Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Uncertainty-aware Collaborative Learning Method for Remote Sensing Image Classification Under Multi-Label Noise

In remote sensing (RS), collecting a large number of reliable training images annotated by multiple land-cover class labels for multi-label classiﬁcation (MLC) is time-consuming and costly. To address this problem, the publicly available thematic products are often used for annotating RS images with zero-labeling-cost. However, in this case the training set can include noisy multi-labels that distort the learning process, resulting in inaccurate predictions. This paper proposes an architect-independent Consensual Collaborative Multi-Label Learning (CCML) method to train deep classiﬁers under input-dependent (heteroscedastic) multi-label noise in the MLC problems. The proposed CCML identiﬁes, ranks, and corrects noisy multi-label images through four main modules: 1) group lasso module; 2) discrepancy module; 3) ﬂipping module; and 4) swap module. The group lasso module detects the potentially noisy labels by estimating the label uncertainty based on the aggregation of two collaborative networks. The discrepancy module ensures that the two networks learn diverse features, while obtaining the same predictions. The ﬂipping module corrects the identiﬁed noisy labels, and the swap module exchanges the ranking information between the two networks. The experiments conducted on the multi-label RS image archive IR-BigEarthNet con-ﬁrm the robustness of the proposed CCML under extreme multi-label noise rates.
Comparing interpretability and explainability for feature selection

A common approach for feature selection is to examine the variable importance scores for a machine learning model, as a way to understand which features are the most relevant for making predictions. Given the significance of feature selection, it is crucial for the calculated importance scores to reflect reality. Falsely overestimating the importance of irrelevant features can lead to false discoveries, while underestimating importance of relevant features may lead us to discard important features, resulting in poor model performance. Additionally, black-box models like XGBoost provide state-of-the art predictive performance, but cannot be easily understood by humans, and thus we rely on variable importance scores or methods for explainability like SHAP to offer insight into their behavior. In this paper, we investigate the performance of variable importance as a feature selection method across various black-box and interpretable machine learning methods. We compare the ability of CART, Optimal Trees, XGBoost and SHAP to correctly identify the relevant subset of variables across a number of experiments. The results show that regardless of whether we use the native variable importance method or SHAP, XGBoost fails to clearly distinguish between relevant and irrelevant features. On the other hand, the interpretable methods are able to correctly and efficiently identify irrelevant features, and thus offer significantly better performance for feature selection.
UIUC_BioNLP at SemEval-2021 Task 11: A Cascade of Neural Models for Structuring Scholarly NLP Contributions

We propose a cascade of neural models that performs sentence classification, phrase recognition, and triple extraction to automatically structure the scholarly contributions of NLP publications. To identify the most important contribution sentences in a paper, we used a BERT-based classifier with positional features (Subtask 1). A BERT-CRF model was used to recognize and characterize relevant phrases in contribution sentences (Subtask 2). We categorized the triples into several types based on whether and how their elements were expressed in text, and addressed each type using separate BERT-based classifiers as well as rules (Subtask 3). Our system was officially ranked second in Phase 1 evaluation and first in both parts of Phase 2 evaluation. After fixing a submission error in Pharse 1, our approach yields the best results overall. In this paper, in addition to a system description, we also provide further analysis of our results, highlighting its strengths and limitations. We make our code publicly available at https://github.com/Liu-Hy/nlp-contrib-graph.
Label Geometry Aware Discriminator for Conditional Generative Networks

