Miracle-dz/UniFAR-FTUs huggingface dataset:
Offline Meta-Reinforcement Learning with Online Self-Supervision

Meta-reinforcement learning (RL) methods can meta-train policies that adapt to new tasks with orders of magnitude less data than standard RL, but meta-training itself is costly and time-consuming. If we can meta-train on offline data, then we can reuse the same static dataset, labeled once with rewards for different tasks, to meta-train policies that adapt to a variety of new tasks at meta-test time. Although this capability would make meta-RL a practical tool for real-world use, offline meta-RL presents additional challenges beyond online meta-RL or standard offline RL settings. Meta-RL learns an exploration strategy that collects data for adapting, and also meta-trains a policy that quickly adapts to data from a new task. Since this policy was meta-trained on a fixed, offline dataset, it might behave unpredictably when adapting to data collected by the learned exploration strategy, which differs systematically from the offline data and thus induces distributional shift. We propose a hybrid offline meta-RL algorithm, which uses offline data with rewards to meta-train an adaptive policy, and then collects additional unsupervised online data, without any reward labels to bridge this distribution shift. By not requiring reward labels for online collection, this data can be much cheaper to collect. We compare our method to prior work on offline meta-RL on simulated robot locomotion and manipulation tasks and find that using additional unsupervised online data collection leads to a dramatic improvement in the adaptive capabilities of the meta-trained policies, matching the performance of fully online meta-RL on a range of challenging domains that require generalization to new tasks.
Stool Studies Don't Pass the Sniff Test: A Systematic Review of Human Gut Microbiome Research Suggests Widespread Misuse of Machine Learning

In the machine learning culture, an independent test set is required for proper model verification. Failures in model verification, including test set omission and test set leakage, make it impossible to know whether or not a trained model is fit for purpose. In this article, we present a systematic review and quantitative analysis of human gut microbiome classification studies, conducted to measure the frequency and impact of test set omission and test set leakage on area under the receiver operating curve (AUC) reporting. Among 102 articles included for analysis, we find that only 12% of studies report a bona fide test set AUC, meaning that the published AUCs for 88% of studies cannot be trusted at face value. Our findings cast serious doubt on the general validity of research claiming that the gut microbiome has high diagnostic or prognostic potential in human disease.
An Embedded Iris Recognition System Optimization using Dynamically ReconfigurableDecoder with LDPC Codes

Extracting and analyzing iris textures for biometric recognition has been extensively studied. As the transition of iris recognition from lab technology to nation-scale applications, most systems are facing high complexity in either time or space, leading to unfitness for embedded devices. In this paper, the proposed design includes a minimal set of computer vision modules and multi-mode QC-LDPC decoder which can alleviate variability and noise caused by iris acquisition and follow-up process. Several classes of QC-LDPC code from IEEE 802.16 are tested for the validity of accuracy improvement. Some of the codes mentioned above are used for further QC-LDPC decoder quantization, validation and comparison to each other. We show that we can apply Dynamic Partial Reconfiguration technology to implement the multi-mode QC-LDPC decoder for the iris recognition system. The results show that the implementation is power-efficient and good for edge applications.
Steady azimuthal flow field induced by a rotating sphere near a rigid disk or inside a gap between two coaxially positioned rigid disks

Geometric confinements play an important role in many physical and biological processes and significantly affect the rheology and behavior of colloidal suspensions at low Reynolds numbers. On the basis of the linear Stokes equations, we investigate theoretically and computationally the viscous azimuthal flow induced by the slow rotation of a small spherical particle located in the vicinity of a rigid no-slip disk or inside a gap between two coaxially positioned rigid no-slip disks of the same radius. We formulate the solution of the hydrodynamic problem as a mixed-boundary-value problem in the whole fluid domain, which we subsequently transform into a system of dual integral equations. Near a stationary disk, we show that the resulting integral equation can be reduced into an elementary Abel integral equation that admits a unique analytical solution. Between two coaxially positioned stationary disks, we demonstrate that the flow problem can be transformed into a system of two Fredholm integral equations of the first kind. The latter are solved by means of numerical approaches. Using our solution, we further investigate the effect of the disks on the slow rotational motion of a colloidal particle and provide expressions of the hydrodynamic mobility as a function of the system geometry. We compare our results with corresponding finite-element simulations and observe very good agreement.
Unsupervised Learning of Optical Flow With Non-Occlusion From Geometry

