Miracle-dz/UniFAR-FTUs huggingface dataset:
Structure and Dynamics of the Golgi Complex at 15 °C: Low Temperature Induces the Formation of Golgi‐Derived Tubules

Immunofluorescence and cryoimmunoelectron microscopy were used to examine the morphologic and functional effects on the Golgi complex when protein transport is blocked at the ERGIC (endoplasmic reticulum‐Golgi intermediate compartment) in HeLa cells incubated at low temperature (15 °C). At this temperature, the Golgi complex showed long tubules containing resident glycosylation enzymes but not matrix proteins. These Golgi‐derived tubules also lacked anterograde (VSV‐G) or retrograde (Shiga toxin) cargo. The formation of tubules was dependent on both energy and intact microtubule and actin cytoskeletons. Conversely, brefeldin A or cycloheximide treatments did not modify the appearance. When examined at the electron microscope, Golgi stacks were long and curved and appeared connected to tubules immunoreactive to galactosyltransferase antibodies but devoid of Golgi matrix proteins. Strikingly, COPI proteins moved from membranes to the cytosol at 15 °C, which could explain the formation of tubules.
Extensions and limitations of randomized smoothing for robustness guarantees

Randomized smoothing, a method to certify a classifier’s decision on an input is invariant under adversarial noise, offers attractive advantages over other certification methods. It operates in a black-box and so certification is not constrained by the size of the classifier’s architecture. Here, we extend the work of Li et al. [26], studying how the choice of divergence between smoothing measures affects the final robustness guarantee, and how the choice of smoothing measure itself can lead to guarantees in differing threat models. To this end, we develop a method to certify robustness against any ${\ell _p}\left( {p \in {\mathbb{N}_{ > 0}}} \right)$ minimized adversarial perturbation. We then demonstrate a negative result, that randomized smoothing suffers from the curse of dimensionality; as p increases, the effective radius around an input one can certify vanishes.
DiffGCN: Graph Convolutional Networks via Differential Operators and Algebraic Multigrid Pooling

Graph Convolutional Networks (GCNs) have shown to be effective in handling unordered data like point cloud and meshes. In this work we propose novel approaches for graph convolution, pooling and unpooling, taking inspiration from finite-elements and algebraic multigrid frameworks. We form a parameterized convolution kernel based on discretized differential operators, leveraging the graph mass, gradient and Laplacian. This way, the parameterization does not depend on the graph structure, only on the meaning of the network convolutions as differential operators. To allow hierarchical representations of the input, we propose pooling and unpooling operations that are based on algebraic multigrid methods. To motivate and explain our method, we compare it to standard Convolutional Neural Networks, and show their similarities and relations in the case of a regular grid. Our proposed method is demonstrated in various experiments like classification and segmentation, achieving on par or better than state of the art results. We also analyze the computational cost of our method compared to other GCNs.
Population dynamics of the garfish, Belone euxini (Belonidae: Belone) from the south-east Black Sea

To describe the age, growth, mortality and distribution of the garfish, Belone euxini and differences in these parameters from other populations, monthly samplings were conducted between December 2011 and July 2013 in the south-east Black Sea. Garfish catch decreased to its lowest levels in spring months, especially after May, and it was completely finished by the summer months. The mean total length (TL) of the female was significantly greater than the mean TL of the male (P < 0.05). The sex-ratio of female:male with size was skewed towards males up to 38 cm length-class, and skewed towards females above the 38 cm class (P < 0.05). The growth pattern of garfish was described by goodness of fit of the data and biological interpretation of growth parameters. The exponential (females: L∞ = 88.3 cm TL, K = 0.108 yr−1, males: L∞ = 71.9 cm TL, K = 0.151 yr−1) and von Bertalanffy (females: L∞ = 81.6 cm TL, K = 0.125 yr−1, t0 = −2.25 year, males: L∞ = 81.6 cm TL, K = 0.125 yr−1, t0 = −2.13 year) models were considered the most suitable growth models. Total instantaneous mortality rate, Z, was estimated as 1.04 yr−1 for females and 1.24 yr−1 for males. Instantaneous natural mortality ratio, M, was estimated higher for males than for females. The exploitation rate of both sexes was also higher than the optimum exploitation (E = 0.5) criterion, which is indicative of overfishing of the garfish population.
Denoising Implicit Feedback for Recommendation

