Miracle-dz/UniFAR-FTUs huggingface dataset:
Shock-Boundary Layer Interactions in Supersonic Turbine Cascades

The physics of shock-boundary layer interactions in a supersonic turbine cascade is investigated through a wall-resolved large eddy simulation. Special attention is given to the characterization of the low-frequency dynamics of the separation bubbles using ﬂow visualization, spectral analysis, space-time cross correlations, and ﬂow modal decomposition. The mean ﬂowﬁeld shows diﬀerent shock structures formed on both sides of the airfoil. On the suction side, an oblique shock impinges on the turbulent boundary layer, whereas a Mach reﬂection interacts with the pressure side boundary layer. Instantaneous ﬂow visualizations illustrate elongated streamwise structures on the incoming boundary layers and their interactions with the shocks and separation bubbles. The passage of high-speed (low-speed) streaks through the recirculation bubbles leads to the downstream (upstream) motion of the separation point on both suction and pressure sides, resulting in spanwise modulation of the bubbles. Space-time cross-correlations reveal that the near-wall streaks drive the suction side separation bubble motion, which in turn promotes the oscillations of the reattachment shock and shear layer ﬂapping. Space-time correlations also indicate the existence of a π phase jump in the pressure ﬂuctuations along the separation bubble on the suction side. After this phase jump, a downstream propagating pressure disturbance is observed, while prior to this point, the pressure disturbances dominantly propagate in the upstream direction. Finally, the organized motions in the shock-boundary layer interactions and their corresponding characteristic frequencies are identiﬁed using proper orthogonal decomposition.
Tracking monocular camera pose and deformation for SLAM inside the human body

Monocular SLAM in deformable scenes will open the way to multiple medical applications like computer-assisted navigation in endoscopy, automatic drug delivery or autonomous robotic surgery. In this paper we propose a novel method to simultaneously track the camera pose and the 3D scene deformation, without any assumption about environment topology or shape. The method uses an illumination-invariant photometric method to track image features and estimates camera motion and deformation combining reprojection error with spatial and temporal regularization of deformations. Our results in simulated colonoscopies show the method's accuracy and robustness in complex scenes under increasing levels of deformation. Our qualitative results in human colonoscopies from Endomapper dataset show that the method is able to successfully cope with the challenges of real endoscopies: deformations, low texture and strong illumination changes. We also compare with previous tracking methods in simpler scenarios from Hamlyn dataset where we obtain competitive performance, without needing any topological assumption.
STRATA: Word Boundaries & Phoneme Recognition From Continuous Urdu Speech using Transfer Learning, Attention, & Data Augmentation

Phoneme recognition is a largely unsolved problem in NLP, especially for low-resource languages like Urdu. The systems that try to extract the phonemes from audio speech require hand-labeled phonetic transcriptions. This requires expert linguists to annotate speech data with its relevant phonetic representation which is both an expensive and a tedious task. In this paper, we propose STRATA, a framework for supervised phoneme recognition that overcomes the data scarcity issue for low resource languages using a seq2seq neural architecture integrated with transfer learning, attention mechanism, and data augmentation. STRATA employs transfer learning to reduce the network loss in half. It uses attention mechanism for word boundaries and frame alignment detection which further reduces the network loss by 4% and is able to identify the word boundaries with 92.2% accuracy. STRATA uses various data augmentation techniques to further reduce the loss by 1.5% and is more robust towards new signals both in terms of generalization and accuracy. STRATA is able to achieve a Phoneme Error Rate of 16.5% and improves upon the state of the art by 1.1% for TIMIT dataset (English) and 11.5% for CSaLT dataset (Urdu).
NICO++: Towards Better Benchmarking for Domain Generalization

