Miracle-dz/UniFAR-FTUs huggingface dataset:
Neural Architecture Search Using Stable Rank of Convolutional Layers

In Neural Architecture Search (NAS), Differentiable ARchiTecture Search (DARTS) has recently attracted much attention due to its high efficiency. It defines an over-parameterized network with mixed edges each of which represents all operator candidates, and jointly optimizes the weights of the network and its architecture in an alternating way. However, this process prefers a model whose weights converge faster than the others, and such a model with fastest convergence often leads to overfitting. Accordingly the resulting model cannot always be well-generalized. To overcome this problem, we propose Minimum Stable Rank DARTS (MSR-DARTS), which aims to find a model with the best generalization error by replacing the architecture optimization with the selection process using the minimum stable rank criterion. Specifically, a convolution operator is represented by a matrix and our method chooses the one whose stable rank is the smallest. We evaluate MSR-DARTS on CIFAR-10 and ImageNet dataset. It achieves an error rate of 2.92% with only 1.7M parameters within 0.5 GPU-days on CIFAR-10, and a top-1 error rate of 24.0% on ImageNet. Our MSR-DARTS directly optimizes an ImageNet model with only 2.6 GPU days while it is often impractical for existing NAS methods to directly optimize a large model such as ImageNet models and hence a proxy dataset such as CIFAR-10 is often utilized.
Complexity in Economic and Social Systems: Cryptocurrency Market at around COVID-19

Social systems are characterized by an enormous network of connections and factors that can influence the structure and dynamics of these systems. Among them the whole economical sphere of human activity seems to be the most interrelated and complex. All financial markets, including the youngest one, the cryptocurrency market, belong to this sphere. The complexity of the cryptocurrency market can be studied from different perspectives. First, the dynamics of the cryptocurrency exchange rates to other cryptocurrencies and fiat currencies can be studied and quantified by means of multifractal formalism. Second, coupling and decoupling of the cryptocurrencies and the conventional assets can be investigated with the advanced cross-correlation analyses based on fractal analysis. Third, an internal structure of the cryptocurrency market can also be a subject of analysis that exploits, for example, a network representation of the market. In this work, we approach the subject from all three perspectives based on data from a recent time interval between January 2019 and June 2020. This period includes the peculiar time of the Covid-19 pandemic; therefore, we pay particular attention to this event and investigate how strong its impact on the structure and dynamics of the market was. Besides, the studied data covers a few other significant events like double bull and bear phases in 2019. We show that, throughout the considered interval, the exchange rate returns were multifractal with intermittent signatures of bifractality that can be associated with the most volatile periods of the market dynamics like a bull market onset in April 2019 and the Covid-19 outburst in March 2020. The topology of a minimal spanning tree representation of the market also used to alter during these events from a distributed type without any dominant node to a highly centralized type with a dominating hub of USDT. However, the MST topology during the pandemic differs in some details from other volatile periods.
A 60 kpc Galactic Wind Cone in NGC 3079

Galactic winds are associated with intense star formation and active galactic nuclei. Depending on their formation mechanism and velocity, they may remove a significant fraction of gas from their host galaxies, thus suppressing star formation, enriching the intergalactic medium, and shaping the circumgalactic gas. However, the long-term evolution of these winds remains mostly unknown. We report the detection of a wind from NGC 3079 to at least 60 kpc from the galaxy. We detect the wind in far-ultraviolet (FUV) line emission to 60 kpc (as inferred from the broad FUV filter in the Galaxy Evolution Explorer) and X-rays to at least 30 kpc. The morphology, luminosities, temperatures, and densities indicate that the emission comes from shocked material, and the O/Fe ratio implies that the X-ray-emitting gas is enriched by Type II supernovae. If so, the speed inferred from simple shock models is about 500 km s−1, which is sufficient to escape the galaxy. However, the inferred kinetic energy in the wind from visible components is substantially smaller than canonical hot superwind models.
Daily red LED illumination improves the quality of cabbage plug seedlings during low-temperature storage

