Miracle-dz/UniFAR-FTUs huggingface dataset:
SciWING– A Software Toolkit for Scientific Document Processing

We introduce SciWING, an open-source soft-ware toolkit which provides access to state-of-the-art pre-trained models for scientific document processing (SDP) tasks, such as citation string parsing, logical structure recovery and citation intent classification. Compared to other toolkits, SciWING follows a full neural pipeline and provides a Python inter-face for SDP. When needed, SciWING provides fine-grained control for rapid experimentation with different models by swapping and stacking different modules. Transfer learning from general and scientific documents specific pre-trained transformers (i.e., BERT, SciBERT, etc.) can be performed. SciWING incorporates ready-to-use web and terminal-based applications and demonstrations to aid adoption and development. The toolkit is available from http://sciwing.io and the demos are available at http://rebrand.ly/sciwing-demo.
Conditioned Source Separation for Musical Instrument Performances

In music source separation, the number of sources may vary for each piece and some of the sources may belong to the same family of instruments, thus sharing timbral characteristics and making the sources more correlated. This leads to additional challenges in the source separation problem. This paper proposes a source separation method for multiple musical instruments sounding simultaneously and explores how much additional information apart from the audio stream can lift the quality of source separation. We explore conditioning techniques at different levels of a primary source separation network and utilize two extra modalities of data, namely presence or absence of instruments in the mixture, and the corresponding video stream data.
Training Neural Networks to Produce Compatible Features

This paper makes a first step towards compatible and hence reusable network components. Rather than training networks for different tasks independently, we adapt the training process to produce network components that are compatible across tasks. We propose and compare several different approaches to accomplish compatibility. Our experiments on CIFAR-10 show that: (i) we can train networks to produce compatible features, without degrading task accuracy compared to training networks independently; (ii) the degree of compatibility is highly dependent on where we split the network into a feature extractor and a classification head; (iii) random initialization has a large effect on compatibility; (iv) we can train incrementally: given previously trained components, we can train new ones which are also compatible with them. This work is part of a larger goal to increase network reusability: we envision that compatibility will enable solving new tasks by mixing and matching suitable components.
Role of Serum Trypsin Level in Diagnosis and Prognosis of Pancreatitis and Compared with Healthy Subjects of Rajasthan

Inflammation of pancreas is called pancreatitis. Cell death is induced directly by intracellular trypsin which is very harmful. Aim of this study was to find out the role of serum trypsin level in diagnosis and prognosis of pancreatitis. Material and methods: 200 subjects were selected out of them 100 were selected as study group and 100 were selected as control group and distributed according to their age and sex. Serum trypsin was estimated by sandwich elisa kit method. Result: meantrypsin level was found to be 587.94 ng /ml in male patients group as well as in all male control group. Value of trypsin was 186.28 ng/ml in female patients group and mean serum trypsin level was 696.34 and mean value of serum trypsin in female control group was 206.20. The P value was Highly significant in this group. (P<0.0001). Serum trypsin level was calculated in many other groups and compared with healthy subjects and each and every time the level of serum trypsin was found to be increased in all patients. Conclusion: We came to the conclusion that serum trypsin activity looks specific and can be used for diagnosis of Alcoholic and gall stone pancreatitis.
$PD_3$-groups and HNN Extensions

We show that if a $PD_3$-group $G$ splits as an HNN extension $A*_C\varphi$ where $C$ is a $PD_3$-group then the Poincare dual in $H^1(G;\mathbb{Z})=Hom(G,\mathbb{Z})$ of the homology class $[C]$ is the epimorphism $f:G\to\mathbb{Z}$ with kernel the normal closure of $A$. We also make several other observations about $PD_3$-groups which split over $PD_2$-groups.
Efficient long-distance relation extraction with DG-SpanBERT