The ubiquity of implicit feedback makes them the default choice to build online recommender systems. While the large volume of implicit feedback alleviates the data sparsity issue, the downside is that they are not as clean in reflecting the actual satisfaction of users. For example, in E-commerce, a large portion of clicks do not translate to purchases, and many purchases end up with negative reviews. As such, it is of critical importance to account for the inevitable noises in implicit feedback for recommender training. However, little work on recommendation has taken the noisy nature of implicit feedback into consideration. In this work, we explore the central theme of denoising implicit feedback for recommender training. We find serious negative impacts of noisy implicit feedback, i.e., fitting the noisy data hinders the recommender from learning the actual user preference. Our target is to identify and prune the noisy interactions, so as to improve the efficacy of recommender training. By observing the process of normal recommender training, we find that noisy feedback typically has large loss values in the early stages. Inspired by this observation, we propose a new training strategy named Adaptive Denoising Training (ADT), which adaptively prunes noisy interactions during training. Specifically, we devise two paradigms for adaptive loss formulation: Truncated Loss that discards the large-loss samples with a dynamic threshold in each iteration; and Reweighted Loss that adaptively lowers the weights of large-loss samples. We instantiate the two paradigms on the widely used binary cross-entropy loss and test the proposed ADT strategies on three representative recommenders. Extensive experiments on three benchmarks demonstrate that ADT significantly improves the quality of recommendation over normal training.
Evaluating the Disentanglement of Deep Generative Models through Manifold Topology

Learning disentangled representations is regarded as a fundamental task for improving the generalization, robustness, and interpretability of generative models. However, measuring disentanglement has been challenging and inconsistent, often dependent on an ad-hoc external model or specific to a certain dataset. To address this, we present a method for quantifying disentanglement that only uses the generative model, by measuring the topological similarity of conditional submanifolds in the learned representation. This method showcases both unsupervised and supervised variants. To illustrate the effectiveness and applicability of our method, we empirically evaluate several state-of-the-art models across multiple datasets. We find that our method ranks models similarly to existing methods.
Fair Classification with Noisy Protected Attributes

Due to the growing deployment of classification algorithms in various social contexts, developing methods that are fair with respect to protected attributes such as gender or race is an important problem. However, the information about protected attributes in datasets may be inaccurate due to either issues with data collection or when the protected attributes used are themselves predicted by algorithms. Such inaccuracies can prevent existing fair classification algorithms from achieving desired fairness guarantees. Motivated by this, we study fair classification problems when the protected attributes in the data may be ``noisy''. In particular, we consider a noise model where any protected type may be flipped to another with some fixed probability. We propose a ``denoised'' fair optimization formulation that can incorporate very general fairness goals via a set of constraints, mitigates the effects of such noise perturbations, and comes with provable guarantees. Empirically, we show that our framework can lead to near-perfect statistical parity with only a slight loss in accuracy for significant noise levels.
A Robust Attentional Framework for License Plate Recognition in the Wild

Recognizing car license plates in natural scene images is an important yet still challenging task in realistic applications. Many existing approaches perform well for license plates collected under constrained conditions, e.g., shooting in frontal and horizontal view-angles and under good lighting conditions. However, their performance drops significantly in an unconstrained environment that features rotation, distortion, occlusion, blurring, shading or extreme dark or bright conditions. In this work, we propose a robust framework for license plate recognition in the wild. It is composed of a tailored CycleGAN model for license plate image generation and an elaborate designed image-to-sequence network for plate recognition. On one hand, the CycleGAN based plate generation engine alleviates the exhausting human annotation work. Massive amount of training data can be obtained with a more balanced character distribution and various shooting conditions, which helps to boost the recognition accuracy to a large extent. On the other hand, the 2D attentional based license plate recognizer with an Xception-based CNN encoder is capable of recognizing license plates with different patterns under various scenarios accurately and robustly. Without using any heuristics rule or post-processing, our method achieves the state-of-the-art performance on four public datasets, which demonstrates the generality and robustness of our framework. Moreover, we released a new license plate dataset, named “CLPD”, with 1200 images from all 31 provinces in mainland China. The dataset can be available from: https://github.com/wangpengnorman/CLPD_dataset.
AdaDeep: A Usage-Driven, Automated Deep Model Compression Framework for Enabling Ubiquitous Intelligent Mobiles

