets. We then evaluate the performance persistence of stance classifiers over time and demonstrate how it decays as the temporal gap between training and testing data increases. We propose a novel approach to mitigate this performance drop, which is based on temporal adaptation of the word embeddings used for training the stance classifier. This enables us to make use of readily available unlabelled data from the current time period instead of expensive annotation efforts. We propose and compare several approaches to embedding adaptation and find that the Incremental Temporal Alignment (ITA) model leads to the best results in reducing performance drop over time.