Miracle-dz/UniFAR-FTUs huggingface dataset:
PROPERTIES AND APPLICATIONS OF NANOCELLULOSE POLYMER COMPOSITES. A SHORT REVIEW

This paper covers some aspects related to the structure of natural cellulose fibers as well the sources, obtaining methods, properties and their applications. In contrast with aramide and other materials resulted from fossil fuels, nanocellulose is completely renewable. The tensile strength of crystalline cellulose is similar to aluminum's. Its stiffness has been shown to be in the order of magnitude which is similar to aramide and better than glass fibers. Nananocellulose may be prepared as films which are expected to show high strength, high stiffness and high strain. Its strength/weight ratio is 8 times greater than that of stainless steel.
Detection and classification of induction motor faults using feed-forward backpropagation network

For this paper artificial neural networks technique is applied to identify broken rotor bar fault of single phase induction motor. Since artificial neural networks can be able to deal with non-linear problem, it is capable for apply to many applications in particular for recognizing patterns positively. Furthermore, fast Fourier transform (FFT) is employed for converting original stator current waveform, which is time domain, to stator current signal, which is frequency domain, that is labeled as motor current signature analysis for collecting essential data in order for sending into artificial neural networks later. Although performance of artificial neural network is related as many factors, three different multilayer neural network with several training algorithms are researched in this paper. Also three training algorithms are focused to study for exempla gradient descent algorithm, Levenberg-Marquardt algorithm and resilient back propagation algorithm. Consequently, the Levenberg Marquardt algorithm can perform very well for every different multilayer of neural networks in term the network mean squares errors (MSE) which having 5.25E-07 percent that comparing with other results of training algorithms.
Search for supersymmetry in events with four or more leptons in 21 fb − 1 of pp collisions at √ s = 8 TeV with the ATLAS detector The ATLAS Collaboration

A search for supersymmetry in events with four or more leptons is presented. The analysis uses 20.7 fb−1 of proton-proton collision data at √ s = 8 TeV recorded by the ATLAS detector at the LHC. No excess above the Standard Model expectation is observed. Exclusion limits at 95% confidence level are placed in various R-parity violating simplified models, where the lightest supersymmetric particle is the lightest neutralino, which decays promptly to leptons. Limits are also placed in R-parity conserving scenarios characterised by the production of heavier neutralinos (χ̃ 0 2 χ̃ 3) decaying to leptons and χ̃ 0 1, and in general gauge mediated supersymmetry breaking models with higgsino-like χ̃ 0 1. c © Copyright 2013 CERN for the benefit of the ATLAS Collaboration. Reproduction of this article or parts of it is allowed as specified in the CC-BY-3.0 license.
Multi-label Classification with High-rank and High-order Label Correlations

Exploiting label correlations is important to multi-label classification. Previous methods capture the high-order label correlations mainly by transforming the label matrix to a latent label space with low-rank matrix factorization. However, the label matrix is generally a full-rank or approximate full-rank matrix, making the low-rank factorization inappropriate. Besides, in the latent space, the label correlations will become implicit. To this end, we propose a simple yet effective method to depict the high-order label correlations explicitly, and at the same time maintain the high-rank of the label matrix. Moreover, we estimate the label correlations and infer model parameters simultaneously via the local geometric structure of the input to achieve mutual enhancement. Comparative studies over ten benchmark data sets validate the effectiveness of the proposed algorithm in multi-label classification. The exploited high-order label correlations are consistent with common sense empirically. Our code is publicly available at https://github.com/601175936/HOMI.
Automatic quantization for physics-based simulation

Quantization has proven effective in high-resolution and large-scale simulations, which benefit from bit-level memory saving. However, identifying a quantization scheme that meets the requirement of both precision and memory efficiency requires trial and error. In this paper, we propose a novel framework to allow users to obtain a quantization scheme by simply specifying either an error bound or a memory compression rate. Based on the error propagation theory, our method takes advantage of auto-diff to estimate the contributions of each quantization operation to the total error. We formulate the task as a constrained optimization problem, which can be efficiently solved with analytical formulas derived for the linearized objective function. Our workflow extends the Taichi compiler and introduces dithering to improve the precision of quantized simulations. We demonstrate the generality and efficiency of our method via several challenging examples of physics-based simulation, which achieves up to 2.5× memory compression without noticeable degradation of visual quality in the results. Our code and data are available at https://github.com/Hanke98/AutoQantizer.
Differential Imaging Forensics: A Feasibility Study

