Miracle-dz/UniFAR-FTUs huggingface dataset:
Choosing observation operators to mitigate model error in Bayesian inverse problems

In statistical inference, a discrepancy between the parameter-to-observable map that generates the data and the parameter-to-observable map that is used for inference can lead to misspecified likelihoods and thus to incorrect estimates. In many inverse problems, the parameter-to-observable map is the composition of a linear state-to-observable map called an `observation operator' and a possibly nonlinear parameter-to-state map called the `model'. We consider such Bayesian inverse problems where the discrepancy in the parameter-to-observable map is due to the use of an approximate model that differs from the best model, i.e. to nonzero `model error'. Multiple approaches have been proposed to address such discrepancies, each leading to a specific posterior. We show how to use local Lipschitz stability estimates of posteriors with respect to likelihood perturbations to bound the Kullback--Leibler divergence of the posterior of each approach with respect to the posterior associated to the best model. Our bounds lead to criteria for choosing observation operators that mitigate the effect of model error for Bayesian inverse problems of this type. We illustrate one such criterion on an advection-diffusion-reaction PDE inverse problem from the literature, and use this example to discuss the importance and challenges of model error-aware inference.
SynMotor: A Benchmark Suite for Object Attribute Regression and Multi-task Learning

In this paper, we develop a novel benchmark suite including both a 2D synthetic image dataset and a 3D synthetic point cloud dataset. Our work is a sub-task in the framework of a remanufacturing project, in which small electric motors are used as fundamental objects. Apart from the given detection, classification, and segmentation annotations, the key objects also have multiple learnable attributes with ground truth provided. This benchmark can be used for computer vision tasks including 2D/3D detection, classification, segmentation, and multi-attribute learning. It is worth mentioning that most attributes of the motors are quantified as continuously variable rather than binary, which makes our benchmark well-suited for the less explored regression tasks. In addition, appropriate evaluation metrics are adopted or developed for each task and promising baseline results are provided. We hope this benchmark can stimulate more research efforts on the sub-domain of object attribute learning and multi-task learning in the future.
Strengthening the Directed Brooks' Theorem for oriented graphs and consequences on digraph redicolouring

Let $D=(V,A)$ be a digraph. We define $\Delta_{\max}(D)$ as the maximum of $\{ \max(d^+(v),d^-(v)) \mid v \in V \}$ and $\Delta_{\min}(D)$ as the maximum of $\{ \min(d^+(v),d^-(v)) \mid v \in V \}$. It is known that the dichromatic number of $D$ is at most $\Delta_{\min}(D) + 1$. In this work, we prove that every digraph $D$ which has dichromatic number exactly $\Delta_{\min}(D) + 1$ must contain the directed join of $\overleftrightarrow{K_r}$ and $\overleftrightarrow{K_s}$ for some $r,s$ such that $r+s = \Delta_{\min}(D) + 1$, except if $\Delta_{\min}(D) = 2$ in which case $D$ must contain a digon. In particular, every oriented graph $\vec{G}$ with $\Delta_{\min}(\vec{G}) \geq 2$ has dichromatic number at most $\Delta_{\min}(\vec{G})$. Let $\vec{G}$ be an oriented graph of order $n$ such that $\Delta_{\min}(\vec{G}) \leq 1$. Given two 2-dicolourings of $\vec{G}$, we show that we can transform one into the other in at most $n$ steps, by recolouring one vertex at each step while maintaining a dicolouring at any step. Furthermore, we prove that, for every oriented graph $\vec{G}$ on $n$ vertices, the distance between two $k$-dicolourings is at most $2\Delta_{\min}(\vec{G})n$ when $k\geq \Delta_{\min}(\vec{G}) + 1$. We then extend a theorem of Feghali, Johnson and Paulusma to digraphs. We prove that, for every digraph $D$ with $\Delta_{\max}(D) = \Delta \geq 3$ and every $k\geq \Delta +1$, the $k$-dicolouring graph of $D$ consists of isolated vertices and at most one further component that has diameter at most $c_{\Delta}n^2$, where $c_{\Delta} = O(\Delta^2)$ is a constant depending only on $\Delta$.
ECSAS: Exploring Critical Scenarios from Action Sequence in Autonomous Driving

Rare critical scenarios are crucial to verify the performance of autonomous driving in different situations. Critical scenario generation requires the ability of sampling critical combinations from an infinite parameter space in the logical scenario. Existing solutions aim to explore the correlation of action parameters in the initial scenario rather than action sequences. How to model action sequences so that one can further consider the effects of different action parameters is the bottleneck of the problem. In this paper, we solve the problem by proposing the ECSAS framework. Specifically, we first propose a description language, BTScenario, allowing us to model action sequences of scenarios. We then use reinforcement learning to search for combinations of critical action parameters. Several optimizations are proposed to increase efficiency, including action mask and replay buffer. Experimental results show that our model with strong collision ability and effectively outperforms the existing methods on various nontrivial scenarios.
An upgraded theory for Helene , Telesto , and Calypso

