Miracle-dz/UniFAR-FTUs huggingface dataset:
Convective-Scale Data Assimilation for the Weather Research and Forecasting Model Using the Local Particle Filter

AbstractParticle filters (PFs) are Monte Carlo data assimilation techniques that operate with no parametric assumptions for prior and posterior errors. A data assimilation method introduced recently, called the local PF, approximates the PF solution within neighborhoods of observations, thus allowing for its use in high-dimensional systems. The current study explores the potential of the local PF for atmospheric data assimilation through cloud-permitting numerical experiments performed for an idealized squall line. Using only 100 ensemble members, experiments using the local PF to assimilate simulated radar measurements demonstrate that the method provides accurate analyses at a cost comparable to ensemble filters currently used in weather models. Comparisons between the local PF and an ensemble Kalman filter demonstrate benefits of the local PF for producing probabilistic analyses of non-Gaussian variables, such as hydrometeor mixing ratios. The local PF also provides more accurate forecasts than the ens.
C ONSISTENCY -B ASED S EMI -S UPERVISED A CTIVE L EARNING : T OWARDS M INIMIZING L ABELING B UD - GET

Active learning (AL) aims to integrate data labeling and model training in a unified way, and to minimize the labeling budget by prioritizing the selection of high value data that can best improve model performance. Readily-available unlabeled data are used to evaluate selection mechanisms, but are not used for model training in conventional pool-based AL. To minimize the labeling budget, we unify unlabeled sample selection and model training based on two principles. First, we exploit both labeled and unlabeled data using semi-supervised learning (SSL) to distill information from unlabeled data that improves representation learning and sample selection. Second, we propose a simple yet effective selection metric that is coherent with the training objective such that the selected samples are effective at improving model performance. Our experimental results demonstrate superior performance with our proposed principles for limited labeled data compared to alternative AL and SSL combinations. In addition, we study the AL phenomena of ‘cold start’, which is becoming an increasingly more important factor to enable optimal unification of data labeling, model training and labeling budget minimization. We propose a measure that is found to be empirically correlated with the AL target loss. This measure can be used to assist in determining the proper start size.
Shearlet-based regularization in sparse dynamic tomography

Classical tomographic imaging is soundly understood and widely employed in medicine, nondestructive testing and security applications. However, it still offers many challenges when it comes to dynamic tomography. Indeed, in classical tomography, the target is usually assumed to be stationary during the data acquisition, but this is not a realistic model. Moreover, to ensure a lower X-ray radiation dose, only a sparse collection of measurements per time step is assumed to be available. With such a set up, we deal with a sparse data, dynamic tomography problem, which clearly calls for regularization, due to the loss of information in the data and the ongoing motion. In this paper, we propose a 3D variational formulation based on 3D shearlets, where the third dimension accounts for the motion in time, to reconstruct a moving 2D object. Results are presented for real measured data and compared against a 2D static model, in the case of fan-beam geometry. Results are preliminary but show that better reconstructions can be achieved when motion is taken into account.
Rigorous expansion of electromagnetic Green’s tensor of lossy resonators in open systems

We obtain an eigenmode expansion of the electromagnetic Green’s tensor G(r,r') for lossy resonators in open systems, which is simple yet complete. This enables rapid simulations by providing the spatial variation of G0(r,r') over both r and r' in one simulation. Few eigenmodes are often necessary for nanostructures, facilitating both analytic calculations and unified insight into computationally intensive phenomena such as Purcell enhancement, radiative heat transfer, van der Waals forces, and Förster resonance energy transfer. We bypass all implementation and completeness issues associated with the alternative quasinormal eigenmode methods, by defining modes with permittivity rather than frequency as the eigenvalue. Thus, modes decay rather than diverge at infinity, and are defined by a linear eigenvalue problem, readily implemented using any numerical method. We demonstrate its general implementation in COMSOL Multiphysics, using the default in-built tools.
The Effect of Asymmetry on the On-Line Multicast Routing Problem

In this paper, we study the problem of multicast routing on directed graphs. We define the asymmetry of a graph to be the maximum ratio of weights on opposite directed edges between a pair of nodes for all node-pairs. We examine three types of problems according the membership behavior: (i) the static, (ii) the join-only, (iii) the join-leave problems. We study the effect of the asymmetry on the worst case performance of two algorithms: the Greedy and Shortest Paths algorithms. The worst case performance of Shortest Paths is poor, but it is affected by neither the asymmetry nor the membership behavior. In contrast, the worst case performance of Greedy is a proportional to the asymmetry in a some cases. We prove an interesting result for the join-only problem: the Greedy algorithm has near-optimal on-line performance.
Measurement of the shape of the Λ[superscript 0][subscript b] → Λ[superscript +][subscript c]μ[superscript −][bar over ν][subscript μ] differential decay rate