In natural language processing, relation extraction seeks to rationally understand unstructured text. Here, we propose a novel SpanBERT-based graph convolutional network (DG-SpanBERT) that extracts semantic features from a raw sentence using the pre-trained language model SpanBERT and a graph convolutional network to pool latent features. Our DG-SpanBERT model inherits the advantage of SpanBERT on learning rich lexical features from large-scale corpus. It also has the ability to capture long-range relations between entities due to the usage of GCN on dependency tree. The experimental results show that our model outperforms other existing dependency-based and sequence-based models and achieves a state-of-the-art performance on the TACRED dataset.
Elastic and inelastic scattering of neutrinos and WIMPs on nuclei

The event rates for WIMP-nucleus and neutrino-nucleus scattering processes, expected to be detected at ton-scale rare-event detectors, are investigated. We focus on nuclear isotopes that correspond to the target nuclei of current and future experiments looking for WIMP- and neutrino-nucleus events. The nuclear structure calculations, performed in the context of the Deformed Shell Model, are based on Hartree-Fock intrinsic states with angular momentum projection and band mixing for both the elastic and inelastic channels. Our predictions in the high recoil energy tail, show that detectable distortions of the measured/expected signal may be interpreted through the inclusion of the non-negligible incoherent channels.
A new example of the effects of a singular background on the zeta function

To motivate our discussion, we consider a 1 + 1 dimensional scalar field interacting with a static Coulomb-type background, so that the spectrum of quantum fluctuations is given by a second-order differential operator on a single coordinate r with a singular coefficient proportional to 1/r. We find that the spectral functions of this operator present an interesting behavior: the ζ function has multiple poles in the complex plane; accordingly, logarithms of the proper time appear in the heat-trace expansion. As a consequence, the ζ function does not provide a finite regularization of the effective action. This work extends similar results previously derived in the context of conical singularities.
Controlled switching of ferroelectric SnSe monolayers at room temperature

Two-dimensional (2D) van der Waals ferroelectrics provide an unprecedented architectural freedom for the creation of artificial multiferroics and non-volatile electronic devices based on vertical and co-planar heterojunctions of 2D ferroic materials. Additionally, their non-centrosymmetric structures lead to a strong non-linear optical response, and also add functionalities to designer spintronic and valleytronic devices. Nevertheless, controlled electrical switching of in-plane polarization--a fundamental requirement for realizing in-plane ferroelectric non-volatile memories--has not yet been seen experimentally in any monolayer-thick 2D ferroelectric. Here we report the discovery of robust ferroelectricity with a critical temperature close to 400 K in SnSe monolayer plates, and the demonstration of controlled ferroelectric switching of their in-plane spontaneous polarization at room temperature using a scanning tunneling microscope (STM). The switching was achieved through creating, moving and eliminating 180° domain walls in the SnSe monolayer plates by applying appropriate bias voltage pulses to an STM tip. This study shows that STM is a powerful tool for detecting and manipulating the in-plane polarization in 2D ferroelectric monolayers, which is difficult to characterize by conventional approaches such as piezoresponse force microscopy, thus facilitating the hunt for other 2D ferroelectric monolayers with in-plane polarization with important technological applications.
Lateral Nasal Capillary Haemangioma a Rare Occurrence—Case Report and Literature Review

Nasal lobular capillary haemangioma (CH), also known as pyogenic granuloma, is a benign lesion of unknown etiology commonly associated with pregnancy, oral contraceptive pills, and trauma. Marked by epistaxis and nasal obstruction, the condition involves benign capillary proliferation with a microscopically distinctive lobular architecture, affecting the skin and mucous membranes of the oral cavity and nasal region. This aim of this article is to report a case of lateral nasal capillary haemangioma in a 39-year-old female, who presented in our facilities having experienced spontaneous epistaxis on the left side of the nose for the previous three months. Endoscopy revealed a protruding left intranasal mass arising from the lateral nasal wall in the region of the middle turbinate and osteo-meatal complex. The patient underwent endoscopic excision of the mass and did excellently well thereafter with no recurrence.
A typed parallel lambda-calculus via 1-depth intermediate proofs

