Miracle-dz/UniFAR-FTUs huggingface dataset:
Beyond 512 Tokens: Siamese Multi-depth Transformer-based Hierarchical Encoder for Document Matching

Many information retrieval and natural language processing problems can be formalized as a semantic matching task. However, the existing work in this area has been focused in large part on the matching between short texts like finding answer spans, sentences and passages given a query or a natural language question. Semantic matching between long-form texts like documents, which can be applied to applications such as document clustering, news recommendation and related article recommendation, is relatively less explored and needs more research effort. In recent years, self-attention based models like Transformers and BERT have achieved state-of-the-art performance in several natural language understanding tasks. These kinds of models, however, are still restricted to short text sequences like sentences due to the quadratic computational time and space complexity of self-attention with respect to the input sequence length. In this paper, we address these issues by proposing the Siamese Multi-depth Transformer-based Hierarchical (SMITH) Encoder for document representation learning and matching, which contains several novel design choices to adapt self-attention models for long text inputs. For model pre-training, we propose the masked sentence block language modeling task in addition to the original masked word language modeling task used in BERT, to capture sentence block relations within a document. The experimental results on several benchmark data sets for long-form document matching show that our proposed SMITH model outperforms the previous state-of-the-art Siamese matching models including hierarchical attention, multi-depth attention-based hierarchical recurrent neural network, and BERT for long-form document matching, and increases the maximum input text length from 512 to 2048 when compared with BERT-based baseline methods.
Dynamically accelerated cover times

Among observables characterising the random exploration of a graph or lattice, the cover time, namely the time to visit every site, continues to attract widespread interest. Much insight about cover times is gained by mapping to the (spaceless) coupon-collector problem, which amounts to ignoring spatio-temporal correlations, and an early conjecture that the limiting cover time distribution of regular random walks on large lattices converges to the Gumbel distribution in $d \geq 3$ was recently proved rigorously. Furthermore, a number of mathematical and numerical studies point to the robustness of the Gumbel universality to modifications of the \textit{spatial} features of the random search processes (e.g.\ introducing persistence and/or intermittence, or changing the graph topology). Here we investigate the robustness of the Gumbel universality to dynamical modification of the \textit{temporal} features of the search, specifically by allowing the random walker to "accelerate" or "decelerate" upon visiting a previously unexplored site. We generalise the mapping mentioned above by relating the statistics of cover times to the roughness of $1/f^\alpha$ Gaussian signals, leading to the conjecture that the Gumbel distribution is but one of a family of cover time distributions, ranging from Gaussian for highly accelerated cover, to exponential for highly decelerated cover. While our conjecture is confirmed by systematic Monte Carlo simulations in dimensions $d > 3$, our results for acceleration in $d=3$ challenge the current understanding of the role of correlations in the cover time problem.
Lipschitz free spaces over locally compact metric spaces

We prove that the Lipschitz free space over a certain type of discrete metric space has the Radon-Nikodým property. We also show that the Lipschitz free space over a complete, locally compact metric space has the Schur or approximation property whenever the Lipschitz free space over each compact subset also has this property.
OR-UNet: an Optimized Robust Residual U-Net for Instrument Segmentation in Endoscopic Images

Segmentation of endoscopic images is an essential processing step for computer and robotics-assisted interventions. The Robust-MIS challenge provides the largest dataset of annotated endoscopic images to date, with 5983 manually annotated images. Here we describe OR-UNet, our optimized robust residual 2D U-Net for endoscopic image segmentation. As the name implies, the network makes use of residual connections in the encoder. It is trained with the sum of Dice and cross-entropy loss and deep supervision. During training, extensive data augmentation is used to increase the robustness. In an 8-fold cross-validation on the training images, our model achieved a mean (median) Dice score of 87.41 (94.35). We use the eight models from the cross-validation as an ensemble on the test set.
Higgs to $\gamma Z$ decay as a probe of flavour changing neutral Yukawa couplings