A measurement of the shape of the differential decay rate and the associated Isgur-Wise function for the decay Λ 0 b → Λ þ c μ − ¯ ν μ is reported, using data corresponding to 3 fb − 1 collected with the LHCb detector in proton-proton collisions. The Λ þ c μ − ¯ ν μ ðþ anything Þ final states are reconstructed through the detection of a muon and a Λ þ c baryon decaying into pK − π þ , and the decays Λ 0 b → Λ þ c π þ π − μ − ¯ ν μ are used to determine contributions from Λ 0 b → Λ (cid:2)þ c μ − ¯ ν μ decays. The measured dependence of the differential decay rate upon the squared four-momentum transfer between the heavy baryons, q 2 , is compared with expectations from heavy-quark effective theory and from unquenched lattice QCD predictions.
Calorimetric investigations in a gas aggregation source

A gas aggregation source based on DC magnetron sputtering was investigated using a passive thermal probe and supplementary diagnostics (Langmuir probe and quartz crystal microbalance). Parameter variations of pressure, axial distance, and magnetron current have been performed for three different targets (pure Cu, pure W, composite Cu/W) in argon discharge. The measurements showed the energy flux to be significantly higher for the case of the pure tungsten and the composite target compared to the copper target, which is likely a result of the strongly increased amount of neutrals being reflected from the heavier targets. Furthermore, gas rarefaction by the sputtered atoms was found to be essential for the understanding of the observed energy flux and that the dominant contributors to the energy flux in the higher pressure regime are comparable to those observed in the conventional lower pressure regime. Selected deposited films have been investigated ex-situ by scanning electron microscopy, which allowed us to gain insight into the nanoparticle formation in relation to the observed energy conversion.A gas aggregation source based on DC magnetron sputtering was investigated using a passive thermal probe and supplementary diagnostics (Langmuir probe and quartz crystal microbalance). Parameter variations of pressure, axial distance, and magnetron current have been performed for three different targets (pure Cu, pure W, composite Cu/W) in argon discharge. The measurements showed the energy flux to be significantly higher for the case of the pure tungsten and the composite target compared to the copper target, which is likely a result of the strongly increased amount of neutrals being reflected from the heavier targets. Furthermore, gas rarefaction by the sputtered atoms was found to be essential for the understanding of the observed energy flux and that the dominant contributors to the energy flux in the higher pressure regime are comparable to those observed in the conventional lower pressure regime. Selected deposited films have been investigated ex-situ by scanning electron microscopy, which allowed u.
Self-Ensembling with GAN-based Data Augmentation for Domain Adaptation in Semantic Segmentation: Supplementary material

In this section, we report extra experimental results, in which experiments on BDD100K [10] is involved. BDD100K is a large-scale driving dataset collected from the United States, under various driving circumstances, weather conditions, and lighting conditions. It has a training set with 7000 images, and a validation set with 1000 images. In our experiment, we used 19 categories of the annotations, which are compatible with the classes of Cityscapes. Since BDD100K is a new dataset compared to GTA5 and SYNTHIA in the main text, there are only a few works [2] that study domain adaptation for semantic segmentation. Our method achieves state-of-the-art segmentation performance. We report IoU for each class and mIoU in Table 1. Compared to the baseline, our method improves the mIoUs by 10.9 %.
Sine-wave gating InGaAs/InP single photon detector with ultralow afterpulse

Sine-wave gating method is widely used in high-speed single photon detection (SPD) but shows extra afterpulse probability compared with the self-differencing technique. Here, we demonstrate that most extra afterpulses of the sine-wave gating SPD come from successive avalanche signals and distortion of electrical filters and also propose an effective method by removing the wider filtered avalanche signals to reduce the afterpulse probability. At 1 GHz gating frequency, the ultralow afterpulse probability is characterized as 1.0% and 0.54% for a photon detection efficiency of 20.7% and 10.9%, respectively.
Block-Online Multi-Channel Speech Enhancement Using DNN-Supported Relative Transfer Function Estimates

This paper addresses the problem of block-online processing for multi-channel speech enhancement. We consider several variants of a system that performs beamforming supported by DNN-based Voice Activity Detection followed by post-filtering. The speaker is targeted through estimating relative transfer functions between microphones. Each block of the input signals is processed independently in order to make the method applicable in highly dynamic environments. The performance loss caused by the short length of the processing block is studied and compared with results achieved when recordings are processed as one block (batch processing). The experimental evaluation of the proposed method is performed on large datasets of CHiME-4 and on another dataset featuring moving target speaker. The experiments are evaluated in terms of objective criteria and Word Error Rate achieved by a baseline Automatic Speech Recognition system, for which the enhancement method serves as a front-end solution. The results indicate that the proposed method is robust with respect to the length of the processing block and yields significant WER improvement even for a short block length of 250 ms.
Classical, Bayesian, and generalized inferences of the reliability of a multicomponent system with censored data