We motivate and develop a new line of digital forensics. In the meanwhile, we propose a novel approach to photographer identification, a rarely explored authorship attribution problem. We report a proof-of-concept study, which shows the feasibility of our method. Our contributions include a new forensic method for photographer de-anonymization and revealing a novel privacy threat which had been ignored before. The success of our creation builds on top of a new optical side-channel which we have discovered, as well as on how to exploit it effectively. We also make the first attempt to bridge side channels and inverse problems, two fields that appear to be completely isolated from each other but have deep connections.
Randomized Kaczmarz Method for Single Particle X-ray Image Phase Retrieval

In this paper, we investigate phase retrieval algorithm for the single particle X-ray imaging data. We present a variance-reduced randomized Kaczmarz (VR-RK) algorithm for phase retrieval. The VR-RK algorithm is inspired by the randomized Kaczmarz method and the Stochastic Variance Reduce Gradient Descent (SVRG) algorithm. Numerical experiments show that the VR-RK algorithm has a faster convergence rate than randomized Kaczmarz algorithm and the iterative projection phase retrieval methods, such as the hybrid input output (HIO) and the relaxed averaged alternating reflections (RAAR) methods. The VR-RK algorithm can recover the phases with higher accuracy, and is robust at the presence of noise. Experimental results on the scattering data from individual particles show that the VR-RK algorithm can recover phases and improve the single particle image identification.
Distributed-Memory Parallel Contig Generation for De Novo Long-Read Genome Assembly

De novo genome assembly, i.e., rebuilding the sequence of an unknown genome from redundant and erroneous short sequences, is a key but computationally intensive step in many genomics pipelines. The exponential growth of genomic data is increasing the computational demand and requires scalable, high-performance approaches. In this work, we present a novel distributed memory algorithm that, from a string graph representation of the genome and using sparse matrices, generates the contig set, i.e., overlapping sequences that form a map representing a region of a chromosome. Using matrix abstraction, we mask branches in the string graph, and compute the connected component to group genomic sequences that belong to the same linear chain (i.e., contig). Then, we perform multiway number partitioning to minimize the load imbalance in local assembly, i.e., concatenation of sequences from a given contig. Based on the assignment obtained by partitioning, we compute the induce subgraph function to redistribute sequences between processes, resulting in a set of local sparse matrices. Finally, we traverse each matrix using depth-first search to concatenate sequences. Our algorithm shows good scaling with parallel efficiency up to 80% on 128 nodes, resulting in uniform genome coverage and showing promising results in terms of assembly quality. Our contig generation algorithm localizes the assembly process to significantly reduce the amount of computation spent on this step. Our work is a step forward for efficient de novo long read assembly of large genomes in a distributed memory.
Size-Ramsey numbers of graphs with maximum degree three

The size-Ramsey number ˆ r ( H ) of a graph H is the smallest number of edges a (host) graph G can have, such that for any red/blue colouring of G , there is a monochromatic copy of H in G . Recently, Conlon, Nenadov and Trujić showed that if H is a graph on n vertices and maximum degree three, then ˆ r ( H ) = O ( n 8 / 5 ) , improving upon the upper bound of n 5 / 3+ o (1) by Kohayakawa, Rödl, Schacht and Szemerédi. In this paper we show that ˆ r ( H ) ≤ n 3 / 2+ o (1) . While the previously used host graphs were vanilla binomial random graphs, we prove our result using a novel host graph construction. Our bound hits a natural barrier of the existing methods.
Linear leaky-integrate-and-fire neuron model based spiking neural networks and its mapping relationship to deep neural networks

Spiking neural networks (SNNs) are brain-inspired machine learning algorithms with merits such as biological plausibility and unsupervised learning capability. Previous works have shown that converting Artificial Neural Networks (ANNs) into SNNs is a practical and efficient approach for implementing an SNN. However, the basic principle and theoretical groundwork are lacking for training a non-accuracy-loss SNN. This paper establishes a precise mathematical mapping between the biological parameters of the Linear Leaky-Integrate-and-Fire model (LIF)/SNNs and the parameters of ReLU-AN/Deep Neural Networks (DNNs). Such mapping relationship is analytically proven under certain conditions and demonstrated by simulation and real data experiments. It can serve as the theoretical basis for the potential combination of the respective merits of the two categories of neural networks.
Bayesian Optimization-based Nonlinear Adaptive PID Controller Design for Robust Mobile Manipulation

In this paper, we propose to use a nonlinear adaptive PID controller to regulate the joint variables of a mobile manipulator. The motion of the mobile base forces undue disturbances on the joint controllers of the manipulator. In designing a conventional PID controller, one should make a trade-off between the performance and agility of the closed-loop system and its stability margins. The proposed nonlinear adaptive PID controller provides a mechanism to relax the need for such a compromise by adapting the gains according to the magnitude of the error without expert tuning. Therefore, we can achieve agile performance for the system while seeing damped overshoot in the output and track the reference as close as possible, even in the presence of external disturbances and uncertainties in the modeling of the system. We have employed a Bayesian optimization approach to choose the parameters of a nonlinear adaptive PID controller to achieve the best performance in tracking the reference input and rejecting disturbances. The results demonstrate that a well-designed nonlinear adaptive PID controller can effectively regulate a mobile manipulator’s joint variables while carrying an unspecified heavy load and an abrupt base movement occurs.
Unveiling the hosts of parsec-scale massive black hole binaries: morphology and electromagnetic signatures

