Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Need for Higher Order Interpolation with Overset Grid Methods

Overset methods, by overlapping several grids, allow large and arbitrary motion of bodies. First developed for the aerospace industry (Benek et al., 1985), it is now used for a wider range of applications like maritime ones (Carrica et al., 2013). At the fringes of each grid, the coupling is done by interpolating field variables. This interpolation step is key in any overset computations since the choice of the interpolation scheme directly influences the accuracy of the solution and the performance. Most overset implementations done for unstructured CFD codes uses 1st or at most 2nd order interpolation schemes (Gatin et al., 2018, Völkner et al., 2017, Chandar et al., 2018) but higher order interpolation schemes (3rd or 4th) are not often studied (Chandar, 2019). This paper evaluates the use of higher order (higher than 2nd order) interpolation schemes for the overset method by comparing the accuracy of different interpolation schemes on a Poiseuille flow test case, no performance study is however conducted here. The overset implementation used in this study is a coupling between the CFD solver ReFRESCO and the overset library Suggar++ (Noack and Boger, 2009). The paper is organised as follows: section 2 presents the overset approach and the different interpolation schemes used in this study. Section 3 discusses the robustness of Least Squares interpolation scheme. Section 4 describes the test cases ran for the study as well as the computational setup. Results are presented in section 5 and finally concluding remarks are given in section 6.
Optimal Decentralized Algorithms for Saddle Point Problems over Time-Varying Networks ∗

Decentralized optimization methods have been in the focus of optimization community due to their scalability, increasing popularity of parallel algorithms and many applications. In this work, we study saddle point problems of sum type, where the summands are held by separate computational entities connected by a network. The network topology may change from time to time, which models real-world network malfunctions. We obtain lower complexity bounds for algorithms in this setup and develop optimal methods which meet the lower bounds.
Ride-Pooling Matching with a Compensatory Cost Function: Implications for Adoption, Efficiency and Level of Service

By utilising vehicle capacity more efficiently, ride-pooling platforms can potentially lead to reduced congestion levels without adversely prolonging travel times. While previous studies concluded that shared rides can offer substantial benefits, initial evidence suggests low adoption levels. We postulate that previous studies that investigated the potential of ride-pooling failed to account for the trade-off that users are likely to make when considering a shared ride. We address this shortcoming by formulating user net benefit stemming from sharing as a compensatory function where the additional travel time and on-board discomfort need to be compensated by the price discount for a traveller to choose a shared ride over a private ride. The proposed formulation is embedded in a method for matching travel requests and vehicles. We conduct a series of experiments investigating how the potential of ride-pooling services depends on travel demand characteristics, user preferences and the pricing policy adopted by the service provider. In particular, the impact of various behavioural settings in terms of users' willingness to share their ride and delay aversion on service adoption and its operational efficiency is assessed. Our results suggest that the total vehicle mileage savings found by previous studies is only attainable when users are very willing to share their ride (i.e. attach low premium to private rides) and are offered a 50% discount for doing so. We find ride-pooling transportation distance savings as low as 15% in less favourable behavioural scenarios.
Escape of cosmic rays from perpendicular shocks in the interstellar magnetic field

We investigate the escape process from a perpendicular shock region of a spherical shock in the interstellar medium (ISM). The diffusive shock acceleration in the perpendicular shock of supernova remnants (SNRs) has been expected to accelerate cosmic rays (CRs) to the PeV scale without an upstream magnetic field amplification. We estimate the maximum energy of CRs limited by the escape from the perpendicular shock region. By performing test particle simulations, we confirm the theoretical estimation, showing that the escape-limited maximum energy in the perpendicular shock is several 10 TeV for the typical type Ia SNRs. Therefore, in order for SNRs in the ISM to accelerate CRs to the PeV scale, an upstream magnetic field amplification is needed. The characteristic energy scale of several 10 TeV could be the origin of the spectral break around 10 TeV, which was reported by recent direct CR observations. In addition, we show that, in the free expansion phase, the rapid perpendicular shock acceleration works on about 20% area of the whole shock surface, which is larger than the size of the superluminal shock region. We also discuss the escape of CR electrons from the perpendicular shock.
MSFNet: Multi-scale features network for monocular depth estimation