With the deeper study of Higgs particle, Higgs precision measurements can be served to probe new physics indirectly. In many new physics models, vector-like quarks $T_L,~T_R$ occur naturally. It is important to probe their couplings with SM particles. In this work, we consider the singlet $T_L,~T_R$ extended models and show how to constrain the $Tth$ couplings through the $h\rightarrow\gamma Z$ decay at HL-LHC. Firstly, we derive the perturbative unitarity bounds on $|y_{L,~R}^{tT}|$ with other couplings set to be zeros simply. In order to optimize the situation, we take $m_T$ = 400GeV and $s_L$ = 0.2 considering the experimental constraints. Under this benchmark point, we find that the future bounds from $h\rightarrow\gamma Z$ decay can limit the real parts of $y_{L,~R}^{tT}$ in the positive direction to be O(1) because of the double enhancement. For the real parts of $y_{L,~R}^{tT}$ in the negative direction, it is always surpassed by the perturbative unitarity. Moreover, we find that top quark CEDM can give more strong bounds (especially the imaginary parts of $y_{L,~R}^{tT}$) than the perturbative unitarity and $h\rightarrow\gamma Z$ decay in the off-axis regions for some scenarios.
Evolving inborn knowledge for fast adaptation in dynamic POMDP problems

Rapid online adaptation to changing tasks is an important problem in machine learning and, recently, a focus of meta-reinforcement learning. However, reinforcement learning (RL) algorithms struggle in POMDP environments because the state of the system, essential in a RL framework, is not always visible. Additionally, hand-designed meta-RL architectures may not include suitable computational structures for specific learning problems. The evolution of online learning mechanisms, on the contrary, has the ability to incorporate learning strategies into an agent that can (i) evolve memory when required and (ii) optimize adaptation speed to specific online learning problems. In this paper, we exploit the highly adaptive nature of neuromodulated neural networks to evolve a controller that uses the latent space of an autoencoder in a POMDP. The analysis of the evolved networks reveals the ability of the proposed algorithm to acquire inborn knowledge in a variety of aspects such as the detection of cues that reveal implicit rewards, and the ability to evolve location neurons that help with navigation. The integration of inborn knowledge and online plasticity enabled fast adaptation and better performance in comparison to some non-evolutionary meta-reinforcement learning algorithms. The algorithm proved also to succeed in the 3D gaming environment Malmo Minecraft.
A Conflict Detection Framework for IoT Services in Multi-resident Smart Homes

We propose a novel framework to detect conflicts among IoT services in a multi-resident smart home. A novel IoT conflict model is proposed considering the functional and non-functional properties of IoT services. We design a conflict ontology that formally represents different types of conflicts. A hybrid conflict detection algorithm is proposed by combining both knowledge-driven and data-driven approaches. Experimental results on real-world datasets show the efficiency of the proposed approach.
Uncovering the dynamics of correlation structures relative to the collective market motion

The measured correlations of financial time series in subsequent epochs change considerably as a function of time. When studying the whole correlation matrices, quasi-stationary patterns, referred to as market states, are seen by applying clustering methods. They emerge, disappear or reemerge, but they are dominated by the collective motion of all stocks. In the jargon, one speaks of the market motion, it is always associated with the largest eigenvalue of the correlation matrices. Thus the question arises, if one can extract more refined information on the system by subtracting the dominating market motion in a proper way. To this end we introduce a new approach by clustering reduced-rank correlation matrices which are obtained by subtracting the dyadic matrix belonging to the largest eigenvalue from the standard correlation matrices. We analyze daily data of 262 companies of the S&P 500 index over a period of almost 15 years from 2002 to 2016. The resulting dynamics is remarkably different, and the corresponding market states are quasi-stationary over a long period of time. Our approach adds to the attempts to separate endogenous from exogenous effects.
POCOVID-Net: Automatic Detection of COVID-19 From a New Lung Ultrasound Imaging Dataset (POCUS)

