Miracle-dz/UniFAR-FTUs huggingface dataset:
Domain Adaptation and Active Learning for Fine-Grained Recognition in the Field of Biodiversity

Deep-learning methods offer unsurpassed recognition performance in a wide range of domains, including fine-grained recognition tasks. However, in most problem areas there are insufficient annotated training samples. Therefore, the topic of transfer learning respectively domain adaptation is particularly important. In this work, we investigate to what extent unsupervised domain adaptation can be used for fine-grained recognition in a biodiversity context to learn a real-world classifier based on idealized training data, e.g. preserved butterflies and plants. Moreover, we investigate the influence of different normalization layers, such as Group Normalization in combination with Weight Standardization, on the classifier. We discovered that domain adaptation works very well for fine-grained recognition and that the normalization methods have a great influence on the results. Using domain adaptation and Transferable Normalization, the accuracy of the classifier could be increased by up to 12.35 % compared to the baseline. Furthermore, the domain adaptation system is combined with an active learning component to improve the results. We compare different active learning strategies with each other. Surprisingly, we found that more sophisticated strategies provide better results than the random selection baseline for only one of the two datasets. In this case, the distance and diversity strategy performed best. Finally, we present a problem analysis of the datasets.
The Sonora Substellar Atmosphere Models. II. Cholla: A Grid of Cloud-free, Solar Metallicity Models in Chemical Disequilibrium for the JWST Era

Exoplanet and brown dwarf atmospheres commonly show signs of disequilibrium chemistry. In the James Webb Space Telescope (JWST) era, high-resolution spectra of directly imaged exoplanets will allow the characterization of their atmospheres in more detail, and allow systematic tests for the presence of chemical species that deviate from thermochemical equilibrium in these atmospheres. Constraining the presence of disequilibrium chemistry in these atmospheres as a function of parameters such as their effective temperature and surface gravity will allow us to place better constraints on the physics governing these atmospheres. This paper is part of a series of works presenting the Sonora grid of atmosphere models. In this paper, we present a grid of cloud-free, solar metallicity atmospheres for brown dwarfs and wide-separation giant planets with key molecular species such as CH4, H2O, CO, and NH3 in disequilibrium. Our grid covers atmospheres with T eff ∈ [500 K, 1300 K], log g ∈ [3.0, 5.5] (cgs) and an eddy diffusion parameter of logKzz=2,4 and 7 (cgs). We study the effect of different parameters within the grid on the temperature and composition profiles of our atmospheres. We discuss their effect on the near-infrared colors of our model atmospheres and the detectability of CH4, H2O, CO, and NH3 using the JWST. We compare our models against existing MKO and Spitzer observations of brown dwarfs and verify the importance of disequilibrium chemistry for T dwarf atmospheres. Finally, we discuss how our models can help constrain the vertical structure and chemical composition of these atmospheres.
Contact Anticipation for Physical Human–Robot Interaction with Robotic Manipulators using Onboard Proximity Sensors

In this paper, we present a framework that unites obstacle avoidance and deliberate physical interaction for robotic manipulators. As humans and robots begin to coexist in work and household environments, pure collision avoidance is insufficient, as human–robot contact is inevitable and, in some situations, desired. Our work enables manipulators to anticipate, detect, and act on contact. To achieve this, we allow limited deviation from the robot’s original trajectory through velocity reduction and motion restrictions. Then, if contact occurs, a robot can detect it and maneuver based on a novel dynamic contact thresholding algorithm. The core contribution of this work is dynamic contact thresholding, which allows a manipulator with onboard proximity sensors to track nearby objects and reduce contact forces in anticipation of a collision. Our framework elicits natural behavior during physical human–robot interaction. We evaluate our system on a variety of scenarios using the Franka Emika Panda robot arm; collectively, our results demonstrate that our contribution is not only able to avoid and react on contact, but also anticipate it.
TADRN: Triple-Attentive Dual-Recurrent Network for Ad-hoc Array Multichannel Speech Enhancement

Deep neural networks (DNNs) have been successfully used for multichannel speech enhancement in ﬁxed array geometries. However, challenges remain for ad-hoc arrays with unknown microphone placements. We propose a deep neural network based approach for ad-hoc array processing: Triple-Attentive Dual-Recurrent Network (TADRN). TADRN uses self-attention across channels for learning spatial information and a dual-path attentive recurrent network (ARN) for temporal modeling. Temporal modeling is done independently for all channels by dividing a signal into smaller chunks and using an intra-chunk ARN for local modeling and an inter-chunk ARN for global modeling. Consequently, TADRN uses triple-path attention: inter-channel, intra-chunk, and inter-chunk, and dual-path recurrence: intra-chunk and inter-chunk. Experimental results show excellent performance of TADRN. We demonstrate that TADRN improves speech enhancement by leveraging additional randomly placed microphones, even at locations far from the target source. Additionally, large improvements in objective scores are observed when poorly placed microphones in the scene are complemented with more effective microphone positions, such as those closer to a target source.
FLiText: A Faster and Lighter Semi-Supervised Text Classification with Convolution Networks