In recent years, monocular depth estimation is applied to understand the surrounding 3D environment and has made great progress. However, there is an ill-posed problem on how to gain depth information directly from a single image. With the rapid development of deep learning, this problem is possible to be solved. Although more and more approaches are proposed one after another, most of existing methods inevitably lost details due to continuous downsampling when mapping from RGB space to depth space. To the end, we design a Multi-scale Features Network (MSFNet), which consists of Enhanced Diverse Attention (EDA) module and Upsample-Stage Fusion (USF) module. The EDA module employs the spatial attention method to learn significant spatial information, while USF module complements low-level detail information with high-level semantic information from the perspective of multi-scale feature fusion to improve the predicted effect. In addition, since the simple samples are always trained to a better effect first, the hard samples are difficult to converge. Therefore, we design a batch-loss to assign large loss factors to the harder samples in a batch. Experiments on NYU-Depth V2 dataset and KITTI dataset demonstrate that our proposed approach is more competitive with the state-of-the-art methods in both qualitative and quantitative evaluation.
Determination of Triazine Herbicides in Soil by HPLC

A method was developed for determining residues of the seven triazine herbicides: atrazine, propazine, simazine, ametryn, prometryn, terbutryn and prometon in soil. Samples were extracted with acetonitrile or methanol-acetonitrile (3:7, by vol) and followed by cleanup on a neutral alumina column, and determination by HPLC with diode array detector at 224 nm. Recovery with this method ranged from 95.0 to 106.9%, and the coefficient of variation ranged from 1.24 to 6.83%. The lowest detectable concentrations in soil were 0.84 to 2.07 μg/kg.
Cautious Policy Programming: Exploiting KL Regularization in Monotonic Policy Improvement for Reinforcement Learning

In this paper, we propose cautious policy programming (CPP), a novel value-based reinforcement learning (RL) algorithm that can ensure monotonic policy improvement during learning. Based on the nature of entropy-regularized RL, we derive a new entropy regularization-aware lower bound of policy improvement that only requires estimating the expected policy advantage function. CPP leverages this lower bound as a criterion for adjusting the degree of a policy update for alleviating policy oscillation. Different from similar algorithms that are mostly theory-oriented, we also propose a novel interpolation scheme that makes CPP better scale in high dimensional control problems. We demonstrate that the proposed algorithm can trade o? performance and stability in both didactic classic control problems and challenging high-dimensional Atari games.
Preparation and in vitro Release of Multilayer Alginate Chitosan Microspheres Loading VEGF and Vancomycin

The core ball carrying vascular epithelial growth factor(VEGF) was first prepared by crosslinking of alginate and calcium.Then,chitosan,alginate,and chitosan again were coated on the surface of the core ball layer by layer using self-assembly technology,which constituted a multi-drug loaded release-controlled system at last,and vancomycin was loaded in the chitosan layer.The effect of the concentration of alginate,calcium and chitosan on microspheres' morphology,size,drug loading and in vitro release was studied by an orthogonal experiment to find the best formula design.Morphologies of the surface and cross section and diameter were observed by scanning electron microscopy(SEM). Infrared absorption spectrum of the multilayer microspheres was also detected by Fourier transformed infrared spectrometer(FTIR). The result showed that the best concentrations of alginate,calcium and chitosan were 0.04,0.15 and 0.01 g / m L,respectively.And in this case,the multilayer microspheres were typical spherical with smooth surface and the internal solid structure with a diameter ranging from 900 to 1100 μm. The entrapment efficiency of VEGF and vancomycin was 61.31% and 3.48%,respectively.In vitro,the release of VEGF could be sustained stably for up to15.5 d with two releasing peak,which is surprising and exciting.As well,the release of vancomycin lasted for 4.5 d,which also meets the clinical needs.The research indicates that VEGF and vancomycin loaded multilayer microspheres possess good controlled release characteristics and have the potentials of controlling infection and promoting vascularization at the same time,which is hopeful for basic research and clinical application of tissue engineering bone(TEB).
Infinite-Dimensional Programmable Quantum Processors

A universal programmable quantum processor uses “program” quantum states to apply an arbitrary quantum channel to an input state. We generalize the concept of a finite-dimensional programmable quantum processor to infinite dimension assuming an energy constraint on the input and output of the target quantum channels. By proving reductions to and from finite-dimensional processors, we obtain upper and lower bounds on the program dimension required to approximately implement energy-limited quantum channels. In particular, we consider the implementation of Gaussian channels. Due to their practical relevance, we investigate the resource requirements for gauge-covariant Gaussian channels. Additionally, we give upper and lower bounds on the program dimension of a processor implementing all Gaussian unitary channels. These lower bounds rely on a direct informationtheoretic argument, based on the generalization from finite to infinite dimension of a certain “replication lemma” for unitaries.
Naturally restricted subsets of nonsignaling correlations: typicality and convergence

