Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning to Model and Calibrate Optics Via a Differentiable Wave Optics Simulator

We present a novel learning-based method to build a differentiable computational model of a real fluorescence microscope. Our model can be used to calibrate a real optical setup directly from data samples and to engineer point spread functions by specifying the desired input-output data. This approach is poised to drastically improve the design of microscopes, because the parameters of current models of optical setups cannot be easily fit to real data. Inspired by the recent progress in deep learning, our solution is to build a differentiable wave optics simulator as a composition of trainable modules, each computing light wave-front (WF) propagation due to a specific optical element. We call our differentiable modules WaveBlocks and show reconstruction results in the case of lenses, wave propagation in air, camera sensors and diffractive elements (e.g., phase-masks).
Extreme Low-Light Imaging with Multi-granulation Cooperative Networks

Low-light imaging is challenging since images may appear to be dark and noised due to low signal-to-noise ratio, complex image content, and the variety in shooting scenes in extreme low-light condition. Many methods have been proposed to enhance the imaging quality under extreme low-light conditions, but it remains difficult to obtain satisfactory results, especially when they attempt to retain high dynamic range (HDR). In this paper, we propose a novel method of multi-granulation cooperative networks (MCN) with bidirectional information flow to enhance extreme low-light images, and design an illumination map estimation function (IMEF) to preserve high dynamic range (HDR). To facilitate this research, we also contribute to create a new benchmark dataset of real-world Dark High Dynamic Range (DHDR) images to evaluate the performance of high dynamic preservation in low light environment. Experimental results show that the proposed method outperforms the state-of-the-art approaches in terms of both visual effects and quantitative analysis.
Spatiotemporal Adaptive Quantization for Video Compression Applications

JCT-VC HEVC HM 16 includes a Coding Unit (CU) level adaptive Quantization Parameter (QP) technique named AdaptiveQP. It is designed to perceptually adjust the QP in Y, Cb and Cr Coding Blocks (CBs) based only on the variance of samples in a luma CB. In this paper, we propose an adaptive quantisation technique that consists of two contributions. The first contribution relates to accounting for the variance of chroma samples, in addition to luma samples, in a CU. The second contribution relates to accounting for CU temporal information as well as CU spatial information. Moreover, we integrate into our method a lambda refined QP technique to reduce complexity associated multiple QP optimizations in the Rate Distortion Optimization process. We evaluate the proposed technique on 4:4:4, 4:2:2, 4:2:0 and 4:0:0 YCbCr test sequences, for which we quantify the results using the Bjontegaard Delta Rate (BD-Rate) metric. Our method achieves a maximum BD-Rate reduction of 23.1% (Y), 26.7% (Cr) and 25.2% (Cb). Furthermore, a maximum encoding time reduction of 4.4% is achieved.
Single-sample writers - "Document Filter" and their impacts on writer identification

The writing can be used as an important biometric modality which allows to unequivocally identify an individual. It happens because the writing of two different persons present differences that can be explored both in terms of graphometric properties or even by addressing the manuscript as a digital image, taking into account the use of image processing techniques that can properly capture different visual attributes of the image (e.g. texture). In this work, perform a detailed study in which we dissect whether or not the use of a database with only a single sample taken from some writers may skew the results obtained in the experimental protocol. In this sense, we propose here what we call "document filter". The "document filter" protocol is supposed to be used as a preprocessing technique, such a way that all the data taken from fragments of the same document must be placed either into the training or into the test set. The rationale behind it, is that the classifier must capture the features from the writer itself, and not features regarding other particularities which could affect the writing in a specific document (i.e. emotional state of the writer, pen used, paper type, and etc.). By analyzing the literature, one can find several works dealing the writer identification problem. However, the performance of the writer identification systems must be evaluated also taking into account the occurrence of writer volunteers who contributed with a single sample during the creation of the manuscript databases. To address the open issue investigated here, a comprehensive set of experiments was performed on the IAM, BFL and CVL databases. They have shown that, in the most extreme case, the recognition rate obtained using the "document filter" protocol drops from 81.80% to 50.37%.
The whitefly‐associated facultative symbiont Hamiltonella defensa suppresses induced plant defences in tomato