Summary Seedling storage can help to match supply with demand. To develop a method to preserve the quality of stored cabbage (Brassica oleracea L.) plug seedlings, we investigated the effects of the duration of daily red light-emitting diode (LED) illumination during storage on shoot growth, carbohydrate concentrations, and photosynthetic characteristics. Seedlings were stored at 10ºC for 3 weeks in the dark or under red LED illumination at a photosynthetic photon flux density of 0.8 µmol m–2 s–1 for 5, 20, 60, 240, or 1,440 min (continuous light) d–1. Shoots elongated and remained succulent during storage when the duration of illumination was < 60 min d–1. LED illumination did not affect shoot dry weight, or the concentrations of starch and soluble sugars. Regardless of its duration, LED illumination maintained high concentrations of chlorophyll and ribulose-1,5-bisphosphate carboxylase/oxygenase in the leaves. After removal from storage, seedlings stored in the dark or under continuous illumination showed lower recovery of their net rates of CO2 fixation than all other seedlings. We conclude that red LED illumination for 60 min d–1 was most effective at suppressing shoot elongation and reducing the loss of photosynthetic ability in cabbage seedlings during low-temperature storage.
Dialogue Distillation: Open-domain Dialogue Augmentation Using Unpaired Data

Recent advances in open-domain dialogue systems rely on the success of neural models that are trained on large-scale data. However, collecting large-scale dialogue data is usually time-consuming and labor-intensive. To address this data dilemma, we propose a novel data augmentation method for training open-domain dialogue models by utilizing unpaired data. Specifically, a data-level distillation process is first proposed to construct augmented dialogues where both post and response are retrieved from the unpaired data. A ranking module is employed to filter out low-quality dialogues. Further, a model-level distillation process is employed to distill a teacher model trained on high-quality paired data to augmented dialogue pairs, thereby preventing dialogue models from being affected by the noise in the augmented data. Automatic and manual evaluation indicates that our method can produce high-quality dialogue pairs with diverse contents, and the proposed data-level and model-level dialogue distillation can improve the performance of competitive baselines.
Weakly Supervised Learning of Nuanced Frames for Analyzing Polarization in News Media

In this paper we suggest a minimally-supervised approach for identifying nuanced frames in news article coverage of politically divisive topics. We suggest to break the broad policy frames suggested by Boydstun et al., 2014 into fine-grained subframes which can capture differences in political ideology in a better way. We evaluate the suggested subframes and their embedding, learned using minimal supervision, over three topics, namely, immigration, gun-control and abortion. We demonstrate the ability of the subframes to capture ideological differences and analyze political discourse in news media.
Quadrupole dominance in the light Sn and in the Cd isotopes

[Background] The BE2 rates of the Sn isotopes for $N\le 64$ exhibit enhancements hitherto unexplained. The same is true for the Cd isotopes. [Purpose] Describe the electromagnetic properties of the Sn and Cd isotopes [Method] Shell model calculations with a minimally renormalized realistic interaction, supplemented by Quasi and Pseudo-SU3 symmetries and Nilsson-SU3 selfconsistent calculations. [Results for $N\le 64$] Shell model calculations with the neutron effective charge as single free parameter describe well the BE2(2>0) and BE2(4>2) rates for $N\le 64$ in the Cd and Sn isotopes. The former exhibit weak permanent deformation corroborating the prediction of a Pseudo-SU3 symmetry, which remains of heuristic value in the latter, where the pairing force erodes the quadrupole dominance. Calculations in $10^7$ and $10^{10}$-dimensional spaces exhibit almost identical patterns: A vindication of the shell model. [Results for $N\ge 64$] Nilsson-SU3 calculations describe BE2 patterns in [112-120]Cd and [116-118]Sn isotopes having sizable quadrupole moment of non-rotational origin denoted as q-vibrations. No calculations are proposed for the heavier species, for which the conventional seniority dscription is assumed for Sn, while in Cd the quadrupole moments change sign. [Conclusion] A radical reexamination of traditional interpretations in the region has been shown to be necessary, in which quadrupole dominance plays a major role. What emerges is a bumpy but coherent view.
Deriving Visual Semantics from Spatial Context: An Adaptation of LSA and Word2Vec to generate Object and Scene Embeddings from Images

