Miracle-dz/UniFAR-FTUs huggingface dataset:
Evolving efficiency and robustness of the international oil trade network

As a vital strategic resource, oil has an essential influence on the world’s economy, diplomacy and military development. Oil resources are unevenly distributed. Therefore, trade is an important way to realize the allocation of oil resources on a global scale. This paper establishes a trading-based network model for international oil trade to study the evolving efficiency, criticality and robustness of economies and the relationships between oil trade partners. The results show that for unweighted international oil trade networks (iOTNs), the efficiency of oil resource allocation gradually increases with growing complexity of the iOTNs, and the weighted efficiency indicators are more capable of highlighting the impact of major events on the iOTNs. The identified critical economies and trade relationships have more important strategic significance in the real market. Simulated deliberate attacks corresponding to national bankruptcy, trade blockade, and economic sanctions have a more significant impact on the robustness than random attacks, and the international oil trade system is becoming more vulnerable. When economies are promoting high-quality economic development and continuously enhancing positions in the iOTN, more attention needs to be paid to the identified critical economies and trade relationships.
Knowledge Distillation and Student-Teacher Learning for Visual Intelligence: A Review and New Outlooks

Deep neural models, in recent years, have been successful in almost every field, even solving the most complex problem statements. However, these models are huge in size with millions (and even billions) of parameters, demanding heavy computation power and failing to be deployed on edge devices. Besides, the performance boost is highly dependent on redundant labeled data. To achieve faster speeds and to handle the problems caused by the lack of labeled data, knowledge distillation (KD) has been proposed to transfer information learned from one model to another. KD is often characterized by the so-called ‘Student-Teacher’ (S-T) learning framework and has been broadly applied in model compression and knowledge transfer. This paper is about KD and S-T learning, which are being actively studied in recent years. First, we aim to provide explanations of what KD is and how/why it works. Then, we provide a comprehensive survey on the recent progress of KD methods together with S-T frameworks typically used for vision tasks. In general, we investigate some fundamental questions that have been driving this research area and thoroughly generalize the research progress and technical details. Additionally, we systematically analyze the research status of KD in vision applications. Finally, we discuss the potentials and open challenges of existing methods and prospect the future directions of KD and S-T learning.
The CaFe Project: Optical Fe ii and Near-infrared Ca ii Triplet Emission in Active Galaxies. I. Photoionization Modeling

Optical Fe ii emission is a strong feature in quasar spectra originating in the broad-line region (BLR). The difficulty in understanding the complex Fe ii pseudo-continuum has led us to search for other reliable, simpler ionic species such as Ca ii. In this first part of the series, we confirm the strong correlation between the strengths of two emission features, the optical Fe ii and the NIR Ca ii, both from observations and photoionization modeling. With the inclusion of an up-to-date compilation of observations with both optical Fe ii and NIR Ca ii measurements, we span a wider and more extended parameter space and confirm the common origin of these two spectral features with our photoionization models using CLOUDY. Taking into account the effect of dust into our modeling, we constrain the BLR parameter space (primarily in terms of the ionization parameter and local cloud density) as a function of the strengths of Fe ii and Ca ii emission.
MLPSVM: A new parallel support vector machine to multi-label learning

Multi-label learning has attracted the attention of the machine learning community. The problem conversion method Binary Relevance converts a familiar single label into a multi-label algorithm. The binary relevance method is widely used because of its simple structure and efficient algorithm. But binary relevance does not consider the links between labels, making it cumbersome to handle some tasks. This paper proposes a multi-label learning algorithm that can also be used for single-label classification. It is based on standard support vector machines and changes the original single decision hyperplane into two parallel decision hyper-planes, which call multi-label parallel support vector machine (MLPSVM). At the end of the article, MLPSVM is compared with other multi-label learning algorithms. The experimental results show that the algorithm performs well on data sets.
Feature Lenses: Plug-and-play Neural Modules for Transformation-Invariant Visual Representations

Convolutional Neural Networks (CNNs) are known to be brittle under various image transformations, including rotations, scalings, and changes of lighting conditions. We observe that the features of a transformed image are drastically different from the ones of the original image. To make CNNs more invariant to transformations, we propose "Feature Lenses", a set of ad-hoc modules that can be easily plugged into a trained model (referred to as the "host model"). Each individual lens reconstructs the original features given the features of a transformed image under a particular transformation. These lenses jointly counteract feature distortions caused by various transformations, thus making the host model more robust without retraining. By only updating lenses, the host model is freed from iterative updating when facing new transformations absent in the training data; as feature semantics are preserved, downstream applications, such as classifiers and detectors, automatically gain robustness without retraining. Lenses are trained in a self-supervised fashion with no annotations, by minimizing a novel "Top-K Activation Contrast Loss" between lens-transformed features and original features. Evaluated on ImageNet, MNIST-rot, and CIFAR-10, Feature Lenses show clear advantages over baseline methods.
Electrohydrodynamic interactions of drop pairs