With the rapid development of COVID-19 into a global pandemic, there is an ever more urgent need for cheap, fast and reliable tools that can assist physicians in diagnosing COVID-19. Medical imaging such as CT can take a key role in complementing conventional diagnostic tools from molecular biology, and, using deep learning techniques, several automatic systems were demonstrated promising performances using CT or X-ray data. Here, we advocate a more prominent role of point-of-care ultrasound imaging to guide COVID-19 detection. Ultrasound is non-invasive and ubiquitous in medical facilities around the globe. Our contribution is threefold. First, we gather a lung ultrasound (POCUS) dataset consisting of (currently) 1103 images (654 COVID-19, 277 bacterial pneumonia and 172 healthy controls), sampled from 64 videos. While this dataset was assembled from various online sources and is by no means exhaustive, it was processed specifically to feed deep learning models and is intended to serve as a starting point for an open-access initiative. Second, we train a deep convolutional neural network (POCOVID-Net) on this 3-class dataset and achieve an accuracy of 89% and, by a majority vote, a video accuracy of 92% . For detecting COVID-19 in particular, the model performs with a sensitivity of 0.96, a specificity of 0.79 and F1-score of 0.92 in a 5-fold cross validation. Third, we provide an open-access web service (POCOVIDScreen) that is available at: this https URL. The website deploys the predictive model, allowing to perform predictions on ultrasound lung images. In addition, it grants medical staff the option to (bulk) upload their own screenings in order to contribute to the growing public database of pathological lung ultrasound images. Dataset and code are available from: this https URL
Singular diffusion with Neumann boundary conditions

In this paper we develop an existence theory for the nonlinear initial-boundary value problem with singular diffusion ∂tu=divk(x)∇G(u) , u| t=0 = u 0 with Neumann boundary conditions k(x)∇G(u) ⋅ ν = 0. Here x∈B⊂Rd , a bounded open set with C 3 boundary, and with ν as the unit outer normal. The function G is Lipschitz continuous and nondecreasing, while k(x) is a diagonal matrix. We show that any two weak entropy solutions u and v satisfy u(t)−v(t)L1(B)⩽u|t=0−v|t=0L1(B)eCt , for almost every t ⩾ 0, and a constant C = C(k, G, B). If we restrict to the case when the entries k i of k depend only on the corresponding component, k i = k i (x i ), and ∂B is C 2, we show that there exists an entropy solution, thus establishing in this case that the problem is well-posed in the sense of Hadamard.
Exploring Nanoscale Ferroelectricity in Doped Hafnium Oxide by Interferometric Piezoeresponse Force Microscopy

Hafnium oxide (HfO2)-based ferroelectrics offer remarkable promise for memory and logic devices in view of their compatibility with traditional silicon CMOS technology, high switchable polarization, good endurance and thickness scalability. These factors have led to steep rise in research on this class of materials over the past number of years. At the same time, only a few reports on the direct sensing of nanoscale ferroelectric properties exist, with many questions remaining regarding the emergence of ferroelectricity in these materials. While piezoresponse force microscopy (PFM) is ideally suited to probe piezo- and ferro-electricity on the nanoscale, it is known to suffer artifacts which complicate quantitative interpretation of results and can even lead to claims of ferroelectricity in materials which are not ferroelectric. In this paper we explore the possibility of using an improved PFM method based on interferometric displacement sensing (IDS) to study nanoscale ferroelectricity in bare Si doped HfO2. Our results indicate a clear difference in the local remnant state of various HfO2 crystallites with reported values for the piezoelectric coupling in range 0.6-1.5 pm/V. In addition, we report unusual ferroelectric polarization switching including possible contributions from electrostriction and Vegard effect, which may indicate oxygen vacancies or interfacial effects influence the emergence of nanoscale ferroelectricity in HfO2.
Early Optical Observations of GRB 150910A: Bright Jet Optical Afterglow and X-Ray Dipole Radiation from a Magnetar Central Engine