A tridimensional model including the perturbation due to Saturn's oblateness provides accurate solutions for the motions of Tethys and Dione's Lagrangian satellites Telesto, Calypso, and Helene. Expanded around the Lagrangian points and compared to numerical simulations to check their reliabilities, the solutions are fitted to almost twenty years of data thanks to recently published observations. User-friendly compact series give Telesto, Calypso, and Helene's positions and velocities in the mean ecliptic and equinox of J2000.
MODIS Cloud-Top Property Refinements for Collection 6

AbstractThis paper summarizes the Collection-6 refinements in the Moderate Resolution Imaging Spectroradiometer (MODIS) operational cloud-top properties algorithm. The focus is on calibration improvements and on cloud macrophysical properties including cloud-top pressure–temperature–height and cloud thermodynamic phase. The cloud phase is based solely on infrared band measurements. In addition, new parameters will be provided in Collection 6, including cloud-top height and a flag for clouds near the tropopause. The cloud parameters are improved primarily through 1) improved knowledge of the spectral response functions for the MODIS 15-μm carbon dioxide bands gleaned from comparison of coincident MODIS and Atmospheric Infrared Sounder (AIRS) radiance measurements and 2) continual comparison of global MODIS and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) instantaneous cloud products throughout the course of algorithm refinement. Whereas the cloud-top macrophysical parameters.
Long-term memory and synapse-like dynamics in two-dimensional nanofluidic channels

Fine-tuned ion transport across nanoscale pores is key to many biological processes, including neurotransmission. Recent advances have enabled the confinement of water and ions to two dimensions, unveiling transport properties inaccessible at larger scales and triggering hopes of reproducing the ionic machinery of biological systems. Here we report experiments demonstrating the emergence of memory in the transport of aqueous electrolytes across (sub)nanoscale channels. We unveil two types of nanofluidic memristors depending on channel material and confinement, with memory ranging from minutes to hours. We explain how large time scales could emerge from interfacial processes such as ionic self-assembly or surface adsorption. Such behavior allowed us to implement Hebbian learning with nanofluidic systems. This result lays the foundation for biomimetic computations on aqueous electrolytic chips. Description Toward fluidic neuromorphic computing There is considerable interest in strategies that mimic the structure of human brain and could lead to the development of next-generation neuromorphic devices. Many recent studies have focused on solid-state devices, although information in biological systems is conveyed by ions solvated in water, an approach now explored in two papers in this issue (see the Perspective by Noy and Darling). Robin et al. created nanofluidic devices consisting of nanometer-thick two-dimensional slits filled with a salt solution, whereas Xiong et al. present a nanofluidic ionic memristor based on confined polyelectrolyte-ion interactions. The two studies are focused on different aspects of neuromorphic engineering, but both show precise control of ion transport in water across nanoscale channels. These studies show promising directions for creating neuromorphic functions using energy-efficient fluidic memristors that could mimic biological systems down to their fundamental principles. —YS Two nanofluidic devices can reproduce Hebbian learning using ions in water as charge carriers, similar to how neurons work.
Ontology-Based Driving Simulation for Traffic Lights Optimization

Traffic lights optimization is one of the principal components to lessen the traffic flow and travel time in an urban area. The present article seeks to introduce a novel procedure to design the traffic lights in a city using evolutionary-based optimization algorithms in combination with an ontology-based driving behavior simulation framework. Accordingly, an ontology-based knowledge base is introduced to provide a machine-understandable knowledge of roads and intersections, traffic rules, and driving behaviors. Then, a simulation environment is developed to inspect car behavior in real time. To optimize the traffic lights, a sine-based equation was defined for each traffic light, and the total travel time of the vehicles was considered as the cost function in the optimization algorithm. The optimization was performed with 5, 10, 15, 20, 25, and 30 vehicles in the urban areas. Based on the results, in contrast to uncontrolled intersections without traffic lights, optimized traffic lights can significantly contribute to total travel time-saving. To conclude, due to an escalation in the number of vehicles, the significance of optimized traffic lights has encountered an increase, and unoptimized traffic lights could increase total travel time even more than a city deprived of any traffic light.
TPFS: A High-Performance Tiered File System for Persistent Memories and Disks