Optical flow estimation is a fundamental problem of computer vision and has many applications in the fields of robot learning and autonomous driving. This paper reveals novel geometric principles of optical flow based on the insight and detailed definition of non-occlusion. Then, two novel loss functions are proposed for the unsupervised learning of optical flow based on the geometric principles of non-occlusion. Specifically, after the occlusion part of the images are masked, the flowing process of pixel coordinate points is carefully considered and geometric constraints are conducted based on the geometric principles of optical flow. First, the optical flow vectors of neighboring pixel coordinate points in the first frame will not intersect during the pixel displacement to the second frame. Secondly, when the cluster containing adjacent four pixel coordinate points in the first frame moves to the second frame, no other pixel coordinate points will flow into the quadrilateral formed by them. According to the two geometrical constraints, the optical flow non-intersection loss and the optical flow non-blocking loss in the non-occlusion regions are proposed. Two loss functions punish the irregular and inexact optical flows in the non-occlusion regions. The experiments on datasets demonstrated that the proposed unsupervised losses of optical flow based on the geometric principles in non-occlusion regions make the estimated optical flow more refined in detail, and improve the performance of unsupervised learning of optical flow. In addition, the experiments training on synthetic data and evaluating on real data show that the generalization ability of optical flow network is improved by our proposed unsupervised approach. Our codes are open sourced at: https://github.com/IRMVLab/NccFlow.
Heterogeneous Global Graph Neural Networks for Personalized Session-based Recommendation

Predicting the next interaction of a short-term interaction session is a challenging task in session-based recommendation. Almost all existing works rely on item transition patterns, and neglect user historical sessions while modeling user preference, which often leads to non-personalized recommendation. And existing personalized session-based recommenders are limited to sessions of the current user, and ignore the useful item-transition patterns from other user's historical sessions. To address these issues, we propose a novel Heterogeneous Global Graph Neural Networks (HG-GNN) to exploit the item transitions over all sessions in a subtle manner for better inferring user preference from the current and historical sessions. To effectively exploit the item transitions over all sessions from users, our global graph contains item transitions of sessions, user-item interactions and global co-occurrence items. Moreover, to capture user preference from sessions comprehensively, we propose a graph augmented preference encoder to learn the session representation. Specifically, we design a novel heterogeneous graph neural network (HGNN) on heterogeneous global graph to learn long-term user preference and item representations with rich semantics. Based on the HGNN, we propose the Personalized Session Encoder to combine the general user preference and temporal interest of the current session to generate the personalized session representation for recommendation. Extensive experimental results on three real-world datasets show that our model outperforms other state-of-the-art methods.
Which molecule traces what: chemical diagnostics of protostellar sources

Context. The physical and chemical conditions in Class 0/I protostars are fundamental in unlocking the protostellar accretion process and its impact on planet formation. Aims. The aim is to determine which physical components are traced by different molecules at subarcsecond scales (<100–400 au). Methods. We used a suite of Atacama Large Millimeter/submillimeter Array (ALMA) datasets in band 6 (1 mm), band 5 (1.8 mm), and band 3 (3 mm) at spatial resolutions 0 . 5–3 for 16 protostellar sources. For a subset of sources, Atacama Compact Array (ACA) data at band 6 with a spatial resolution of 6 (cid:48)(cid:48) were added. The availability of low- and high-excitation lines on small and larger full
Non co-preservation of the $1/2$&$1/(2l+1)$-rational caustics along deformations of circles