Despite the remarkable performance that modern deep neural networks have achieved on independent and identically distributed (I.I.D.) data, they can crash under distribution shifts. Most current evaluation methods for do-main generalization (DG) adopt the leave-one-out strategy as a compromise on the limited number of domains. We propose a large-scale benchmark with extensive labeled domains named $NICO^{++}$ along with more rational evaluation methods for comprehensively evaluating DC algorithms. To evaluate DG datasets, we propose two metrics to quantify covariate shift and concept shift, respectively. Two novel generalization bounds from the perspective of data construction are proposed to prove that limited concept shift and significant covariate shift favor the evaluation capability for generalization. Through extensive experiments, $NlCO^{++}$ shows its superior evaluation capability compared with current DG datasets and its contribution in alleviating unfairness caused by the leak of oracle knowledge in model selection. The data and code for the benchmark based on $NICO^{++}$ are available at https://github.com/xxgege/NICO-plus.
CHAI: A CHatbot AI for Task-Oriented Dialogue with Offline Reinforcement Learning

Conventionally, generation of natural language for dialogue agents may be viewed as a statistical learning problem: determine the patterns in human-provided data and generate appropriate responses with similar statistical properties. However, dialogue can also be regarded as a goal directed process, where speakers attempt to accomplish a specific task. Reinforcement learning (RL) algorithms are designed specifically for solving such goal-directed problems, but the most direct way to apply RL, through trial-and-error learning in human conversations, is costly. In this paper, we study how offline reinforcement learning can instead be used to train dialogue agents entirely using static datasets collected from human speakers. Our experiments show that recently developed offline RL methods can be combined with language models to yield realistic dialogue agents that better accomplish task goals.
Astrometric Gravitational-Wave Detection via Stellar Interferometry

We evaluate the potential for gravitational-wave (GW) detection in the frequency band from 10 nHz to 1 µ Hz using extremely high-precision astrometry of a small number of stars. In particular, we argue that non-magnetic, photometrically stable, hot white dwarfs (WD) located at ∼ kpc distances may be optimal targets for this approach. Previous studies of astrometric GW detection have focused on the potential for less precise surveys of large numbers of stars; our work provides an alternative optimization approach to this problem. Interesting GW sources in this band are expected at characteristic strains around h c ∼ 10 − 17 × ( µ Hz /f gw ). The astrometric angular precision required to see these sources is ∆ θ ∼ h c after integrating for a time T ∼ 1 /f gw . We show that jitter in the photometric center of WD of this type due to starspots is bounded to be small enough to permit this high-precision, small- N approach. We discuss possible noise arising from stellar reﬂex motion induced by orbiting objects and show how it can be mitigated. The only plausible technology able to achieve the requisite astrometric precision is a space-based stellar interferometer. Such a future mission with few-meter-scale collecting dishes and baselines of O (100 km) is suﬃcient to achieve the target precision. This collector size is broadly in line with the collectors proposed for some formation-ﬂown, space-based astrometer or optical synthetic-aperture imaging-array concepts proposed for other science reasons. The proposed baseline is however somewhat larger than the km-scale baselines discussed for those concepts, but we see no fundamental technical obstacle to utilizing such baselines. A mission of this type thus also holds the promise of being one of the few ways to access interesting GW sources in this band.
Recurrent neural networks that generalize from examples and optimize by dreaming

: The gap between the huge volumes of data needed to train artiﬁcial neural networks and the relatively small amount of data needed by their biological counterparts is a central puzzle in machine learning. Here, inspired by biological information-processing, we introduce a generalized Hopﬁeld network where pairwise couplings between neurons are built according to Hebb’s prescription for on-line learning and allow also for (suitably stylized) oﬀ-line sleeping mechanisms. Moreover, in order to retain a learning framework, here the patterns are not assumed to be available, instead, we let the network experience solely a dataset made of a sample of noisy examples for each pattern. We analyze the model by statistical-mechanics tools and we obtain a quantitative picture of its capabilities as functions of its control parameters: the resulting network is an associative memory for pattern recognition that learns from examples on-line, generalizes and optimizes its storage capacity by oﬀ-line sleeping. Remarkably, the sleeping mechanisms always signiﬁcantly reduce (up to ≈ 90%) the dataset size required to correctly generalize, further, there are memory loads that are prohibitive to Hebbian networks without sleeping (no matter the size and quality of the provided examples), but that are easily handled by the present “rested”
Stress-Testing LiDAR Registration