Gamma-ray burst (GRB) 150910A was detected by Swift/Burst Alert Telescope (BAT), and then rapidly observed by Swift/XRT, Swift/Ultraviolet-Optical Telescope, and ground-based telescopes. We report Lick Observatory spectroscopic and photometric observations of GRB 150910A, and we investigate the physical origins of both the optical and X-ray afterglows, incorporating data obtained with BAT and XRT. The light curves show that the jet-emission episode lasts ∼360 s with a sharp pulse from BAT to XRT (Episode I). In Episode II, the optical emission has a smooth onset bump followed by a normal decay (αR,2 ≈ −1.36), as predicted in the standard external shock model, while the X-ray emission exhibits a plateau (αX,1 ≈ −0.36) followed by a steep decay (αX,2 ≈ −2.12). The light curves show obvious chromatic behavior with an excess in the X-ray flux. Our results suggest that GRB 150910A is an unusual GRB driven by a newly born magnetar with its extremely energetic magnetic dipole (MD) wind in Episode II, which overwhelmingly dominates the observed early X-ray plateau. The radiative efficiency of the jet prompt emission is ηγ ≈ 11%. The MD wind emission was detected in both the BAT and XRT bands, making it the brightest among the current sample of MD winds seen by XRT. We infer the initial spin period (P0) and the surface polar cap magnetic field strength (Bp) of the magnetar as 1.02 × 1015 G ≤ Bp ≤ 1.80 × 1015 G and 1 ms ≤ P0v ≤ 1.77 ms, and the radiative efficiency of the wind is ηw ≥ 32%.
Symplectic fillings of virtually overtwisted contact structures on lens spaces

Symplectic fillings of standard tight contact structures on lens spaces are understood and classified. The situation is different if one considers non-standard tight structures (i.e. those that are virtually overtwisted), for which a classification scheme is still missing. In this work we use different approaches and employ various techniques to improve our knowledge of symplectic fillings of virtually overtwisted contact structures. We study curves configurations on surfaces to solve the problem in the case of a specific family of lens spaces. Then we give general constraints on the topology of Stein fillings of any lens space by looking at algebraic properties of integer lattices and at geometric slicing of solid tori. Furthermore, we try to place these manifolds in the context of algebraic geometry, in order to determine whether Stein fillings can be realized as Milnor fibers of hypersurfce singularities, finding a series of necessary conditions for this to happen. In the concluding part of the thesis, we focus on the connections between planar contact 3-manifolds and the theory of Artin presentations.
Analyzing COVID-19 pandemic with a new growth model for population ecology

We have proposed a new form of growth rate for population ecology. Generally, the growth rate is dependent on the size of the population at that particular epoch. We have introduced an alternative time-dependent form of growth rate. This form satisfies essential conditions to represent population growth and can be an alternative form for growth models to analyze population dynamics. We have employed the generalized Richards model as a guideline to compare our results. Further, we have applied our model in the case of epidemics. To check the efficacy of our model, we have verified the 2003 SARS data. This model has estimated the final epidemic size with good accuracy. Thereafter, we intend to describe the present COVID-2019 pandemic. We have performed our analysis with data for Italy, Spain, and Germany. Following, we have tried to predict the number of COVID-19 cases and the turning point for the USA, UK, and India.
Remote Photoplethysmography: Rarely Considered Factors

Remote Photoplethysmography (rPPG) is a fast-growing technique of vital sign estimation by analyzing video of a person. Several major phenomena affecting rPPG signals have been studied (e.g. video compression, distance from person to camera, skin tone, head motions). However, to develop a highly accurate rPPG method, new, minor, factors should be investigated. First considered factor is irregular frame rate of video recordings. Despite of PPG signal transformation by frame rate irregularity, no significant distortion of PPG signal spectra was found in the experiments. Second factor is rolling shutter effect which generates tiny phase shift of the same PPG signal in different parts of the frame caused by progressive scanning. In particular conditions effect of this artifact could be of the same order of magnitude as physiologically caused phase shifts. Third factor is a size of temporal windows, which could significantly influence the estimated error of vital sign evaluation. It follows that one should account difference in size of processing windows when comparing rPPG methods. Short series of experiments were conducted to estimate importance of these phenomena and to determine necessity of their further comprehensive study.
藍藻 Trichodesmium の分類について