Multi-domain image-to-image translation with conditional Generative Adversarial Networks (GANs) can generate highly photo realistic images with desired target classes, yet these synthetic images have not always been helpful to improve downstream supervised tasks such as image classification. Improving downstream tasks with synthetic examples requires generating images with high fidelity to the unknown conditional distribution of the target class, which many labeled conditional GANs attempt to achieve by adding soft-max cross-entropy loss based auxiliary classifier in the discriminator. As recent studies suggest that the soft-max loss in Euclidean space of deep feature does not leverage their intrinsic angular distribution, we propose to replace this loss in auxiliary classifier with an additive angular margin (AAM) loss that takes benefit of the intrinsic angular distribution, and promotes intra-class compactness and inter-class separation to help generator synthesize high fidelity images. We validate our method on RaFD and CIFAR-100, two challenging face expression and natural image classification data set. Our method outperforms state-of-the-art methods in several different evaluation criteria including recently proposed GAN-train and GAN-test metrics designed to assess the impact of synthetic data on downstream classification task, assessing the usefulness in data augmentation for supervised tasks with prediction accuracy score and average confidence score, and the well known FID metric.
Sample-Efficient Device-Independent Quantum State Verification and Certification

Aleksandra Gočanin,1 Ivan Šupić,2, 3 and Borivoje Dakić4, 5 1Faculty of Physics, University of Belgrade, Studentski Trg 12-16, 11000 Belgrade, Serbia 2CNRS, LIP6, Sorbonne Université, 4 place Jussieu, 75005 Paris, France 3Département de Physique Appliquée, Université de Genève, 1211 Genève, Switzerland 4University of Vienna, Faculty of Physics, Vienna Center for Quantum Science and Technology, Boltzmanngasse 5, 1090 Vienna, Austria 5Institute for Quantum Optics and Quantum Information (IQOQI), Austrian Academy of Sciences, Boltzmanngasse 3, 1090 Vienna, Austria (Dated: February 8, 2022)
Dwarf Satellites of High-z Lyman Break Galaxies: A Free Lunch for JWST

We show that the James Webb Space Telescope (JWST) will be able to detect dwarf satellites of high-z Lyman break galaxies (LBGs). To this end, we use cosmological simulations following the evolution of a typical M ⋆ ≃ 1010 M ⊙ LBG up to z ≃ 6, and analyze the observational properties of its five satellite dwarf galaxies (107 M ⊙ < M ⋆ < 109 M ⊙). Modeling their stellar emission and dust attenuation, we reconstruct their rest-frame ultraviolet-optical spectra for 6 < z < 6.5. JWST/NIRCam synthetic images show that the satellites can be spatially resolved from their host, and their emission is detectable by planned deep surveys. Moreover, we build synthetic spectral energy distributions and color–magnitude diagrams for the satellites. We conclude that the color F200W–F356W is a powerful diagnostic tool for understanding their physical properties once they have been identified. For example, F200W–F356W ≲ −0.25 can be used to identify star-bursting (SFR ∼ 5 M ⊙ yr−1), low-mass (M ⋆ ≲ 5 × 108 M ⊙) systems, with ∼80% of their stars being young and metal poor ].
Experimental Investigation of Aortic Pressure Variations Following a Simulated Thoracic Impact

Blunt traumatic aortic rupture is a heart injury that can occur in falls, automobile accidents, and sporting injuries involving impact to the thorax. Despite its severity and high morbidity rate, the research still does not provide a consistent description of the mechanism of rupture. In this study, a crash testing dummy with an in vitro pumping heart, 3D printed ribcage, and ballistic gel damping layer was developed to reproduce a realistic response to thoracic impact. Testing was performed using a standardized pendulum used for calibration of crash test dummies, with the location of impact being the middle of the sternum. Different impact severities were tested by adjusting the kinetic energy at impact with the initial height of the pendulum. Measurements of the dummy include instantaneous aortic pressure waveforms prior, during and following the impact. The results of this experiment show that aortic pressure experiences significant changes in magnitude during simulated impact. This work could help contribute towards a better understanding of the mechanisms leading to blunt traumatic aortic rupture and the development of preventative measures.
The Overfullness of Graphs with Small Minimum Degree and Large Maximum Degree