Point cloud registration (PCR) is an important task in many ﬁelds including autonomous driving with LiDAR sen-sors. PCR algorithms have improved signiﬁcantly in recent years, by combining deep-learned features with robust estimation methods. These algorithms succeed in scenar-ios such as indoor scenes and object models registration. However, testing in the automotive LiDAR setting, which presents its own challenges, has been limited. The standard benchmark for this setting, KITTI-10m, has essentially been saturated by recent algorithms: many of them achieve near-perfect recall. In this work, we stress-test recent PCR techniques with LiDAR data. We propose a method for selecting balanced registration sets, which are challenging sets of frame-pairs from LiDAR datasets. They contain a balanced representation of the different relative motions that appear in a dataset, i.e. small and large rotations, small and large offsets in space and time, and various combinations of these. We perform a thorough comparison of accuracy and run-time on these benchmarks. Perhaps unexpectedly, we ﬁnd that the fastest and simultaneously most accurate approach is a version of advanced RANSAC. We further improve results with a novel pre-ﬁltering method. Full code is available 1 .
The Tree Loss: Improving Generalization with Many Classes

Multi-class classification problems often have many semantically similar classes. For example, 90 of ImageNet's 1000 classes are for different breeds of dog. We should expect that these semantically similar classes will have similar parameter vectors, but the standard cross entropy loss does not enforce this constraint. We introduce the tree loss as a drop-in replacement for the cross entropy loss. The tree loss re-parameterizes the parameter matrix in order to guarantee that semantically similar classes will have similar parameter vectors. Using simple properties of stochastic gradient descent, we show that the tree loss's generalization error is asymptotically better than the cross entropy loss's. We then validate these theoretical results on synthetic data, image data (CIFAR100, ImageNet), and text data (Twitter).
On Acceleration of Gradient-Based Empirical Risk Minimization using Local Polynomial Regression

We study the acceleration of the Local Polynomial Interpolation-based Gradient Descent method (LPI-GD) recently proposed for the approximate solution of empirical risk minimization problems (ERM). We focus on loss functions that are strongly convex and smooth with condition number $\sigma$. We additionally assume the loss function is $\eta$-Hölder continuous with respect to the data. The oracle complexity of LPI-GD is $\tilde{O}(\sigma m^{d}\log(1/\varepsilon))$ for a desired accuracy $\varepsilon$, where $d$ is the dimension of the parameter space, and $m$ is the cardinality of an approximation grid. The factor $m^{d}$ can be shown to scale as $O((1/\varepsilon)^{d/2\eta})$. LPI-GD has been shown to have better oracle complexity than gradient descent (GD) and stochastic gradient descent (SGD) for certain parameter regimes. We propose two accelerated methods for the ERM problem based on LPI-GD and show an oracle complexity of $\tilde{O}(\sqrt{\sigma}m^{d}\log(1/\varepsilon))$. Moreover, we provide the first empirical study on local poly-nomial interpolation-based gradient methods and corroborate that LPI-GD has better performance than GD and SGD in some scenarios, and the proposed methods achieve acceleration.
A software pilot application to calculate the parameters involved in subsidiaries interconnection based on FSO technology

Free Space Optics (FSO) technology transmits information using free space optical signals similar to those that travel through optical fibers. The capacity of FSO to connect buildings close together is covered in this paper. Four optical wireless links between three buildings of a fictitious company, considering a building that obstructs the sight line between two of them, are designed here. As part of the design process, several issues such as distance between the equipment, weather conditions, some features of the FSO equipment, and system losses were addressed; moreover, an application that performs the calculations and displays the results of the most important aspects in the design was implemented. The importance of such links is given by their flexibility, low implementation costs, mobility, rapid deployment, high throughput, great bandwidth, reliability, and high security level due to their operating principles, among others; therefore, this type of links are an ideal option to implement a backbone for the proposed network.
Metallicity of Galactic RR Lyrae from Optical and Infrared Light Curves. II. Period–Fourier–Metallicity Relations for First Overtone RR Lyrae

