Miracle-dz/UniFAR-FTUs huggingface dataset:
Nonlocal Co-occurrence for Image Downscaling

—Image downscaling is one of the widely used operations in image processing and computer graphics. It was recently demonstrated in the literature that kernel-based convolutional ﬁlters could be modiﬁed to develop efﬁcient image downscaling algorithms. In this work, we present a new downscaling technique which is based on kernel-based image ﬁltering concept. We propose to use pairwise co-occurrence similarity of the pixelpairs as the range kernel similarity in the ﬁltering operation. The co-occurrence of the pixel-pair is learned directly from the input image. This co-occurrence learning is performed in a neighborhood based fashion all over the image. The proposed method can preserve the high-frequency structures, which were present in the input image, into the downscaled image. The resulting images retain visually-important details and do not suffer from edge-blurring artifact. We demonstrate the effectiveness of our proposed approach with extensive experiments on a large number of images downscaled with various downscaling factors.
Cs 598csc: Algorithms for Big Data

We have seen a linear sketching estimate for F 2 estimation that uses O(log n) space. Indyk [1] obtained a technically sophisticated and interesting sketch for F k estimation where 0 < p ≤ 2 (note that p can be a real number) which uses polylog(n) space. Since the details are rather technical we will only give the high-level approach and refer the reader to the paper and related notes for more details. Note that for p > 2 there is a lower bound of Ω(n 1−2/p) on the space required. To describe the sketch for 0 < p ≤ 2 we will revisit the F 2 estimate via the JL Lemma approach that uses properties of the normal distribution. be sampled independenty from the N (0, 1) distribution z ← 0 While (stream is not empty) do (i j , ∆ j) is current token z ← z + ∆ j · Y ij endWhile Output z 2 Let Z = i∈[n] x i Y i be the random variable that represents the value of z at the end of the stream. The variable Z is a sum of independent normal variables and by the properties of the normal distribution Z ∼ i x 2 i · N (0, 1). Normal distribution is called 2-stable for this reason. More generally a distribution D is said to be p-stable if the following property holds: Let Z 1 , Z 2 ,. , Z n be independent random variables distributed according to D. Then i x i Z i has the same distribution as x p Z where Z ∼ D. Note that a p-stable distribution will be symmetric around 0. It is known that p-stable distributions exist for all p ∈ (0, 2] and not for any p > 2. The p-stable distributions do not have, in general, an analytical formula except in some cases. We have already seen that the standard normal distribution is 2-stable. The 1-stable distribution is the Cauchy distribution which is the distribution of the ratio of two independent standard normal random variables. The density function of the Cauchy distribution is known to be 1 π(1+x 2) ; note that the Cauchy distribution does not have a finite mean or variance. We use D p to denote a p-stable distribution. Although a general p-stable distribution does not have an analytical formula it is known that …
The Yet-Unobserved Multi-Messenger Gravitational-Wave Universe

Observations with next-generation ground-based detectors further enhanced with multi-messenger (electromagnetic and neutrino) detections will allow us to probe new extreme astrophysics. Target sources included: core-collapse supernovae, continuous emission from isolated or accreting neutron stars, and bursts from magnetars and other pulsars.
Three Ways to Improve Semantic Segmentation with Self-Supervised Depth Estimation (Supplementary Materials)