ABSTRACT In this study, we discuss the classical, Bayesian, and generalized inference of the reliability parameter At least s of the exceed of an s-out-of-k:G system with strength components subjected to a common stress Y whose probability densities are independent two-parameter general class of exponentiated inverted exponential distributions. These statistical analyses are carried out based on the progressively type-II right censored data with uniformly random removals. Under squared error and LINEX loss functions, Bayes estimates are developed by using Lindley's approximation and the Markov Chain Monte Carlo method due to the lack of closed forms of the posterior distributions. Generalized inferences are performed based on the generalized variable method. Simulation studies and real-world data analyses are given to illustrate the proposed procedures. The size of the test, adjusted and unadjusted power of the test, coverage probability and expected confidence lengths of the confidence interval, and biases of the estimator are also discussed. Comparison and contrast among the classical, Bayesian, and generalized inferences of the reliability parameter in the multicomponent stress-strength model are performed.
Personalized Graph Neural Networks With Attention Mechanism for Session-Aware Recommendation

The problem of session-aware recommendation aims to predict users’ next click based on their current session and historical sessions. Existing session-aware recommendation methods have defects in capturing complex item transition relationships. Other than that, most of them fail to explicitly distinguish the effects of different historical sessions on the current session. To this end, we propose a novel method, named Personalized Graph Neural Networks with Attention Mechanism (A-PGNN) for brevity. A-PGNN mainly consists of two components: one is Personalized Graph Neural Network (PGNN), which is used to extract the personalized structural information in each user behavior graph, compared with the traditional Graph Neural Network (GNN) model, which considers the role of the user when the node embedding is updated. The other is Dot-Product Attention mechanism, which draws on the Transformer net to explicitly model the effect of historical sessions on the current session. Extensive experiments conducted on two real-world data sets show that A-PGNN evidently outperforms the state-of-the-art personalized session-aware recommendation methods.
Characterizing Structural Regularities of Labeled Data in Overparameterized Models

The details on model architectures, data set information and hyper-parameters used in the experiments for empirical estimation of the C-score can be found in Table 2. We implement our experiment in Tensorflow (Abadi et al., 2015). The holdout subroutine used in the empirical C-score estimation is based on the estimator proposed in Feldman & Zhang (2020), and listed in Algorithm 1. Most of the training jobs for C-score estimation are run on single NVidia® Tesla P100 GPUs. The ImageNet training jobs are run with 8 P100 GPUs using single-node multi-GPU data parallelization.
Pattern-Based Compaction for ProbLog Inference (Extended Abstract)

Combining logic and probabilities resulted in the field of Probabilistic Logic Programming (PLP). PLP programs are logic programs in which some of the facts are annotated with probabilities. Each of these (probabilistic) facts can either be true or false. A probabilistic program gives rise to an exponential number of possible worlds (a specific decision on the truth values of all probabilistic facts). A query is true in a subset of the possible worlds:
Discovering Subsequence Patterns for Next POI Recommendation

Next Point-of-Interest (POI) recommendation plays an important role in location-based services. State-of-the-art methods learn the POI-level sequential patterns in the user's check-in sequence but ignore the subsequence patterns that often represent the socio-economic activities or coherence of preference of the users. However, it is challenging to integrate the semantic subsequences due to the difficulty to predefine the granularity of the complex but meaningful subsequences. In this paper, we propose Adaptive Sequence Partitioner with Power-law Attention (ASPPA) to automatically identify each semantic subsequence of POIs and discover their sequential patterns. Our model adopts a state-based stacked recurrent neural network to hierarchically learn the latent structures of the user's check-in sequence. We also design a power-law attention mechanism to integrate the domain knowledge in spatial and temporal contexts. Extensive experiments on two real-world datasets demonstrate the effectiveness of our model.
A Comprehensive Study of Deep Learning for Image

Generating a description of an image is called image captioning. Image captioning requires to recognize the important objects, their attributes and their relationships in an image. It also needs to generate syntactically and semantically correct sentences. Deep learning-based techniques are capable of handling the complexities and challenges of image captioning. In this survey paper, we aim to present a comprehensive review of existing deep learning-based image captioning techniques. We discuss the foundation of the techniques to analyze their performances, strengths and limitations. We also discuss the datasets and the evaluation metrics popularly used in deep learning based automatic image captioning.
Evolution of the Quasar Luminosity Function: Implications for EoR-21cm