For any given positive integer l, we prove that every plane deformation of a circle which preserves the 1{2and 1{p2l 1q–rational caustics is trivial i.e. the deformation consists only of similarities (rescalings and isometries). MSC2020 numbers: 37C83, 37E40, 37J51, 70H09
The effective mass problem for the Landau–Pekar equations

We provide a definition of the effective mass for the classical polaron described by the Landau–Pekar (LP) equations. It is based on a novel variational principle, minimizing the energy functional over states with given (initial) velocity. The resulting formula for the polaron’s effective mass agrees with the prediction by LP (1948 J. Exp. Theor. Phys. 18 419–423).
COMBO: A New Module for EUD Parsing

We introduce the COMBO-based approach for EUD parsing and its implementation, which took part in the IWPT 2021 EUD shared task. The goal of this task is to parse raw texts in 17 languages into Enhanced Universal Dependencies (EUD). The proposed approach uses COMBO to predict UD trees and EUD graphs. These structures are then merged into the final EUD graphs. Some EUD edge labels are extended with case information using a single language-independent expansion rule. In the official evaluation, the solution ranked fourth, achieving an average ELAS of 83.79%. The source code is available at https://gitlab.clarin-pl.eu/syntactic-tools/combo.
Proceedings of the First Workshop on Weakly Supervised Learning (WeaSuL)

The paradigm of pre-training followed by finetuning has become a standard procedure for NLP tasks, with a known problem of domain shift between the pre-training and downstream corpus. Previous works have tried to mitigate this problem with additional pre-training, either on the downstream corpus itself when it is large enough, or on a manually curated unlabeled corpus of a similar domain. In this paper, we address the problem for the case when the downstream corpus is too small for additional pre-training. We propose TADPOLE, a task adapted pre-training framework based on data selection techniques adapted from Domain Adaptation. We formulate the data selection as an anomaly detection problem that unlike existing methods works well when the downstream corpus is limited in size. It results in a scalable and efficient unsupervised technique that eliminates the need for any manual data curation. We evaluate our framework on eight tasks across four different domains: Biomedical, Computer Science, News, and Movie reviews, and compare its performance against competitive baseline techniques from the area of Domain Adaptation. Our framework outperforms all the baseline methods. On large datasets we get an average gain of 0.3% in performance but on small datasets with less than 5K training examples, we get a much higher gain of 1.8%. This shows the efficacy of domain adapted finetuning when the task dataset is small.
Asymptotic normality of robust M-estimators with convex penalty

This paper develops asymptotic normality results for individual coordinates of robust M-estimators with convex penalty in high-dimensions, where the dimension p is at most of the same order as the sample size n, i.e, p/n ≤ γ for some fixed constant γ > 0. The asymptotic normality requires a bias correction and holds for most coordinates of the M-estimator for a large class of loss functions including the Huber loss and its smoothed versions regularized with a strongly convex penalty. The asymptotic variance that characterizes the width of the resulting confidence intervals is estimated with data-driven quantities. This estimate of the variance adapts automatically to low (p/n→ 0) or high (p/n ≤ γ) dimensions and does not involve the proximal operators seen in previous works on asymptotic normality of M-estimators. For the Huber loss, the estimated variance has a simple expression involving an effective degrees-of-freedom as well as an effective sample size. The case of the Huber loss with Elastic-Net penalty is studied in details and a simulation study confirms the theoretical findings. The asymptotic normality results follow from Stein formulae for high-dimensional random vectors on the sphere developed in the paper which are of independent interest.
Relation-Based Associative Joint Location for Human Pose Estimation in Videos