Network Architecture The neural network combines a DeepLabv3 [3] with a U-Net [10] decoder for depth and segmentation prediction each. As encoder, a ResNet101 with dilated (instead of strided) convolutions in the last block is used, following [3]. Features from multiple scales are aggregated by an ASPP [3] block with dilation rates of 6, 12, and 18. Similar to U-Net [10], the decoder has five upsampling blocks with skip connections. Each upsampling block consists of a 3x3 convolution layer (except the first block, which is the ASPP), a bilinear upsampling operation, a concatenation with the encoder features of the corresponding size (skip connection), and another 3x3 convolution layer. Both convolutional layers are followed by an ELU non-linearity. The number of output channels for the blocks are 256, 256, 128, 128, and 64. The last four blocks also have another 3x3 convolutional layer followed by a sigmoid activation attached to their output for the purpose of predicting the disparity at the respective scale. For effective multi-task learning, we additionally follow PADNet [11] and deploy an attention-guided multi-modal distillation module with additional side output for semantic segmentation after the third decoder block. In experiments without multi-task learning, only the semantic segmentation decoder is used. For pose estimation, we use a lightweight ResNet18 encoder followed by four convolutions to produce the translation and the rotation in angle-axis representation as suggested in [5]. Table S1. Training and inference time on an Nvidia Tesla P100 averaged over 100 iterations or 500 images, respectively. D-T: SDE Transfer Learning, D-M SDE Transfer and Multi-Task Learning, P: Pseudo-Labelling, X-D: Mix Depth
Non-Rigid Neural Radiance Fields: Reconstruction and Novel View Synthesis of a Deforming Scene from Monocular Video

In this tech report, we present the current state of our ongoing work on reconstructing Neural Radiance Fields (NERF) of general non-rigid scenes via ray bending. Non-rigid NeRF (NR-NeRF) takes RGB images of a deforming object ( e.g., from a monocular video) as input and then learns a geometry and appearance representation that not only allows to reconstruct the input sequence but also to re-render any time step into novel camera views with high fidelity. In particular, we show that a consumer-grade camera is sufficient to synthesize convincing bullet-time videos of short and simple scenes. In addition, the resulting representation enables correspondence estimation across views and time, and provides rigidity scores for each point in the scene. We urge the reader to watch the supplemental videos for qualitative results. We will release our code.
ESIR : End-to-end Scene Text Recognition via Iterative Rectification

Automated recognition of various texts in scenes has been a research challenge for years, largely due to arbitrary text appearance variations in term of perspective distortion, text line curvature, text styles as well as different types of imaging artifacts. The recently developed Convolutional Neural Networks (CNNs) are capable of learning robust representations under the presence of imaging artifacts and changes in text fonts, sizes and colors, but still face various problems while dealing with scene texts with perspective and curvature distortions. In this work, we propose an end-to-end trainable scene text recognition system via iterative rectification (ESIR), which iteratively removes perspective distortion and text line curvature as driven by better scene text recognition performance. An innovative rectification network is developed, where a line-fitting transformation is designed to estimate the pose of text lines in scenes. In addition, an iterative rectification framework is developed which corrects perspective distortion and text line curvature iteratively towards a fronto-parallel view and optimal scene text recognition. Further, the ESIR is robust to parameter initialization and convenient to train, where the training needs only scene text images and word-level annotations as required by most scene text recognition systems. Extensive experiments over a number of public datasets show that the ESIR is capable of correcting various scene text distortions accurately, achieving superior scene text recognition performance for both normal scene text images and those suffering from severe perspective and curvature distortions.
Unsupervised Domain Adaptation for Visual Navigation

Recent advances in artiﬁcial intelligence, especially in the ﬁelds of computer vision and reinforcement learning, have made it possible to train visual navigation agents with great performance in a wide variety of navigation tasks. For example, in computer photo-realistic simulation of real-world apartments, the trained agent can reliably navigate to a speciﬁed coordinate, or a room of a speciﬁed type such as kitchen and bathroom. When asked to explore as much area as possible under a ﬁxed time budget, the trained agent exhibit great memory of where it has been to and strategic planning. All these tasks require the agent to process raw ﬁrst-person images to construct a meaningful understanding and representation of the room such as where the walls and obstacles are located, and conduct structural and semantic reasoning to determine its path, the room type, or the ﬂoor plan. However, for most learning-based navigation agents, the training and testing are done in the same simulation environment. In order for these methods to be practical in the real world, they need to be transferable to unseen environments and non-simulated environments. We propose an unsupervised domain adaptation method for visual navigation, which trains an image translation model that translates the images of the evaluation environment that the agent is never trained on, into images of the training environment where the agent learns to perform the task, so that the agent can recognize the translated images and achieve good performance in the evaluation environment. The image translation model is trained given an already trained agent, so that it could take advantage of the task-relevant representations learned by the agent to ensure those representations are preserved during translation. We conduct both simulation-to-simulation and simulation-to-real-world experiments to demonstrate the effectiveness of our method in helping the trained agents adapt to unseen environments. In the simulation-to-simulation environment, the proposed method outperforms several baselines including direct transfer and popular generic image translation methods such as CycleGAN, across two different visual navigation tasks. In the simulation-to-real-world experiment, the agent enhanced by our method achieves signiﬁcantly better performance than those without the enhancement.
Parameterization of polylines in unorganized cross-sections and open surface reconstruction

