Miracle-dz/UniFAR-FTUs huggingface dataset:
CRACT: Cascaded Regression-Align-Classification for Robust Visual Tracking

High quality object proposals are crucial in visual tracking algorithms that utilize region proposal network (RPN). Refinement of these proposals, typically by box regression and classification in parallel, has been popularly adopted to boost tracking performance. However, it still meets problems when dealing with complex and dynamic background. Thus motivated, in this paper we introduce an improved proposal refinement module, Cascaded Regression-Align-Classification (CRAC), which yields new state-of-the-art performances on many benchmarks. First, having observed that the offsets from box regression can serve as guidance for proposal feature refinement, we design CRAC as a cascade of box regression, feature alignment and box classification. The key is to bridge box regression and classification via an alignment step, which leads to more accurate features for proposal classification with improved robustness. To address the variation in object appearance, we introduce an identification-discrimination component for box classification, which leverages offline reliable fine-grained template and online rich background information to distinguish the target from background. Moreover, we present pyramid RoIAlign that benefits CRAC by exploiting both the local and global cues of proposals. During inference, tracking proceeds by ranking all refined proposals and selecting the best one. In experiments on seven benchmarks including OTB-2015, UAV123, NfS, VOT-2018, TrackingNet, GOT-10k and LaSOT, our CRACT exhibits very promising results in comparison with state-of-the-art competitors and runs in real-time.
Experiments on transfer learning architectures for biomedical relation extraction

Relation extraction (RE) consists in identifying and structuring automatically relations of interest from texts. Recently, BERT improved the top performances for several NLP tasks, including RE. However, the best way to use BERT, within a machine learning architecture, and within a transfer learning strategy is still an open question since it is highly dependent on each specific task and domain. Here, we explore various BERT-based architectures and transfer learning strategies (i.e., frozen or fine-tuned) for the task of biomedical RE on two corpora. Among tested architectures and strategies, our *BERT-segMCNN with finetuning reaches performances higher than the state-of-the-art on the two corpora (1.73 % and 32.77 % absolute improvement on ChemProt and PGxCorpus corpora respectively). More generally, our experiments illustrate the expected interest of fine-tuning with BERT, but also the unexplored advantage of using structural information (with sentence segmentation), in addition to the context classically leveraged by BERT.
Geom-Spider-EM: Faster Variance Reduced Stochastic Expectation Maximization for Nonconvex Finite-Sum Optimization

The Expectation Maximization (EM) algorithm is a key reference for inference in latent variable models; unfortunately, its computational cost is prohibitive in the large scale learning setting. In this paper, we propose an extension of the Stochastic Path-Integrated Differential EstimatoR EM (SPIDER-EM) and derive complexity bounds for this novel algorithm, designed to solve smooth nonconvex finite-sum optimization problems. We show that it reaches the same state of the art complexity bounds as SPIDER-EM; and provide conditions for a linear rate of convergence. Numerical results support our findings.
MetaSensing: Intelligent Metasurface Assisted RF 3D Sensing by Deep Reinforcement Learning

Using RF signals for wireless sensing has gained increasing attention. However, due to the unwanted multi-path fading in uncontrollable radio environments, the accuracy of RF sensing is limited. Instead of passively adapting to the environment, in this paper, we consider the scenario where an intelligent metasurface is deployed for sensing the existence and locations of 3D objects. By programming its beamformer patterns, the metasurface can provide desirable propagation properties. However, achieving a high sensing accuracy is challenging, since it requires the joint optimization of the beamformer patterns and mapping of the received signals to the sensed outcome. To tackle this challenge, we formulate an optimization problem for minimizing the cross-entropy loss of the sensing outcome, and propose a deep reinforcement learning algorithm to jointly compute the optimal beamformer patterns and the mapping of the received signals. Simulation results verify the effectiveness of the proposed algorithm and show how the size of the metasurface and the target space influence the sensing accuracy.
Lethean Attack: An Online Data Poisoning Technique