In natural language processing (NLP), state-of-the-art (SOTA) semi-supervised learning (SSL) frameworks have shown great performance on deep pre-trained language models such as BERT, and are expected to significantly reduce the demand for manual labeling. However, our empirical studies indicate that these frameworks are not suitable for lightweight models such as TextCNN, LSTM and etc. In this work, we develop a new SSL framework called FLiText, which stands for Faster and Lighter semi-supervised Text classification. FLiText introduces an inspirer network together with the consistency regularization framework, which leverages a generalized regular constraint on the lightweight models for efficient SSL. As a result, FLiText obtains new SOTA performance for lightweight models across multiple SSL benchmarks on text classification. Compared with existing SOTA SSL methods on TextCNN, FLiText improves the accuracy of lightweight model TextCNN from 51.00% to 90.49% on IMDb, 39.8% to 58.06% on Yelp-5, and from 55.3% to 65.08% on Yahoo! Answer. In addition, compared with the fully supervised method on the full dataset, FLiText just uses less than 1% of labeled data to improve the accuracy by 6.59%, 3.94%, and 3.22% on the datasets of IMDb, Yelp-5, and Yahoo! Answer respectively.
Multidimensional representations in late-life depression: convergence in neuroimaging, cognition, clinical symptomatology and genetics

Importance: Late-life depression (LLD) is characterized by considerable heterogeneity in clinical manifestation. Unraveling such heterogeneity would aid in elucidating etiological mechanisms and pave the road to precision and individualized medicine. Objective: We sought to delineate, cross-sectionally and longitudinally, disease-related heterogeneity in LLD linked to neuroanatomy, cognitive functioning, clinical symptomatology, and genetic profiles. Design & setting: The iSTAGING study is an international multicenter consortium investigating brain aging in pooled and harmonized data from 13 studies with over 35,000 participants, including a subset of individuals with major depressive disorders. Participants: Multimodal data from a multicentre sample (N=996), including neuroimaging, neurocognitive assessments, and genetics: 501 LLD participants (332 women, mean age 67.39 ± 5.56 years) and 495 healthy controls (333 women, mean age 66.53 ± 5.16 years) were analyzed. A semi-supervised clustering method (HYDRA) was applied to regional grey matter (GM) brain volumes to derive dimensional representations. Exposure: None Main outcome and Measure: Two dimensions were identified, which accounted for the LLDrelated heterogeneity in voxel-wise GM maps, white matter (WM) fractional anisotropy (FA), neurocognitive functioning, clinical phenotype, and genetics. Results: Dimension one (Dim1) demonstrated relatively preserved brain anatomy without WM disruptions relative to healthy controls. In contrast, dimension two (Dim2) showed widespread brain atrophy and WM integrity disruptions, along with cognitive impairment and higher depression severity. Moreover, one de novo independent genetic variant (rs13120336) was
Primordial neutrino asymmetry evolution with full mean-field effects and collisions

Neutrino oscillations and mean-field effects considerably enrich the phenomenology of neutrino evolution in the early Universe. Taking into account these effects, most notably the neutrino self-interaction mean-field contribution, we revisit the problem of the evolution of primordial neutrino asymmetries including for the first time the complete expression for collisions, which describe scattering and annihilations with electrons/positrons and reactions among (anti)neutrinos. We show that a generalisation of the adiabatic transfer of averaged oscillations (ATAO) scheme, a numerical method previously developed without neutrino degeneracy and based on the large separation of time scales in this problem, is sufficient to reach the same accuracy as the full quantum kinetic equation integration, but is notably faster. This approximation highlights the physics of synchronous oscillations at play in the evolution of neutrino chemical potentials, especially in the particular case with only two-neutrino mixing. In particular, it allows to understand what controls the beginning and the amplitude of oscillations, but also why there is a subsequent regime of collective oscillations with larger frequencies. We also find that it is very important to use the full collision term instead of relying on damping-like approximations, in order not to overestimate how collisions reduce these synchronous oscillations. Finally we study qualitatively how mixing parameters affect the final neutrino configuration, and in particular we show that the CP-violating Dirac phase cannot substantially affect the final N eff nor the final electronic (anti)-neutrino spectrum, and thus should not affect cosmological observables.
A Machine Learning Framework Towards Transparency in Experts' Decision Quality