Recent breakthroughs in deep neural networks (DNNs) have fueled a tremendously growing demand for bringing DNN-powered intelligence into mobile platforms. While the potential of deploying DNNs on resource-constrained platforms has been demonstrated by DNN compression techniques, the current practice suffers from two limitations: 1) merely stand-alone compression schemes are investigated even though each compression technique only suit for certain types of DNN layers; and 2) mostly compression techniques are optimized for DNNs’ inference accuracy, without explicitly considering other application-driven system performance (e.g., latency and energy cost) and the varying resource availability across platforms (e.g., storage and processing capability). To this end, we propose AdaDeep, a usage-driven, automated DNN compression framework for systematically exploring the desired trade-off between performance and resource constraints, from a holistic system level. Specifically, in a layer-wise manner, AdaDeep automatically selects the most suitable combination of compression techniques and the corresponding compression hyperparameters for a given DNN. Thorough evaluations on six datasets and across twelve devices demonstrate that <inline-formula><tex-math notation="LaTeX">${\sf AdaDeep}$</tex-math><alternatives><mml:math><mml:mi mathvariant="sans-serif">AdaDeep</mml:mi></mml:math><inline-graphic xlink:href="liu-ieq1-2999956.gif"/></alternatives></inline-formula> can achieve up to <inline-formula><tex-math notation="LaTeX">$18.6\times$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>18</mml:mn><mml:mo>. </mml:mo><mml:mn>6</mml:mn><mml:mo>×</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="liu-ieq2-2999956.gif"/></alternatives></inline-formula> latency reduction, <inline-formula><tex-math notation="LaTeX">$9.8\times$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>9</mml:mn><mml:mo>. </mml:mo><mml:mn>8</mml:mn><mml:mo>×</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="liu-ieq3-2999956.gif"/></alternatives></inline-formula> energy-efficiency improvement, and <inline-formula><tex-math notation="LaTeX">$37.3\times$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>37</mml:mn><mml:mo>. </mml:mo><mml:mn>3</mml:mn><mml:mo>×</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="liu-ieq4-2999956.gif"/></alternatives></inline-formula> storage reduction in DNNs while incurring negligible accuracy loss. Furthermore, <inline-formula><tex-math notation="LaTeX">${\sf AdaDeep}$</tex-math><alternatives><mml:math><mml:mi mathvariant="sans-serif">AdaDeep</mml:mi></mml:math><inline-graphic xlink:href="liu-ieq5-2999956.gif"/></alternatives></inline-formula> also uncovers multiple novel combinations of compression techniques.
Single-single gravitational-wave captures in globular clusters: Eccentric deci-Hertz sources observable by DECIGO and Tian-Qin

We study the formation rate of binary black hole mergers formed through gravitational-wave emission between unbound, single black holes in globular clusters. While the formation of these binaries in very dense systems such as galactic nuclei has been well studied, we show here that this process can operate in lower-density stellar systems as well, forming binaries at a rate similar to other proposed pathways for creating eccentric mergers. Recent advances in post-Newtonian cluster dynamics indicate that a large fraction of dynamically assembled binary black holes merge inside their host clusters during weak and strong binary-single and binary-binary interactions, and that these systems may retain measurable eccentricities as they travel through the LIGO and LISA sensitivity bands. Using an analytic approach to modeling binary black holes from globular clusters, we show that the formation of merging binaries from previously unbound black holes can operate at a similar rate to mergers forming during strong binary encounters, and that these binaries inhabit a unique region of the gravitational-wave frequency space which can be identified by proposed deci-Hertz space-based detectors. (Less)
Efficient Evaluation of Natural Stochastic Policies in Offline Reinforcement Learning