Summary Maternally inherited bacterial symbionts are present in many, if not most, insect species. While there is rapidly accumulating evidence that facultative, heritable symbionts often protect insect hosts from natural enemies, there have been few clear examples where facultative symbionts mediate herbivore–plant interactions. The phloem-feeding whitefly, Bemisia tabaci, is a major agricultural pest that frequently harbours facultative symbionts, including Hamiltonella defensa. While H. defensa and other facultative symbionts have been shown to improve whitefly performance on particular food plants, no direct and specific roles for symbiont-mediated interactions with food plants have been identified. Here, we conducted a series of assays to determine whether infection with H. defensa improved whitefly performance on tomato (Solanum lycopersicum) and whether this benefit was associated with symbiont effects on induced plant defences. Finally, we tested whether impacts on induced defences involved in the modulation of a salivary factor. The downregulation of plant defences was associated with increased whitefly fecundity and survival. Feeding by H. defensa-infected whiteflies suppressed JA and JA-related anti-herbivore-induced defences in tomato relative to uninfected controls. That saliva-only treatments of damaged tomato leaves from H. defensa-infected whiteflies suppressed induced plant defences compared to saliva from uninfected controls, suggesting that elicitors in saliva were responsible. Characterization of the putative salivary factor(s) revealed they are likely small (< 3-kDa) and nonproteinaceous. Interestingly, suppression of defences was not observed in SA-deficient NahG plants, indicating that suppression of JA-regulated defences depends on the SA signalling pathway. This finding reveals an intriguing example of the crosstalk between SA and JA signalling pathways and suggests that infection with facultative symbionts can result in the manipulation of induced plant defences to the benefit of the insect host and heritable symbiont. Our results show that the bacterial symbiont H. defensa mediates whitefly–plant interactions by suppressing induced plant defences in tomato. This finding is among the first showing a direct role for facultative symbionts in mediating plant–herbivore interactions and demonstrates a novel tactic for insect herbivores to circumvent plant defences. Symbiont-mediated suppression of plant defences may enhance the deleterious effects of insect pests feeding on important crops.
RPD: A Distance Function Between Word Embeddings

It is well-understood that different algorithms, training processes, and corpora produce different word embeddings. However, less is known about the relation between different embedding spaces, i.e. how far different sets of em-beddings deviate from each other. In this paper, we propose a novel metric called Relative Pairwise Inner Product Distance (RPD) to quantify the distance between different sets of word embeddings. This unitary-invariant metric has a unified scale for comparing different sets of word embeddings. Based on the properties of RPD, we study the relations of word embeddings of different algorithms systematically and investigate the influence of different training processes and corpora. The results shed light on the poorly understood word embeddings and justify RPD as a measure of the distance of embedding space.
Streaming Transformer-based Acoustic Models Using Self-attention with Augmented Memory

Transformer-based acoustic modeling has achieved great suc-cess for both hybrid and sequence-to-sequence speech recogni-tion. However, it requires access to the full sequence, and thecomputational cost grows quadratically with respect to the in-put sequence length. These factors limit its adoption for stream-ing applications. In this work, we proposed a novel augmentedmemory self-attention, which attends on a short segment of theinput sequence and a bank of memories. The memory bankstores the embedding information for all the processed seg-ments. On the librispeech benchmark, our proposed methodoutperforms all the existing streamable transformer methods bya large margin and achieved over 15% relative error reduction,compared with the widely used LC-BLSTM baseline. Our find-ings are also confirmed on some large internal datasets.
An Algebraic Model For Quorum Systems

Quorum systems are a key mathematical abstraction in distributed fault-tolerant computing for capturing trust assumptions. A quorum system is a collection of subsets of all processes, called quorums, with the property that each pair of quorums have a non-empty intersection. They can be found at the core of many reliable distributed systems, such as cloud computing platforms, distributed storage systems and blockchains. In this paper we give a new interpretation of quorum systems, starting with classical majority-based quorum systems and extending this to Byzantine quorum systems. We propose an algebraic representation of the theory underlying quorum systems making use of multivariate polynomial ideals, incorporating properties of these systems, and studying their algebraic varieties. To achieve this goal we will exploit properties of Boolean Groebner bases. The nice nature of Boolean Groebner bases allows us to avoid part of the combinatorial computations required to check consistency and availability of quorum systems. Our results provide a novel approach to test quorum systems properties from both algebraic and algorithmic perspectives.
Phylogenetic analysis of the tribe Bovini (Mammalia: Artiodactyla)

A matrix of 57 (mainly cranial) characters and 32 taxa of fossil and Recent Bovini (buffaloes, bison and cattle) has been analysed using the parsimony program HENNIG86. Among the best established results are the exclusion of Parabos, polyphyly of Leptobos, monophyly of the Bos sensu lato+ buffaloes group (a clade including all Recent Bovini), probable monophyly of the Bubalina and Syncerina, and the close relationship between bison and yak. Some other interesting questions raised include the apparent absence of a close link between Pliocene African bovines (except “Leptobos” syrticus) and later African buffaloes, and the possible monophyly of Pelorovis oldowayensis+Bos sensu lato.
A new subgenus of the genus Colastes Haliday (Hymenoptera: Braconidae: Exothecinae) for species reared from bracket fungi, with description of two new species from Europe