Embeddings are an important tool for the representation of word meaning. Their effectiveness rests on the distributional hypothesis: words that occur in the same context carry similar semantic information. Here, we adapt this approach to index visual semantics in images of scenes. To this end, we formulate a distributional hypothesis for objects and scenes: Scenes that contain the same objects (object context) are semantically related. Similarly, objects that appear in the same spatial context (within a scene or subregions of a scene) are semantically related. We develop two approaches for learning object and scene embeddings from annotated images. In the first approach, we adapt LSA and Word2vec's Skipgram and CBOW models to generate two sets of embeddings from object co-occurrences in whole images, one for objects and one for scenes. The representational space spanned by these embeddings suggests that the distributional hypothesis holds for images. In an initial application of this approach, we show that our image-based embeddings improve scene classification models such as ResNet18 and VGG-11 (3.72\% improvement on Top5 accuracy, 4.56\% improvement on Top1 accuracy). In the second approach, rather than analyzing whole images of scenes, we focus on co-occurrences of objects within subregions of an image. We illustrate that this method yields a sensible hierarchical decomposition of a scene into collections of semantically related objects. Overall, these results suggest that object and scene embeddings from object co-occurrences and spatial context yield semantically meaningful representations as well as computational improvements for downstream applications such as scene classification.
Frequency-explicit a posteriori error estimates for finite element discretizations of Maxwell's equations

We consider residual-based a posteriori error estimators for Galerkin-type discretizations of time-harmonic Maxwell's equations. We focus on configurations where the frequency is high, or close to a resonance frequency, and derive reliability and efficiency estimates. In contrast to previous related works, our estimates are frequency-explicit. In particular, our key contribution is to show that even if the constants appearing in the reliability and efficiency estimates may blow up on coarse meshes, they become independent of the frequency for sufficiently refined meshes. Such results were previously known for the Helmholtz equation describing scalar wave propagation problems and we show that they naturally extend, at the price of many technicalities in the proofs, to Maxwell's equations. Our mathematical analysis is performed in the 3D case, and covers conforming Nedelec discretizations of the first and second family, as well as first-order (and hybridizable) discontinuous Galerkin schemes. We also present numerical experiments in the 2D case, where Maxwell's equations are discretized with Nedelec elements of the first family. These illustrating examples perfectly fit our key theoretical findings, and suggest that our estimates are sharp.
Detailed Dynamic Model of Antagonistic PAM System and Its Experimental Validation: Sensorless Angle and Torque Control With UKF

This article proposes a detailed nonlinear mathematical model of an antagonistic pneumatic artificial muscle (PAM) actuator system for estimating the joint angle and torque using an unscented Kalman filter (UKF). The proposed model is described in a hybrid state-space representation. It includes the contraction force of the PAM, joint dynamics, fluid dynamics of compressed air, mass flows of a valve, and friction models. A part of the friction models is modified to obtain a novel form of the Coulomb friction depending on the inner pressure of the PAM. For model validation, offline and online UKF estimations and sensorless tracking control of the joint angle and torque are conducted to evaluate the estimation accuracy and tracking control performance. The estimation error is less than 7.91 %, and the steady-state tracking control performance is more than 94.75 %. These results confirm that the proposed model is detailed and could be used as the state estimator of an antagonistic PAM system.
Carbon, isotopic ratio 12C/13C, and nitrogen in solar twins: constraints for the chemical evolution of the local disc

