Miracle-dz/UniFAR-FTUs huggingface dataset:
Structural Coupling for Microservices

Cloud-native Applications are 'distributed, elastic and horizontal-scalable systems composed of (micro)services which isolate states in a minimum of stateful components'. Hence, an important property is to ensure a low coupling and a high cohesion among the (micro)services composing the cloud-native application. Loosely coupled and highly cohesive services allow development teams to work in parallel, reducing the communication overhead between teams. However, despite both practitioners and researchers agree on the importance of this general property, there are no validated metrics to effectively measure or test the actual coupling level between services. In this work, we propose ways to compute and visualize the coupling between microservices, by extending and adapting the concepts behind the computation of the traditional structural coupling. We validate these measures with a case study involving 17 open-source projects and we provide an automatic approach to measure them. The results of this study highlight how these metrics provide to practitioners a quantitative and visual view of services compositions, which can be useful to conceive advanced systems to monitor the evolution of the service.
Development of an Energy-Sensitive Detector for the Atom Probe Tomography

Abstract A position and energy-sensitive detector has been developed for atom probe tomography (APT) instruments in order to deal with some mass peak overlap issues encountered in APT experiments. Through this new type of detector, quantitative and qualitative improvements could be considered for critical materials with mass peak overlaps, such as nitrogen and silicon in TiSiN systems, or titanium and carbon in cemented carbide materials. This new detector is based on a thin carbon foil positioned on the front panel of a conventional MCP-DLD detector. According to several studies, it has been demonstrated that the impact of ions on thin carbon foils has the effect of generating a number of transmitted and reflected secondary electrons. The number generated mainly depends on both the kinetic energy and the mass of incident particles. Despite the fact that this phenomenon is well known and has been widely discussed for decades, no studies have been performed to date for using it as a means to discriminate particles energy. Therefore, this study introduces the first experiments on a potential new generation of APT detectors that would be able to resolve mass peak overlaps through the energy-sensitivity of thin carbon foils.
High-Resolution Segmentation of Tooth Root Fuzzy Edge Based on Polynomial Curve Fitting with Landmark Detection

As the most economical and routine auxiliary examination in the diagnosis of root canal treatment, oral X-ray has been widely used by stomatologists. It is still challenging to segment the tooth root with a blurry boundary for the traditional image segmentation method. To this end, we propose a model for high-resolution segmentation based on polynomial curve fitting with landmark detection (HS-PCL). It is based on detecting multiple landmarks evenly distributed on the edge of the tooth root to fit a smooth polynomial curve as the segmentation of the tooth root, thereby solving the problem of fuzzy edge. In our model, a maximum number of the shortest distances algorithm (MNSDA) is proposed to automatically reduce the negative influence of the wrong landmarks which are detected incorrectly and deviate from the tooth root on the fitting result. Our numerical experiments demonstrate that the proposed approach not only reduces Hausdorff95 (HD95) by 33.9% and Average Surface Distance (ASD) by 42.1% compared with the stateof-the-art method, but it also achieves excellent results on the minute quantity of datasets, which greatly improves the feasibility of automatic root canal therapy evaluation by medical image computing.
Growing a Test Corpus with Bonsai Fuzzing

This paper presents a coverage-guided grammar-based fuzzing technique for automatically synthesizing a corpus of concise test inputs. We walk-through a case study of a compiler designed for education and the corresponding problem of generating meaningful test cases to provide to students. The prior state-of-the-art solution is a combination of fuzzing and test-case reduction techniques such as variants of delta-debugging. Our key insight is that instead of attempting to minimize convoluted fuzzer-generated test inputs, we can instead grow concise test inputs by construction using a form of iterative deepening. We call this approach bonsai fuzzing. Experimental results show that bonsai fuzzing can generate test corpora having inputs that are 16–45% smaller in size on average as compared to a fuzz-then-reduce approach, while achieving approximately the same code coverage and fault-detection capability.
Warm millimetre dust in protoplanetary discs near massive stars

Dust plays a key role in the formation of planets and its emission also provides one of our most accessible views of protoplanetary discs. If set by radiative equilibrium with the central star, the temperature of dust in the disc plateaus at around 10 − 20K in the outer regions. However sufficiently nearby massive stars can heat the outer disc to substantially higher temperatures. In this paper we study the radiative equilibrium temperature of discs in the presence of massive external sources and gauge the effect that it has on millimetre dust mass estimates. Since millimetre grains are not entrained in any wind we focus on geometrically simple 2Daxisymmetric disc models using radiative transfer calculations with both the host star and an external source. Recent surveys have searched for evidence of massive stars influencing disc evolution using disc properties as a function of projected separation. In assuming a disc temperature of 20K for a disc a distance D from a strong radiation source, disc masses are overestimated by a factor that scales with D−1/2 interior to the separation that external heating becomes important. This could significantly alter dust mass estimates of discs in close proximity to θ1C in the Orion Nebular Cluster. We also make an initial assessment of the effect upon snow lines. Within a parsec of an O star like θ1C a CO snow line no longer exists, though the water snow line is virtually unaffected except for very close separations of ≤ 0.01 pc.
Carbon-free high-loading silicon anodes enabled by sulfide solid electrolytes