We study the efficient off-policy evaluation of natural stochastic policies, which are defined in terms of deviations from the unknown behaviour policy. This is a departure from the literature on off-policy evaluation that largely consider the evaluation of explicitly specified policies. Crucially, offline reinforcement learning with natural stochastic policies can help alleviate issues of weak overlap, lead to policies that build upon current practice, and improve policies' implementability in practice. Compared with the classic case of a prespecified evaluation policy, when evaluating natural stochastic policies, the efficiency bound, which measures the best-achievable estimation error, is inflated since the evaluation policy itself is unknown. In this paper we derive the efficiency bounds of two major types of natural stochastic policies: tilting policies and modified treatment policies. We then propose efficient nonparametric estimators that attain the efficiency bounds under lax conditions and enjoy a partial double robustness property.
Robust Face Verification via Disentangled Representations

We introduce a robust algorithm for face verification, i.e., deciding whether twoimages are of the same person or not. Our approach is a novel take on the idea ofusing deep generative networks for adversarial robustness. We use the generativemodel during training as an online augmentation method instead of a test-timepurifier that removes adversarial noise. Our architecture uses a contrastive loss termand a disentangled generative model to sample negative pairs. Instead of randomlypairing two real images, we pair an image with its class-modified counterpart whilekeeping its content (pose, head tilt, hair, etc.) intact. This enables us to efficientlysample hard negative pairs for the contrastive loss. We experimentally show that, when coupled with adversarial training, the proposed scheme converges with aweak inner solver and has a higher clean and robust accuracy than state-of-the-art-methods when evaluated against white-box physical attacks.
Planetary mass–radius relations across the galaxy

Context. Planet formation theory suggests that planet bulk compositions are likely to reflect the chemical abundance ratios of their host star’s photosphere. Variations in the abundance of particular chemical species in stellar photospheres between different galactic stellar populations demonstrate that there are differences among the expected solid planet bulk compositions. Aims. We aim to present planetary mass-radius relations of solid planets for kinematically differentiated stellar populations, namely, the thin disc, thick disc, and halo. Methods. Using two separate internal structure models, we generated synthetic planets using bulk composition inputs derived from stellar abundances. We explored two scenarios, specifically iron-silicate planets at 0.1 AU and silicate-iron-water planets at 4 AU. Results. We show that there is a persistent statistical difference in the expected mass-radius relations of solid planets among the different galactic stellar populations. At 0.1 AU for silicate-iron planets, there is a 1.51–2.04% mean planetary radius difference between the thick and thin disc stellar populations, whilst for silicate-iron-water planets past the ice line at 4 AU, we calculate a 2.93–3.26% difference depending on the models. Between the halo and thick disc, we retrieve at 0.1 AU a 0.53–0.69% mean planetary radius difference, and at 4 AU we find a 1.24–1.49% difference depending on the model. Conclusions. Future telescopes (such as PLATO) will be able to precisely characterize solid exoplanets and demonstrate the possible existence of planetary mass-radius relationship variability between galactic stellar populations.
The Lipschitz Constant of Self-Attention

Lipschitz constants of neural networks have been explored in various contexts in deep learning, such as provable adversarial robustness, estimating Wasserstein distance, stabilising training of GANs, and formulating invertible neural networks. Such works have focused on bounding the Lipschitz constant of fully connected or convolutional networks, composed of linear maps and pointwise non-linearities. In this paper, we investigate the Lipschitz constant of self-attention, a non-linear neural network module widely used in sequence modelling. We prove that the standard dot-product self-attention is not Lipschitz, and propose an alternative L2 self-attention that is Lipschitz. We derive an upper bound on the Lipschitz constant of L2 self-attention and provide empirical evidence for its asymptotic tightness. To demonstrate the practical relevance of the theory, we formulate invertible self-attention and use it in a Transformer-based architecture for a character-level language modelling task.
Comparing the Effects of Landscape Fragmentation by Forestry and Agriculture on Predation of Artificial Nests