We present new period-ϕ 31-[Fe/H] relations for first-overtone RRL stars (RRc), calibrated over a broad range of metallicities (−2.5 ≲ [Fe/H] ≲ 0.0) using the largest currently available set of Galactic halo field RRL with homogeneous spectroscopic metallicities. Our relations are defined in the optical (ASAS-SN V band) and, inaugurally, in the infrared (WISE W1 and W2 bands). Our V-band relation can reproduce individual RRc spectroscopic metallicities with a dispersion of 0.30 dex over the entire metallicity range of our calibrator sample (an rms smaller than what we found for other relations in literature including nonlinear terms). Our infrared relation has a similar dispersion in the low- and intermediate-metallicity range ([Fe/H] ≲ −0.5), but tends to underestimate the [Fe/H] abundance around solar metallicity. We tested our relations by measuring both the metallicity of the Sculptor dSph and a sample of Galactic globular clusters, rich in both RRc and RRab stars. The average metallicity we obtain for the combined RRL sample in each cluster is within ±0.08 dex of their spectroscopic metallicities. The infrared and optical relations presented in this work will enable deriving reliable photometric RRL metallicities in conditions where spectroscopic measurements are not feasible; e.g., in distant galaxies or reddened regions (observed with upcoming Extremely Large Telescopes and the James Webb Space Telescope), or in the large sample of new RRL that will be discovered in large-area time-domain photometric surveys (such as the LSST and the Roman space telescope).
Multiple-environment Self-adaptive Network for Aerial-view Geo-localization

Aerial-view geo-localization tends to determine an unknown position through matching the drone-view image with the geo-tagged satellite-view image. This task is mostly regarded as an image retrieval problem. The key underpinning this task is to design a series of deep neural networks to learn discriminative image descriptors. However, existing methods meet large performance drops under realistic weather, such as rain and fog, since they do not take the domain shift between the training data and multiple test environments into consideration. To minor this domain gap, we propose a Multiple-environment Self-adaptive Network (MuSe-Net) to dynamically adjust the domain shift caused by environmental changing. In particular, MuSe-Net employs a two-branch neural network containing one multiple-environment style extraction network and one self-adaptive feature extraction network. As the name implies, the multiple-environment style extraction network is to extract the environment-related style information, while the self-adaptive feature extraction network utilizes an adaptive modulation module to dynamically minimize the environment-related style gap. Extensive experiments on two widely-used benchmarks, i.e., University-1652 and CVUSA, demonstrate that the proposed MuSe-Net achieves a competitive result for geo-localization in multiple environments. Furthermore, we observe that the proposed method also shows great potential to the unseen extreme weather, such as mixing the fog, rain and snow.
Augmentation Invariance and Adaptive Sampling in Semantic Segmentation of Agricultural Aerial Images

In this paper, we investigate the problem of Semantic Segmentation for agricultural aerial imagery. We observe that the existing methods used for this task are designed without considering two characteristics of the aerial data: (i) the top-down perspective implies that the model cannot rely on a fixed semantic structure of the scene, because the same scene may be experienced with different rotations of the sensor; (ii) there can be a strong imbalance in the distribution of semantic classes because the relevant objects of the scene may appear at extremely different scales (e.g., a field of crops and a small vehicle). We propose a solution to these problems based on two ideas: (i) we use together a set of suitable augmentation and a consistency loss to guide the model to learn semantic representations that are invariant to the photometric and geometric shifts typical of the top-down perspective (Augmentation Invariance); (ii) we use a sampling method (Adaptive Sampling) that selects the training images based on a measure of pixel-wise distribution of classes and actual network confidence. With an extensive set of experiments conducted on the Agriculture-Vision dataset, we demonstrate that our proposed strategies improve the performance of the current state-of-the-art method. 1.
BLISS: Robust Sequence-to-Sequence Learning via Self-Supervised Input Representation