Given a simple graph $G$, denote by $\Delta(G)$, $\delta(G)$, and $\chi'(G)$ the maximum degree, the minimum degree, and the chromatic index of $G$, respectively. We say $G$ is \emph{$\Delta$-critical} if $\chi'(G)=\Delta(G)+1$ and $\chi'(H)\le \Delta(G)$ for every proper subgraph $H$ of $G$; and $G$ is \emph{overfull} if $|E(G)|>\Delta \lfloor |V(G)|/2 \rfloor$. Since a maximum matching in $G$ can have size at most $\lfloor |V(G)|/2 \rfloor$, it follows that $\chi'(G) = \Delta(G) +1$ if $G$ is overfull. Conversely, let $G$ be a $\Delta$-critical graph. The well known overfull conjecture of Chetwynd and Hilton asserts that $G$ is overfull provided $\Delta(G)>|V(G)|/3$. In this paper, we show that any $\Delta$-critical graph $G$ is overfull if $\Delta(G) - 7\delta(G)/4\ge(3|V(G)|-17)/4$.
Chemodynamical Signatures of Bar Resonances in the Galactic Disk: Current Data and Future Prospects

The Galactic disk exhibits complex chemical and dynamical substructure thought to be induced by the bar, spiral arms, and satellites. Here, we explore the chemical signatures of bar resonances in action and velocity space, and characterize the differences between the signatures of corotation (CR) and higher-order resonances using test particle simulations. Thanks to recent surveys, we now have large data sets containing metallicities and kinematics of stars outside the solar neighborhood. We compare the simulations to the observational data from Gaia EDR3 and LAMOST DR5 and find weak evidence for a slow bar with the “hat” moving group (250 km s−1 ≲ v ϕ ≲ 270 km s−1) associated with its outer Lindblad resonance and “Hercules” (170 km s−1 ≲ v ϕ ≲ 195 km s−1) with CR. While constraints from current data are limited by their spatial footprint, stars closer in azimuth than the Sun to the bar’s minor axis show much stronger signatures of the bar’s outer Lindblad and CR resonances in test particle simulations. Future data sets with greater azimuthal coverage, including the final Gaia data release, will allow reliable chemodynamical identification of bar resonances.
Playing Quantum Nonlocal Games with Six Noisy Qubits on the Cloud

Nonlocal games are extensions of Bell inequalities, aimed at demonstrating quantum advantage. These games are well suited for noisy quantum computers because they only require the preparation of a shallow circuit, followed by the measurement of non‐commuting observable. Here, the minimal implementation of the nonlocal game proposed in Science 362, 308 (2018) is considered. This game is tested by preparing a 6‐qubit cluster state using quantum computers on the cloud by IBM, IonQ, and Honeywell. The present implementation includes several levels of optimization, such as circuit identities and error mitigation, and allows us to cross the classical threshold and demonstrate quantum advantage in one quantum computer. The quantum nature of the cluster state is further studied by a Bell inequality that allows us to observe quantum advantage in less accurate quantum computers, at the expense of probing a larger number of circuits.
Looking at CTR Prediction Again: Is Attention All You Need?

Click-through rate (CTR) prediction is a critical problem in web search, recommendation systems and online advertisement displaying. Learning good feature interactions is essential to reflect user's preferences to items. Many CTR prediction models based on deep learning have been proposed, but researchers usually only pay attention to whether state-of-the-art performance is achieved, and ignore whether the entire framework is reasonable. In this work, we use the discrete choice model in economics to redefine the CTR prediction problem, and propose a general neural network framework built on self-attention mechanism. It is found that most existing CTR prediction models align with our proposed general framework. We also examine the expressive power and model complexity of our proposed framework, along with potential extensions to some existing models. And finally we demonstrate and verify our insights through some experimental results on public datasets.
Encoding Explanatory Knowledge for Zero-shot Science Question Answering

This paper describes N-XKT (Neural encoding based on eXplanatory Knowledge Transfer), a novel method for the automatic transfer of explanatory knowledge through neural encoding mechanisms. We demonstrate that N-XKT is able to improve accuracy and generalization on science Question Answering (QA). Specifically, by leveraging facts from background explanatory knowledge corpora, the N-XKT model shows a clear improvement on zero-shot QA. Furthermore, we show that N-XKT can be fine-tuned on a target QA dataset, enabling faster convergence and more accurate results. A systematic analysis is conducted to quantitatively analyze the performance of the N-XKT model and the impact of different categories of knowledge on the zero-shot generalization task.
Thematic recommendations on knowledge graphs using multilayer networks

