Miracle-dz/UniFAR-FTUs huggingface dataset:
Image Scrambling through Two Level Arnold Transform

-In today’s world of multimedia applications, digital image security is of utmost importance. This paper proposes a two level image scrambling algorithm using Arnold transform with different number of iterations. Pixels are scrambled for each iteration in the conventional Arnold scrambling. Due to its periodic property, the original image can be retrieved by experimenting the inverse Arnold transform on the scrambled image. The main objective of this paper is to improve the complexity of malicious decryption by considering Arnold scrambling for pixels and blocks. In the first level, Arnold pixel scrambling is performed on the original image of size N × N, where the pixels are scrambled based on number of iterations. In the second level the scrambled image is divided in to 8×8 blocks for which Arnold scrambling is conducted block-wise. In the decryption process, inverse Arnold block scrambling is applied first for the scrambled image and Arnold pixel scrambling is conducted second. Decryption process needs to be carried out with proper order of scrambling and iterations. This order of scrambling and number of iterations increase the complexity of malicious decryption. Correlation coefficient, number of pixel change rate and unified average changing intensity are computed between the scrambled and original images for performance analysis. The proposed method is also analysed in each level for its robustness .
IID SQuAD QANet Project

Our goal is to create a QANet [1] model for the reading comprehension task where given a context paragraph and question sentence, the model will find the start and ending word in the context paragraph that answers the question. We chose the QANet architecture for its novel use of transformer components on the question answering (Q&A) task. Previous and current models for Q&A heavily utilized RNNs with attention which lead to longer training and interference times in order to achieve good results. The QANet architecture avoids this by replacing the RNN with self-attention and residual blocks, thereby attaining a speedup in training time with comparable accuracy. Our final QANet model achieved an F1-score of 62.57 on the test non-PCE leader board.
Relation-Aware Multi Channel Attention Based Graph Convolutional Network for Breast Cancer Image Classification

Breast cancer is a life-threatening human disease. Timely diagnosis of breast cancer is related to disease prognosis, and correct classification of benign and malignant pathology images is of great importance for precision treatment. Although convolutional neural network (CNN) based deep learning can capture detailed local features in images, the weight sharing and local perception of the convolutional kernel itself limits the ability of CNN-based models to perceive global semantics. Not only that, the relevance and consistency of high-dimensional image semantics are lost because the correlation that exists in the high-dimensional image semantic space cannot be captured in the deep CNN architecture. To mitigate these constraints, we introduce a multi-channel attention mechanism to address the shortcomings of ignoring relevance and consistency in the high-dimensional semantic space based on the CNN model itself. Furthermore, we introduce the graph convolutional network (GCN) model which utilizes a pixel-based graph structure representation to solve the problem of global feature perception of images. Experiments demonstrate that our method achieves 82.8% accuracy on the Databiox dataset, improves the performance of classification tasks, and has wide generalizability and excellent application prospects.
Transmission of IPv6 Packets over PLC Networks

Power Line Communication (PLC), namely using the electric-power lines for indoor and outdoor communications, has been widely applied to support Advanced Metering Infrastructure (AMI), especially smart meters for electricity. The inherent advantage of existing electricity infrastructure facilitates the expansion of PLC deployments, and moreover, a wide variety of accessible devices raises the potential demand of IPv6 for future applications. This document describes how IPv6 packets are transported over constrained PLC networks, such as ITU-T G.9903, IEEE 1901.1, IEEE 1901.2 and IEEE 1901.2a.
Inductive Biases for Object-Centric Representations in the Presence of Complex Textures

Understanding which inductive biases could be helpful for the unsupervised learning of object-centric representations of natural scenes is challenging. In this paper, we systematically investigate the performance of two models on datasets where neural style transfer was used to obtain objects with complex textures while still retaining ground-truth annotations. We find that by using a single module to reconstruct both the shape and visual appearance of each object, the model learns more useful representations and achieves better object separation. In addition, we observe that adjusting the latent space size is insufficient to improve segmentation performance. Finally, the downstream usefulness of the representations is significantly more strongly correlated with segmentation quality than with reconstruction accuracy.
When Is Partially Observable Reinforcement Learning Not Scary?