Typical computer-assisted design software reconstructs 3D surfaces by interpolating polylines hand-picked in unorganized cross-sections (not necessarily parallel or orthogonal). Modern approaches mainly consider re-constructing closed surfaces [1]. However, open geological surfaces are frequently found in oil & gas exploration. Some point cloud interpolation methods can reconstruct open surfaces [2]. The authors indeed parameterize points of space x i ∈ R 3 into a convex planar domain where each associated point u i ∈ R 2 is made the “barycen-ter” of its 3D geometrical neighbors x j ∈ N Gi as in equation 1. Interpolation is then performed in parameter space, and the resulting surface transformed back in R 3 . λ i,j are built using the inverse distance in R 3 [2].
3-D STRUCTURE OF INDIAN FORESTS – PERSPECTIVES FROM EXPERIMENTS ON THE FIRST FULLY-POLARIMETRIC TANDEM-X TOMOGRAMS

Abstract. This paper provides a first hand view of the 3-D structure of the forests when viewed by X-band SAR data. Tomograms are generated using multi-polarimetric space-borne TerraSAR-X/TanDEM-X acquisitions and analysed over a multi-species forest range. The paper analysed these generated tomograms and puts forth-interesting observations of these unique forest species. The high- and low-canopy density plantations provide unique tomograms and vertical structure profiles where the effect of varying extinction is observed in X-band. Further, the scattering powers are shown relative to their backscatter powers. In-depth analysis in currently underway and would be reported in future.
Vertical Structure of Quasi-Stationary Planetary Waves in Several Winters

Zonal harmonic analysis of the structure of quasi-stationary planetary waves in the winter stratosphere of the Northern Hemisphere made by Hirota and Sato (1969) is extended to the cases of eight winters (1963-1970) inclusive of 500mb data. Each winter is formed of three months: January, February and March. From the analysis of monthly mean states of the amplitude, the position of trough and the wave energy, it is found that the "node-like structure" of planetary waves of zonal wavenumber 1 appears at about 100mb level at 60*N in many months among 24 months. Thepresence of nodes and the intensity of the zonal westerlies at upper levels are highly correlated; the node appears when the intensity of the mean zonal wind at 10mb level exceeds about 40meter/sec. This suggests that the node appears as the result of the interferences of waves propagating upwards and downwards. In such situation, reflective indices of amplitudes are simply estimated to be between 35 and 60%. The presence of the node-like structure is not evident in the wave 1 at 40*N. It is not found in waves 2 and 3. The vertical distribution of the wave energy of waves 2 and 3 shows the maximum at about 300-500mb level and decreases with increasing height above the level. The year to year variation of the vertical structure of planetary waves is discussed, and is also compared with the result of van Loon et al. (1973).
Effect sizes of the differences between means without assuming the variance equality and between a mean and a constant.