A new Holarctic subgenus (Fungivenator subgen. nov.) of the genus Colastes Haliday, 1833 (Hymenoptera: Braconidae: Exothecinae) is proposed for a group of species that, as far as is known, has its larvae living on hosts in bracket fungi. The type species (Colastes sandei sp. nov. from Netherlands, Germany and England) and a second species (C. fritzeni sp. nov.) from Finland are described and illustrated. A key to the five known species is included.
Blockchain Vulnerabilities and Recent Security Challenges: A Review Paper

Blockchain is a relatively new technology, a distributed database used for sharing between nodes of computer networks. A blockchain stores all information in automated digital format as a database. Blockchain innovation ensures the accuracy and security of the data record and generates trust without the need for a trusted third party. The objectives of this paper are to determine the security risk of the blockchain systems, analyze the vulnerabilities exploited on the blockchain, and identify recent security challenges that the blockchain faces. This review paper presents some of the previous studies of the security threats that blockchain faces and reviews the security enhancement solutions for blockchain vulnerabilities. There are some studies on blockchain security issues, but there is no systematic examination of the problem, despite the blockchain system’s security threats. An observational research methodology was used in this research. Through this methodology, many research related to blockchain threats and vulnerabilities obtained. The outcomes of this research are to Identify the most important security threats faced by the blockchain and consideration of security recently vulnerabilities. Processes and methods for dealing with security concerns are examined. Intelligent corporate security academic challenges and limitations are covered throughout this review. The goal of this review is to serve as a platform as well as a reference point for future work on blockchain-based security.
Hybrid-DNNs: Hybrid Deep Neural Networks for Mixed Inputs

Rapid development of big data and high-performance computing have encouraged explosive studies of deep learning in geoscience. However, most studies only take single-type data as input, frittering away invaluable multisource, multi-scale information. We develop a general architecture of hybrid deep neural networks (HDNNs) to support mixed inputs. Regarding as a combination of feature learning and target learning, the new proposed networks provide great capacity in high-hierarchy feature extraction and in-depth data mining. Furthermore, the hybrid architecture is an aggregation of multiple networks, demonstrating good flexibility and wide applicability. The configuration of multiple networks depends on application tasks and varies with inputs and targets. Concentrating on reservoir production prediction, a specific HDNN model is configured and applied to an oil development block. Considering their contributions to hydrocarbon production, core photos, logging images and curves, geologic and engineering parameters can all be taken as inputs. After preprocessing, the mixed inputs are prepared as regular-sampled structural and numerical data. For feature learning, convolutional neural networks (CNN) and multilayer perceptron (MLP) network are configured to separately process structural and numerical inputs. Learned features are then concatenated and fed to subsequent networks for target learning. Comparison with typical MLP model and CNN model highlights the superiority of proposed HDNN model with high accuracy and good generalization.
The Power Of Triply Complementary Priors For Image Compressive Sensing

Recent works that utilized deep models have achieved superior results in various image restoration applications. Such approach is typically supervised which requires a corpus of training images with distribution similar to the images to be recovered. On the other hand, the shallow methods which are usually unsupervised remain promising performance in many inverse problems, e.g., image compressive sensing (CS), as they can effectively leverage non-local self-similarity priors of natural images. However, most of such methods are patch-based leading to the restored images with various ringing artifacts due to naive patch aggregation. Using either approach alone usually limits performance and generalizability in image restoration tasks. In this paper, we propose a joint low-rank and deep (LRD) image model, which contains a pair of triply complementary priors, namely external and internal, deep and shallow, and local and nonlocal priors. We then propose a novel hybrid plug-and-play (H-PnP) framework based on the LRD model for image CS. To make the optimization tractable, a simple yet effective algorithm is proposed to solve the proposed H-PnP based image CS problem. Extensive experimental results demonstrate that the proposed H-PnP algorithm significantly outperforms the state-of-the-art techniques for image CS recovery such as SCSNet and WNNM.
Accelerating Ill-Conditioned Low-Rank Matrix Estimation via Scaled Gradient Descent