We introduce a Curry-Howard correspondence for a large class of intermediate logics characterized by intuitionistic proofs with non-nested applications of rules for classical disjunctive tautologies (1-depth intermediate proofs). The resulting calculus, we call it $\lambda_{\parallel}$, is a strongly normalizing parallel extension of the simply typed $\lambda$-calculus. Although simple, the $\lambda_{\parallel}$ reduction rules can model arbitrary process network topologies, and encode interesting parallel programs ranging from numeric computation to algorithms on graphs.
When, and Why, Simple Methods Fail. Lessons Learned from Hyperparameter Tuning in Software Analytics (and Elsewhere)

Tuning a data miner for for software analytics is something of a black art. Recent research has shown that some of that tuning can be achieved via automatic tools, called "hyperparameter optimizers". Much of that research has used tools developed from outside of SE. Hence, here, we ask how and when we can exploit the special properties of SE data to build faster and better optimizers. Specifically, we apply hyperparameter optimization for 120 data sets addressing problems like bad smell detection, predicting Github issue close time, bug report analysis, defect prediction and dozens of other non-SE problems. To these, we applied a tool developed using SE data which (a) out-performs the state-of-the-art for these SE problems yet (b) fails badly on non-SE problems. From this experience, we can infer a simple rule for when to use/avoid different kinds of optimizers. SE data is often about infrequent issues, like the occasional defect, or the rarely exploited security violation, or the requirement that holds for one special case. But as we show, the same was not observed when we applied it on non-SE data. Our conclusion will be that we can exploit this special properties of SE to great effect; specifically, to find better optimizations for SE tasks via a tactic called "dodging" (explained in this paper).
Towards Evaluating the Robustness of Chinese BERT Classifiers

Recent advances in large-scale language representation models such as BERT have improved the state-of-the-art performances in many NLP tasks. Meanwhile, character-level Chinese NLP models, including BERT for Chinese, have also demonstrated that they can outperform the existing models. In this paper, we show that, however, such BERT-based models are vulnerable under character-level adversarial attacks. We propose a novel Chinese char-level attack method against BERT-based classifiers. Essentially, we generate "small" perturbation on the character level in the embedding space and guide the character substitution procedure. Extensive experiments show that the classification accuracy on a Chinese news dataset drops from 91.8% to 0% by manipulating less than 2 characters on average based on the proposed attack. Human evaluations also confirm that our generated Chinese adversarial examples barely affect human performance on these NLP tasks.
Disentangling Electronic and Magnetic Order in NdNiO$_3$ at Ultrafast Timescales

Understanding how emergent phases arise from microscopic interactions in quantum materials is of fundamental importance. Long range order can be strongly influenced by cooperative or competing interactions, an important example being lattice and magnetic degrees of freedom operative in numerous materials exhibiting insulator-to-metal transitions (IMTs). Ultrashort x-ray pulses provide a powerful approach to temporally disentangle magnetic and structural dynamics, yielding new insights into the subtleties of cooperative or competitive behavior that influence IMTs. Here we utilize femtosecond soft x-ray pulses to simultaneously probe the magnetic and electronic order following optical excitation in NdNiO$_3$ (NNO), a model nickelate exhibiting structural and magnetic dynamics that conspire to induce an IMT. During the course of the photoinduced IMT (above gap excitation), we observe an ultrafast ($<$ 180fs) quenching of magnetic order followed by a slower collapse of the insulating phase probed by X-ray absorption and THz transmission ($\sim$450fs). Pump wavelength dependence indicates that an intersite charge transfer (ICT) excitation is a crucial ingredient to initiate the non-thermal IMT dynamics. Density functional theory (DFT) calculations reveal a consistent scenario where ICT drives a collapse of antiferromagnetic order, dynamically preparing NNO for subsequent octahedral breathing mode relaxation that leads to the metallic phase.
Sky Quality Meter and satellite correlation for night cloud-cover analysis at astronomical sites