Emerging fast, byte-addressable persistent memory (PM) promises substantial storage performance gains compared with traditional disks. We present TPFS, a tiered file system that combines PM and slow disks to create a storage system with near-PM performance and large capacity. TPFS steers incoming file input/output (I/O) to PM, dynamic random access memory (DRAM), or disk depending on the synchronicity, write size, and read frequency. TPFS profiles the application’s access stream online to predict the behavior of file access. In the background, TPFS estimates the “temperature” of file data and migrates the write-cold and read-hot file data from PM to disks. To fully utilize disk bandwidth, TPFS coalesces data blocks into large, sequential writes. Experimental results show that with a small amount of PM and a large solid-state drive (SSD), TPFS achieves up to 7.3× and 7.9× throughput improvement compared with EXT4 and XFS running on an SSD alone, respectively. As the amount of PM grows, TPFS’s performance improves until it matches the performance of a PM-only file system.
Miscellaneous EEG Preprocessing and Machine Learning for Pilots' Mental States Classification: Implications

Higher cognitive process efforts may result in mental exhaustion, poor performance, and long-term health issues. An EEG-based methods for detecting a pilot's mental state have recently been created utilizing machine learning algorithms. EEG signals include a significant noise component, and these approaches either ignore this or use a random mix of preprocessing techniques to reduce noise. In the absence of uniform preprocessing procedures for cleaning, it would be impossible to compare the efficacy of machine learning models across research, even if they employ data obtained from the same experiment. In this study, we intend to evaluate how preprocessing approaches affect the performance of machine learning models. To do this, we concentrated on fundamental preprocessing techniques, such as a band-pass filter and independent component analysis. Using a publicly accessible actual physiological dataset gathered from a pilot who was exposed to a variety of mental events, we explore the influence of these preprocessing strategies on two machine learning models, SVMs and ANNs. Our findings indicate that the performance of the models is unaffected by preprocessing techniques. Moreover, our findings indicate that the models were able to anticipate the mental states from merged data collected in two environments. These findings demonstrate the necessity for a standardized methodological framework for the application of machine learning models to EEG inputs.
Parametric dependence of the edge radial electric field in the DIII-D tokamak

High spatially resolved measurements of the radial electric field, Er, have been made across the transition from L mode to H mode plasmas for many different plasma parameters and conditions. The evolution of the well-like structure of the Er profile formed at the L-H transition has been investigated. No distinct variation in the shape or width of the Er well at the L-H transition is observed as a function of the plasma parameters investigated, such as the plasma current, the toroidal magnetic field and the plasma density. The value of Er is negative just inside the last closed flux surface (LCFS) for all the plasmas studied. There is a variation in the depth of the Er well for different conditions. The experimental results have been compared with theoretical predictions for suppression of plasma turbulence by sheared E × B plasma flow.
Determinants of Foreign Language Teachers’ Intention to Adopt Online Teaching: An empirical study by the combined model of TAM and TTF

The purpose of this study is to explore the factors that influence foreign language teachers’ intention to use (IU) the online platform by integrating Technology Acceptance Model (TAM) and Task Technology Fit (TTF). A sample of 358 foreign language teachers from Chinese universities were selected by distributing a questionnaire and the research hypotheses were tested by multiple regression analysis. The results showed that perceived usefulness (PU), perceived ease of use (PEOU) and TTF all significantly influenced teachers’ IU the online platform. What's more, TTF was an important mediator for teachers’ intention to utilize the virtual platform. In addition, we found the specific region and specialized foreign language also play a role in the teachers’ intention of using the technology. The results of our study helps to facilitate regional higher education development and to informalize foreign language education policies in Chinese universities.
Optimizing Ethanol Production in Escherichia Coli Using a Hybrid of Particle Swarm Optimization and Artificial Bee Colony

Metabolic engineering for biomass production using microorganisms’ cell has received considerable attention in recent years. This is due to the biomass products being extensively used in the field of food additives, supplements, pharmaceuticals, and polymer materials. In this paper, ethanol production in Escherichia coli (E. coli) is the desired product. Sugarcane and corn are often used to produce ethanol. However, one of the problems to produce adequate amounts of ethanol is that large areas are needed to plant sugarcane and corn. Furthermore, the amount of time for the process of dry milling and wet milling is high, which are 40 to 50 hours and 24 to 48 hours, respectively. The wet laboratory is also having limitation on the production of ethanol in microorganisms because the amount of the ethanol produced is not satisfying. Hence, a lot of metabolic engineering techniques is introduced to enhance the production of ethanol in E. coli, such as gene knockout strategy, but the production is yet to meet the demand. Therefore, this paper proposes a hybrid algorithm of Particle Swarm Optimization with the Artificial Bee Colony algorithm (PSOABC) to identify the optimal set of gene knockout strategy to improve the ethanol production in E. coli. A list of genes to knockout, production of the desired product, and growth rate are presented in this paper. PSOABC has shown better performance in terms of production, growth rate and accuracy.
VCSN: Virtual Circuit-Switching Network for Flexible and Simple-to-Operate Communication in HPC FPGA Cluster