The present paper reviews historically the taxonomy of the genus Trichodesmium. The genus was established by EHRENBERG in 1830 on the basis of the type species T. erythraeum. When GOMONT made the revision of Oscillatoriaceae in 1892, he recognized the genus, describing three species, T. erythraeum EHRENB. , T. Hildebrandtii GOM. , and T. Thiebautii GOM. In 1932 GEITLER combined the genus Trichodesmium into the genus Oscillatoria. It is as follows: Oscillatoria Thiebautii (GOM.) GEITL. (= Trichodesmium Thiebautz'i GOM.; =Xanthotrichum contortum WILLE in SCHUT ’ r); 0. erythraea (EHRENB.) GEITL. ( = Trichodesmium erythraeum EHRENB.); and 0. Hildebrandtii (GOM.) GEITL . (=Trichodesmium Hildebrandtii GOM.). However, in 1942 he reestablished the genus Trichodesmium in accordance with Gomont ’ s opinion. As done in 1925, he retained the freshwater species Trichodesmium lacustre KLEB. under the genus Osei ・ 'llatoria. In 1938 J. DE TONI gave the new name Skujaella for the genus Trichodesmium and combined all the species of Trichodesmium, which had been described since the establishment of the genus in 1830. However, GEITLER (1942) argued that the generic name Skujaella is invalid, according to the law of priority in view of the fact that generic names Xanthotrichum (WILLE in SCHUTT, 1893) and Heliotrichum (WILLE in SCHUTT, 1893) have been used earlier synonyms of the genus Trichodesmium.
Neural Head Reenactment with Latent Pose Descriptors

We propose a neural head reenactment system, which is driven by a latent pose representation and is capable of predicting the foreground segmentation alongside the RGB image. The latent pose representation is learned as a part of the entire reenactment system, and the learning process is based solely on image reconstruction losses. We show that despite its simplicity, with a large and diverse enough training dataset, such learning successfully decomposes pose from identity. The resulting system can then reproduce mimics of the driving person and, furthermore, can perform cross-person reenactment. Additionally, we show that the learned descriptors are useful for other pose-related tasks, such as keypoint prediction and pose-based retrieval.
CascadeTabNet: An approach for end to end table detection and structure recognition from image-based documents

An automatic table recognition method for interpretation of tabular data in document images majorly involves solving two problems of table detection and table structure recognition. The prior work involved solving both problems independently using two separate approaches. More recent works signify the use of deep learning-based solutions while also attempting to design an end to end solution. In this paper, we present an improved deep learning-based end to end approach for solving both problems of table detection and structure recognition using a single Convolution Neural Network (CNN) model. We propose CascadeTabNet: a Cascade mask Region-based CNN High-Resolution Network (Cascade mask R-CNN HRNet) based model that detects the regions of tables and recognizes the structural body cells from the detected tables at the same time. We evaluate our results on ICDAR 2013, ICDAR 2019 and Table-Bank public datasets. We achieved 3rd rank in ICDAR 2019 post-competition results for table detection while attaining the best accuracy results for the ICDAR 2013 and Table-Bank dataset. We also attain the highest accuracy results on the ICDAR 2019 table structure recognition dataset. Additionally, we demonstrate effective transfer learning and image augmentation techniques that enable CNNs to achieve very accurate table detection results. Code and dataset has been made available at: https://github.com/DevashishPrasad/CascadeTabNet
All Word Embeddings from One Embedding

In neural network-based models for natural language processing (NLP), the largest part of the parameters often consists of word embeddings. Conventional models prepare a large embedding matrix whose size depends on the vocabulary size. Therefore, storing these models in memory and disk storage is costly. In this study, to reduce the total number of parameters, the embeddings for all words are represented by transforming a shared embedding. The proposed method, ALONE (all word embeddings from one), constructs the embedding of a word by modifying the shared embedding with a filter vector, which is word-specific but non-trainable. Then, we input the constructed embedding into a feed-forward neural network to increase its expressiveness. Naively, the filter vectors occupy the same memory size as the conventional embedding matrix, which depends on the vocabulary size. To solve this issue, we also introduce a memory-efficient filter construction approach. We indicate our ALONE can be used as word representation sufficiently through an experiment on the reconstruction of pre-trained word embeddings. In addition, we also conduct experiments on NLP application tasks: machine translation and summarization. We combined ALONE with the current state-of-the-art encoder-decoder model, the Transformer, and achieved comparable scores on WMT 2014 English-to-German translation and DUC 2004 very short summarization with less parameters.