Description Silicon anode solid-state batteries Research on solid-state batteries has focused on lithium metal anodes. Alloy-based anodes have received less attention in part due to their lower specific capacity even though they should be safer. Tan et al. developed a slurry-based approach to create films from micrometer-scale silicon particles that can be used in anodes with carbon binders. When incorporated into solid-state batteries, they showed good performance across a range of temperatures and excellent cycle life in full cells. —MSL Solid electrolytes enable the use of microparticle silicon anodes for stable, high-performance all solid-state batteries. The development of silicon anodes for lithium-ion batteries has been largely impeded by poor interfacial stability against liquid electrolytes. Here, we enabled the stable operation of a 99.9 weight % microsilicon anode by using the interface passivating properties of sulfide solid electrolytes. Bulk and surface characterization, and quantification of interfacial components, showed that such an approach eliminates continuous interfacial growth and irreversible lithium losses. Microsilicon full cells were assembled and found to achieve high areal current density, wide operating temperature range, and high areal loadings for the different cells. The promising performance can be attributed to both the desirable interfacial property between microsilicon and sulfide electrolytes and the distinctive chemomechanical behavior of the lithium-silicon alloy.
Design, Optimal Guidance and Control of a Low-cost Re-usable Electric Model Rocket

In the last decade, autonomous vertical take-off and landing (VTOL) vehicles have become increasingly important as they lower mission costs thanks to their re-usability. However, their development is complex, rendering even the basic experimental validation of the required advanced guidance and control (G & C) algorithms prohibitively time-consuming and costly. In this paper, we present the design of an inexpensive small-scale VTOL platform that can be built from off-the-shelf components for less than 1000 USD. The vehicle design mimics the first stage of a reusable launcher, making it a perfect test-bed for G & C algorithms. To control the vehicle during ascent and descent, we propose a real-time optimization-based G & C algorithm. The key features are a real-time minimum fuel and free-final-time optimal guidance combined with an offset-free tracking model predictive position controller. The vehicle hardware design and the G & C algorithm are experimentally validated both indoors and outdoor, showing reliable operation in a fully autonomous fashion with all computations done on-board and in real-time.
Orthogonal Attention: A Cloze-Style Approach to Negation Scope Resolution

Negation Scope Resolution is an extensively researched problem, which is used to locate the words affected by a negation cue in a sentence. Recent works have shown that simply finetuning transformer-based architectures yield state-of-the-art results on this task. In this work, we look at Negation Scope Resolution as a Cloze-Style task, with the sentence as the Context and the cue words as the Query. We also introduce a novel Cloze-Style Attention mechanism called Orthogonal Attention, which is inspired by Self Attention. First, we propose a framework for developing Orthogonal Attention variants, and then propose 4 Orthogonal Attention variants: OA-C, OA-CA, OA-EM, and OA-EMB. Using these Orthogonal Attention layers on top of an XLNet backbone, we outperform the finetuned XLNet state-of-the-art for Negation Scope Resolution, achieving the best results to date on all 4 datasets we experiment with: BioScope Abstracts, BioScope Full Papers, SFU Review Corpus and the *sem 2012 Dataset (Sherlock).
Generalizing trial evidence to target populations in non‐nested designs: Applications to AIDS clinical trials

Comparative effectiveness evidence from randomized trials may not be directly generalizable to a target population of substantive interest when, as in most cases, trial participants are not randomly sampled from the target population. Motivated by the need to generalize evidence from two trials conducted in the AIDS Clinical Trials Group (ACTG), we consider weighting, regression and doubly robust estimators to estimate the causal effects of HIV interventions in a specified population of people living with HIV in the USA. We focus on a non‐nested trial design and discuss strategies for both point and variance estimation of the target population average treatment effect. Specifically in the generalizability context, we demonstrate both analytically and empirically that estimating the known propensity score in trials does not increase the variance for each of the weighting, regression and doubly robust estimators. We apply these methods to generalize the average treatment effects from two ACTG trials to specified target populations and operationalize key practical considerations. Finally, we report on a simulation study that investigates the finite‐sample operating characteristics of the generalizability estimators and their sandwich variance estimators.
Lectures on Symmetric Tensor Categories