Applications of Reinforcement Learning (RL), in which agents learn to make a sequence of decisions despite lacking complete information about the latent states of the controlled system, that is, they act under partial observability of the states, are ubiquitous. Partially observable RL can be notoriously difficult -- well-known information-theoretic results show that learning partially observable Markov decision processes (POMDPs) requires an exponential number of samples in the worst case. Yet, this does not rule out the existence of large subclasses of POMDPs over which learning is tractable. In this paper we identify such a subclass, which we call weakly revealing POMDPs. This family rules out the pathological instances of POMDPs where observations are uninformative to a degree that makes learning hard. We prove that for weakly revealing POMDPs, a simple algorithm combining optimism and Maximum Likelihood Estimation (MLE) is sufficient to guarantee polynomial sample complexity. To the best of our knowledge, this is the first provably sample-efficient result for learning from interactions in overcomplete POMDPs, where the number of latent states can be larger than the number of observations.
Emotions-Based Interactions: design challeges for increasing well-being

Design is a way of creating meaningful interactions. Cultivating a self-development approach through creative thinking, optimism and a positive attitude is part of that process. This emotional experience is an iterative activity in which body-mind and social life are connected. In this context interactive emotional-based interfaces can move people to be part of the design process, more effective in their daily life and involved into a positive revolution. This poster wants to frame the theoretical key concepts and starts a collection of practical insights useful to guide interaction design to increase experiences in ways to flourish.
Pure Interchange Oscillations of Thin Filaments in an Average Magnetosphere

This paper describes magnetospheric waves of very long wavelength in thin magnetic ﬁlaments. We consider an average magnetospheric conﬁguration with zero ionospheric conductance and calculate waves using two di ﬀ erent formulations: classic interchange theory and ideal MHD. Classic interchange theory, which is developed in detail in this paper, is basically analytic and is relatively straightforward to determine computationally, but it can’t o ﬀ er very high accuracy. The two formalisms agree well for the plasma sheet and also for the inner magnetosphere. The eigenfrequencies range over about a factor of seven, but the formulations generally agree with a root-mean-square di ﬀ erence between the logarithms of interchange and MHD frequencies to be ∼ 0 . 054. The pressure perturbations in the classic interchange theory are assumed constant along each ﬁeld line, but the MHD computed pressure perturbations along the ﬁeld line vary in a range ∼ 30% in the plasma sheet but are larger in the inner magnetosphere. The parallel and perpendicular displacements, which are very di ﬀ erent in the plasma sheet and inner magnetosphere, show good qualitative agreement between the two approaches. In the plasma sheet, the perpendicular displacements are strongly concentrated in the equatorial plane, whereas the parallel displacements are spread through most of the plasma sheet away from the equatorial plane; and can be regarded as buoyancy waves. In the inner magnetosphere, the displacements are more sinusoidal and are more like conventional slow modes. The di ﬀ erent forms of the waves are best characterized by the ﬂux tube entropy PV γ . limited set of conditions, as it applies to the limit of long wavelengths and assumes pressure is constant along each ﬁeld line. Focusing on waves and using a thin ﬁlament approach, we have extended the classic theory of interchange by calculating eigenfrequencies and eigenfunctions for pure interchange models using an energy approach. The results are applied on an average force-balanced magnetosphere conﬁguration and are compared to MHD ballooning oscillation eigenfrequencies and eigenfunctions. The two approaches show good agreement in the plasma sheet, but less so in the inner magnetosphere, where the MHD results qualitatively resemble MHD classic slow modes rather than buoyancy waves.
A Re-Analysis of Repeatability and Reproducibility in the Ames-USDOE-FBI Study

Abstract Forensic firearms identification, the determination by a trained firearms examiner as to whether or not bullets or cartridges came from a common weapon, has long been a mainstay in the criminal courts. Reliability of forensic firearms identification has been challenged in the general scientific community, and, in response, several studies have been carried out aimed at showing that firearms examination is accurate, that is, has low error rates. Less studied has been the question of consistency, of whether two examinations of the same bullets or cartridge cases come to the same conclusion, carried out by an examiner on separate occasions—intrarater reliability or repeatability—or by two examiners—interrater reliability or reproducibility. One important study, described in a 2020 Report by the Ames Laboratory-USDOE to the Federal Bureau of Investigation, went beyond considerations of accuracy to investigate firearms examination repeatability and reproducibility. The Report’s conclusions were paradoxical. The observed agreement of examiners with themselves or with other examiners appears mediocre. However, the study concluded repeatability and reproducibility are satisfactory, on grounds that the observed agreement exceeds a quantity called the expected agreement. We find that appropriately employing expected agreement as it was intended does not suggest satisfactory repeatability and reproducibility, but the opposite.
Unsupervised Motor Imagery Saliency Detection Based on Self-Attention Mechanism

Detecting the salient parts of motor-imagery electroencephalogram (MI-EEG) signals can enhance the performance of the brain-computer interface (BCI) system and reduce the computational burden required for processing lengthy MI-EEG signals. In this paper, we propose an unsupervised method based on the self-attention mechanism to detect the salient intervals of MI-EEG signals automatically. Our suggested method can be used as a preprocessing step within any BCI algorithm to enhance its performance. The effectiveness of the suggested method is evaluated on the most widely used BCI algorithm, the common spatial pattern (CSP) algorithm, using dataset 2a from BCI competition IV. The results indicate that the proposed method can effectively prune MI-EEG signals and significantly enhance the performance of the CSP algorithm in terms of classification accuracy.
New method for directly computing reduced density matrices