We present a framework to generate and evaluate thematic recommendations based on multilayer network representations of knowledge graphs (KGs). In this representation, each layer encodes a different type of relationship in the KG, and directed interlayer couplings connect the same entity in different roles. The relative importance of different types of connections is captured by an intuitive salience matrix that can be estimated from data, tuned to incorporate domain knowledge, address different use cases, or respect business logic. We apply an adaptation of the personalised PageRank algorithm to multilayer models of KGs to generate item-item recommendations. These recommendations reflect the knowledge we hold about the content and are suitable for thematic and/or cold-start recommendation settings. Evaluating thematic recommendations from user data presents unique challenges that we address by developing a method to evaluate recommendations relying on user-item ratings, yet respecting their thematic nature. We also show that the salience matrix can be estimated from user data. We demonstrate the utility of our methods by significantly improving consumption metrics in an AB test where collaborative filtering delivered subpar performance. We also apply our approach to movie recommendation using publicly-available data to ensure the reproducibility of our results. We demonstrate that our approach outperforms existing thematic recommendation methods and is even competitive with collaborative filtering approaches.
Mining Legacy Issues in Open Pit Mining Sites: Innovation & Support of Renaturalization and Land Utilization

and actively learn their representation in our dataset. We evaluate the OCR, Active Learning and Text Classiﬁcation separately to report the performance of the system. Active Learning and text classiﬁcation results are twofold: Whereas our categories about restrictions work sufﬁcient ( > .85 F1), the seven topic-oriented categories were complicated for human coders and hence the results achieved mediocre evaluation scores ( < .70 F1).
Fractal Rosette: A Stable Space-Ground Network Structure in Mega-Constellation

We present F-Rosette, a stable space-ground network structure for low-earth orbit (LEO) satellite mega-constellations at scale. Due to the dynamic many-to-many space-ground mapping in high mobility, existing LEO mega-constellations with IP protocol stack suffer from frequent user IP address changes (every 133~510s per user) and network routing re-convergence (<20% network usability). To provably stabilize the space-ground network under high mobility and many-to-many dynamics, F-Rosette adopts a recursive structure over the Rosette constellation, derives a hierarchical and time-invariant network address space from a new geographical coordinate system, and ensures efficient and stable routing via geographical-to-topological routing embedding without re-convergence. Our hardware-in-the-loop, trace-driven emulations validate F-Rosette's stability, near-optimal routing (<1.4% additional delays), and marginal overhead (<1% CPU,<2MB memory) for resource-constrained satellites.
The generalized strong subadditivity of the von Neumann entropy for bosonic quantum Gaussian systems

We prove a generalization of the strong subadditivity of the von Neumann entropy for bosonic quantum Gaussian systems. Such generalization determines the minimum values of linear combinations of the entropies of subsystems associated to arbitrary linear functions of the quadratures, and holds for arbitrary quantum states including the scenario where the entropies are conditioned on a memory quantum system. We apply our result to prove new entropic uncertainty relations with quantum memory, a generalization of the quantum Entropy Power Inequality, and the linear time scaling of the entanglement entropy produced by quadratic Hamiltonians.
Variation of Isotope Coefficient with Number of CuO2 Layers in High TC Superconductors

In this paper, an effort has been made to study the isotope coefficient in high TC Superconductors by using the variation of isotopic coefficient with the number of CuO2 layers and the variation of isotope coefficients on transition temperature TC . The Hamiltonian for CuO2 layers using BCS type model and extra term of interlayer interaction between CuO2 layers has been considered. Expressions for isotope effect (α) and transition temperature (TC) are obtained and numerically calculated for experimental values by using Green’s function technique.
Electrotactile feedback for hand interactions: A systematic review, meta-analysis, and future directions