Hedges' d, an existing unbiased effect size of the difference between means, assumes the variance equality. However, the assumption of the variance equality is fragile, and is often violated in practical applications. Here, we define e, a new effect size of the difference between means, which does not assume the variance equality. In addition, another novel statistic c is defined as an effect size of the difference between a mean and a known constant. Hedges' g, our c, and e correspond to Student's unpaired two-sample t test, Student's one-sample t test, and Welch's t test, respectively. An R package is also provided to compute these effect sizes with their variance and confidence interval.
SYMPAIS: SYMbolic Parallel Adaptive Importance Sampling for Probabilistic Program Analysis

Probabilistic software analysis aims at quantifying the probability of a target event occurring during the execution of a program processing uncertain incoming data or written itself using probabilistic programming constructs. Recent techniques combine classic static analysis methods with inference procedure to obtain accurate quantification of the probability of rare target events, such as failures in a mission-critical system. However, current techniques face several scalability and applicability limitations when analyzing software processing with high-dimensional multivariate distributions. In this paper, we present SYMbolic Parallel Adaptive Importance Sampling (SYMPAIS), a new algorithm that combines symbolic execution with adaptive importance sampling to analyze probabilistic programs. Our method provides a general solution that scales to systems with high-dimensional inputs and demonstrates superior performance in quantifying rare events compared to prior work. Preliminary experimental results support the potential efficacy of our solution.
A Minimum Distance Estimator for Dynamic Conditional Correlations

The crucial problem in estimating dynamic conditional correlation models is the need to guarantee a positive-definite covariance matrix. In order to avoid any violation of this property, many estimators impose strong restrictions on the model. In addition, many models do not parameterize the correlations directly but the covariance. This paper avoids this problem in proposing a minimum distance estimator (MDE) to estimate dynamic conditional correlations or multivariate GARCH models. The model allows full flexibility in the estimation. Violations of the positive-definiteness of the covariance matrices are part of the specification tests. A simulation study shows the performance of the estimator and the empirical section compares estimates of the MDE with the DCC estimator of Engle (2002). This paper does not only demonstrate an alternative estimator but also outlines how this model can be used to analyze the influence of the restrictions on the estimates in multivariate GARCH models. JEL classification: C32, C52
Further results on the accessibility of a satellite with two reaction wheels

This article deals with the contributes to answer to the question satellite with two reaction wheels is no a five dimensional sub-manifold is sus exhibited and two control objectives r any attitude state to any state at rest configuration with a constant spin ab article gives constructive demonstrat path planning compatible with these approach. echnologically, the actuators use exchange devices or gas jet propellers. types of technology are drastically diffe transfers via a momentum conservation reaction forces generating actual externa the case of actuator failures dealt with s [4]. In particular, the second author sho fails in the case of fewer than three actu spacecraft under-actuated with gas jets [ In this paper, we focus our attention on case, if the satellite is in general not con zero. Thus, the most of the solutions tod this restricted case. Moreover, because d non-smooth ones [8,9]. Contrarily to th Further Results on the Accessibility of a Satellite with Two Reaction Wheels T r Frédéric Boyer and Mazen Alami attitude open loop control of a satellite actuated by two reaction wheels and of [1]: “what is the most one can expect in such a case? ” As a matter of fact, the t controllable on the whole state space of the spacecraft attitudes. Nevertheless, ceptible to be accessible from any state. In this article, this sub-manifold will be elated to it, will be studied. The first one corresponds to steer the satellite from . The second one corresponds to steer the spacecraft from any state to a given out the unactuated spacecraft axis. More than giving qualitative answers, the ions of accessibility since it exhibits explicit open loop control laws based on a two objectives. Finally, some simulation results illustrate the feasibility of the
PSD2 Explainable AI Model for Credit Scoring