This is an expanded version of the notes by the second author of the lectures on symmetric tensor categories given by the first author at Ohio State University in March 2019 and later at ICRA-2020 in November 2020. We review some aspects of the current state of the theory of symmetric tensor categories and discuss their applications, including ones unavailable in the literature.
Observation of the tradeoff between internal quantum nonseparability and external classical correlations

Jie Zhu, Yue Dai, 3 S. Camalet, ∗ Cheng-Jie Zhang, † Bi-Heng Liu, Chuan-Feng Li, Guang-Can Guo, and Yong-Sheng Zhang ‡ Key Laboratory of Quantum Information, University of Science and Technology of China, CAS, Hefei, 230026, China, and CAS Center for Excellence in Quantum Information and Quantum Physics, Hefei, 230026, China School of Physical Science and Technology, Ningbo University, Ningbo, 315211, China School of Physical Science and Technology, Soochow University, Suzhou, 215006, China Sorbonne Université, CNRS, Laboratoire de Physique Théorique de la Matière Condensée, LPTMC, F-75005 Paris, France (Dated: March 9, 2021)
Local word statistics affect reading times independently of surprisal

Surprisal theory has provided a unifying framework for understanding many phenomena in sentence processing (Hale, 2001; Levy, 2008a), positing that a word's conditional probability given all prior context fully determines processing difficulty. Problematically for this claim, one local statistic, word frequency, has also been shown to affect processing, even when conditional probability given context is held constant. Here, we ask whether other local statistics have a role in processing, or whether word frequency is a special case. We present the first clear evidence that more complex local statistics, word bigram and trigram probability, also affect processing independently of surprisal. These findings suggest a significant and independent role of local statistics in processing. Further, it motivates research into new generalizations of surprisal that can also explain why local statistical information should have an outsized effect.
Self-scattering on large, porous grains in protoplanetary disks with dust settling

Context. Observations of protoplanetary disks in the sub-millimetre wavelength range suggest that polarisation is caused by scattering of thermal re-emission radiation. Most of the dust models that are used to explain these observations have major drawbacks: they either use much smaller grain sizes than expected from dust evolution models, or result in polarisation degrees that are lower than observed. Aims. We investigate the effect of dust grain porosity on the observable polarisation due to scattering at sub-millimetre wavelengths arising from grain size distributions up to millimetre sizes, as they are expected to be present close to the midplane of protoplanetary disks. Methods. Using the effective medium theory, we calculated the optical properties of porous dust and used them to predict the behaviour of the observable polarisation degree due to scattering. Subsequently, Monte Carlo radiative transfer simulations for protoplanetary disks with porous dust grains were performed to analyse the additional effect of the optical depth structure, and thus the effect of multiple scattering events and inhomogeneous temperature distributions on the net observable polarisation degree. Results. We find that porous dust grains with moderate filling factors of about 10% increase the degree of polarisation compared to compact grains. For higher grain porosities, that is, grains with a filling factor of 1% or lower, the extinction opacity decreases, as does the optical depth of a disk with constant mass. Consequently, the unpolarised direct radiation dominates the scattered flux, and the degree of polarisation drops rapidly. Even though the simulated polarisation degree is higher than in the case of compact grains, it is still below the typical observed values for face-on disks. However, the polarisation degree can be increased when crucial model assumptions derived from disk and dust evolution theories, for instance, dust settling and millimetre-sized dust grains, are dropped. In the case of inclined disks, however, our reference model is able to achieve polarisation degrees of about 1%, and using higher disk masses, even higher than this.
Reinforcement Learning, Bit by Bit

Reinforcement learning agents have demonstrated remarkable achievements in simulated environments. Data efficiency poses an impediment to carrying this success over to real environments. The design of data-efficient agents calls for a deeper understanding of information acquisition and representation. We discuss concepts and regret analysis that together offer principled guidance. This line of thinking sheds light on questions of what information to seek, how to seek that information, and what information to retain. To illustrate concepts, we design simple agents that build on them and present computational results that highlight data efficiency.
PyRCN: Exploration and Application of ESNs

—As a family member of Recurrent Neural Networks and similar to Long-Short-Term Memory cells, Echo State Networks (ESNs) are capable of solving temporal tasks, but with a substantially easier training paradigm based on linear regression. However, optimizing hyper-parameters and efﬁciently implementing the training process might be somewhat over-whelming for the ﬁrst-time users of ESNs. This paper aims to facilitate the understanding of ESNs in theory and practice. Treating ESNs as non-linear ﬁlters, we explain the effect of the hyper-parameters using familiar concepts such as impulse responses. Furthermore, the paper introduces the Python toolbox PyRCN (Python Reservoir Computing Network) for developing, training and analyzing ESNs on arbitrarily large datasets. The tool is based on widely-used scientiﬁc packages, such as numpy and scipy and offers an interface to scikit-learn. Example code and results for classiﬁcation and regression tasks are provided.
Improving Global Adversarial Robustness Generalization With Adversarially Trained GAN