Abstract We present predictions for the spatial distribution of 21 cm brightness temperature fluctuations from high-dynamic-range simulations for AGN-dominated reionization histories that have been tested against available Lyα and CMB data. We model AGN by extrapolating the observed Mbh–σ relation to high redshifts and assign them ionizing emissivities consistent with recent UV luminosity function measurements. AGN-dominated reionization histories increase the variance of the 21 cm emission by a factor of up to ten compared to similar reionization histories dominated by faint galaxies, to values close to 100 mK2 at scales accessible to experiments (k ≲ 1 cMpc−1h). This is lower than the sensitivity reached by ongoing experiments by only a factor of about two or less. AGN dominated reionization should be easily detectable by LOFAR (and later HERA and SKA1) at their design sensitivity.
Landscape Pattern and Ecological Risk Assessment in Guangxi Based on Land Use Change

Due to ecological environmental fragility and soil erosion in Guangxi, studies of landscape patterns and associated ecological risks are needed to guide sustainable land development and ecologically sensitive land management. This study assesses dynamic spatial and temporal change patterns in land use and ecological risks based on 30 m land-use data, analyzes spatial correlations with ecological risks, and explores natural and socio-economic factor impacts on ecological risks. The results reveal: (1) A rapid and sizeable construction land increase in Guangxi from 2000 to 2018 associated mainly with loss of woodland and grassland. (2) Guangxi had the highest number of arable land patches from 2000 to 2018, and the distribution tended to be fragmented; moreover, the construction land gradually expanded outward from concentrated areas to form larger aggregates with increasing internal stability each year. (3) Guangxi ecological risk levels were low, low–medium, and medium, with significantly different spatial distributions observed for areas possessing different ecological risk levels. Regional ecological risk gradually decreased from the middle Guangxi regions to the surrounding areas and was positively correlated with spatial distribution. (4) Socio-economic factor impacts on ecological risk exceeded natural factor impacts. These results provide guidance toward achieving ecologically sensitive regional land-use management and ecological risk reduction and control, it can also provide a reference for ecological risk research in other similar regions in the world.
Common factors and spatial dependence: an application to US house prices

Abstract This article considers panel data models with cross-sectional dependence arising from both spatial autocorrelation and unobserved common factors. It proposes estimation methods that employ cross-sectional averages as factor proxies, including the 2SLS, Best 2SLS, and GMM estimations. The proposed estimators are robust to unknown heteroskedasticity and serial correlation in the disturbances, unrequired to estimate the number of unknown factors, and computationally tractable. The article establishes the asymptotic distributions of these estimators and compares their consistency and efficiency properties. Extensive Monte Carlo experiments lend support to the theoretical findings and demonstrate the satisfactory finite sample performance of the proposed estimators. The empirical section of the article finds strong evidence of spatial dependence of real house price changes across 377 Metropolitan Statistical Areas in the US from 1975Q1 to 2014Q4.
Counting all self-avoiding walks on a finite lattice strip of width one and two

In this paper, a closed-form expression for counting all SAWs, irrespective of length, but restricted to the finite lattice strip (-a, . . ., 0, . . ., b) × (0, 1), shall be obtained in terms of the non-negative integer parameters a and b. In addition, the argument used to prove this result will be extended to establish an enumerating formula for counting all SAWs, irrespective of length, but restricted to the half-finite lattice strip of width two (0, 1, . . ., n) × (0, 1, 2), in terms of n.
Continuous-time Signal Temporal Logic Planning with Control Barrier Functions

Temporal Logic (TL) guided control problems have gained enormous interests in recent years. A wide range of properties, such as liveness and safety, can be specified through TL. On the other hand, Control Barrier Functions (CBF) have shown success in the context of safety critical applications that require constraints on the system states. In this paper, we consider linear cyber-physical systems with continuous dynamics, where controls are generated by digital computers in discrete time. We propose an offline trajectory planner for such systems subject to linear constraints given as Signal Temporal Logic (STL) formulas. The proposed planner is based on a Mixed Integer Quadratic Programming (MIQP) formulation that utilizes CBFs to produce system trajectories that are valid in continuous time; moreover we allow STL predicates with arbitrary time constraints, in which asynchronous control updates are allowed. We validate our theoretical results through numerical simulations.
Scratches from the Past: Inflationary Archaeology through Features in the Power Spectrum of Primordial Fluctuations

Inflation may provide unique insight into the physics at the highest available energy scales that cannot be replicated in any realistic terrestrial experiment. Features in the primordial power spectrum are generically predicted in a wide class of models of inflation and its alternatives, and are observationally one of the most overlooked channels for finding evidence for non-minimal inflationary models. Constraints from observations of the cosmic microwave background cover the widest range of feature frequencies, but the most sensitive constraints will come from future large-scale structure surveys that can measure the largest number of linear and quasi-linear modes.