Abundances of light elements in dwarf stars of different ages are important constraints for stellar yields, Galactic chemical evolution and exoplanet chemical composition studies. We have measured C and N abundances and $^{12}$C/$^{13}$C ratios for a sample of 63 solar twins spanning a wide range in age, based on spectral synthesis of a comprehensive list of CH\,A-X and CN\,B-X features using HARPS spectra. The analysis of 55 thin disc solar twins confirms the dependences of [C/Fe] and [N/Fe] on [Fe/H]. [N/Fe] is investigated as a function of [Fe/H] and age for the first time for these stars. Our derived correlation [C/Fe]-age agrees with works for solar-type stars and solar twins, but the [N/Fe]-age correlation does not. The relations [C,N/Fe]-[Fe/H] and [C,N/Fe]-age for the solar twins lay under-solar. $^{12}$C/$^{13}$C is found correlated with [Fe/H] and seems to have decreased along the evolution of the local thin disc. Predictions from chemical evolution models for the solar vicinity corroborate the relations [C,N/Fe]-[Fe/H], $^{12}$C/$^{13}$C-age and [N/O]-[O/H], but do not for the $^{12}$C/$^{13}$C-[Fe/H] and [C/O]-[O/H] relations. The N/O ratio in the Sun is placed at the high end of the homogeneous distribution of solar twins, which suggests uniformity in the N-O budget for the formation of icy planetesimals, watery super-earths and giant planets. C and N had different nucleosynthetic origins along the thin disc evolution, as shown by the relations of [C/N], [C/O] and [N/O] against [O/H] and age. [C/N] and [C/O] are particularly observed increasing in time for solar twins younger than the Sun.
Robust Decentralized Learning for Neural Networks

In decentralized learning, data is distributed among local clients which collaboratively train a shared prediction model using secure aggregation. To preserve the privacy of the clients, modern decentralized learning paradigms require each client to maintain a private local training data set and only upload their summarized model updates to the server. However, this can quickly lead to a degenerate model and collapse in performance when corrupted updates (e.g., adversarial manipulations) are aggregated at the server. In this work, we present a robust decentralized learning framework, Decent_BVA, using bias-variance based adversarial training via asymmetrical communications between each client and the server. The experiments are conducted on neural networks with cross-entropy loss. Nevertheless, the proposed framework allows the use of various classification loss functions (e.g., cross-entropy loss, mean squared error loss) where the gradients of the bias and variance are tractable to be estimated from local clients' models. In this case, any gradient-based adversarial training strategies could be used by taking the bias-variance oriented adversarial examples into consideration, e.g., bias-variance based FGSM and PGD proposed in this paper. Experiments show that Decent_BVA is robust to the classical adversarial attacks when the level of corruption is high while being competitive compared with conventional decentralized learning in terms of the model's accuracy and efficiency.
Massive MIMO Channel Prediction: Kalman Filtering Vs. Machine Learning

This paper focuses on channel prediction techniques for massive multiple-input multiple-output (MIMO) systems. Previous channel predictors are based on theoretical channel models, which would be deviated from realistic channels. In this paper, we develop and compare a vector Kalman filter (VKF)-based channel predictor and a machine learning (ML)-based channel predictor using the realistic channels from the spatial channel model (SCM), which has been adopted in the 3GPP standard for years. First, we propose a low-complexity mobility estimator based on the spatial average using a large number of antennas in massive MIMO. The mobility estimate can be used to determine the complexity order of developed predictors. The VKF-based channel predictor developed in this paper exploits the autoregressive (AR) parameters estimated from the SCM channels based on the Yule-Walker equations. Then, the ML-based channel predictor using the linear minimum mean square error (LMMSE)-based noise pre-processed data is developed. Numerical results reveal that both channel predictors have substantial gain over the outdated channel in terms of the channel prediction accuracy and data rate. The ML-based predictor has larger overall computational complexity than the VKF-based predictor, but once trained, the operational complexity of ML-based predictor becomes smaller than that of VKF-based predictor.
Evolution of Secondary Sexual Characters and Sexual Behavior Patterns in a Family of Viviparous Fishes (Cyprinodontiformes: Poeciliidae)