Parsec-scale massive black hole binaries (MBHBs) are expected to form in hierarchical models of structure formation. Even though different observational strategies have been designed to detect these systems, a theoretical study is a further guide for their search and identification. In this work, we investigate the hosts properties and the electromagnetic signatures of massive black holes gravitationally bound on parsec-scales with primary mass $\rm {>}\, 10^7\, M_{\odot }$. For that, we construct a full-sky lightcone by the use of the semi-analytical model L-Galaxies in which physically motivated prescriptions for the formation and evolution of MBHBs have been included. Our predictions show that the large majority of the MBHBs are placed either in spiral galaxies with a classical bulge structure or in elliptical galaxies. Besides, the scaling relations followed by MBHBs are indistinguishable from the ones of single massive black holes. We find that the occupation fraction of parsec-scale MBHBs reaches up to ${\sim }\, 50\%$ in galaxies with $\rm M_{stellar}\, {>}\, 10^{11}\, M_{\odot }$ and drops below 10% for $\rm M_{stellar}\, {<}\, 10^{11}\, M_{\odot }$. Our model anticipates that the majority of parsec-scale MBHBs are unequal mass systems and lie at z ∼ 0.5, with ∼ 20 objects per $\rm deg^2$ in the sky. However, most of these systems are inactive, and only 1 − 0.1 objects per $\rm deg^2$ have an electromagnetic counterpart with a bolometric luminosity in excess of 1043 erg/s. Very luminous phases of parsec-scale MBHBs are more common at z > 1, but the number of binaries per $\rm deg^2$ is ≲ 0.01 at $\rm L_{\rm bol}\, {>}\, 10^{45} \rm erg/s$.
CEG4N: Counter-Example Guided Neural Network Quantization Refinement

Neural networks are essential components of learning-based software systems. However, their high compute, memory, and power requirements make using them in low resources domains challenging. For this reason, neural networks are often quantized before deployment. Existing quantization techniques tend to degrade the network accuracy. We propose Counter-Example Guided Neural Network Quantization Refinement (CEG4N). This technique combines search-based quantization and equivalence verification: the former minimizes the computational requirements, while the latter guarantees that the network's output does not change after quantization. We evaluate CEG4N~on a diverse set of benchmarks, including large and small networks. Our technique successfully quantizes the networks in our evaluation while producing models with up to 72% better accuracy than state-of-the-art techniques.
BLANT: Basic Local Alignment of Network Topology, Part 1: Seeding local alignments with unambiguous 8-node graphlets

BLAST is a standard tool in bioinformatics for creating local sequence alignments using a"seed-and-extend"approach. Here we introduce an analogous seed-and-extend algorithm that produces local network alignments: BLANT, for Basic Local Alignment of Network Topology. This paper introduces BLANT-seed: given an input graph, BLANT-seed uses network topology alone to create a limited, high-specificity index of k-node induced subgraphs called k-graphlets (analogous to BLASTS's k-mers). The index is constructed so that, if significant common network topology exists between two graphs, their indexes are likely to overlap. BLANT-seed then queries the indexes of two networks to generate a list of common k-graphlets which, when paired, form a seed pair. Our companion paper (submitted elsewhere) describes BLANT-extend, which"grows"these seeds to larger local alignments, again using only topological information.
Structural Inference of Networked Dynamical Systems with Universal Differential Equations

Networked dynamical systems are common throughout science in engineering; e.g., biological networks, reaction networks, power systems, and the like. For many such systems, nonlinearity drives populations of identical (or near-identical) units to exhibit a wide range of nontrivial behaviors, such as the emergence of coherent structures (e.g., waves and patterns) or otherwise notable dynamics (e.g., synchrony and chaos). In this work, we seek to infer (i) the intrinsic physics of a base unit of a population, (ii) the underlying graphical structure shared between units, and (iii) the coupling physics of a given networked dynamical system given observations of nodal states. These tasks are formulated around the notion of the Universal Differential Equation, whereby unknown dynamical systems can be approximated with neural networks, mathematical terms known a priori (albeit with unknown parameterizations), or combinations of the two. We demonstrate the value of these inference tasks by investigating not only future state predictions but also the inference of system behavior on varied network topologies. The effectiveness and utility of these methods are shown with their application to canonical networked nonlinear coupled oscillators.
Adaptive Virtual Neuroarchitecture