We study the pairwise interactions of drops in an applied uniform DC electric field within the framework of the leaky dielectric model. We develop three-dimensional numerical simulations using the boundary integral method and an analytical theory assuming small drop deformations. We apply the simulations and the theory to explore the electrohydrodynamic interactions between two identical drops with arbitrary orientation of the their line of centers relative to the applied field direction. Our results show complex dynamics depending on the conductivities and permittivities of the drops and suspending fluids, and the initial drop pair alignment with the applied electric field.
CSI learning based active secure coding scheme for detectable wiretap channel

In this paper, we consider the problem of secure and reliable communication with uncertain channel state information (CSI) and present a new solution named active secure coding which combines the machine learning methods with the traditional physical layer secure coding scheme. First, we build a detectable wiretap channel model by combining the hidden Markov model with the compound wiretap channel model, in which the varying of channel block CSI is a Markov process and the detected information is a stochastic emission from the current CSI. Next, we present a CSI learning scheme to learn the CSI from the detected information by the Baum-Welch and Viterbi algorithms. Then we construct explicit secure polar codes based on the learned CSI, and combine it with the CSI learning scheme to form the active secure polar coding scheme. Simulation results show that an acceptable level of reliability and security can be achieved by the proposed active secure polar coding scheme.
Classifying Constructive Comments

We introduce the Constructive Comments Corpus (C3), comprised of 12,000 annotated news comments, intended to help build new tools for online communities to improve the quality of their discussions. We define constructive comments as high-quality comments that make a contribution to the conversation. We explain the crowd worker annotation scheme and de ne a taxonomy of subcharacteristics of constructiveness. The quality of the annotation scheme and the resulting dataset is evaluated using measurements of inter-annotator agreement, expert assessment of a sample, and by the constructiveness sub-characteristics, which we show provide a proxy for the general constructiveness concept. We provide models for constructiveness trained on C3 using both feature-based and a variety of deep learning approaches and demonstrate, through domain adaptation experiments, that these models capture general rather than topic- or domain-specific characteristics of constructiveness. We also examine the role that length plays in our models, as comment length could be easily gamed if models depend heavily upon this feature. By examining the errors made by each model and their distribution by length, we show that the best performing models are effective independently of comment length. The constructiveness corpus and our experiments pave the way for a moderation tool focused on promoting comments that make a meaningful contribution, rather than only filtering out undesirable content.
Geodesic fields for Pontryagin type C0-Finsler manifolds

Let M be a differentiable manifold, T x M be its tangent space at x ε M and TM={(x,y);x ε M; y ε T x M} be its tangent bundle. A C 0 -Finsler structure is a continuous function F:TM → [0, ∞ ) such that F(x, · ):T x M → [0, ∞ ) is an asymmetric norm. In this work we introduce the Pontryagin type C 0 -Finsler structures, which are structures that satisfy the minimum requirements of Pontryagin's maximum principle for the problem of minimizing paths. We define the extended geodesic field E on the slit cotangent bundle T * M\0 of (M,F) , which is a generalization of the geodesic spray of Finsler geometry. We study the case where E is a locally Lipschitz vector field. We show some examples where the geodesics are more naturally represented by E than by a similar structure on TM . Finally we show that the maximum of independent Finsler structures is a Pontryagin type C 0 -Finsler structure where E is a locally Lipschitz vector field.
Joint Translation and Unit Conversion for End-to-end Localization

A variety of natural language tasks require processing of textual data which contains a mix of natural language and formal languages such as mathematical expressions. In this paper, we take unit conversions as an example and propose a data augmentation technique which lead to models learning both translation and conversion tasks as well as how to adequately switch between them for end-to-end localization.
Quantum biology revisited

We revise the interpretation of coherence signals in photosynthetic systems and clarify what they tell us about light harvesting. Photosynthesis is a highly optimized process from which valuable lessons can be learned about the operating principles in nature. Its primary steps involve energy transport operating near theoretical quantum limits in efficiency. Recently, extensive research was motivated by the hypothesis that nature used quantum coherences to direct energy transfer. This body of work, a cornerstone for the field of quantum biology, rests on the interpretation of small-amplitude oscillations in two-dimensional electronic spectra of photosynthetic complexes. This Review discusses recent work reexamining these claims and demonstrates that interexciton coherences are too short lived to have any functional significance in photosynthetic energy transfer. Instead, the observed long-lived coherences originate from impulsively excited vibrations, generally observed in femtosecond spectroscopy. These efforts, collectively, lead to a more detailed understanding of the quantum aspects of dissipation. Nature, rather than trying to avoid dissipation, exploits it via engineering of exciton-bath interaction to create efficient energy flow.
Draft Genome Sequence of Pseudarthrobacter phenanthrenivorans Strain MHSD1, a Bacterial Endophyte Isolated From the Medicinal Plant Pellaea calomelanos