Data augmentations (DA) are the cores to achieving robust sequence-to-sequence learning on various natural language processing (NLP) tasks. However, most of the DA approaches force the decoder to make predictions conditioned on the perturbed input representation, underutilizing supervised information provided by perturbed input. In this work, we propose a framework-level robust sequence-to-sequence learning approach, named BLISS, via self-supervised input representation, which has the great potential to complement the data-level augmentation approaches. The key idea is to supervise the sequence-to-sequence framework with both the \textit{supervised} ("input$\rightarrow$output") and \textit{self-supervised} ("perturbed input$\rightarrow$input") information. We conduct comprehensive experiments to validate the effectiveness of BLISS on various tasks, including machine translation, grammatical error correction, and text summarization. The results show that BLISS outperforms significantly the vanilla Transformer and consistently works well across tasks than the other five contrastive baselines. Extensive analyses reveal that BLISS learns robust representations and rich linguistic knowledge, confirming our claim. Source code will be released upon publication.
Actions at the Edge: Jointly Optimizing the Resources in Multi-Access Edge Computing

Multi-access edge computing (MEC) is an emerging paradigm that pushes resources for sensing, communications, computing, storage, and intelligence to the premises closer to the end users (i.e., the edge) so that they can leverage the nearby rich resources to improve their quality of experience. Due to the growing emerging applications targeted at intelligentizing life-sustaining cyber-physical systems, this paradigm has become a hot research topic, particularly when MEC is utilized to provide edge intelligence and real-time processing and control. This article elaborates the research issues along this line, including basic concepts and performance metrics, killer applications, architectural design, modeling approaches and solutions, and future research directions. It is hoped that this article provides a quick introduction to this fruitful research area, particularly for beginning researchers.
Sparsely Activated Mixture-of-Experts are Robust Multi-Task Learners

Traditional multi-task learning (MTL) methods use dense networks that use the same set of shared weights across several different tasks. This often creates interference where two or more tasks compete to pull model parameters in different directions. In this work, we study whether sparsely activated Mixture-of-Experts (MoE) improve multi-task learning by specializing some weights for learning shared representations and using the others for learning task-specific information. To this end, we devise task-aware gating functions to route examples from different tasks to specialized experts which share subsets of network weights conditioned on the task. This results in a sparsely activated multi-task model with a large number of parameters, but with the same computational cost as that of a dense model. We demonstrate such sparse networks to improve multi-task learning along three key dimensions: (i) transfer to low-resource tasks from related tasks in the training mixture; (ii) sample-efficient generalization to tasks not seen during training by making use of task-aware routing from seen related tasks; (iii) robustness to the addition of unrelated tasks by avoiding catastrophic forgetting of existing tasks.
A Variational Approach to Bayesian Phylogenetic Inference

Bayesian phylogenetic inference is currently done via Markov chain Monte Carlo (MCMC) with simple proposal mechanisms. This hinders exploration efficiency and often requires long runs to deliver accurate posterior estimates. In this paper, we present an alternative approach: a variational framework for Bayesian phylogenetic analysis. We propose combining subsplit Bayesian networks, an expressive graphical model for tree topology distributions, and a structured amortization of the branch lengths over tree topologies for a suitable variational family of distributions. We train the variational approximation via stochastic gradient ascent and adopt gradient estimators for continuous and discrete variational parameters separately to deal with the composite latent space of phylogenetic models. We show that our variational approach provides competitive performance to MCMC, while requiring much less computation due to a more efficient exploration mechanism enabled by variational inference. Experiments on a benchmark of challenging real data Bayesian phylogenetic inference problems demonstrate the effectiveness and efficiency of our methods.