Expert workers make non-trivial decisions with significant implications. Experts’ decision accuracy is thus a fundamental aspect of their judgment quality, key to both management and consumers of experts’ services. Yet, in many important settings, transparency in experts’ decision quality is rarely possible because ground truth data for evaluating the experts’ decisions is costly and available only for a limited set of decisions. Furthermore, different experts typically handle exclusive sets of decisions, and thus prior solutions that rely on the aggregation of multiple experts’ decisions for the same instance are inapplicable. We first formulate the problem of estimating experts’ decision accuracy in this setting and then develop a machine-learning-based framework to address it. Our method effectively leverages both abundant historical data on workers’ past decisions, and scarce decision instances with ground truth information. We conduct extensive empirical evaluations of our method’s performance relative to alternatives using both semi-synthetic data based on publicly available datasets, and purposefully compiled dataset on real workers’ decisions. The results show that our approach is superior to existing alternatives across diverse settings, including different data domains, experts’ qualities, and the amount of ground truth data. To our knowledge, this paper is the first to posit and address the problem of estimating experts’ decision accuracies from historical data with scarcely available ground truth, and it is the first to offer comprehensive results for this problem setting, establishing the performances that can be achieved across settings, as well as the state-of-the-art performance on which future work can build.
Categorizing Items with Short and Noisy Descriptions using Ensembled Transferred Embeddings

Item categorization is a machine learning task which aims at classifying e-commerce items, typically represented by textual attributes, to their most suitable category from a predefined set of categories. An accurate item categorization system is essential for improving both the user experience and the operational processes of the company. In this work, we focus on item categorization settings in which the textual attributes representing items are noisy and short, and labels (i.e., accurate classification of items into categories) are not available. In order to cope with such settings, we propose a novel learning framework, Ensembled Transferred Embeddings (ETE), which relies on two key ideas: 1) labeling a relatively small sample of the target dataset, in a semi-automatic process, and 2) leveraging other datasets from related domains or related tasks that are large-scale and labeled, to extract"transferable embeddings". Evaluation of ETE on a large-scale real-world dataset provided to us by PayPal, shows that it significantly outperforms traditional as well as state-of-the-art item categorization methods.
Convergence to weak solutions of a space-time hybridized discontinuous Galerkin method for the incompressible Navier-Stokes equations

We prove that a space-time hybridized discontinuous Galerkin method for the evolutionary Navier--Stokes equations converges to a weak solution as the time step and mesh size tend to zero. Moreover, we show that this weak solution satisfies the energy inequality. To perform our analysis, we make use of discrete functional analysis tools and a discrete version of the Aubin--Lions--Simon theorem.
Model Inspired Autoencoder for Unsupervised Hyperspectral Image Super-Resolution

This article focuses on hyperspectral image (HSI) super-resolution that aims to fuse a low-spatial-resolution HSI and a high-spatial-resolution multispectral image to form a high-spatial-resolution HSI (HR-HSI). Existing deep learning-based approaches are mostly supervised that rely on a large number of labeled training samples, which is unrealistic. The commonly used model-based approaches are unsupervised and flexible but rely on handcrafted priors. Inspired by the specific properties of model, we make the first attempt to design a model-inspired deep network for HSI super-resolution in an unsupervised manner. This approach consists of an implicit autoencoder network built on the target HR-HSI that treats each pixel as an individual sample. The nonnegative matrix factorization (NMF) of the target HR-HSI is integrated into the autoencoder network, where the two NMF parts, spectral and spatial matrices, are treated as decoder parameters and hidden outputs, respectively. In the encoding stage, we present a pixelwise fusion model to estimate hidden outputs directly and then reformulate and unfold the model’s algorithm to form the encoder network. With the specific architecture, the proposed network is similar to a manifold prior-based model and can be trained patch by patch rather than the entire images. Moreover, we propose an additional unsupervised network to estimate the point spread function and spectral response function. Experimental results conducted on both synthetic and real datasets demonstrate the effectiveness of the proposed approach.
Exploiting Cross-Modal Prediction and Relation Consistency for Semisupervised Image Captioning