Convolutional neural networks (CNNs) have achieved beyond human-level accuracy in the image classification task and are widely deployed in real-world environments. However, CNNs show vulnerability to adversarial perturbations that are well-designed noises aiming to mislead the classification models. In order to defend against the adversarial perturbations, adversarially trained GAN (ATGAN) is proposed to improve the adversarial robustness generalization of the state-of-the-art CNNs trained by adversarial training. ATGAN incorporates adversarial training into standard GAN training procedure to remove obfuscated gradients which can lead to a false sense in defending against the adversarial perturbations and are commonly observed in existing GANs-based adversarial defense methods. Moreover, ATGAN adopts the image-to-image generator as data augmentation to increase the sample complexity needed for adversarial robustness generalization in adversarial training. Experimental results in MNIST SVHN and CIFAR-10 datasets show that the proposed method doesn't rely on obfuscated gradients and achieves better global adversarial robustness generalization performance than the adversarially trained state-of-the-art CNNs.
Discovering Diverse Multi-Agent Strategic Behavior via Reward Randomization

We propose a simple, general and effective technique, Reward Randomization for discovering diverse strategic policies in complex multi-agent games. Combining reward randomization and policy gradient, we derive a new algorithm, Reward-Randomized Policy Gradient (RPG). RPG is able to discover multiple distinctive human-interpretable strategies in challenging temporal trust dilemmas, including grid-world games and a real-world game Agar.io, where multiple equilibria exist but standard multi-agent policy gradient algorithms always converge to a fixed one with a sub-optimal payoff for every player even using state-of-the-art exploration techniques. Furthermore, with the set of diverse strategies from RPG, we can (1) achieve higher payoffs by fine-tuning the best policy from the set; and (2) obtain an adaptive agent by using this set of strategies as its training opponents. The source code and example videos can be found in our website: https://sites.google. com/view/staghuntrpg.
IRON: Invariant-based Highly Robust Point Cloud Registration

In this paper, we present IRON (Invariant-based global Robust estimation and OptimizatioN), a non-minimal and highly robust solution for point cloud registration with a great number of outliers among the correspondences. To realize this, we decouple the registration problem into the estimation of scale, rotation and translation, respectively. Our first contribution is to propose RANSIC (RANdom Samples with Invariant Compatibility), which employs the invariant compatibility to seek inliers among random samples and robustly estimates the scale between two sets of point clouds in the meantime. Once the scale is estimated, our second contribution is to relax the non-convex global registration problem into a convex Semi-Definite Program (SDP) in a certifiable way using Sum-of-Squares (SOS) Relaxation and show that the relaxation is tight. For robust estimation, we further propose RT-GNC (Rough Trimming and Graduated Non-Convexity), a global outlier rejection heuristic having better robustness and time-efficiency than traditional GNC, as our third contribution. With these contributions, we can render our registration algorithm, IRON. Through experiments over real datasets, we show that IRON is efficient, highly accurate and robust against as many as 99% outliers whether the scale is known or unknown, outperforming the existing state-of-the-art algorithms.
Joint Noise-Tolerant Learning and Meta Camera Shift Adaptation for Unsupervised Person Re-Identification

This paper considers the problem of unsupervised person re-identification (re-ID), which aims to learn discriminative models with unlabeled data. One popular method is to obtain pseudo-label by clustering and use them to optimize the model. Although this kind of approach has shown promising accuracy, it is hampered by 1) noisy labels produced by clustering and 2) feature variations caused by camera shift. The former will lead to incorrect optimization and thus hinders the model accuracy. The latter will result in assigning the intra-class samples of different cameras to different pseudo-label, making the model sensitive to camera variations. In this paper, we propose a unified framework to solve both problems. Concretely, we propose a Dynamic and Symmetric Cross-Entropy loss (DSCE) to deal with noisy samples and a camera-aware meta-learning algorithm (MetaCam) to adapt camera shift. DSCE can alleviate the negative effects of noisy samples and accommodate the change of clusters after each clustering step. MetaCam simulates cross-camera constraint by splitting the training data into meta-train and meta-test based on camera IDs. With the interacted gradient from meta-train and meta-test, the model is enforced to learn camera-invariant features. Extensive experiments on three re-ID benchmarks show the effectiveness and the complementary of the proposed DSCE and MetaCam. Our method outperforms the state-of-the-art methods on both fully unsupervised re-ID and unsupervised domain adaptive re-ID.