We demonstrate the power of a first principle-based and practicable method that allows for the perturbative computation of reduced density matrix elements of an open quantum system without making use of any master equations. The approach is based on techniques from non-equilibrium quantum field theory like thermo field dynamics, the Schwinger-Keldsyh formalism, and the Feynman-Vernon influence functional. It does not require the Markov approximation and is essentially a Lehmann-Szymanzik-Zimmermann-like reduction. In order to illustrate this method, we consider a real scalar field as an open quantum system interacting with an environment comprising another real scalar field. We give a general formula that allows for the perturbative computation of density matrix elements for any number of particles in a momentum basis. Finally, we consider a simple toy model and use this formula to obtain expressions for some of the system's reduced density matrix elements.
The Escape of Globular Clusters from the Satellite Dwarf Galaxies of the Milky Way

Using numerical simulations, we have studied the escape of globular clusters (GCs) from the satellite dwarf spheroidal galaxies (dSphs) of the Milky Way (MW). We start by following the orbits of a large sample of GCs around dSphs in the presence of the MW potential ﬁeld. We then obtain the fraction of GCs leaving their host dSphs within a Hubble Time. We model dSphs by a Hernquist density proﬁle with masses between 10 7 M (cid:12) and 7 × 10 9 M (cid:12) . All dSphs lie on the Galactic disc plane, but they have diﬀerent orbital eccentricities and apogalactic distances. We compute the escape fraction of GCs from 13 of the most massive dSphs of the MW, using their realistic orbits around the MW (as determined by Gaia ). The escape fraction of GCs from 13 dSphs is in the range 12% to 93%. The average escape time of GCs from these dSphs was less than 8 Gyrs, indicating that the escape process of GCs from dSphs was over. We then adopt a set of observationally-constrained density proﬁles for speciﬁc case of the Fornax dSph. According to our results, the escape fraction of GCs shows a negative correlation with both the mass and the apogalactic distance of the dSphs, as well as a positive correlation with the orbital eccentricity of dSphs. In particular, we ﬁnd that the escape fraction of GCs from the Fornax dSph is between 13% and 38%. Finally, we observe that when GCs leave their host dSphs, their ﬁnal orbit around the MW does not diﬀer much from their host dSphs.
Architecture of Planetary Systems Predicted from Protoplanetary Disks Observed with ALMA. II. Evolution Outcomes and Dynamical Stability

Recent ALMA observations on disk substructures suggest the presence of embedded protoplanets in a large number of disks. The primordial configurations of these planetary systems can be deduced from the morphology of the disk substructure and serve as initial conditions for numerical investigation of their future evolution. Starting from the initial configurations of 12 multiplanetary systems deduced from ALMA disks, we carried out two-stage N-body simulation to investigate the evolution of the planetary systems at the disk stage, as well as the long-term orbital stability after the disk dispersal. At the disk stage, our simulation includes both the orbital migration and pebble/gas accretion effects. We found that a variety of planetary systems are produced and can be categorized into distant giant planets, Jupiter-like planets, Neptune-like planets, and distant small planets. We found that the disk-stage evolution and the final configurations are sensitive to both the initial mass assignments and viscosity. After the disk stage, we implement only mutual gravity between star and planets and introduce stochastic perturbative forces. All systems are integrated for up to 10 Gyr to test their orbital stability. Most planetary systems are found to be stable for at least 10 Gyr, with perturbative force in a reasonable range. Our result implies that a strong perturbation source such as stellar flybys is required to drive the planetary system unstable. We discuss the implications of our results on both the disk and planet observation, which may be confirmed by the next-generation telescopes such as JWST and ngVLA.
Building Odia Shallow Parser

Shallow parsing is an essential task for many NLP applications like ma-chine translation, summarization, sen-timent analysis, aspect identification and many more. Quality annotated corpora is critical for building accu-rate shallow parsers. Many Indian languages are resource poor with respect to the availability of corpora in gen-eral. So, this paper is an attempt towards creating quality corpora for shallow parsers. The contribution of this paper is two folds: creation pos and chunk annotated corpora for Odia and development of baseline systems for pos tagging and chunking in Odia.
Self-organized critical dynamics of RNA virus evolution (preprint)