Haptic feedback is critical in a broad range of human-machine/computer-interaction applications. However, the high cost and low portability/wearability of haptic devices remains an unresolved issue, severely limiting the adoption of this otherwise promising technology. Electrotactile interfaces have the advantage of being more portable and wearable due to its reduced actuators’ size, as well as benefiting from lower power consumption and manufacturing cost. The usages of electrotactile feedback have been explored in human-computer interaction and human machine-interaction for facilitating hand-based interactions in applications such as prosthetics, virtual reality, robotic teleoperation, surface haptics, portable devices, and rehabilitation. This paper presents a systematic review and meta-analysis of electrotactile feedback systems for hand based interactions in the last decade. We categorize the different electrotactile systems according to their type of stimulation and implementation/application. We also present and discuss a quantitative congregation of the findings, so as to offer a high-level overview into the state-of-art and suggest future directions. Electrotactile feedback was successful in rendering and/or augmenting most tactile sensations, eliciting perceptual processes, and improving performance in many scenarios, especially in those where the wearability/portability of the system is important. However, knowledge gaps, technical drawbacks, and methodological limitations were detected, which should be addressed in future studies.
On chain recurrence classes of endomorphisms of ℙ^{𝕜}

We prove that the minimal chain recurrence classes of a holomorphic endomorphism of $\mathbb P^k$ have finitely many connected components. We also obtain results on arbitrary classes. These strong constraints on the topological dynamics in the phase space are all deduced from the associated action on a space of currents.
Integrated metabolome and transcriptome revealed the flavonoid biosynthetic pathway in developing Vernonia amygdalina leaves

Background Vernonia amygdalina as a tropical horticultural crop has been widely used for medicinal herb, feed, and vegetable. Recently, increasing studies revealed that this species possesses multiple pharmacological properties. Notably, V. amygdalina leaves possess an abundance of flavonoids, but the specific profiles of flavonoids and the mechanisms of fl avonoid bi osynthesis in developing leaves are largely unknown. Methods The total flavonoids of V. amygdalina leaves were detected using ultraviolet spectrophotometer. The temporal flavonoid profiles of V. amygdalina leaves were analyzed by LC-MS. The transcriptome analysis of V. amygdalina leaves was performed by Illumina sequencing. Functional annotation and differential expression analysis of V. amygdalina genes were performed by Blast2GO v2.3. 5 and RSEM v1.2. 31, respectively. qRT-PCR analysis was used to verify the gene expressions in developing V. amygdalina leaves. Results By LC-MS analysis, three substrates (p-coumaric acid, trans-cinnamic acid, and phenylalanine) for flavonoid biosynthesis were identified in V. amygdalina leaves. Additionally, 42 flavonoids were identified from V. amygdalina leaves, including six dihydroflavones, 14 flavones, eight isoflavones, nine flavonols, two xanthones, one chalcone, one cyanidin, and one dihydroflavonol. Glycosylation and methylation were common at the hydroxy group of C3, C7, and C4’ positions. Moreover, dynamic patterns of different flavonoids showed diversity. By Illumina sequencing, the obtained over 200 million valid reads were assembled into 60,422 genes. Blast analysis indicated that 31,872 genes were annotated at least in one of public databases. Greatly increasing molecular resources makes up for the lack of gene information in V. amygdalina. By digital expression profiling and qRT-PCR, we specifically characterized some key enzymes, such as Va-PAL1, Va-PAL4, Va-C4H1, Va-4CL3, Va-ACC1, Va-CHS1, Va-CHI, Va-FNSII, and Va-IFS3, involved in flavonoid biosynthesis. Importantly, integrated metabolome and transcriptome data of V. amygdalina leaves, we systematically constructed a flavonoid biosynthetic pathway with regards to material supplying, flavonoid scaffold biosynthesis, and flavonoid modifications. Our findings contribute significantly to understand the underlying mechanisms of flavonoid biosynthesis in V. amygdalina leaves, and also provide valuable information for potential metabolic engineering.