Data poisoning is an adversarial scenario where an attacker feeds a specially crafted sequence of samples to an online model in order to subvert learning. We introduce Lethean Attack, a novel data poisoning technique that induces catastrophic forgetting on an online model. We apply the attack in the context of Test-Time Training, a modern online learning framework aimed for generalization under distribution shifts. We present the theoretical rationale and empirically compare it against other sample sequences that naturally induce forgetting. Our results demonstrate that using lethean attacks, an adversary could revert a test-time training model back to coin-flip accuracy performance using a short sample sequence.
Enhanced Scene Specificity with Sparse Dynamic Value Estimation

Multi-scene reinforcement learning involves training the RL agent across multiple scenes / levels from the same task, and has become essential for many generalization applications. However, the inclusion of multiple scenes leads to an increase in sample variance for policy gradient computations, often resulting in suboptimal performance with the direct application of traditional methods (e.g. PPO, A3C). One strategy for variance reduction is to consider each scene as a distinct Markov decision process (MDP) and learn a joint value function dependent on both state (s) and MDP (M). However, this is non-trivial as the agent is usually unaware of the underlying level at train / test times in multi-scene RL. Recently, Singh et al. [1] tried to address this by proposing a dynamic value estimation approach that models the true joint value function distribution as a Gaussian mixture model (GMM). In this paper, we argue that the error between the true scene-specific value function and the predicted dynamic estimate can be further reduced by progressively enforcing sparse cluster assignments once the agent has explored most of the state space. The resulting agents not only show significant improvements in the final reward score across a range of OpenAI ProcGen environments, but also exhibit increased navigation efficiency while completing a game level.
Continual learning with direction-constrained optimization

This paper studies a new design of the optimization algorithm for training deep learning models with a fixed architecture of the classification network in a continual learning framework, where the training data is non-stationary and the non-stationarity is imposed by a sequence of distinct tasks. This setting implies the existence of a manifold of network parameters that correspond to good performance of the network on all tasks. Our algorithm is derived from the geometrical properties of this manifold. We first analyze a deep model trained on only one learning task in isolation and identify a region in network parameter space, where the model performance is close to the recovered optimum. We provide empirical evidence that this region resembles a cone that expands along the convergence direction. We study the principal directions of the trajectory of the optimizer after convergence and show that traveling along a few top principal directions can quickly bring the parameters outside the cone but this is not the case for the remaining directions. We argue that catastrophic forgetting in a continual learning setting can be alleviated when the parameters are constrained to stay within the intersection of the plausible cones of individual tasks that were so far encountered during training. Enforcing this is equivalent to preventing the parameters from moving along the top principal directions of convergence corresponding to the past tasks. For each task we introduce a new linear autoencoder to approximate its corresponding top forbidden principal directions. They are then incorporated into the loss function in the form of a regularization term for the purpose of learning the coming tasks without forgetting. We empirically demonstrate that our algorithm performs favorably compared to other state-of-art regularization-based continual learning methods, including EWC and SI.
Early modified gravity in light of the 
H0
 tension and LSS data

We present a model of early modified gravity (EMG) consisting in a scalar field $\ensuremath{\sigma}$ with a nonminimal coupling to the Ricci curvature of the type ${M}_{\mathrm{pl}}^{2}+\ensuremath{\xi}{\ensuremath{\sigma}}^{2}$ plus a cosmological constant and a small effective mass and demonstrate its ability to alleviate the ${H}_{0}$ tension while providing a good fit to cosmic microwave background (CMB) anisotropies and baryon acoustic oscillations (BAO) data. In this model the scalar field, frozen deep in the radiation era, grows around the redshift of matter-radiation equality because of the coupling to nonrelativistic matter. The small effective mass, which we consider here as induced by a quartic potential, then damps the scalar field into coherent oscillations around its minimum at $\ensuremath{\sigma}=0$, leading to a weaker gravitational strength at early times and naturally recovering the consistency with laboratory and Solar System tests of gravity. We analyze the capability of EMG with positive $\ensuremath{\xi}$ to fit current cosmological observations and compare our results to the case without an effective mass and to the popular early dark energy models with $\ensuremath{\xi}=0$. We show that EMG with a quartic coupling of the order of $\ensuremath{\lambda}\ensuremath{\sim}\mathcal{O}({\mathrm{eV}}^{4}/{M}_{\mathrm{pl}}^{4})$ can substantially alleviate the ${H}_{0}$ tension also when the full shape of the matter power spectrum is included in the fit in addition to CMB and Supernovae (SN) data.
Enhancing deep neural networks with morphological information

