Miracle-dz/UniFAR-FTUs huggingface dataset:
Complexity-Oriented Per-Shot Video Coding Optimization

Current per-shot encoding schemes aim to improve the compression efficiency by shot-level optimization. It splits a source video sequence into shots and imposes optimal sets of encoding parameters on each shot. Per-shot encoding achieved approximately 20% bitrate savings over baseline fixed QP encoding at the expense of pre-processing complexity. However, the adjustable parameter space of the current per-shot encoding schemes only has spatial resolution and QP/CRF, resulting in a lack of encoding flexibility. In this paper, we extend the per-shot encoding framework in the complexity dimension. We believe that per-shot encoding with flexible complexity will help in deploying user-generated content. We propose a rate-distortion-complexity optimization process for encoders and a methodology to determine the coding parameters under the constraints of complexities and bitrate ladders. Experimental results show that our proposed method achieves complexity constraints ranging from 100% to 3% in a dense form compared to the slowest per-shot anchor. With similar complexities of the per-shot scheme fixed in specific presets, our proposed method achieves BDrate gain up to −19.17%.
Towards Fully Declarative Program Analysis via Source Code Transformation

Advances in logic programming and increasing industrial uptake of Datalog-inspired approaches demonstrate the emerging need to express powerful code analyses more easily. Declarative program analysis frameworks (e.g., using logic programming like Datalog) significantly ease defining analyses compared to imperative implementations. However, the declarative benefits of these frameworks only materialize after parsing and translating source code to generate facts. Fact generation remains a non-declarative precursor to analysis where imperative implementations first parse and interpret program structures (e.g., abstract syntax trees and control-flow graphs). The procedure of fact generation thus remains opaque and difficult for non-experts to understand or modify. We present a new perspective on this analysis workflow by proposing declarative fact generation to ease specification and exploration of lightweight declarative analyses. Our approach demonstrates the first venture towards fully declarative analysis specification across multiple languages. The key idea is to translate source code directly to Datalog facts in the analysis domain using declarative syntax transformation. We then reuse existing Datalog analyses over generated facts, yielding an end-to-end declarative pipeline. As a first approximation we pursue a syntax-driven approach and demonstrate the feasibility of generating and using lightweight versions of liveness and call graph reachability properties. We then discuss the workability of extending declarative fact generation to also incorporate semantic information.
Two-level approach for solving the inverse problem of defect identification in eddy current testing-type NDT

An inverse problem of 3D crack identification inside a conductive material from the eddy current measurements is investigated. In order to accelerate the time-consuming direct optimization, the reconstruction is provided by the minimization of a last-square functional of the data-model misfit using space mapping (SM) methodology. This technique enables to shift the optimization load from a time consuming and accurate model to the less precise but faster coarse surrogate. In this work, the finite element method (FEM) is used as a fine model, while the model based on the volume integral method (VIM) serves as a coarse model. The application of this method to the shape reconstruction allows to shorten the evaluation time that is required to provide the proper parameter estimation of surface defects.
More is Better: A Novel Multi-view Framework for Domain Generalization

Aiming to generalize the model trained in source domains to unseen target domains, domain generalization (DG) has attracted lots of attention recently. The key issue of DG is how to prevent overﬁtting to the observed source domains because target domain is unavailable during training. We investigate that overﬁtting not only causes the inferior generalization ability to unseen target domains but also leads unstable prediction in the test stage. In this paper, we observe that both sampling multiple tasks in training stage and generating augmented images in test stage largely beneﬁt generalization performance. Thus, by treating tasks and images as different views, we propose a novel multi-view DG framework. Speciﬁcally, in training stage, to enhance generalization ability, we develop a multi-view regularized meta-learning algorithm that employs multiple tasks to produce a suitable optimization direction during updating model. In test stage, to alleviate unstable prediction, we utilize multiple augmented images to yield multi-view prediction, which signiﬁcantly promotes model reliability via fusing the results of different views of a test image. Extensive experiments on three benchmark datasets validate our method outperforms several state-of-the-art approaches.
Comprehensive Movie Recommendation System