RNA virus (e.g., SARS-CoV-2) evolves in a complex manner. Studying RNA virus evolution is vital for understanding molecular evolution and medicine development. Scientists lack, however, general frameworks to characterize the dynamics of RNA virus evolution directly from empirical data and identify potential physical laws. To fill this gap, we present a theory to characterize the RNA virus evolution as a physical system with absorbing states and avalanche behaviors. This approach maps accessible biological data (e.g., phylogenetic tree and infection) to a general stochastic process of RNA virus infection and evolution, enabling researchers to verify potential self-organized criticality underlying RNA virus evolution. We apply our framework to SARS-CoV-2, the virus accounting for the global epidemic of COVID-19. We find that SARS-CoV-2 exhibits scale-invariant avalanches as mean-field theory predictions. The observed scaling relation, universal collapse, and slowly decaying auto-correlation suggest a self-organized critical dynamics of SARS-CoV-2 evolution. Interestingly, the lineages that emerge from critical evolution processes coincidentally match with threatening lineages of SARS-CoV-2 (e.g., the Delta virus). We anticipate our approach to be a general formalism to portray RNA virus evolution and help identify potential virus lineages to be concerned.
Asymptotic Independence of the Quadratic form and Maximum of Independent Random Variables with Applications to High-Dimensional Tests

This paper establishes the asymptotic independence between the quadratic form and maximum of a sequence of independent random variables. Based on this theoretical result, we find the asymptotic joint distribution for the quadratic form and maximum, which can be applied into the high-dimensional testing problems. By combining the sum-type test and the max-type test, we propose the Fisher's combination tests for the one-sample mean test and two-sample mean test. Under this novel general framework, several strong assumptions in existing literature have been relaxed. Monte Carlo simulation has been done which shows that our proposed tests are strongly robust to both sparse and dense data.
Deep learning-based surrogate model for 3-D patient-specific computational fluid dynamics

Optimization and uncertainty quantification have been playing an increasingly important role in computational hemodynamics. However, existing methods based on principled modeling and classic numerical techniques have faced significant challenges, particularly when it comes to complex 3-dimensional (3-D) patient-specific shapes in the real world. First, it is notoriously challenging to parameterize the input space of arbitrarily complex 3-D geometries. Second, the process often involves massive forward simulations, which are extremely computationally demanding or even infeasible. We propose a novel deep learning surrogate modeling solution to address these challenges and enable rapid hemodynamic predictions. Specifically, a statistical generative model for 3-D patient-specific shapes is developed based on a small set of baseline patient-specific geometries. An unsupervised shape correspondence solution is used to enable geometric morphing and scalable shape synthesis statistically. Moreover, a simulation routine is developed for automatic data generation by automatic meshing, boundary setting, simulation, and post-processing. An efficient supervised learning solution is proposed to map the geometric inputs to the hemodynamics predictions in latent spaces. Numerical studies on aortic flows are conducted to demonstrate the effectiveness and merit of the proposed techniques.
Learning Similarity Preserving Binary Codes for Recommender Systems

Hashing-based Recommender Systems (RSs) are widely studied to provide scalable services. The existing methods for the systems combine three modules to achieve efficiency: feature extraction, interaction modeling, and binarization. In this paper, we study an unexplored module combination for the hashing-based recommender systems, namely Compact Cross-Similarity Recommender (CCSR). Inspired by cross-modal retrieval, CCSR utilizes Maximum a Posteriori similarity instead of matrix factorization and rating reconstruction to model interactions between users and items. We conducted experiments on MovieLens1M, Amazon product review, Ichiba purchase dataset and confirmed CCSR outperformed the existing matrix factorization-based methods. On the Movielens1M dataset, the absolute performance improvements are up to 15.69% in NDCG and 4.29% in Recall. In addition, we extensively studied three binarization modules: $sign$, scaled tanh, and sign-scaled tanh. The result demonstrated that although differentiable scaled tanh is popular in recent discrete feature learning literature, a huge performance drop occurs when outputs of scaled $tanh$ are forced to be binary.
Compressed Empirical Measures (in finite dimensions)

We study approaches for compressing the empirical measure in the context of finite dimensional reproducing kernel Hilbert spaces (RKHSs). In this context, the empirical measure is contained within a natural convex set and can be approximated using convex optimization methods.Such an approximation gives under certain conditions rise to a coreset of data points. A key quantity that controls how large such a coreset has to be is the size of the largest ball around the empirical measure that is contained within the empirical convex set. The bulk of our work is concerned with deriving high probability lower bounds on the size of such a ball under various conditions. We complement this derivation of the lower bound by developing techniques that allow us to apply the compression approach to concrete inference problems such as kernel ridge regression. We conclude with a construction of an infinite dimensional RKHS for which the compression is poor, highlighting some of the difficulties one faces when trying to move to infinite dimensional RKHSs.