Deep learning approaches are superior in natural language processing due to their ability to extract informative features and patterns from languages. The two most successful neural architectures are LSTM and transformers, used in large pretrained language models such as BERT. While cross-lingual approaches are on the rise, most current natural language processing techniques are designed and applied to English, and less-resourced languages are lagging behind. In morphologically rich languages, information is conveyed through morphology, for example, through affixes modifying stems of words. The existing neural approaches do not explicitly use the information on word morphology. We analyse the effect of adding morphological features to LSTM and BERT models. As a testbed, we use three tasks available in many less-resourced languages: named entity recognition (NER), dependency parsing (DP) and comment filtering (CF). We construct baselines involving LSTM and BERT models, which we adjust by adding additional input in the form of part of speech (POS) tags and universal features. We compare the models across several languages from different language families. Our results suggest that adding morphological features has mixed effects depending on the quality of features and the task. The features improve the performance of LSTM-based models on the NER and DP tasks, while they do not benefit the performance on the CF task. For BERT-based models, the added morphological features only improve the performance on DP when they are of high quality (i.e., manually checked) while not showing any practical improvement when they are predicted. Even for high-quality features, the improvements are less pronounced in language-specific BERT variants compared to massively multilingual BERT models. As in NER and CF datasets manually checked features are not available, we only experiment with predicted features and find that they do not cause any practical improvement in performance.
Vasculopathy-related cutaneous lesions and intrahepatic cholestasis as synchronous manifestations in a COVID-19 patient; a case report

Today, COVID-19 pneumonia causes global concern. The World Health Organization (WHO) has reported many mortalities from this disease all around the world. Therefore, recognizing new cases of COVID-19 is crucial during this pandemic. Many studies have shown that COVID-19 has a broad spectrum of signs and symptoms, including GI and cutaneous manifestations. Previous studies have reported liver enzyme changes as well as diarrhea as a common GI manifestation of COVID-19. However, there are few reports about COVID-19 synchronous cutaneous and liver involvement. Additionally, there are few reports about intrahepatic cholestasis in COVID-19 patients. In this article, a confirmed case of COVID-19 with vasculopathy-related cutaneous manifestation and liver cholestasis is reported. A 67-year-old Iranian woman was admitted to Taleghani Hospital with epigastric pain, vomiting, anosmia, rising liver enzyme levels, fever, itching, and skin rashes. Skin and liver biopsies were performed during the patient’s admission; the results suggested vasculopathy-related cutaneous lesion and liver cholestasis. Plasmapheresis was initiated and all manifestations disappeared after treatment. All atypical presentations, including cutaneous lesions and liver manifestations, should be considered as COVID-19 and evaluated.
The cosmic neutrino background as a collection of fluids in large-scale structure simulations