The analysis of the night cloud cover is very important for astronomical observation in real time, considering a typical observation time of about 15 minutes, and to have a statistics of the night cloud cover. In this paper we use the SQM (Sky Quality Meter) for high resolution temporal analysis of the La Silla and Asiago (Ekar observatory) sky: 3 and 5 minutes respectively. We investigate the annual temporal evolution of the natural contributions of the sky in a site not influenced by artificial light at night (ALAN) and one highly influenced respectively. We also make a correlation between GOES and AQUA satellites data and ground-based SQM data to confirm a relationship between the SQM data and cloud cover. We develop an algorithm that allows the use of the SQM for night cloud detection and we reach a correlation of 97.2\% at La Silla and 94.6\% at Asiago with the nighttime cloud cover detected by the GOES and AQUA satellites. Our algorithm also classifies the photometric (PN) and spectroscopic nights (SN). We measure 59.1\% PN and 21.7\% SN for a total percentage of clear nights of 80.8\% at La Silla in 2018. The respective Ekar observatory values are 31.1\% PN, 24.0\% SN and 55.1\% of total clear nights time. Application to the SQM network would involve the development of long-term statistics and big data forecasting models, for site testing and real-time astronomical observation.
New Opportunities for the Formal Proof of Computational Real Geometry? (Extended Abstract)

The purpose of this paper is to explore the question "to what extent could we produce formal, machine-verifiable, proofs in real algebraic geometry? " The question has been asked before but as yet the leading algorithms for answering such questions have not been formalised. We present a thesis that a new algorithm for ascertaining satisfiability of formulae over the reals via Cylindrical Algebraic Coverings [Abraham, Davenport, England, Kremer, \emph{Deciding the Consistency of Non-Linear Real Arithmetic Constraints with a Conflict Driver Search Using Cylindrical Algebraic Coverings}, 2020] might provide trace and outputs that allow the results to be more susceptible to machine verification than those of competing algorithms.
Prevalence of Risk Factors for Diabetes Mellitus and Hypertension Among Adult in Tabuk - Kingdom of Saudi Arabia

BACKGROUND: Cardiovascular disease (CVD) is a common but chronic condition that can cause death, and is seen as a substantial source of disability and health costs. A balance between prevention and intervention, as is the case with other infectious diseases, is the best way to stem the increasing burden of CVD. AIM: This study assesses the prevalence of diabetes mellitus, hypertension, in a sample from the University of Tabuk. METHODS: A cross-sectional study was done in 2018, with 120 employees and students at the University of Tabuk (in Tabuk City, Saudi Arabia), for those over age 20 (60 males and 60 females) from different faculties and departments at the university. RESULTS: Assessment was done with 120 participants to assess the prevalence of hypertension and diabetes mellitus, at the University of Tabuk, in which the prevalence is actually higher than in most cities of Saudi Arabia. Ten percent of participants are hyperglycemic and 10% are developing problems in that area, with no significant difference between males and females. CONCLUSION: This study shows that cardiovascular risk factors will influence those with major health disorders in the future. Consequently, a nationwide campaign encouraging wholesome eating, better lifestyles, as well as physical activity, is a healthcare priority.
Parto pretérmino: causas y medidas de prevención

Preterm birth is the final common pathway of a series of different physiopathological processes, so it is considered a syndrome. Spontaneous preterm birth represents two thirds of preterm deliveries. Prevention can be primary or secondary, and is basically focused on prediction, which is currently done by risk calculators that combine medical history, cervical length and fetal fibronectin. Vaginal progesterone reduces the rate of spontaneous preterm birth to 35-40%, if used from week 16 through week 34.
DMLO: Deep Matching LiDAR Odometry

LiDAR odometry is a fundamental task for various areas such as robotics, autonomous driving. This problem is difficult since it requires the systems to be highly robust running in noisy real-world data. Existing methods are mostly local iterative methods. Feature-based global registration methods are not preferred since extracting accurate matching pairs in the nonuniform and sparse LiDAR data remains challenging. In this paper, we present Deep Matching LiDAR Odometry (DMLO), a novel learning-based framework which makes the feature matching method applicable to LiDAR odometry task. Unlike many recent learning-based methods, DMLO explicitly enforces geometry constraints in the framework. Specifically, DMLO decomposes the 6-DoF pose estimation into two parts, a learning-based matching network which provides accurate correspondences between two scans and rigid transformation estimation with a close-formed solution by Singular Value Decomposition (SVD). Comprehensive experimental results on real-world datasets KITTI and Argoverse demonstrate that our DMLO dramatically outperforms existing learning-based methods and is comparable with the state-of-the-art geometry- based approaches.
Improved YOLOv3 Object Classification in Intelligent Transportation System