A recommender system, also known as a recommendation system, is a type of information filtering system that attempts to forecast a user's rating or preference for an item. This article designs and implements a complete movie recommendation system prototype based on the Genre, Pearson Correlation Coefficient, Cosine Similarity, KNN-Based, Content-Based Filtering using TFIDF and SVD, Collaborative Filtering using TFIDF and SVD, Surprise Library based recommendation system technology. Apart from that in this paper, we present a novel idea that applies machine learning techniques to construct a cluster for the movie based on genres and then observes the inertia value number of clusters were defined. The constraints of the approaches discussed in this work have been described, as well as how one strategy overcomes the disadvantages of another. The whole work has been done on the dataset Movie Lens present at the group lens website which contains 100836 ratings and 3683 tag applications across 9742 movies. These data were created by 610 users between March 29, 1996, and September 24, 2018.
Adaptive Modeling Against Adversarial Attacks

Adversarial training, the process of training a deep learning model with adversarial data, is one of the most successful adversarial defense methods for deep learning models. We have found that the robustness to white-box attack of an adversarially trained model can be further improved if we fine tune this model in inference stage to adapt to the adversarial input, with the extra information in it. We introduce an algorithm that"post trains"the model at inference stage between the original output class and a"neighbor"class, with existing training data. The accuracy of pre-trained Fast-FGSM CIFAR10 classifier base model against white-box projected gradient attack (PGD) can be significantly improved from 46.8% to 64.5% with our algorithm.
Multigrid solvers for isogeometric discretizations of the second biharmonic problem

We develop a multigrid solver for the second biharmonic problem in the context of Isogeometric Analysis (IgA), where we also allow a zero-order term. In a previous paper, the authors have developed an analysis for the first biharmonic problem based on Hackbusch's framework. This analysis can only be extended to the second biharmonic problem if one assumes uniform grids. In this paper, we prove a multigrid convergence estimate using Bramble's framework for multigrid analysis without regularity assumptions. We show that the bound for the convergence rate is independent of the scaling of the zero-order term and the spline degree. It only depends linearly on the number of levels, thus logarithmically on the grid size. Numerical experiments are provided which illustrate the convergence theory and the efficiency of the proposed multigrid approaches.
A New Bound in the Littlewood–Offord Problem

The paper deals with studying a connection of the Littlewood–Offord problem with estimating the concentration functions of some symmetric infinitely divisible distributions. It is shown that the concentration function of a weighted sum of independent identically distributed random variables is estimated in terms of the concentration function of a symmetric infinitely divisible distribution whose spectral measure is concentrated on the set of plus-minus weights.
Customising Ranking Models for Enterprise Search on Bilingual Click-Through Dataset

In this work, we provide the details about the process of establishing an end-to-end system for enterprise search on bilingual click-through dataset. The first part of the paper will be about the high-level workflow of the system. Then, in the second part we will elaborately mention about the ranking models to improve the search results in the vertical search of the technical documents in enterprise domain. Throughout the paper, we will mention the way which we combine the methods in IR literature. Finally, in the last part of the paper we will report our results using different ranking algorithms with $NDCG@k$ where k is the cut-off value.
A tomographic spherical mass map emulator of the KiDS-1000 survey using conditional generative adversarial networks

Large sets of matter density simulations are becoming increasingly important in large-scale structure cosmology. Matter power spectra emulators, such as the Euclid Emulator and CosmicEmu, are trained on simulations to correct the non-linear part of the power spectrum. Map-based analyses retrieve additional non-Gaussian information from the density field, whether through human-designed statistics such as peak counts, or via machine learning methods such as convolutional neural networks. The simulations required for these methods are very resource-intensive, both in terms of computing time and storage. This creates a computational bottleneck for future cosmological analyses, as well as an entry barrier for testing new, innovative ideas in the area of cosmological information retrieval. Map-level density field emulators, based on deep generative models, have recently been proposed to address these challenges. In this work, we present a novel mass map emulator of the KiDS-1000 survey footprint, which generates noise-free spherical maps in a fraction of a second. It takes a set of cosmological parameters (Ω M , σ 8) as input and produces a consistent set of 5 maps, corresponding to the KiDS-1000 tomographic redshift bins. To construct the emulator, we use a conditional generative adversarial network architecture and the spherical convolutional neural network DeepSphere, and train it on N-body-simulated mass maps. We compare its performance using an array of quantitative comparison metrics: angular power spectra Cℓ , pixel/peaks distributions, Cℓ correlation matrices, and Structural Similarity Index. Overall, the average agreement on these summary statistics is <10% for the cosmologies at the centre of the simulation grid, and degrades slightly on grid edges. However, the quality of the generated maps is worse at high negative κ values or large scale, which can significantly affect summaries sensitive to such observables. Finally, we perform a mock cosmological parameter estimation using the emulator and the original simulation set. We find good agreement in these constraints, for both likelihood and likelihood-free approaches. The emulator is available at tfhub.dev/cosmo-group-ethz/models/kids-cgan.
GW190521 as a black-hole merger coincident with the ZTF19abanrhr flare