Fragmentation of forested landscapes can reduce the reproductive success of birds occupying residual forest patches. Previous studies, however, have focused primarily on how nest predation can change when landscapes become fragmented by agriculture rather than by logging. To determine if predation on artificial nests in forest patches was influenced by the surrounding landscape, we placed ground and shrub nests along the edge and interior of forest patches located in agricultural, logged, and contiguous forest landscapes within a single region of the southern boreal mixedwood forest of central Canada. Nest predators were identified using remotely triggered cameras and by marks left in plasticine eggs, whereas the relative abundance of nest predators such as corvids and small mammals was estimated by surveys and live‐trapping. The percentage of ground nests destroyed at the edge and interior of patches in the logged and contiguous forest landscapes was significantly lower than at the edge or interior of patches in the agricultural landscape. No differences in predation rate were observed for shrub nests among landscapes. We attributed higher rates of predation in the agricultural landscape to higher densities of red squirrels ( Tamiasciurus hudsonicus) in the interior and a greater diversity of predators along edges. The similarity in predation rate between logged and contiguous forest landscapes suggests that fragmentation caused by logging may result in little change in predator communities and in this respect, might be similar to fragmentation caused by natural disturbance regimes such as fire. In contrast, fragmentation by agriculture seems to have more serious consequences for nesting birds. Policy changes that limit the growth of agriculture in the southern boreal mixedwood forest are required to conserve the diverse avifauna of this region.
Star Formation Timescales of the Halo Populations from Asteroseismology and Chemical Abundances* * Based on data collected with the Subaru Telescope, which is operated by the National Astronomical Observatory of Japan.

We combine asteroseismology, optical high-resolution spectroscopy, and kinematic analysis for 26 halo red giant branch stars in the Kepler field in the range of −2.5 < [Fe/H] < −0.6. After applying theoretically motivated corrections to the seismic scaling relations, we obtain an average mass of 0.97 ± 0.03 M ⊙ for our sample of halo stars. Although this maps into an age of ∼7 Gyr, significantly younger than independent age estimates of the Milky Way stellar halo, we considered this apparently young age to be due to the overestimation of stellar mass in the scaling relations. There is no significant mass dispersion among lower red giant branch stars (log g > 2), which constrains the relative age dispersion to <18%, corresponding to <2 Gyr. The precise chemical abundances allow us to separate the stars with [Fe/H] > −1.7 into two [Mg/Fe] groups. While the [α/Fe] and [Eu/Mg] ratios are different between the two subsamples, [s/Eu], where s stands for Ba, La, Ce, and Nd, does not show a significant difference. These abundance ratios suggest that the chemical evolution of the low-Mg population is contributed by Type Ia supernovae, but not by low- to intermediate-mass asymptotic giant branch stars, providing a constraint on its star formation timescale as 100 Myr < τ < 300 Myr. We also do not detect any significant mass difference between the two [Mg/Fe] groups, thus suggesting that their formation epochs are not separated by more than 1.5 Gyr.
Rapid Task-Solving in Novel Environments

When thrust into an unfamiliar environment and charged with solving a series of tasks, an effective agent should (1) leverage prior knowledge to solve its current task while (2) efficiently exploring to gather knowledge for use in future tasks, and then (3) plan using that knowledge when faced with new tasks in that same environment. We introduce two domains for conducting research on this challenge, and find that state-of-the-art deep reinforcement learning (RL) agents fail to plan in novel environments. We develop a recursive implicit planning module that operates over episodic memories, and show that the resulting deep-RL agent is able to explore and plan in novel environments, outperforming the nearest baseline by factors of 2-3 across the two domains. We find evidence that our module (1) learned to execute a sensible information-propagating algorithm and (2) generalizes to situations beyond its training experience.