Miracle-dz/UniFAR-FTUs huggingface dataset:
The distribution of syntactic dependency distances

ABSTRACT The syntactic structure of a sentence can be represented as a graph where vertices are words and edges indicate syntactic dependencies between them. In this setting, the distance between two syntactically linked words can be defined as the difference between their positions. Here we want to contribute to the characterization of the actual distribution of syntactic dependency distances, and unveil its relationship with short-term memory limitations. We propose a new double-exponential model in which decay in probability is allowed to change after a break-point. This transition could mirror the transition from the processing of words chunks to higher-level structures. We find that a two-regime model – where the first regime follows either an exponential or a power-law decay – is the most likely one in all 20 languages we considered, independently of sentence length and annotation style. Moreover, the break-point is fairly stable across languages and averages values of 4-5 words, suggesting that the amount of words that can be simultaneously processed abstracts from the specific language to a high degree. Finally, we give an account of the relation between the best estimated model and the closeness of syntactic dependencies, as measured by a recently introduced optimality score.
Cross-Field Transformer for Diabetic Retinopathy Grading on Two-field Fundus Images

Automatic diabetic retinopathy (DR) grading based on fundus photography has been widely explored to benefit the routine screening and early treatment. Existing researches generally focus on single-field fundus images, which have limited field of view for precise eye examinations. In clinical applications, ophthalmologists adopt two-field fundus photography as the dominating tool, where the information from each field (i.e., macula-centric and optic disc-centric) is highly correlated and complementary, and benefits comprehensive decisions. However, automatic DR grading based on two-field fundus photography remains a challenging task due to the lack of publicly available datasets and effective fusion strategies. In this work, we first construct a new benchmark dataset (DRTiD) for DR grading, consisting of 3,100 two-field fundus images. To the best of our knowledge, it is the largest public DR dataset with diverse and high-quality two-field images. Then, we propose a novel DR grading approach, namely Cross-Field Transformer (CrossFiT), to capture the correspondence between two fields as well as the long-range spatial correlations within each field. Considering the inherent two-field geometric constraints, we particularly define aligned position embeddings to preserve relative consistent position in fundus. Besides, we perform masked cross-field attention during interaction to filter the noisy relations between fields. Extensive experiments on our DRTiD dataset and a public DeepDRiD dataset demonstrate the effectiveness of our CrossFiT network. The new dataset and the source code of CrossFiT will be publicly available at https://github.com/DU-VTS/DRTiD.
Discovering Unsupervised Behaviours from Full-State Trajectories

Improving open-ended learning capabilities is a promising approach to enable robots to face the unbounded complexity of the real-world. Among existing methods, the ability of Quality-Diversity algorithms to generate large collections of diverse and high-performing skills is instrumental in this context. However, most of those algorithms rely on a hand-coded behavioural descriptor to characterise the diversity, hence requiring prior knowledge about the considered tasks. In this work, we propose an additional analysis of Autonomous Robots Realising their Abilities; a Quality-Diversity algorithm that autonomously ﬁnds behavioural characterisations. We evaluate this approach on a simulated robotic environment, where the robot has to autonomously discover its abilities from its full-state trajectories. All algorithms were applied to three tasks: navigation, moving forward with a high velocity, and performing half-rolls. The experimental results show that the algorithm under study discovers autonomously collections of solutions that are diverse with respect to all tasks. More speciﬁcally, the analysed approach autonomously ﬁnds policies that make the robot move to diverse positions, but also utilise its legs in diverse ways, and even perform half-rolls.
GADMSL: Graph Anomaly Detection on Attributed Networks via Multi-scale Substructure Learning

—Recently, graph anomaly detection has attracted increasing attention in data mining and machine learning com-munities. Apart from existing attribute anomalies, graph anomaly detection also captures suspicious topological-abnormal nodes that differ from the major counterparts. Although massive graph- based detection approaches have been proposed, most of them focus on node-level comparison while pay insufﬁcient attention on the surrounding topology structures. Nodes with more dissimilar neighborhood substructures have more suspicious to be abnormal. To enhance the local substructure detection ability, we propose a novel Graph Anomaly Detection framework via Multi-scale Substructure Learning (GADMSL for abbreviation). Unlike previous algorithms, we manage to capture anomalous substructures where the inner similarities are relatively low in dense-connected regions. Speciﬁcally, we adopt a region proposal module to ﬁnd high-density substructures in the network as suspicious regions. Their inner-node embedding similarities indicate the anomaly degree of the detected substructures. Generally, a lower degree of embedding similarities means a higher prob- ability that the substructure contains topology anomalies. To distill better embeddings of node attributes, we further introduce a graph contrastive learning scheme, which observes attribute anomalies in the meantime. In this way, GADMSL can detect both topology and attribute anomalies. Ultimately, extensive experiments on benchmark datasets show that GADMSL greatly improves detection performance (up to 7.30% AUC and 17.46% AUPRC gains) compared to state-of-the-art attributed networks anomaly detection algorithms.
Volume and heat kernel fluctuations for the three-dimensional uniform spanning tree

Let U be the uniform spanning tree on Z 3 . We show the occurrence of log-logarithmic ﬂuctuations around the leading order for the volume of intrinsic balls in U . As an application, we obtain similar ﬂuctuations for the quenched heat kernel of the simple random walk on U .
A quantum algorithm to estimate the closeness to the Strict Avalanche criterion in Boolean functions

We propose a quantum algorithm (in the form of a quantum oracle) that estimates the closeness of a given Boolean function to one that satisfies the ``strict avalanche criterion'' (SAC). This algorithm requires $n$ queries of the Boolean function oracle, where $n$ is the number of input variables, this is fewer than the queries required by the classical algorithm to perform the same task. We compare our approach with other quantum algorithms that may be used for estimating the closeness to SAC and it is shown our algorithm verifies SAC with the fewest possible calls to quantum oracle and requires the fewest samples for a given confidence bound.
A sound transducer with a flat, flexible diaphragm working with bending waves

From the time of the idea to the finally working sound transducer, a period of over 20 years was necessary. The time has been needed for development and research in manufacturing and materials. It took a long time until new synthetic and magnetic materials were available with their best fitting mechanical behaviors. Nowadays it is possible to present a wideband sound transducer working from 100 Hz up to 35 kHz. It follows time precise without any mechanical energy storage the incoming signal. The special structure of the flat and flexible diaphragm works concentrically only with bending waves. A simple mechanical model, theoretical equations and measurements will be presented in comparison to the omnipresent Rice–Kellogg piston loudspeaker. The advantages in perception and hearing will be shown with regard to physiological behaviors of the hearing sense.
Flip Initial Features: Generalization of Neural Networks for Semi-supervised Node Classification

Graph neural networks (GNNs) have been widely used under semi-supervised settings. Prior studies have mainly focused on finding appropriate graph filters (e.g., aggregation schemes) to generalize well for both homophilic and heterophilic graphs. Even though these approaches are essential and effective, they still suffer from the sparsity in initial node features inherent in the bag-of-words representation. Common in semi-supervised learning where the training samples often fail to cover the entire dimensions of graph filters (hyperplanes), this can precipitate over-fitting of specific dimensions in the first projection matrix. To deal with this problem, we suggest a simple and novel strategy; create additional space by flipping the initial features and hyperplane simultaneously. Training in both the original and in the flip space can provide precise updates of learnable parameters. To the best of our knowledge, this is the first attempt that effectively moderates the overfitting problem in GNN. Extensive experiments on real-world datasets demonstrate that the proposed technique improves the node classification accuracy up to 40.2 %.
Dynkin abelianisations of Lie algebras of exceptional type

We consider degenerations of all simple Lie algebras of exceptional type obtained by embedding into aﬃne Lie algebras. We give a ﬁltration to consider this as an abelianisation of the original Lie algebra. We then show that the associated graded of simple, ﬁnite-dimensional modules are isomorphic to Demazure modules.
Parameter Estimation of the Heston Volatility Model with Jumps in the Asset Prices

The parametric estimation of stochastic differential equations (SDEs) has been the subject of intense studies already for several decades. The Heston model, for instance, is based on two coupled SDEs and is often used in financial mathematics for the dynamics of asset prices and their volatility. Calibrating it to real data would be very useful in many practical scenarios. It is very challenging, however, since the volatility is not directly observable. In this paper, a complete estimation procedure of the Heston model without and with jumps in the asset prices is presented. Bayesian regression combined with the particle filtering method is used as the estimation framework. Within the framework, we propose a novel approach to handle jumps in order to neutralise their negative impact on the estimates of the key parameters of the model. An improvement in the sampling in the particle filtering method is discussed as well. Our analysis is supported by numerical simulations of the Heston model to investigate the performance of the estimators. In addition, a practical follow-along recipe is given to allow finding adequate estimates from any given data.
EasyMLServe: Easy Deployment of REST Machine Learning Services

Various research domains use machine learning approaches because they can solve complex tasks by learning from data. Deploying machine learning models, however, is not trivial and developers have to implement complete solutions which are often installed locally and include Graphical User Interfaces (GUIs). Distributing software to various users on-site has several problems. Therefore, we propose a concept to deploy software in the cloud. There are several frameworks available based on Representational State Transfer (REST) which can be used to implement cloud-based machine learning services. However, machine learning services for scientific users have special requirements that state-of-the-art REST frameworks do not cover completely. We contribute an EasyMLServe software framework to deploy machine learning services in the cloud using REST interfaces and generic local or web-based GUIs. Furthermore, we apply our framework on two real-world applications, \ie, energy time-series forecasting and cell instance segmentation. The EasyMLServe framework and the use cases are available on GitHub.
EEGLog: Lifelogging EEG Data When You Listen to Music

Self-tracking has been long discussed, which can monitor daily activities and help users to recall previous experiences. Such data-capturing technique is no longer limited to photos, text messages, or personal diaries in recent years. With the development of wearable EEG devices, we introduce a novel modality of logging EEG data while listening to music, and bring up the idea of the neural-centric way of life with the designed data analysis application named EEGLog. Four consumer-grade wearable EEG devices are explored by collecting EEG data from 24 participants. Three modules are introduced in EEGLog, including the summary module of EEG data, emotion reports, music listening activities, and memorial moments, the emotion detection module, and the music recommendation module. Feedback from interviews about using EEG devices and EEGLog were obtained and analyzed for future EEG logging development.
Evolution of Resistive Switching Characteristics in WO3-x-based MIM Devices by Tailoring Oxygen Deficiency

We report on resistive switching (RS) characteristics of W/WO 3-x /Pt-based thin film memristors modulated by precisely controlled oxygen non-stoichiometry. RS properties of the devices with varied oxygen vacancy (V O ) concentration have been studied by measuring their DC current voltage properties. Switchability of the resistance states in the memristors have been found to depend strongly on the V O s concentration in the WO 3-x layer. Depending on x, the memristors exhibited forming-free bipolar, forming-required bipolar and non-formable characteristics. Devices with high V O s concentration (~1×10 21 cm -3 ) exhibited lower initial resistance and memory window of only 15, which has been increased to ~6500 with reducing V O s concentration to ~5.8×10 20 cm -3 . Forming-free, stable RS with memory window of ~2000 have been realized for a memristor possessing V O s concentration of ~6.2×10 20 cm -3 . Investigation of the conduction mechanism suggests that tailoring V O s concentration modifies the formation and dimension of the conducting filaments as well as the Schottky barrier height at WO 3-x /Pt interface which deterministically modulates RS characteristics of the WO 3-x based memristors.
Quenching jets increases their flavor

The widespread notion that jets quenched in a Quark-Gluon-Plasma (QGP) are similar in their parton flavor composition to jets in vacuum is critically examined. We demonstrate that while the soft to semi-hard [low to intermediate transverse momentum ($p_T$)] sector of vacuum jets are predominantly bosonic i.e., composed of gluons, \emph{sufficiently} quenched jets can have an intermediate momentum sector that is predominantly fermionic, dominated by quarks and antiquarks. We demonstrate, using leading order perturbative QCD processes, that the rate of flavor conversion from a gluon traversing the QGP as part of a jet, to a quark or antiquark, versus the reverse process, grows steadily with falling $p_T$. Simple diagrammatic estimates are followed by a variety of realistic simulations in static media. The relation of this increase in flavor to the observed baryon enhancement at intermediate $p_T$ is studied in a fully realistic simulation.
Image Restoration Quality Assessment Based on Regional Differential Information Entropy

With the development of image recovery models, especially those based on adversarial and perceptual losses, the detailed texture portions of images are being recovered more naturally. However, these restored images are similar but not identical in detail texture to their reference images. With traditional image quality assessment methods, results with better subjective perceived quality often score lower in objective scoring. Assessment methods suffer from subjective and objective inconsistencies. This paper proposes a regional differential information entropy (RDIE) method for image quality assessment to address this problem. This approach allows better assessment of similar but not identical textural details and achieves good agreement with perceived quality. Neural networks are used to reshape the process of calculating information entropy, improving the speed and efficiency of the operation. Experiments conducted with this study’s image quality assessment dataset and the PIPAL dataset show that the proposed RDIE method yields a high degree of agreement with people’s average opinion scores compared with other image quality assessment metrics, proving that RDIE can better quantify the perceived quality of images.
Interpretation of fingerprint image quality features extracted by self-organizing maps

Accurate prediction of fingerprint quality is of significant importance to any fingerprint-based biometric system. Ensuring high quality samples for both probe and reference can substantially improve the system's performance by lowering false non-matches, thus allowing finer adjustment of the decision threshold of the biometric system. Furthermore, the increasing usage of biometrics in mobile contexts demands development of lightweight methods for operational environment. A novel two-tier computationally efficient approach was recently proposed based on modelling block-wise fingerprint image data using Self-Organizing Map (SOM) to extract specific ridge pattern features, which are then used as an input to a Random Forests (RF) classifier trained to predict the quality score of a propagated sample. This paper conducts an investigative comparative analysis on a publicly available dataset for the improvement of the two-tier approach by proposing additionally three feature interpretation methods, based respectively on SOM, Generative Topographic Mapping and RF. The analysis shows that two of the proposed methods produce promising results on the given dataset.
Neural Font Rendering