The technology of vehicle and driver detection in Intelligent Transportation System(ITS) is a hot topic in recent years. In particular, the driver detection is still a challenging problem which is conductive to supervising traffic order and maintaining public safety. In this paper, an algorithm based on YOLOv3 is proposed to realize the detection and classification of vehicles, drivers, and people on the highway, so as to achieve the purpose of distinguishing driver and passenger and form a one-to-one correspondence between vehicles and drivers. The proposed model and contrast experiment are conducted on our self-build traffic driver's face database. The effectiveness of our proposed algorithm is validated by extensive experiments and verified under various complex highway conditions. Compared with other advanced vehicle and driver detection technologies, the model has a good performance and is robust to road blocking, different attitudes, and extreme lighting.
The Logical Syntax of (IT) Architectures

Despite several (accepted) standards, core notions typically employed in information technology (IT) architectures lack the precise and exact foundations encountered in logic, algebra, and other branches of mathematics. In this contribution we define the term "architecture" in a mathematically rigorous way. We motivate our particular choice by demonstrating (i) how commonly understood and expected properties of an architecture can be suitably defined or derived within our formalization, and (ii) how our concept is fully compatible with real life (business) architectures. Based on our fundamental definitions we further develop a rigorous notion of architectural \emph{similarity} based on the notion of "homomorphisms" between architectures. We demonstrate the (theoretical) applicability by deriving some theorems on the characterization n-tier architectures.
Decoding EEG Rhythms During Action Observation, Motor Imagery, and Execution for Standing and Sitting

Event-related desynchronization and synchronization (ERD/S) and movement-related cortical potential (MRCP) play an important role in brain-computer interfaces (BCI) for lower limb rehabilitation, particularly in standing and sitting. However, little is known about the differences in the cortical activation between standing and sitting, especially how the brain’s intention modulates the pre-movement sensorimotor rhythm as they do for switching movements. In this study, we aim to investigate the decoding of continuous EEG rhythms during action observation (AO), motor imagery (MI), and motor execution (ME) for the actions of standing and sitting. We developed a behavioral task in which participants were instructed to perform both AO and MI/ME in regard to the transitioning actions of sit-to-stand and stand-to-sit. Our results demonstrated that the ERD was prominent during AO, whereas ERS was typical during MI at the alpha band across the sensorimotor area. A combination of the filter bank common spatial pattern (FBCSP) and support vector machine (SVM) for classification was used for both offline and classifier testing analyses. The offline analysis indicated the classification of AO and MI providing the highest mean accuracy at 82.73±2.54% in the stand-to-sit transition. By applying the classifier testing analysis, we demonstrated the higher performance of decoding neural intentions from the MI paradigm in comparison to the ME paradigm. These observations led us to the promising aspect of using our developed tasks based on the integration of both AO and MI to build future exoskeleton-based rehabilitation systems.
Learning Discrete Structured Representations by Adversarially Maximizing Mutual Information

We propose learning discrete structured representations from unlabeled data by maximizing the mutual information between a structured latent variable and a target variable. Calculating mutual information is intractable in this setting. Our key technical contribution is an adversarial objective that can be used to tractably estimate mutual information assuming only the feasibility of cross entropy calculation. We develop a concrete realization of this general formulation with Markov distributions over binary encodings. We report critical and unexpected findings on practical aspects of the objective such as the choice of variational priors. We apply our model on document hashing and show that it outperforms current best baselines based on discrete and vector quantized variational autoencoders. It also yields highly compressed interpretable representations.