It is well-known that in a Bell experiment, the observed correlation between measurement outcomes – as predicted by quantum theory – can be stronger than that allowed by local causality, yet not fully constrained by the principle of relativistic causality. In practice, the characterization of the set Q of quantum correlations is carried out, often, through a converging hierarchy of outer approximations. On the other hand, some subsets of Q arising from additional constraints [e.g., originating from quantum states having positive-partial-transposition (PPT) or being finite-dimensional maximally entangled (MES)] turn out to be also amenable to similar numerical characterizations. How, then, at a quantitative level, are all these naturally restricted subsets of nonsignaling correlations different? Here, we consider several bipartite Bell scenarios and numerically estimate their volume relative to that of the set of nonsignaling correlations. Within the number of cases investigated, we have observed that (1) for a given number of inputs ns (outputs no), the relative volume of both the Bell-local set and the quantum set increases (decreases) rapidly with increasing no (ns) (2) although the so-called macroscopically local set Q1 may approximate Q well in the two-input scenarios, it can be a very poor approximation of the quantum set when ns>no (3) the almost-quantum set Q~1 is an exceptionally-good approximation to the quantum set (4) the difference between Q and the set of correlations originating from MES is most significant when no=2, whereas (5) the difference between the Bell-local set and the PPT set generally becomes more significant with increasing no. This last comparison, in particular, allows us to identify Bell scenarios where there is little hope of realizing the Bell violation by PPT states and those that deserve further exploration.
Worst-Case Welfare of Item Pricing in the Tollbooth Problem

We study the worst-case welfare of item pricing in the tollbooth problem. The problem was first introduced by Guruswami et al. [27], and is a special case of the combinatorial auction in which (i) each of the m items in the auction is an edge of some underlying graph; and (ii) each of the n buyers is single-minded and only interested in buying all edges of a single path. We consider the competitive ratio between the hindsight optimal welfare and the optimal worst-case welfare among all item-pricing mechanisms, when the order of the arriving buyers is adversarial. We assume that buyers own the tie-breaking power, i.e. they can choose whether or not to buy the demand path at 0 utility. We prove a tight competitive ratio of 3/2 when the underlying graph is a single path (also known as the highway problem), whereas item-pricing can achieve the hindsight optimal if the seller is allowed to choose a proper tie-breaking rule to maximize the welfare [6, 11]. Moreover, we prove an O(1) upper bound of competitive ratio when the underlying graph is a tree. For general graphs, we prove an Ω(m1/8) lower bound of the competitive ratio. We show that an mΩ(1) competitive ratio is unavoidable even if the graph is a grid, or if the capacity of every edge is augmented by a constant factor c. The results hold even if the seller has tie-breaking power.
Experience Report: Deep Learning-based System Log Analysis for Anomaly Detection

Logs have been an imperative resource to ensure the reliability and continuity of many software systems, especially large-scale distributed systems. They faithfully record runtime information to facilitate system troubleshooting and behavior understanding. Due to the large scale and complexity of modern software systems, the volume of logs has reached an unprecedented level. Consequently, for log-based anomaly detection, conventional manual inspection methods or even traditional machine learning-based methods become impractical, which serve as a catalyst for the rapid development of deep learning-based solutions. However, there is currently a lack of rigorous comparison among the representative log-based anomaly detectors that resort to neural networks. Moreover, the re-implementation process demands non-trivial efforts, and bias can be easily introduced. To better understand the characteristics of different anomaly detectors, in this paper, we provide a comprehensive review and evaluation of five popular neural networks used by six state-of-the-art methods. Particularly, four of the selected methods are unsupervised, and the remaining two are supervised. These methods are evaluated with two publicly available log datasets, which contain nearly 16 million log messages and 0.4 million anomaly instances in total. We believe our work can serve as a basis in this field and contribute to future academic research and industrial applications.
Revisiting Uncertainty-based Query Strategies for Active Learning with Transformers

Active learning is the iterative construction of a classification model through targeted labeling, enabling significant labeling cost savings. As most research on active learning has been carried out before transformer-based language models (“transformers”) became popular, despite its practical importance, comparably few papers have investigated how transformers can be combined with active learning to date. This can be attributed to the fact that using state-of-the-art query strategies for transformers induces a prohibitive runtime overhead, which effectively nullifies, or even outweighs the desired cost savings. For this reason, we revisit uncertainty-based query strategies, which had been largely outperformed before, but are particularly suited in the context of fine-tuning transformers. In an extensive evaluation, we connect transformers to experiments from previous research, assessing their performance on five widely used text classification benchmarks. For active learning with transformers, several other uncertainty-based approaches outperform the well-known prediction entropy query strategy, thereby challenging its status as most popular uncertainty baseline in active learning for text classification.
BiSTF: Bilateral-Branch Self-Training Framework for Semi-Supervised Large-scale Fine-Grained Recognition