Video-based human pose estimation (VHPE) is a vital yet challenging task. While deep learning algorithms have made tremendous progress for the VHPE, lots of these approaches to this task implicitly model the long-range interaction between joints by expanding the receptive field of the convolution or designing a graph manually. Unlike prior methods, we design a lightweight and plug-and-play joint relation extractor (JRE) to explicitly and automatically model the associative relationship between joints. The JRE takes the pseudo heatmaps of joints as input and calculates their similarity. In this way, the JRE can flexibly learn the correlation between any two joints, allowing it to learn the rich spatial configuration of human poses. Furthermore, the JRE can infer invisible joints according to the correlation between joints, which is beneficial for locating occluded joints. Then, combined with temporal semantic continuity modeling, we propose a Relation-based Pose Semantics Transfer Network (RPSTN) for video-based human pose estimation. Specifically, to capture the temporal dynamics of poses, the pose semantic information of the current frame is transferred to the next with a joint relation guided pose semantics propagator (JRPSP). The JRPSP can transfer the pose semantic features from the non-occluded frame to the occluded frame. The proposed RPSTN achieves state-of-the-art or competitive results on the video-based Penn Action, Sub-JHMDB, PoseTrack2018, and HiEve datasets. Moreover, the proposed JRE improves the performance of backbones on the image-based COCO2017 dataset. Code is available at https://github.com/YHDang/pose-estimation.
Longitudinal Study of an IP Geolocation Database

IP geolocation – the process of mapping network identifiers to physical locations – has myriad applications. We examine a large collection of snapshots from a popular geolocation database and take a first look at its longitudinal properties. We define metrics of IP geo-persistence, prevalence, coverage, and movement, and analyze 10 years of geolocation data at different location granularities. Across different classes of IP addresses, we find that significant location differences can exist even between successive instances of the database – a previously underappreciated source of potential error when using geolocation data: 47% of end users IP addresses move by more than 40 km in 2019. To assess the sensitivity of research results to the instance of the geo database, we reproduce prior research that depended on geolocation lookups. In this case study, which analyzes geolocation database performance on routers, we demonstrate impact of these temporal effects: median distance from ground truth shifted from 167 km to 40 km when using a two months apart snapshot. Based on our findings, we make recommendations for best practices when using geolocation databases in order to best encourage reproducibility and sound measurement.
SSSE: Efficiently Erasing Samples from Trained Machine Learning Models

The availability of large amounts of user-provided data has been key to the success of machine learning for many real-world tasks. Recently, an increasing awareness has emerged that users should be given more control about how their data is used. In particular, users should have the right to prohibit the use of their data for training machine learning systems, and to have it erased from already trained systems. While several sample erasure methods have been proposed, all of them have drawbacks which have prevented them from gaining widespread adoption. Most methods are either only applicable to very specific families of models, sacrifice too much of the original model's accuracy, or they have prohibitive memory or computational requirements. In this paper, we propose an efficient and effective algorithm, SSSE, for samples erasure, that is applicable to a wide class of machine learning models. From a second-order analysis of the model's loss landscape we derive a closed-form update step of the model parameters that only requires access to the data to be erased, not to the original training set. Experiments on three datasets, CelebFaces attributes (CelebA), Animals with Attributes 2 (AwA2) and CIFAR10, show that in certain cases SSSE can erase samples almost as well as the optimal, yet impractical, gold standard of training a new model from scratch with only the permitted data.
Experiments in Surface Gravity–Capillary Wave Turbulence

The last decade has seen a significant increase in the number of studies devoted to wave turbulence. Many deal with water waves, as modeling of ocean waves has historically motivated the development of weak turbulence theory, which addresses the dynamics of a random ensemble of weakly nonlinear waves in interaction. Recent advances in experiments have shown that this theoretical picture is too idealized to capture experimental observations. While gravity dominates much of the oceanic spectrum, waves observed in the laboratory are in fact gravity–capillary waves, due to the restricted size of wave basins. This richer physics induces many interleaved physical effects far beyond the theoretical framework, notably in the vicinity of the gravity–capillary crossover. These include dissipation, finite–system size effects, and finite nonlinearity effects. Simultaneous space-and-time-resolved techniques, now available, open the way for a much more advanced analysis of these effects. Expected final online publication date for the Annual Review of Fluid Mechanics, Volume 54 is January 2022. Please see http://www.annualreviews. org/page/journal/pubdates for revised estimates.
Smart Healthcare in the Age of AI: Recent Advances, Challenges, and Future Prospects