Low-rank matrix estimation is a canonical problem that finds numerous applications in signal processing, machine learning and imaging science. A popular approach in practice is to factorize the matrix into two compact low-rank factors, and then optimize these factors directly via simple iterative methods such as gradient descent and alternating minimization. Despite nonconvexity, recent literatures have shown that these simple heuristics in fact achieve linear convergence when initialized properly for a growing number of problems of interest. However, upon closer examination, existing approaches can still be computationally expensive especially for ill-conditioned matrices: the convergence rate of gradient descent depends linearly on the condition number of the low-rank matrix, while the per-iteration cost of alternating minimization is often prohibitive for large matrices. The goal of this paper is to set forth a competitive algorithmic approach dubbed Scaled Gradient Descent (ScaledGD) which can be viewed as pre-conditioned or diagonally-scaled gradient descent, where the pre-conditioners are adaptive and iteration-varying with a minimal computational overhead. With tailored variants for low-rank matrix sensing, robust principal component analysis and matrix completion, we theoretically show that ScaledGD achieves the best of both worlds: it converges linearly at a rate independent of the condition number of the low-rank matrix similar as alternating minimization, while maintaining the low per-iteration cost of gradient descent. Our analysis is also applicable to general loss functions that are restricted strongly convex and smooth over low-rank matrices. To the best of our knowledge, ScaledGD is the first algorithm that provably has such properties over a wide range of low-rank matrix estimation tasks.
Learning the gravitational force law and other analytic functions

Large neural network models have been successful in learning functions of importance in many branches of science, including physics, chemistry and biology. Recent theoretical work has shown explicit learning bounds for wide networks and kernel methods on some simple classes of functions, but not on more complex functions which arise in practice. We extend these techniques to provide learning bounds for analytic functions on the sphere for any kernel method or equivalent infinitely-wide network with the corresponding activation function trained with SGD. We show that a wide, one-hidden layer ReLU network can learn analytic functions with a number of samples proportional to the derivative of a related function. Many functions important in the sciences are therefore efficiently learnable. As an example, we prove explicit bounds on learning the many-body gravitational force function given by Newton's law of gravitation. Our theoretical bounds suggest that very wide ReLU networks (and the corresponding NTK kernel) are better at learning analytic functions as compared to kernel learning with Gaussian kernels. We present experimental evidence that the many-body gravitational force function is easier to learn with ReLU networks as compared to networks with exponential activations.
Sudden Attention Shifts on Wikipedia Following COVID-19 Mobility Restrictions

We study how the coronavirus disease 2019 (COVID-19) pandemic, alongside the severe mobility restrictions that ensued, has impacted information access on Wikipedia, the world's largest online encyclopedia. A longitudinal analysis that combines pageview statistics for 12 Wikipedia language editions with mobility reports published by Apple and Google reveals a massive increase in access volume, accompanied by a stark shift in topical interests. Health- and entertainment- related topics are found to have gained, and sports- and transportation- related topics, to have lost attention. Interestingly, while the interest in health-related topics was transient, that in entertainment topics is lingering and even increasing. These changes began at the time when mobility was restricted and are most pronounced for language editions associated with countries, in which the most severe mobility restrictions were implemented, indicating that the interest shift might be caused by people's spending more time at home. Our results highlight the utility of Wikipedia for studying reactions to the pandemic across the globe, and illustrate how the disease is rippling through society.
Atom Search Optimization with Simulated Annealing - a Hybrid Metaheuristic Approach for Feature Selection

'Hybrid meta-heuristics' is one of the most interesting recent trends in the field of optimization and feature selection (FS). In this paper, we have proposed a binary variant of Atom Search Optimization (ASO) and its hybrid with Simulated Annealing called ASO-SA techniques for FS. In order to map the real values used by ASO to the binary domain of FS, we have used two different transfer functions: S-shaped and V-shaped. We have hybridized this technique with a local search technique called, SA We have applied the proposed feature selection methods on 25 datasets from 4 different categories: UCI, Handwritten digit recognition, Text, non-text separation, and Facial emotion recognition. We have used 3 different classifiers (K-Nearest Neighbor, Multi-Layer Perceptron and Random Forest) for evaluating the strength of the selected featured by the binary ASO, ASO-SA and compared the results with some recent wrapper-based algorithms. The experimental results confirm the superiority of the proposed method both in terms of classification accuracy and number of selected features.
A Scientific Information Extraction Dataset for Nature Inspired Engineering

Nature has inspired various ground-breaking technological developments in applications ranging from robotics to aerospace engineering and the manufacturing of medical devices. However, accessing the information captured in scientific biology texts is a time-consuming and hard task that requires domain-specific knowledge. Improving access for outsiders can help interdisciplinary research like Nature Inspired Engineering. This paper describes a dataset of 1,500 manually-annotated sentences that express domain-independent relations between central concepts in a scientific biology text, such as trade-offs and correlations. The arguments of these relations can be Multi Word Expressions and have been annotated with modifying phrases to form non-projective graphs. The dataset allows for training and evaluating Relation Extraction algorithms that aim for coarse-grained typing of scientific biological documents, enabling a high-level filter for engineers.