Semi-supervised Fine-Grained Recognition is a challenge task due to the difficulty of data imbalance, high inter-class similarity and domain mismatch. Recent years, this field has witnessed great progress and many methods has gained great performance. However, these methods can hardly generalize to the large-scale datasets, such as Semi-iNat, as they are prone to suffer from noise in unlabeled data and the incompetence for learning features from imbalanced fine-grained data. In this work, we propose Bilateral-Branch Self-Training Framework (BiSTF), a simple yet effective framework to improve existing semi-supervised learning methods on class-imbalanced and domain-shifted fine-grained data. By adjusting the update frequency through stochastic epoch update, BiSTF iteratively retrains a baseline SSL model with a labeled set expanded by selectively adding pseudo-labeled samples from an unlabeled set, where the distribution of pseudo-labeled samples are the same as the labeled data. We show that BiSTF outperforms the existing state-of-the-art SSL algorithm on Semi-iNat dataset.
A Framework for Machine Learning of Model Error in Dynamical Systems

The development of data-informed predictive models for dynamical systems is of widespread interest in many disciplines. We present a unifying framework for blending mechanistic and machine-learning approaches to identify dynamical systems from noisily and partially observed data. We compare pure data-driven learning with hybrid models which incorporate imperfect domain knowledge, referring to the discrepancy between an assumed truth model and the imperfect mechanistic model as model error. Our formulation is agnostic to the chosen machine learning model, is presented in both continuous- and discrete-time settings, and is compatible both with model errors that exhibit substantial memory and errors that are memoryless. First, we study memoryless linear (w.r.t. parametric-dependence) model error from a learning theory perspective, defining excess risk and generalization error. For ergodic continuous-time systems, we prove that both excess risk and generalization error are bounded above by terms that diminish with the square-root of T T , the time-interval over which training data is specified. Secondly, we study scenarios that benefit from modeling with memory, proving universal approximation theorems for two classes of continuous-time recurrent neural networks (RNNs): both can learn memory-dependent model error, assuming that it is governed by a finite-dimensional hidden variable and that, together, the observed and hidden variables form a continuous-time Markovian system. In addition, we connect one class of RNNs to reservoir computing, thereby relating learning of memory-dependent error to recent work on supervised learning between Banach spaces using random features. Numerical results are presented (Lorenz ’63, Lorenz ’96 Multiscale systems) to compare purely data-driven and hybrid approaches, finding hybrid methods less datahungry and more parametrically efficient. We also find that, while a continuous-time framing allows for robustness to irregular sampling and desirable domain- interpretability, a discrete-time framing can provide similar or better predictive performance, especially when data are undersampled and the vector field defining the true dynamics cannot be identified. Finally, we demonstrate numerically how data assimilation can be leveraged to learn hidden dynamics from noisy, partially-observed data, and illustrate challenges in representing memory by this approach, and in the training of such models.
Deep Learning Assisted Compact Modeling of Nanoscale Transistor

Transistors are the basic building blocks for all electronics. Accurate prediction of their current-voltage (IV) characteristics enables circuit simulations before the expensive silicon tape-out. In this work, we propose using deep neural network to improve the accuracy for the conventional, physics-based compact model for nanoscale transistors. Physics-driven requirements on the neural network are discussed. Using finite element simulation as the input dataset, together with a neural network with roughly 30 neurons, the final IV model can well-predict the IV to within 1%. The trained model can readily be implemented by the hardware description language (HDL) such as VerilogA for circuit simulation.
Ranks of abelian varieties in cyclotomic twist families

Let $A$ be an abelian variety over a number field $F$, and suppose that $\mathbb Z[\zeta_n]$ embeds in $\mathrm{End}_{\bar F} A$, for some root of unity $\zeta_n$ of order $n = 3^m$. Assuming that the Galois action on the finite group $A[1-\zeta_n]$ is sufficiently reducible, we bound the average rank of the Mordell--Weil groups $A_d(F)$, as $A_d$ varies through the family of $\mu_{2n}$-twists of $A$. Combining this with the recently proved uniform Mordell--Lang conjecture, we prove near-uniform bounds for the number of rational points in twist families of bicyclic trigonal curves $y^3 = f(x^2)$, as well as in twist families of theta divisors of cyclic trigonal curves $y^3 = f(x)$. Our main technical result is the determination of the average size of a $3$-isogeny Selmer group in a family of $\mu_{2n}$-twists.