Juan Calderón Bustillo, 2, ∗ Samson H.W. Leong, Koustav Chandra, Barry McKernan, 5, 6, 7 and K. E. S. Ford 5, 6, 7 Instituto Galego de F́ısica de Altas Enerx́ıas, Universidade de Santiago de Compostela, 15782 Santiago de Compostela, Galicia, Spain Department of Physics, The Chinese University of Hong Kong, Shatin, N.T. , Hong Kong Department of Physics, Indian Institute of Technology Bombay, Powai, Mumbai, Maharashtra 400076, India Department of Science, CUNY-BMCC, 199 Chambers St., New York, NY 10007, USA Department of Astrophysics, American Museum of Natural History, Central Park West, New York, NY 10028, USA Physics Program, The Graduate Center, CUNY, New York, NY 10016, USA Center for Computational Astrophysics, Flatiron Institute, New York, NY 10010, USA
A rodent paradigm for studying perceptual decisions under asymmetric reward

Many real-life decisions involve both perceptual processes and weighing the consequences of different actions. However, the neural mechanisms underlying perceptual decisions have typically been examined separately from those underlying economic decisions. Here, we trained rats to make choices informed by both perceptual and value cues on a trial-by-trial basis. As in typical perceptual tasks, subjects were rewarded for correctly categorizing a tone relative to a learned threshold. To add an economic component, a light indicated, on each trial, whether correct responses to one side gave higher rewards than correct responses to the other side. As such, on trials with some perceptual uncertainty, it could be worthwhile to choose the unlikely option, if it had higher expected value. We found that, despite subjects sensitivity to the frequency of the cue and the reward sizes, their behavior was not optimal: subjects tended to shift their choices in a stimulus-independent way following light flashes. Moreover, subjects tended to under-shift, which could be interpreted as being over-confident in their perceptual beliefs or as being risk-averse. 1/25 ar X iv :2 11 2. 12 27 8v 1 [ qbi o. N C ] 2 2 D ec 2 02 1
Variational integration of learned dynamical systems

The principle of least action is one of the most fundamental physical principle. It says that among all possible motions connecting two points in a phase space, the system will exhibit those motions which extremise an action functional. Many qualitative features of dynamical systems, such as the presence of conservation laws and energy balance equations, are related to the existence of an action functional. Incorporating variational structure into learning algorithms for dynamical systems is, therefore, crucial in order to make sure that the learned model shares important features with the exact physical system. In this paper we show how to incorporate variational principles into trajectory predictions of learned dynamical systems. The novelty of this work is that (1) our technique relies only on discrete position data of observed trajectories. Velocities or conjugate momenta do not need to be observed or approximated and no prior knowledge about the form of the variational principle is assumed. Instead, they are recovered using backward error analysis. (2) Moreover, our technique compensates discretisation errors when trajectories are computed from the learned system. This is important when moderate to large step-sizes are used and high accuracy is required. For this, we introduce and rigorously analyse the concept of inverse modiﬁed Lagrangians by developing an inverse version of variational backward error analysis. (3) Finally, we introduce a method to perform system identiﬁcation from position observations only, based on variational backward error analysis.
Compact triple‐band bandpass filter using pseudointerdigital trisection stepped impedance resonators

This letter presents a compact triple‐band bandpass filter using pseudointerdigital trisection stepped impedance resonators (SIR). By tuning the impedance ratio and physical length of trisection SIRs, the BPF has good triple‐band performances at 1.57‐/2.45‐/5.25‐GHz and high isolation among the three passbands. It is shown that the triple‐band bandpass filter has a small size in comparison of previous works. Good agreement is shown between the simulated and measured results. © 2008 Wiley Periodicals, Inc. Microwave Opt Technol Lett 50: 2462–2465, 2008; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.23691
Excitons under strain: light absorption and emission in strained hexagonal boron nitride

Hexagonal boron nitride is an indirect band gap material with a strong luminescence in the ultraviolet. This luminescence originates from bound excitons recombination assisted by different phonon modes. The coupling between excitons and phonons is so strong that the resulting light emission is as efficient as the one of direct band gap materials. In this manuscript we investigate how uniaxial strain modifies the electronic and optical properties of this material, and in particular how it affects the exciton-phonon coupling. Using a formulation of this coupling based on finite-difference displacements, recently developed by some of us, we investigate how phonon-assisted transitions change under strain. Our results open the way to the study of phonon-assisted luminescence in strained materials from first principles. Our findings are important both for experiments that directly probe h-BN under strain or for those in which it is used as substrate for other 2D material with a lattice mismatch.
Cointeracting dark matter and conformally coupled light scalars