Pseudarthrobacter phenanthrenivorans strain MHSD1 is a bacterial endophyte isolated from sterilized leaves of Pellaea calomelanos, a medicinal plant capable of growing in arid environments. Here, we report the draft genome sequence and annotation of this bacterial endophyte. The draft genome sequence of P. phenanthrenivorans strain MHSD1 has 4 450 468 bp with a G + C content of 65.30%. The National Center for Biotechnology Information Prokaryotic Genome Annotation Pipeline identified a total of 4004 protein-coding genes, 56 genes coding for RNAs, and 82 pseudogenes. Biosynthesis pathways for various phytohormones such as auxin, salicylic acid, ethylene, cytokinin, jasmonic acid, abscisic acid, and gibberellins were identified. Putative genes involved in various characteristics of bacterial endophyte lifestyle such as transport, motility, adhesion, membrane proteins, secretion and delivery systems, plant cell wall modification, and detoxification were identified. Phylogenomic analysis showed P. phenanthrenivorans strain MHSD1 to be a subspecies of P. phenanthrenivorans Sphe3.
Atorvastatin protects against contrast-induced acute kidney injury via upregulation of endogenous hydrogen sulfide

Abstract Background Contrast-induced acute kidney injury (CIAKI) is the third leading cause of acute renal failure in hospitalized patients. This study was aimed to investigate whether atorvastatin could upregulate the expression of hydrogen sulfide (H2S) and hence protect against CIAKI. Methods We treated male rats and NRK-52E cells by iopromide to establish in vivo and in vitro models of CIAKI. Pretreatment with atorvastatin was given in CIAKI rats to investigate its effect on CIAKI. We collected serum and urine samples to detect renal function. We obtained kidney tissue for histological analysis and detection of protein concentration. We tested the serum concentration of H2S and renal expression of two H2S synthetases [cystathionine γ-lyase (CSE) and cystathionine-β synthase (CBS)]. NaHS was pretreated in NRK-52E cells to testify its underlying effect on contrast-induced injury. Results Atorvastatin significantly ameliorated renal dysfunction and morphological changes in CIAKI rats, as well as inflammation, apoptosis, and excessive oxidative stress. Atorvastatin also markedly increased the serum concentration of H2S and renal expression of CSE and CBS. Moreover, pretreatment with NaHS in NRK-52E cells considerably attenuated contrast-induced cell death and inflammation. Conclusion Atorvastatin protects against CIAKI via upregulation of endogenous hydrogen sulfide.
Complete aggregation pathway of amyloid β (1-40) and (1-42) resolved on an atomically clean interface

High-resolution atomic force microscopy provides clean imaging of amyloid aggregates at graphene-water interface. To visualize amyloid β (Aβ) aggregates requires an uncontaminated and artifact-free interface. This paper demonstrates the interface between graphene and pure water (verified to be atomically clean using tunneling microscopy) as an ideal platform for resolving size, shape, and morphology (measured by atomic force microscopy) of Aβ-40 and Aβ-42 peptide assemblies from 0.5 to 150 hours at a 5-hour time interval with single-particle resolution. After confirming faster aggregation of Aβ-42 in comparison to Aβ-40, a stable set of oligomers with a diameter distribution of ~7 to 9 nm was prevalently observed uniquely for Aβ-42 even after fibril appearance. The interaction energies between a distinct class of amyloid aggregates (dodecamers) and graphene was then quantified using molecular dynamics simulations. Last, differences in Aβ-40 and Aβ-42 networks were resolved, wherein only Aβ-42 fibrils were aligned through lateral interactions over micrometer-scale lengths, a property that could be exploited in the design of biofunctional materials.
Geriatric Opioid Harm Reduction: Interprofessional Student Learning Outcomes

Objective: Opioid harm reduction is increasingly important in the care of the older adults, who are at higher risk for negative opioid-related outcomes due to high prevalence of pain, multimorbidity, polypharmacy, and age-changes in metabolism. Our project aims to develop, implement, and evaluate an interprofessional opioid harm reduction service training. Method: This evaluation occurs in context of the Richmond Health and Wellness Program (RHWP), a community-based interprofessional wellness care coordination equity initiative, within buildings designated for low-income and disabled older adults. The geriatric opioid harm reduction training was delivered online and inperson, and followed up with case-discussions and practice. Findings: Pre (n = 69)/post (n = 62) student assessments indicated that after the training, there was an increase in knowledge. At follow-up, 60% recognized tramadol as an opioid, 50% at baseline. About 97% correctly indicated that MME represents morphine milligram equivalent, 80% at baseline. About 93% indicated that 50 MME level greatly increases opioid overdose risk, 62% at baseline. Only 20%, change from 60% at baseline, reported not being able to calculate MME at post assessment. Conclusion: Findings indicate that geriatric opioid harm reduction training within community-based wellness care coordination is feasible. