Miracle-dz/UniFAR-FTUs huggingface dataset:
The normalized Laplacian spectrum and eigentime identities of hype-cubes

Many popular graph metrics encode average properties of individual network elements. Complementing these conventional graph metrics, the eigenvalue spectrum of the normalized Laplacian describes a network's structure directly at a systems level, without referring to individual nodes or connections. In this paper, we study the spectrum and their applications of normalized Laplacian matrices of hype-cubes, a special kind of Cayley graphs. We determine explicitly all the eigenvalues and their corresponding multiplicities by a recursive method. By using the relation between normalized Laplacian spectrum and eigentime identity, we derive the explicit formula to the eigentime identity for random walks on the hype-cubes and show that it grows linearly with the network size. Moreover, we compute the number of spanning trees of the hype-cubes.
CV QKD with discretized modulations in the strong noise regime.

We consider a general family of quantum key distribution (QKD) protocols utilizing displaced thermal states with discretized modulations. Separating the effects of the Gaussian channel and the non-Gaussian distribution, we have studied the dependence of the secret key generation rate on the magnitude of modulations (the strength of the modulated signal). We show that in the limit of strong signal, QKD is impossible: from the perspective of an efficient eavesdropper, the ensemble of transmitted states is effectively classical. This constitutes a quantum correction to performance of finite-length QKD protocols. We demonstrate that two regimes must be distinguished: weak and strong thermal noise. In the case of strong noise, the security boundary is mostly determined by the weak-signal limit. When the noise is weak, however, QKD may become possible only when the signal strength exceeds some critical value.
Special Orthogonal Group SO(3), Euler Angles, Angle-axis, Rodriguez Vector and Unit-Quaternion: Overview, Mapping and Challenges

The attitude of a rigid-body in the three dimensional space has a unique and global definition on the Special Orthogonal Group SO (3). This paper gives an overview of the rotation matrix, attitude kinematics and parameterization. The four most frequently used methods of attitude representations are discussed with detailed derivations, namely Euler angles, angle-axis parameterization, Rodriguez vector, and unit-quaternion. The mapping from one representation to others including SO (3) is given. Also, important results which could be useful for the process of filter and/or control design are given. The main weaknesses of attitude parameterization using Euler angles, angle-axis parameterization, Rodriguez vector, and unit-quaternion are illustrated. Keywords: Special Orthogonal Group 3, Euler angles, Angle-axis, Rodriguez Vector, Unit-quaternion, SO(3), Mapping, Parameterization, Attitude, Control, Filter, Observer, Estimator, Rotation, Rotational matrix, Transformation matrix, Orientation, Transformation, Roll, Pitch, Yaw, Quad-rotor, Unmanned aerial vehicle, Robot, spacecraft, satellite, UAV, Underwater vehicle, autonomous, system, Pose, literature review, survey, overview, comparison, comparative study, body frame, identity, origin, dynamics, kinematics, Lie group, inertial frame, zero, filter, control, estimate, observation, measurement, 3D, three dimensional space, advantage, disadvantage.
Automatic Generation of Pull Request Descriptions

Enabled by the pull-based development model, developers can easily contribute to a project through pull requests (PRs). When creating a PR, developers can add a free-form description to describe what changes are made in this PR and/or why. Such a description is helpful for reviewers and other developers to gain a quick understanding of the PR without touching the details and may reduce the possibility of the PR being ignored or rejected. However, developers sometimes neglect to write descriptions for PRs. For example, in our collected dataset with over 333K PRs, more than 34% of the PR descriptions are empty. To alleviate this problem, we propose an approach to automatically generate PR descriptions based on the commit messages and the added source code comments in the PRs. We regard this problem as a text summarization problem and solve it using a novel sequence-to-sequence model. To cope with out-of-vocabulary words in software artifacts and bridge the gap between the training loss function of the sequence-to-sequence model and the evaluation metric ROUGE, which has been shown to correspond to human evaluation, we integrate the pointer generator and directly optimize for ROUGE using reinforcement learning and a special loss function. We build a dataset with over 41K PRs and evaluate our approach on this dataset through ROUGE and a human evaluation. Our evaluation results show that our approach outperforms two baselines by significant margins.
Latent Distance Estimation for Random Geometric Graphs

Random geometric graphs are a popular choice for a latent points generative model for networks. Their definition is based on a sample of $n$ points $X_1,X_2,\cdots,X_n$ on the Euclidean sphere~$\mathbb{S}^{d-1}$ which represents the latent positions of nodes of the network. The connection probabilities between the nodes are determined by an unknown function (referred to as the ``link'' function) evaluated at the distance between the latent points. We introduce a spectral estimator of the pairwise distance between latent points and we prove that its rate of convergence is the same as the nonparametric estimation of a function on $\mathbb{S}^{d-1}$, up to a logarithmic factor. In addition, we provide an efficient spectral algorithm to compute this estimator without any knowledge on the nonparametric link function. As a byproduct, our method can also consistently estimate the dimension $d$ of the latent space.
Loam livox: A fast, robust, high-precision LiDAR odometry and mapping package for LiDARs of small FoV

LiDAR odometry and mapping (LOAM) has been playing an important role in autonomous vehicles, due to its ability to simultaneously localize the robot’s pose and build high-precision, high-resolution maps of the surrounding environment. This enables autonomous navigation and safe path planning of autonomous vehicles. In this paper, we present a robust, real-time LOAM algorithm for LiDARs with small FoV and irregular samplings. By taking effort on both frontend and back-end, we address several fundamental challenges arising from such LiDARs, and achieve better performance in both precision and efficiency compared to existing baselines. To share our findings and to make contributions to the community, we open source our codes on Github1.
Superconductivity in High-Entropy-Alloy Telluride AgInSnPbBiTe5

A polycrystalline sample of a high-entropy-alloy-type telluride AgInSnPbBiTe5 was synthesized by high-pressure synthesis. Elemental and structural analyses revealed that five metals are solved in a metal site of the NaCl-type structure, which satisfies with the criterion of high-entropy-alloy state. A superconducting transition with a transition temperature of 2.5 K was observed in AgInSnPbBiTe5.
A Convolutional Transformation Network for Malware Classification

Modern malware evolves various detection avoidance techniques to bypass the state-of-the-art detection methods. An emerging trend to deal with this issue is the combination of image transformation and machine learning techniques to classify and detect malware. However, existing works in this field only perform simple image transformation methods that limit the accuracy of the detection. In this paper, we introduce a novel approach to classify malware by using a deep network on images transformed from binary samples. In particular, we first develop a novel hybrid image transformation method to convert binaries into color images that convey the binary semantics. The images are trained by a deep convolutional neural network that later classifies the test inputs into benign or malicious categories. Through the extensive experiments, our proposed method surpasses all baselines and achieves 99.14% in terms of accuracy on the testing set.
Coupling Rendering and Generative Adversarial Networks for Artificial SAS Image Generation

There is a growing demand for large-scale Synthetic Aperture Sonar (SAS) datasets. This demand stems from data-driven applications such as Automatic Target Recognition (ATR) [1]–[3], segmentation [4] and oceanographic research of the seafloor, simulation for sensor prototype development and calibration [5], and even potential higher level tasks such as motion estimation [6] and micronavigation [7]. Unfortunately, the acquisition of SAS data is bottlenecked by the costly deployment of SAS imaging systems, and even when data acquisition is possible, the data is often skewed towards containing barren seafloor rather than objects of interest. This skew introduces a data imbalance problem wherein a dataset can have as much as a 1000-to-1 ratio of seafloor background to object-of-interest SAS image chips.
Comments on anomalies in supersymmetric theories

We analyse the relation between anomalies in their manifestly supersymmetric formulation in superspace and their formulation in Wess–Zumino (WZ) gauges. We show that there is a one-to-one correspondence between the solutions of the cohomology problem in the two formulations and that they are related by a particular choice of a superspace counterterm (‘scheme’). Any apparent violation of Q-supersymmetry is due to an explicit violation by the counterterm which defines the scheme equivalent to the WZ gauge. It is therefore removable.
Modeling Collaboration for Robot-assisted Dressing Tasks

We investigated the application of haptic aware feedback control and deep reinforcement learning to robot assisted dressing in simulation. We did so by modeling both human and robot control policies as separate neural networks and training them both via TRPO. We show that co-optimization, training separate human and robot control policies simultaneously, can be a valid approach to finding successful strategies for human/robot cooperation on assisted dressing tasks. Typical tasks are putting on one or both sleeves of a hospital gown or pulling on a T-shirt. We also present a method for modeling human dressing behavior under variations in capability including: unilateral muscle weakness, Dyskinesia, and limited range of motion. Using this method and behavior model, we demonstrate discovery of successful strategies for a robot to assist humans with a variety of capability limitations.
Multilingual ASR with Massive Data Augmentation

Towards developing high-performing ASR for low-resource languages, approaches to address the lack of resources are to make use of data from multiple languages, and to augment the training data by creating acoustic variations. In this work we present a single grapheme-based ASR model learned on 7 geographically proximal languages, using standard hybrid BLSTM-HMM acoustic models with lattice-free MMI objective. We build the single ASR grapheme set via taking the union over each language-specific grapheme set, and we find such multilingual ASR model can perform language-independent recognition on all 7 languages, and substantially outperform each monolingual ASR model. Secondly, we evaluate the efficacy of multiple data augmentation alternatives within language, as well as their complementarity with multilingual modeling. Overall, we show that the proposed multilingual ASR with various data augmentation can not only recognize any within training set languages, but also provide large ASR performance improvements.
Embedding theorems in the fractional Orlicz-Sobolev space and applications to non-local problems

In the present paper, we deal with a new continuous and compact embedding theorems for the fractional Orlicz-Sobolev spaces, also, we study the existence of infinitely many nontrivial solutions for a class of non-local fractional Orlicz-Sobolev Schrodinger equations whose simplest prototype is $$(-\triangle)^{s}_{m}u+V(x)m(u)=f(x,u),\ x\in\mathbb{R}^{d},$$ where $0<s<1$, $d\geq2$ and $(-\triangle)^{s}_{m}$ is the fractional $M$-Laplace operator. The proof is based on the variant Fountain theorem established by Zou.
AN INVESTIGATION OF “PERCEIVED FEEDBACK” IN CLINICAL EDUCATION OF MIDWIFERY STUDENTS IN ISFAHAN UNIVERSITY OF MEDICAL SCIENCES

Introduction: The main purpose of education is to develop clinical competencies among the students of health sciences. Meaningful and constructive feedback is necessary to the learners to attain constructive knowledge. The goal of this study was to assess "the perceived feedback" by the midwifery students of Isfahan University of Medical Sciences. Methods: We performed this descriptive cross-sectional study on 25 senior midwifery students studying at Isfahan University of Medical Sciences in 2013-2014 academic years. Participants were selected through census and data were collected using a researcher-made questionnaire. Medical education specialists and midwifery experts assessed the content and face validity of the questionnaire and its reliability was measured through Cronbach’s alpha coefficient (α = 0.86). The five-point Likert Scale was used (Never=1, Rarely=2, Sometimes=3, Very Often=4, Always=5) in the questionnaire. The expected mean was considered 4 (above the average). The collected data were analyzed using descriptive statistics tests, one-sample t-test and Anova. Results: 24 students of midwifery completed 180 questionnaires according to the classification of courses and instructors. The mean scores obtained for perceived feedback were 3.69±0.66 for the content of feedback area, 3.71±0.55 for the method of providing feedback, and 3.94±0.67 for the skills of providing feedback area. No significant difference was found between the different courses regarding the mean scores in different areas. Conclusion: The results showed that basic rules of providing feedback in all three areas of content, method, and skills of providing feedback were not implemented appropriately and at a desirable level. Therefore, it seems necessary to increase the instructors’ knowledge on the importance of presenting feedback and training them on appropriate methods of giving feedback in order to improve the quality of clinical education in midwifery.
Learning Invariants through Soft Unification

Human reasoning involves recognising common underlying principles across many examples by utilising variables. The by-products of such reasoning are invariants that capture patterns across examples such as "if someone went somewhere then they are there" without mentioning specific people or places. Humans learn what variables are and how to use them at a young age, and the question this paper addresses is whether machines can also learn and use variables solely from examples without requiring human pre-engineering. We propose Unification Networks that incorporate soft unification into neural networks to learn variables and by doing so lift examples into invariants that can then be used to solve a given task. We evaluate our approach on four datasets to demonstrate that learning invariants captures patterns in the data and can improve performance over baselines.
Entity-Consistent End-to-end Task-Oriented Dialogue System with KB Retriever

Querying the knowledge base (KB) has long been a challenge in the end-to-end task-oriented dialogue system. Previous sequence-to-sequence (Seq2Seq) dialogue generation work treats the KB query as an attention over the entire KB, without the guarantee that the generated entities are consistent with each other. In this paper, we propose a novel framework which queries the KB in two steps to improve the consistency of generated entities. In the first step, inspired by the observation that a response can usually be supported by a single KB row, we introduce a KB retrieval component which explicitly returns the most relevant KB row given a dialogue history. The retrieval result is further used to filter the irrelevant entities in a Seq2Seq response generation model to improve the consistency among the output entities. In the second step, we further perform the attention mechanism to address the most correlated KB column. Two methods are proposed to make the training feasible without labeled retrieval data, which include distant supervision and Gumbel-Softmax technique. Experiments on two publicly available task oriented dialog datasets show the effectiveness of our model by outperforming the baseline systems and producing entity-consistent responses.
Balancing Error and Dissipation in Highly-Reliable Computing

Modern digital electronics support remarkably reliable computing, especially given the challenge of controlling nanoscale logical components that interact in fluctuating environments. However, the high-reliability limit is subject to a fundamental error-energy-efficiency tradeoff that arises from time-symmetric control. Requiring a low probability of error causes energy consumption to diverge as logarithm of the inverse error rate for nonreciprocal logical transitions. The reciprocity (self-invertibility) of a computation is a stricter condition for thermodynamic efficiency than logical reversibility (invertibility), the latter being the root of Landauer's work bound. In fact, the average energy required for reliable erasure is well above that bound. Explicit simulations of work production closely track the error-dissipation tradeoff, diverging from the Landauer bound as the error rate decreases. And, unlike the Landauer work, which can be recovered, the nonreciprocal work must be irreversibly dissipated. Analogous bounds hold for the universal NAND gate and extend to circuits of logical gates. That said, strictly-reciprocal logic gates, such as communication relays and NOT gates, are exempt and can be efficiently implemented via time-symmetric protocols. For all other computations, though, time-asymmetric control must be used to avoid dissipation arising from nonreciprocity. The lesson is that highly-reliable computation under time-symmetric control cannot reach, and is often far above, the Landauer limit. In this way, time-asymmetry becomes a design principle for thermodynamically-efficient computing. We also demonstrate that the time-reversal symmetries of the memory elements themselves play an essential role in determining the minimal energy necessary to implement a computation.
ReduKtor: How We Stopped Worrying About Bugs in Kotlin Compiler

Bug localization is well-known to be a difficult problem in software engineering, and specifically in compiler development, where it is beneficial to reduce the input program to a minimal reproducing example; this technique is more commonly known as delta debugging. What additionally contributes to the problem is that every new programming language has its own unique quirks and foibles, making it near impossible to reuse existing tools and approaches with full efficiency. In this experience paper we tackle the delta debugging problem w.r. t. Kotlin, a relatively new programming language from JetBrains. Our approach is based on a novel combination of program slicing, hierarchical delta debugging and Kotlin-specific transformations, which are synergistic to each other. We implemented it in a prototype called ReduKtor and did extensive evaluation on both synthetic and real Kotlin programs; we also compared its performance with classic delta debugging techniques. The evaluation results support the practical usability of our approach to Kotlin delta debugging and also shows the importance of using both language-agnostic and language-specific techniques to achieve best reduction efficiency and performance.
Learning Controls Using Cross-Modal Representations: Bridging Simulation and Reality for Drone Racing

—Machines are a long way from robustly solving open-world perception-control tasks, such as ﬁrst-person view (FPV) drone racing. While recent advances in Machine Learning, especially Reinforcement and Imitation Learning show promise, they are constrained by the need of large amounts of difﬁcult to collect real-world data for learning robust behaviors in diverse scenarios. In this work we propose to learn rich representations and policies by leveraging unsupervised data, such as video footage from an FPV drone, together with easy to generate simulated labeled data. Our approach takes a cross-modal perspective, where separate modalities correspond to the raw camera sensor data and the system states relevant to the task, such as the relative pose gates to the UAV. We fuse both data modalities into a novel factored architecture that learns a joint low-dimensional representation via Variational Auto Encoders. Such joint representations allow us to leverage rich labeled information from simulations together with the diversity of possible experiences via the unsupervised real-world data. We present experiments in simulation that provide insights into the rich latent spaces learned with our proposed representations, and also show that the use of our cross-modal architecture improves control policy performance in over 5X in comparison with end-to-end learning or purely unsupervised feature extractors. Finally, we present real-life results for drone navigation, showing that the learned representations and policies can generalize across simulation and reality.
ISCOs and OSCOs in the presence of positive cosmological constant

Petarpa Boonserm , Tritos Ngampitipan , Alex Simpson 4 ID , and Matt Visser 4 ID 1 Department of Mathematics and Computer Science, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand. 2 Faculty of Science, Chandrakasem Rajabhat University, Bangkok 10900, Thailand. 3 Thailand Center of Excellence in Physics, Ministry of Education, Bangkok 10400, Thailand 4 School of Mathematics and Statistics, Victoria University of Wellington, PO Box 600, Wellington 6140, New Zealand E-mail: petarpa.boonserm@gmail.com, tritos.ngampitipan@gmail.com, alex.simpson@sms.vuw. ac.nz, matt.visser@sms.vuw. ac.nz
Lubricated friction of textured soft substrates

The understanding of sliding friction for wet, patterned surfaces from first principles is challenging. While emerging applications have sought design principles from biology, a general framework is lacking because soft interfaces experience a multiphysics coupling between solid deformation and fluid dissipation. We investigate the elastohydrodynamic sliding of >50 patterned sliding pairs comprising elastomers, thermosets, and hydrogels, and discover that texturing induces a critical transition in the macroscopic friction coefficient. This critical friction scales universally, without any fitting parameters, with the reduced elastic modulus and the pattern geometry. To capture the frictional dissipation, we separate the flow curve into two regimes and account for the contributions of shear and normal forces applied by the fluid on the patterns. Our model combines Reynolds' equations and elastic deformation to provide physical insights that allow engineering of the elastohydrodynamic friction in a class of soft tribopairs using pattern geometry, material elasticity, and fluid properties.