The task of image captioning aims to generate captions directly from images via the automatically learned cross-modal generator. To build a well-performing generator, existing approaches usually need a large number of described images (i.e., supervised image-sentence pairs), requiring a huge effects on manual labeling. However, in real-world applications, a more general scenario is that we only have limited amount of described images and a large number of undescribed images. Therefore, a resulting challenge is how to effectively combine the undescribed images into the learning of cross-modal generator (i.e., semisupervised image captioning). To solve this problem, we propose a novel image captioning method by exploiting the cross-modal prediction and relation consistency (CPRC), which aims to utilize the raw image input to constrain the generated sentence in the semantic space. In detail, considering that the heterogeneous gap between modalities always leads to the supervision difficulty while using the global embedding directly, CPRC turns to transform both the raw image and corresponding generated sentence into the shared semantic space, and measure the generated sentence from two aspects: 1) prediction consistency: CPRC utilizes the prediction of raw image as soft label to distill useful supervision for the generated sentence, rather than employing the traditional pseudo labeling and 2) relation consistency: CPRC develops a novel relation consistency between augmented images and corresponding generated sentences to retain the important relational knowledge. In result, CPRC supervises the generated sentence from both the informativeness and representativeness perspectives, and can reasonably use the undescribed images to learn a more effective generator under the semisupervised scenario. The experiments show that our method outperforms state-of-the-art comparison methods on the MS-COCO “Karpathy” offline test split under complex nonparallel scenarios, for example, CPRC achieves at least 6% improvements on the CIDEr-D score.
Validation and parameterization of a novel physics-constrained neural dynamics model applied to turbulent fluid flow

In fluid physics, data-driven models to enhance or accelerate time to solution are becoming increasingly popular for many application domains, such as alternatives to turbulence closures, system surrogates, or for new physics discovery. In the context of reduced order models of high-dimensional time-dependent fluid systems, machine learning methods grant the benefit of automated learning from data, but the burden of a model lies on its reduced-order representation of both the fluid state and physical dynamics. In this work, we build a physics-constrained, data-driven reduced order model for the Navier-Stokes equations to approximate spatio-temporal fluid dynamics in the canonical case of isotropic turbulence in a triply periodic box. The model design choices mimic numerical and physical constraints by, for example, implicitly enforcing the incompressibility constraint and utilizing continuous Neural Ordinary Differential Equations for tracking the evolution of the governing differential equation. We demonstrate this technique on three-dimensional, moderate Reynolds number turbulent fluid flow. In assessing the statistical quality and characteristics of the machine-learned model through rigorous diagnostic tests, we find that our model is capable of reconstructing the dynamics of the flow over large integral timescales, favoring accuracy at the larger length scales. More significantly, comprehensive diagnostics suggest that physically-interpretable model parameters, corresponding to the representations of the fluid state and dynamics, have attributable and quantifiable impact on the quality of the model predictions and computational complexity.
Finite-Time Complexity of Online Primal-Dual Natural Actor-Critic Algorithm for Constrained Markov Decision Processes

We consider a discounted cost constrained Markov decision process (CMDP) policy optimization problem, in which an agent seeks to maximize a discounted cumulative reward subject to a number of constraints on discounted cumulative utilities. To solve this constrained optimization program, we study an online actor-critic variant of a classic primal-dual method where the gradients of both the primal and dual variables are estimated using samples from a single trajectory generated by the underlying time-varying Markov processes. This online primal-dual natural actor-critic algorithm maintains and iteratively updates three variables: a dual variable (or Lagrangian multiplier), a primal variable (or actor), and a critic variable used to estimate the gradients of both primal and dual variables. These variables are updated simultaneously but on different time scales (using different step sizes) and they are all intertwined with each other. Our main contribution is to derive a finite-time analysis for the convergence of this algorithm to the global optimum of a CMDP problem. Specifically, we show that with a proper choice of step sizes the optimality gap and constraint violation converge to zero in expectation at a rate O(1/K1/6), where K is the number of iterations. To our knowledge, this paper is the first to study the finite-time complexity of an online primal-dual actor-critic method for solving a CMDP problem. We also validate the effectiveness of this algorithm through numerical simulations.
Node package manager's dependency network robustness

The robustness of npm dependency network is a crucial property, since many projects and web applications heavily rely on the functionalities of packages, especially popular ones that have many dependant packages. In the past, there have been instances where the removal or update of certain npm packages has caused widespread chaos and web-page downtime on the internet. Our goal is to track the network's resilience to such occurrences through time and figure out whether the state of the network is trending towards a more robust structure. We show that the network is not robust to targeted attacks, since a security risk in a few crucial nodes affects a large part of the network. Because such packages are often backed up by serious communities with high standards, the issue is not alarming and is a consequence of power law distribution of the network. The current trend in average number of dependencies and effect of important nodes on the rest of the network is decreasing, which further improves the resilience and sets a positive path in development. Furthermore, we show that communities form around the most important packages, although they do not conform well to the common community definition using modularity. We also provide guidelines for package development that increases the robustness of the network and reduces the possibility of introducing security risks.