A significant challenge for modelling the massive neutrino as a hot dark matter is its large velocity dispersion. In this work, we investigate and implement a multi-fluid perturbation theory that treats the cosmic neutrino population as a collection of fluids with a broad range of bulk velocities. These fluids respond linearly to the clustering of cold matter, which may be linear and described by standard linear perturbation theory, or non-linear, described using either higher-order perturbation theory or N-body simulations. We verify that such an alternative treatment of neutrino perturbations agrees closely with state-of-the-art neutrino linear response calculations in terms of power spectrum and bispectrum predictions. Combining multi-fluid neutrino linear response with a non-linear calculation for the cold matter clustering, we find for a reference νΛCDM cosmology with neutrino mass sum ∑ m ν = 0.93 eV an enhancement of the small-scale neutrino power by an order of magnitude relative to a purely linear calculation. The corresponding clustering enhancement in the cold matter, however, is a modest ∼ 0.05%. Importantly, our multi-fluid approach uniquely enables us to identify that the slowest-moving 25% of the neutrino population clusters strongly enough to warrant a non-linear treatment. Such a precise calculation of neutrino clustering on small scales accompanied by fine-grained velocity information would be invaluable for experiments such as PTOLEMY that probe the local neutrino density and velocity in the solar neighbourhood.
Neural Representations for Modeling Variation in English Speech

Variation in speech is often represented and investigated using phonetic transcriptions, but transcribing speech is time-consuming and error prone. To create reliable representations of speech independent from phonetic transcriptions, we investigate the extraction of acoustic embeddings from several self-supervised neural models. We use these representations to compute word-based pronunciation differences between non-native and native speakers of English, and evaluate these differences by comparing them with human native-likeness judgments. We show that Transformer-based speech representations lead to significant performance gains over the use of phonetic transcriptions, and find that feature-based use of Transformer models is most effective with one or more middle layers instead of the final layer. We also demonstrate that these neural speech representations not only capture segmental differences, but also intonational and durational differences that cannot be represented by a set of discrete symbols used in phonetic transcriptions.
A Bayesian semiparametric approach for inference on the population partly conditional mean from longitudinal data with dropout

Summary Studies of memory trajectories using longitudinal data often result in highly nonrepresentative samples due to selective study enrollment and attrition. An additional bias comes from practice effects that result in improved or maintained performance due to familiarity with test content or context. These challenges may bias study findings and severely distort the ability to generalize to the target population. In this study, we propose an approach for estimating the finite population mean of a longitudinal outcome conditioning on being alive at a specific time point. We develop a flexible Bayesian semiparametric predictive estimator for population inference when longitudinal auxiliary information is known for the target population. We evaluate the sensitivity of the results to untestable assumptions and further compare our approach to other methods used for population inference in a simulation study. The proposed approach is motivated by 15-year longitudinal data from the Betula longitudinal cohort study. We apply our approach to estimate lifespan trajectories in episodic memory, with the aim to generalize findings to a target population.
Handbook on the physics and chemistry of rare earths

The first volumes of the "Handbook on the Physics and Chemistry of Rare Earths" were published in 1978 and 1979. Since then, approximately one volume per year has been published, making a total of 15 volumes to date, containing 104 chapters with little duplication of topics covered. Some of the discoveries of marked technological importance of the 1980s include the excellent permanent magnetic properties of the Nd-Fe-B alloys, the magneto-optic behaviour of Co (or Fe)-Gd (or Tb) amorphous thin films, and the existence of superconductivity at temperatures well above liquid nitrogen (77 K) in YBa2Cu3O7-x; and these topics have been reviewed in some of the 104 chapters. To increase the usefulness of this serial publication, this volume contains comprehensive subject and author indexes for the whole series, up to and including volume 15. The value of the index volume has been enhanced by the inclusion of a comprehensive tabulation of most of the physical properties of the rare earth metals (14 tables). Also included is an errata listing all corrections of mistakes discovered while compiling this index, and those errors pointed out by authors, editors and users of the Handbook.
Signal Sets on Time Scales with Application to Hybrid Systems

Recently, time scales calculus is developed to unify continuous and discrete analysis. By extending the definition of time scales properly, this paper introduces the concept of a signal set as well as its stability properties in terms of the so-called pseudo distance measure. This leads to more general Lyapunov like conditions to check stability properties of systems with hybrid nature. By way of examples, the proposed framework is used to model hybrid systems with simplicity and flexibility to characterize trajectories in the behavior of hybrid systems.
Treewidth of the Generalized Kneser Graphs