Aronson and Clark (1952) and Clark, Aronson and Gordon (1954) summarized the extensive behavioral work on these fishes, and they and Clark and Kamrin (1951) and Hubbs and Reynolds (1957) specifically studied the behavioral significance of fin and genitalic structure in Poeciliidae. Rosen and Gordon (1953) reviewed the mechanics of poeciliid genitalic and fin action in relation to copulatory motor patterns and provided an evolutionary interpretation of their findings. The present paper takes its origin from two separate reports, one behavioral and the other systematic3. It concerns the integration of structure and behavior in the evolution of the highly specialized, viviparous poeciliid fishes from a generalized, probably oviparous ancestor. The results of this study show, not unexpectedly, that the combination of comparative studies of behavior and functional anatomy may furnish insights on questions of evolution that do not emerge from either alone. The demonstrable effec-
Making Images Undiscoverable from Co-Saliency Detection

In recent years, co-saliency object detection (CoSOD) has achieved significant progress and played a key role in the retrieval-related tasks, e.g., image retrieval and video foreground detection. Nevertheless, it also inevitably posts a totally new safety and security problem, i.e., how to prevent high-profile and personal-sensitive contents from being extracted by the powerful CoSOD methods. In this paper, we address this problem from the perspective of adversarial attack and identify a novel task, i.e., adversarial co-saliency attack: given an image selected from an image group containing some common and salient objects, how to generate an adversarial version that can mislead CoSOD methods to predict incorrect co-salient regions. Note that, compared with general adversarial attacks for classification, this new task introduces two extra challenges for existing whitebox adversarial noise attacks: (1) low success rate due to the diverse appearance of images in the image group; (2) low transferability across CoSOD methods due to the considerable difference between CoSOD pipelines. To address these challenges, we propose the very first blackbox joint adversarial exposure & noise attack (Jadena) where we jointly and locally tune the exposure and additive perturbations of the image according to a newly designed high-feature-level contrast-sensitive loss function. Our method, without any information of the state-of-the-art CoSOD methods, leads to significant performance degradation on various co-saliency detection datasets and make the co-salient objects undetectable, which can be strongly practical in nowadays where large-scale personal photos are shared on the internet and should be properly and securely preserved.
Safety-Critical Kinematic Control of Robotic Systems

Over the decades, kinematic controllers have proven to be practically useful for applications like set-point and trajectory tracking in robotic systems. To this end, we formulate a novel safety-critical paradigm by extending the methodology of control barrier functions (CBFs) to kinematic equations governing robotic systems. We demonstrate a purely kinematic implementation of a velocity-based CBF, and subsequently introduce a formulation that guarantees safety at the level of dynamics. This is achieved through a new form of CBFs that incorporate kinetic energy with the classical forms, thereby minimizing model dependence and conservativeness. The approach is then extended to underactuated systems. This method and the purely kinematic implementation are demonstrated in simulation on two robotic platforms: a 6-DOF robotic manipulator, and a cart-pole system.
A Tutorial on Quantum Convolutional Neural Networks (QCNN)

Convolutional Neural Network (CNN) is a popular model in computer vision and has the advantage of making good use of the correlation information of data. However, CNN is challenging to learn efficiently if the given dimension of data or model becomes too large. Quantum Convolutional Neural Network (QCNN) provides a new solution to a problem to solve with CNN using a quantum computing environment, or a direction to improve the performance of an existing learning model. The first study to be introduced proposes a model to effectively solve the classification problem in quantum physics and chemistry by applying the structure of CNN to the quantum computing environment. The research also proposes the model that can be calculated with O(log(n)) depth using Multi-scale Entanglement Renormalization Ansatz (MERA). The second study introduces a method to improve the model’s performance by adding a layer using quantum computing to the CNN learning model used in the existing computer vision. This model can also be used in small quantum computers, and a hybrid learning model can be designed by adding a quantum convolution layer to the CNN model or replacing it with a convolution layer. This paper also verifies whether the QCNN model is capable of efficient learning compared to CNN through training using the MNIST dataset through the TensorFlow Quantum platform.