Our surrounding environment impacts our cognitive-emotional processes on a daily basis and shapes our physical, psychological and social wellbeing. Although the effects of the built environment on our psycho-physiological processes are well studied, virtual environment design with a potentially similar impact on the user, has received limited attention. Based on the influence of space design on a user and combining that with the dynamic affordances of virtual spaces, we present the idea of adaptive virtual neuroarchitecture (AVN), where virtual environments respond to the user and the user's real world context while simultaneously influencing them both in realtime. To show how AVN has been explored in current research, we present a sampling of recent work that demonstrates reciprocal relationships using physical affordances (space, objects), the user's state (physiological, cognitive, emotional), and the virtual world used in the design of novel virtual reality experiences. We believe AVN has the potential to help us learn how to design spaces and environments that can enhance the wellbeing of their inhabitants.
PSP-HDRI+: A Synthetic Dataset Generator for Pre-Training of Human-Centric Computer Vision Models

We introduce a new synthetic data generator PSP-HDRI$+$ that proves to be a superior pre-training alternative to ImageNet and other large-scale synthetic data counterparts. We demonstrate that pre-training with our synthetic data will yield a more general model that performs better than alternatives even when tested on out-of-distribution (OOD) sets. Furthermore, using ablation studies guided by person keypoint estimation metrics with an off-the-shelf model architecture, we show how to manipulate our synthetic data generator to further improve model performance.
State estimation with the Interacting Multiple Model (IMM) method

For model-based estimation methods, the modeling that is as close to reality as possible makes a vital estimation result. In simple applications, it is sufficient to model a system with a single state space model. However, there are applications in which a system changes its behavior deterministically or stochastically. A previously defined model then describes the behavior of the system only inaccurately or is even no longer valid. The state of the art is to use more than one system models in parallel and to derive a suitable system estimate from them. In the literature, this is generally referred to as the Multiple Model (MM) method. Depending on the application and requirements, different methods exist for this purpose, which determine a single state estimate from a set of models. A frequently used representative of these methods is the Interacting Multiple Model (IMM) method which will be presented in this paper.
Variational Approach for Intensity Domain Multi-exposure Image Fusion

Recent innovations shows that blending of details captured by single Low Dynamic Range (LDR) sensor overcomes the limitations of standard digital cameras to capture details from high dynamic range scene. We present a method to produce well-exposed fused image that can be displayed directly on conventional display devices. The ambition is to preserve details in poorly illuminated and brightly illuminated regions. Proposed approach does not require true radiance reconstruction and tone manipulation steps. The aforesaid objective is achieved by taking into account local information measure that select well-exposed regions across input exposures. In addition, Contrast Limited Adaptive Histogram equalization (CLAHE) is introduced to improve uniformity of input multi-exposure image prior to fusion.
Cross-Attention Transformer for Video Interpolation

We propose TAIN (Transformers and Attention for video INterpolation), a residual neural network for video interpolation, which aims to interpolate an intermediate frame given two consecutive image frames around it. We first present a novel vision transformer module, named Cross Similarity (CS), to globally aggregate input image features with similar appearance as those of the predicted interpolated frame. These CS features are then used to refine the interpolated prediction. To account for occlusions in the CS features, we propose an Image Attention (IA) module to allow the network to focus on CS features from one frame over those of the other. TAIN outperforms existing methods that do not require flow estimation and performs comparably to flow-based methods while being computationally efficient in terms of inference time on Vimeo90k, UCF101, and SNU-FILM benchmarks.
DCCF: Deep Comprehensible Color Filter Learning Framework for High-Resolution Image Harmonization

Image color harmonization algorithm aims to automatically match the color distribution of foreground and background images captured in different conditions. Previous deep learning based models neglect two issues that are critical for practical applications, namely high resolution (HR) image processing and model comprehensibility. In this paper, we propose a novel Deep Comprehensible Color Filter (DCCF) learning framework for high-resolution image harmonization. Specifically, DCCF first downsamples the original input image to its low-resolution (LR) counter-part, then learns four human comprehensible neural filters (i.e. hue, saturation, value and attentive rendering filters) in an end-to-end manner, finally applies these filters to the original input image to get the harmonized result. Benefiting from the comprehensible neural filters, we could provide a simple yet efficient handler for users to cooperate with deep model to get the desired results with very little effort when necessary. Extensive experiments demonstrate the effectiveness of DCCF learning framework and it outperforms state-of-the-art post-processing method on iHarmony4 dataset on images' full-resolutions by achieving 7.63% and 1.69% relative improvements on MSE and PSNR respectively.