The increasing observational pressure on the standard cosmological model motivates analyses going beyond the paradigm of the collision-less cold dark matter (DM). Since the only clear evidence for the existence of DM is based on gravitational interactions, it seems particularly fitting to study them in this sector where extensions to the standard model can be naturally introduced. A promising avenue can be obtained using modifications of the space-time metric coupled to DM and induced by the presence of a new ultra-light scalar field $\phi$. The $\phi$ field can contribute to the DM density and can couple all the matter species universally, including additional heavy DM particles. We present a simple two-component DM model employing derivative conformal interactions between the two DM species. This can simultaneously: 1) guarantee the necessary symmetries to stabilize the dark species, 2) predict a subdominant thermal relic density of the heavy DM component, and 3) alleviate small-scale structure tensions of the cold DM scenario due to the possible co-interactions in the dark sector. The scenario is highly predictive with future observational prospects ranging from the Large Hadron Collider (LHC) to gravitational-wave searches, and can be generalized to more rich and realistic dark sector models.
Your Face Mirrors Your Deepest Beliefs-Predicting Personality and Morals through Facial Emotion Recognition

Can we really “read the mind in the eyes”? Moreover, can AI assist us in this task? This paper answers these two questions by introducing a machine learning system that predicts personality characteristics of individuals on the basis of their face. It does so by tracking the emotional response of the individual’s face through facial emotion recognition (FER) while watching a series of 15 short videos of different genres. To calibrate the system, we invited 85 people to watch the videos, while their emotional responses were analyzed through their facial expression. At the same time, these individuals also took four well-validated surveys of personality characteristics and moral values: the revised NEO FFI personality inventory, the Haidt moral foundations test, the Schwartz personal value system, and the domain-specific risk-taking scale (DOSPERT). We found that personality characteristics and moral values of an individual can be predicted through their emotional response to the videos as shown in their face, with an accuracy of up to 86% using gradient-boosted trees. We also found that different personality characteristics are better predicted by different videos, in other words, there is no single video that will provide accurate predictions for all personality characteristics, but it is the response to the mix of different videos that allows for accurate prediction.
Judging a book by its cover: Predicting the marginal impact of title on Reddit post popularity

Several factors influence the popularity of content on social media, including the what, when, and who of a post. Of these factors, the what and when of content are easiest to customize in order to maximize viewership and reach. Further, the title of a post (part of the what) is the easiest to tailor, compared to the post's body, which is often fixed. So, in this paper, we assess the impact of a post's title on its popularity while controlling for the time of posting (the when) by proposing an interpretable attention-based model. Our approach achieves state-of-the-art performance on predicting the popularity of posts on multiple online communities of various sizes, topics, and formats while still being parsimonious. Interpretation of our model's attention weights sheds light on the heterogeneous patterns in how the specific words in a post's title shape its popularity across different communities. Our results highlight the power of sentiment alignment, personal storytelling, and even personality politics in propelling content to virality.
Neuroevolution deep learning architecture search for estimation of river surface elevation from photogrammetric Digital Surface Models

Development of the new methods of surface water observation is crucial in the perspective of increasingly frequent extreme hydrological events related to global warming and increasing demand for water. Orthophotos and digital surface models (DSMs) obtained using UAV photogrammetry can be used to determine the Water Surface Elevation (WSE) of a river. However, this task is difficult due to disturbances of the water surface on DSMs caused by limitations of photogrammetric algorithms. In this study, machine learning was used to extract a WSE value from disturbed photogrammetric data. A brand new dataset has been prepared specifically for this purpose by hydrology and photogrammetry experts. The new method is an important step toward automating water surface level measurements with high spatial and temporal resolution. Such data can be used to validate and calibrate of hydrological, hydraulic and hydrodynamic models making hydrological forecasts more accurate, in particular predicting extreme and dangerous events such as floods or droughts. For our knowledge this is the first approach in which dataset was created for this purpose and deep learning models were used for this task. Additionally, neuroevolution algorithm was set to explore different architectures to find local optimal models and non-gradient search was performed to fine-tune the model parameters. The achieved results have better accuracy compared to manual methods of determining WSE from photogrammetric DSMs.