Let $n$, $k$ and $t$ be integers with $1\leq t< k \leq n$. The generalized Kneser graph $K(n,k,t)$ is a graph whose vertices are the $k$-subsets of a fixed $n$-set, where two $k$-subsets $A$ and $B$ are adjacent if $|A\cap B|<t$. The graph $K(n,k,1)$ is the well-known Kneser graph. In 2014, Harvey and Wood determined the exact treewidth of the Kneser graphs for large $n$ with respect to $k$. In this paper, we give the exact treewidth of the generalized Kneser graphs for $t\geq2$ and large $n$ with respect to $k$ and $t$. In the special case when $t=k-1$, the graph $K(n,k,k-1)$ usually denoted by $\overline{J(n,k)}$ which is the complement of the Johnson graph $J(n,k)$. We give a more precise result for the exact value of the treewidth of $\overline{J(n,k)}$ for any $n$ and $k$.
The dynamic magnetosphere of Swift J1818.0–1607

Radio-loud magnetars display a wide variety of radio-pulse phenomenology seldom seen among the population of rotation-powered pulsars. Spectropolarimetry of the radio pulses from these objects has the potential to place constraints on their magnetic topology and unveil clues about the magnetar radio emission mechanism. Here, we report on eight observations of the magnetar Swift J1818.0–1607 taken with the Parkes Ultra-Wideband Low receiver covering a wide frequency range from 0.7 to 4 GHz over a period of 5 months. The magnetar exhibits significant temporal profile evolution over this period, including the emergence of a new profile component with an inverted spectrum, two distinct types of radio emission mode switching, detected during two separate observations, and the appearance and disappearance of multiple polarization modes. These various phenomena are likely a result of ongoing reconfiguration of the plasma content and electric currents within the magnetosphere. Geometric fits to the linearly polarized position angle indicate we are viewing the magnetar at an angle of ∼99○ from the spin axis, and its magnetic and rotation axes are misaligned by ∼112○. While conducting these fits, we found the position angle swing had reversed direction on MJD 59062 compared to observations taken 15 d earlier and 12 d later. We speculate this phenomena may be evidence the radio emission from this magnetar originates from magnetic field lines associated with two co-located magnetic poles that are connected by a coronal loop.
Cosine series quantum sampling method with applications in signal and image processing

A novel family of Cosine series Quantum Sampling (QCoSamp) operators appropriate for quantum computing is described. The development of quantum algorithms, analogous to classical algorithms, we apply to the harmonic analysis of signals. We show quantum sampling through measurements of a quantum system, and after operators of the family are applied, allow for input signal mapping with a Fourier series representation. Technical methodologies employed, facilitating the implementation of each QCoSamp algorithm to a quantum computer and application to the field of signal and image processing we also described. Keywords: quantum computing, quantum information theory, quantum operator, quantum sampling, Fourier sine-cosine series, signal processing, image processing
Carleman estimates for a class of variable coefficient degenerate elliptic operators with applications to unique continuation

In this paper, we obtain new Carleman estimates for a class of variable coefficient degenerate elliptic operators whose constant coefficient model at one point is the so called Baouendi-Grushin operator. This generalizes the results obtained by the two of us with Garofalo in [10] where similar estimates were established for the "constant coefficient" Baouendi-Grushin operator. Consequently, we obtain: (ⅰ) a Bourgain-Kenig type quantitative uniqueness result in the variable coefficient setting; (ⅱ) and a strong unique continuation property for a class of degenerate sublinear equations. We also derive a subelliptic version of a scaling critical Carleman estimate proven by Regbaoui in the Euclidean setting using which we deduce a new unique continuation result in the case of scaling critical Hardy type potentials.