Recent advances in deep learning techniques and applications have revolutionized artistic creation and manipulation in many domains (text, images, music); however, fonts have not yet been integrated with deep learning architectures in a manner that supports their multi-scale nature. In this work we aim to bridge this gap, proposing a network architecture capable of rasterizing glyphs in multiple sizes, potentially paving the way for easy and accessible creation and manipulation of fonts.
Algebraic criterion for absolute stability, optimality and passivity of dynamic systems

The paper presents a solution to the fundamental algebraic problem: find the necessary and sufficient conditions on the coefficients of a real polynomial II(Ω) so that it is nonnegative, i.e.II(Ω)≥0 for all real ω≥0The solution leads to a unique algebraic criterion for absolute stability, optimality and passivity of dynamic systems, which is an alternative to various analytic frequency criteria developed in modern system theory. The algebraic criterion is superior to the analytic criteria since it avoids any graphical construction and is suitable for machine computations.
COVID-19 Deep Clustering: An Ontology construction clustering method with dynamic medical labeling

This paper introduces a novel clustering-based framework for COVID-19 ontology construction using Pubmed LitCovid scientific research articles data. Our study uses a semantic approach with hierarchical clustering to construct a more effective COVID-19 documents ontology with medical labeling and search. We believe this study may initiate a future development for an advanced COVID-19 domain-specific ontology. The significant contribution from this research addresses solving the limitations in manual classification tasks of the everyday fast-increasing number of scientific papers and the overloading of their unclassified knowledge. With this research, our provision will help scholars with a better search mechanism to retrieve highly relevant expert information about their favorite topics in the COVID-19-related literature. To our best knowledge, this approach is the first successful attempt to apply auto clustering with labeling and search on the COVID-19 research papers. Moreover, in text processing, we propose a systematical evaluation without dependence on standard data collection to evaluate our methodology.
Decision graph for timely delivery of multi-AGVs in healthcare environment

The availability of Automated Guided Vehicles (AGVs) in flexible manufacturing systems is becoming the norm. However, in human-aware environments, e.g. healthcare environments, the movement of both AGVs and humans would inflict mutual harm. To reduce the harmfulness and the earliness/tardiness of ill-timed arrivals, a centralized control system needs to not only forecast the forthcoming traffic situation but also avoid abusing the emergency mode of AGVs. We propose a decision graph to reflect geometric topology and the ongoing traffic situation in the environment. This graph also allows the centralized control system to decide which AGV will be in emergency mode. Given this graph and a chosen regular shortest-path algorithm, the system could find out the best paths for AGVs to finish timely delivery and mitigate harmful effects on humans.
Leveraging Reinforcement Learning and Generative Adversarial Networks to Craft Mutants of Windows Malware against Black-box Malware Detectors

To build an effective malware detector, it is required to collect a diversity of malware samples and their evolution, since malware authors always try to evade detectors through strategies of malware mutation. So, this paper explores the ability to craft mutants of malware for gathering numerous mutated samples in training a machine learning (ML)-based malware detector. Specifically, we leverage Reinforcement Learning (RL) and Generative Adversarial Networks (GAN) to generate adversarial malware samples against ML-based detectors. The more we use this approach with different targeted antivirus and malware samples in training the RL agent as a malware mutator, the more it learns how to avoid black box malware detectors. The experimental results in real-world dataset indicate that RL can help GAN in crafting variants of malware with executability preservation to evade ML-based detectors and VirusTotal. Finally, this approach can be used as an automated tool for benchmarking the robustness of malware detectors against the metamorphic malwares.
Barriers and Supports to Offering Computer Science in High Schools: A Case Study of Structures and Agents

In the age of computing, there still exist many schools that do not offer computer science courses. The reason can be esoteric to designers of interventions, curricula, and policies. This study aims to answer the research question: What do school officials perceive as barriers to and supports for offering computer science courses at their school? This article provides a case study of four public high schools in the state of Georgia to better understand the barriers to adopting computer science. Interviews conducted with school principals, counselors, and teachers were thematically analyzed to study each case. The pattern that emerged from these themes is framed by structuration theory, wherein the structures (school policies, resources, and courses) influence the agents (teachers, students, the community), and in turn, the agents influence the structures. This framing provides insights into what attributes can be supports for or barriers against schools offering computer science courses.