The aim of this project is to develop and test advanced analytical methods to improve the prediction accuracy of Credit Risk Models, preserving at the same time the model interpretability. In particular, the project focuses on applying an explainable machine learning model to bank-related databases. The input data were obtained from open data. Over the total proven models, CatBoost has shown the highest performance. The algorithm implementation produces a GINI of 0.68 after tuning the hyper-parameters. SHAP package is used to provide a global and local interpretation of the model predictions to formulate a human-comprehensive approach to understanding the decision-maker algorithm. The 20 most important features are selected using the Shapley values to present a full human-understandable model that reveals how the attributes of an individual are related to its model prediction.
OF W IKIPEDIA : K NOWLEDGE - POWERED C ONVERSATIONAL AGENTS

In open-domain dialogue intelligent agents should exhibit the use of knowledge, however there are few convincing demonstrations of this to date. The most popular sequence to sequence models typically “generate and hope” generic utterances that can be memorized in the weights of the model when mapping from input utterance(s) to output, rather than employing recalled knowledge as context. Use of knowledge has so far proved difficult, in part because of the lack of a supervised learning benchmark task which exhibits knowledgeable open dialogue with clear grounding. To that end we collect and release a large dataset with conversations directly grounded with knowledge retrieved from Wikipedia. We then design architectures capable of retrieving knowledge, reading and conditioning on it, and finally generating natural responses. Our best performing dialogue models are able to conduct knowledgeable discussions on open-domain topics as evaluated by automatic metrics and human evaluations, while our new benchmark allows for measuring further improvements in this important research direction.
HADES: A long lived particle detector concept for the FCC-ee or CEPC

The next generation of circular high energy collider is expected to be a lepton collider, FCC-ee at CERN or CEPC in China. However, the civil engineering concepts foresee to equip these colliders with bigger detector caverns than one would need for a lepton collider, so that they can be used for a hadron collider that may be installed in the same tunnel without further civil engineering. This opens up the possibility to install extra instrumentation at the cavern walls to search for new long lived particles at the lepton collider. We use the example of heavy neutral leptons to show that such an installation could improve the sensitivity squared mixing by almost half an order of magnitude.
Komparasi Akurasi Metode Correlated Naive Bayes Classifier dan Naive Bayes Classifier untuk Diagnosis Penyakit Diabetes

Penyakit diabetes merupakan salah satu penyakit paling banyak diderita oleh manusia seluruh dunia. Setiap tahun terjadi peningkatan kematian yang disebabkan oleh penyakit diabetes. Penyakit diabetes terjadi disebabkan oleh tubuh tidak menghasilkan insulin dalam jumlah yang cukup. Salah satu cara yang digunakan untuk mengurangi jumlah kematian yang disebabkan oleh penyakit diabetes adalah melakukan diagnosis secara dini. Salah satu teknik yang bisa digunakan adalah memanfaatkan teknik data mining. Untuk melakukan diagnosis penyakit diabetes dibutuhkan suatu metode yang memiliki akurasi terbaik. Pada penelitian ini melakukan komparasi metode Correlated-Naive Bayes Classifier dan Naive Bayes Classifier untuk mendapatkan akurasi terbaik sehingga dapat digunakan untuk diagnosis penyakit diabetes. Berdasarkan pengujian yang telah dilakukan menunjukkan bahwa metode Correlated Naive Bayes Classifier (CNBC) memperoleh akurasi terbaik dibandingkan dengan metode Naive Bayes Classifier (NBC) untuk Dataset Pima indian Diabetes. Tingkat akurasi metode Correlated Naive Bayes Classifier (CNBC) sebesar 67,15%, sedangkan metode Naive Bayes Classifier (NBC) sebesar 64,33%. Metode Correlated Naive Bayes Classifier (C-NBC) memiliki akurasi lebih tinggi dibandingkan metode Naïve Bayes Classifier (NBC) karena pada metode Correlated Naïve Bayes Classifier memperhitungkan nilai korelasi dari masing-masing atribut dataset terhadap Kelasnya. Dengan demikian penggunaan metode Correlated Naïve Bayes Classifier (C-NBC) dapat digunakan untuk melakukan diagnosis penyakit diabetes karena memiliki tingkat akurasi yang bagus dibandigkan metode Naive Bayes Classifier.