FPGA clusters promise to play a critical role in high-performance computing (HPC) systems in the near future due to their flexibility and high power efficiency. The operation of large-scale general-purpose FPGA clusters on which multiple users run diverse applications requires flexible network topology to be divided and reconfigured. This paper proposes Virtual Circuit-Switching Network (VCSN) that provides an arbitrarily reconfigurable network topology and simple-to-operate network system among FPGA nodes. With virtualization, user logic on FPGAs can communicate with each other as if a circuit-switching network was available. This paper demonstrates that VCSN with 100 Gbps Ethernet achieves highly-efficient point-to-point communication among FPGAs due to its unique and efficient communication protocol. We compare VCSN with a direct connection network (DCN) that connects FPGAs directly. We also show a concrete procedure to realize collective communication on an FPGA cluster with VCSN. We demonstrate that the flexible virtual topology provided by VCSN can accelerate collective communication with simple operations. Furthermore, based on experimental results, we model and estimate communication performance by DCN and VCSN in a large FPGA cluster. The result shows that VCSN has the potential to accelerate gather communication up to about 1.97 times more than DCN.
The rotational break for G giants

New high-resolution spectroscopic observations have been obtained for 73 G giants. Fourier analysis of their spectral lines yields rotation velocities and macroturbulence dispersions. Combined with data from an earlier study, total of 86 analyses of luminosity class III giants is now available. The existence of a rotational discontinuity for luminosity class III giants is confirmed, but it is found to be near G0 III rathr than G5 III, as indicated in the earlier work. Evidence for rotation being a single-valued function of spectral type is strengthened. The observations are interpreted in terms of a dynamo-generated magnetic brake and a rotostat phenomenon. 45 refs.
The Effectiveness of Information Technology and Teaching Strategies in Distance Learning to Improve the Learning Effect of K-12 Students: Instructional Strategies and Application of Information Technology in Distance Learning

Since the outbreak of COVID-19, many countries began to try to use online platforms and digital tools for distance learning. This study focuses on the effectiveness of information technology and teaching strategies in distance learning to improve the learning effect of K-12 students. By using the meta-analysis method and screening criteria of empirical research, it focuses on methodological and substantive research, and examines the relationship between information technology and learning effect, and the relationship between teaching strategies and learning effect in distance learning. The final analysis included 85 qualified studies with a total sample size of 79,928 K-12 students: 35 primary school studies (n = 35,155) and 50 secondary school studies (n = 44,773). The results show that compared with offline learning, the application of information technology in distance learning usually has a positive impact (ES=+2.26). However, the impact may vary depending on the type of information technology. From the perspective of teaching strategies, case-based teaching strategies can improve students' learning effect. Therefore, when applying information technology and teaching strategies to students' distance learning effect, we should strengthen the design of process objectives, pay attention to the role of sharing and co-construction technologies in distance learning, and adopt case-based teaching strategies to stimulate students' high-level cognitive activities and cognitive achievements, so as to promote distance learning.
Testing uniformity versus a monotone density

The paper is concerned with testing uniformity versus a monotone density. This problem arises in two important contexts, after transformations, testing whether a sample is a simple random sample or a biased sample, and testing whether the intensity function of a nonhomogeneous Poisson process is constant against monotone alternatives. A penalized likelihood ratio test (P-test) and a Dip likelihood test (D-test) are developed. The D-test is analogous to Hartigan and Hartigan's (1985) Dip test for bump hunting problems. While nonparametric, both the P- and D-tests are quite efficient in comparison to the most powerful (MP) tests for some simple alternatives and also the Laplace test developed for nonhomogeneous Poisson process. The P- and D-tests have higher power than the above MP tests under different sets of monotone alternatives and so have greater applicability. Moderate sample size performance and applications of our tests are illustrated via simulations and examination of an air-conditioning equipment data set.
The route expropriation problem

This paper considers the problem of selecting obnoxious routes (e.g., routes used to transport hazardous material) on a transportation network assuming that population centers on or outside the network within a certain distance from the selected routes can be expropriated at a given price. The objective is to select the routes so as to minimize the total weighted transportation and expropriation costs. For the single-flow problem, a polynomial algorithm is developed. For the multiple-flow problem, a branch-and-price algorithm using column generation is developed and its efficiency is tested with computational experiments.