The significant increase in the number of individuals with chronic ailments (including the elderly and disabled) has dictated an urgent need for an innovative model for healthcare systems. The evolved model will be more personalized and less reliant on traditional brick-and-mortar healthcare institutions such as hospitals, nursing homes, and long-term healthcare centers. The smart healthcare system is a topic of recently growing interest and has become increasingly required due to major developments in modern technologies, especially in artificial intelligence (AI) and machine learning (ML). This paper is aimed to discuss the current state-of-the-art smart healthcare systems highlighting major areas like wearable and smartphone devices for health monitoring, machine learning for disease diagnosis, and the assistive frameworks, including social robots developed for the ambient assisted living environment. Additionally, the paper demonstrates software integration architectures that are very significant to create smart healthcare systems, integrating seamlessly the benefit of data analytics and other tools of AI. The explained developed systems focus on several facets: the contribution of each developed framework, the detailed working procedure, the performance as outcomes, and the comparative merits and limitations. The current research challenges with potential future directions are addressed to highlight the drawbacks of existing systems and the possible methods to introduce novel frameworks, respectively. This review aims at providing comprehensive insights into the recent developments of smart healthcare systems to equip experts to contribute to the field.
Multiwavelength Properties of Miras

We comprehensively study the variability of Miras in the Large Magellanic Cloud by simultaneously analyzing light curves in 14 bands in the range of 0.5–24 μm. We model over 20 yr long, high-cadence I-band light curves collected by the Optical Gravitational Lensing Experiment (OGLE) and fit them to light curves collected in the remaining optical/near-infrared/mid-infrared bands to derive both the variability amplitude ratio and phase lag as a function of wavelength. We show that the variability amplitude ratio declines with increasing wavelength for both oxygen-rich (O-rich) and carbon-rich (C-rich) Miras, while the variability phase lag increases slightly with increasing wavelength. In a significant number of Miras, mostly the C-rich ones, the spectral energy distributions (SEDs) require the presence of a cool component (dust) in order to match the mid-IR data. Based on SED fits for a golden sample of 140 Miras, we calculated synthetic period–luminosity relations (PLRs) in 42 bands for the existing and future sky surveys that include OGLE, the VISTA Near-Infrared YJK s Survey of the Magellanic Clouds System, Legacy Survey of Space and Time, Gaia, Spitzer, the Wide-field Infrared Survey Explorer, the James Webb Space Telescope, the Nancy Grace Roman Space Telescope (formerly WFIRST), and the Hubble Space Telescope. We show that the synthetic PLR slope decreases with increasing wavelength for both the O-rich and C-rich Miras in the range of 0.1–40 μm. Finally, we show the location and motions of Miras on the color–magnitude and color–color diagrams.
Improved bounds for colouring circle graphs

We prove the first χ-bounding function for circle graphs that is optimal up to a constant factor. To be more precise, we prove that every circle graph with clique number at most ω has chromatic number at most 2ω log 2 (ω) + 2ω log 2 (log 2 (ω)) + 10ω.
Large System Analysis of Box-Relaxation in Correlated Massive MIMO Systems Under Imperfect CSI

In this paper, we study the mean square error (MSE) and the bit error rate (BER) performance ofthe box-relaxation decoder in massive multiple-input-multiple-output (MIMO) systems under the assumptions of imperfect channel state information (CSI) and receive-side channel correlation. Our analysis assumes that the number of transmit and receive antennas (n, and m) grow simultaneously large while their ratio remains fixed. For simplicity of the analysis, we consider binary phase shift keying (BPSK) modulated signals. The asymptotic approximations of the MSE and BER enable us to derive the optimal power allocation scheme under MSE/BER minimization. Numerical simulations suggest that the asymptotic approximations are accurate even for small n and m.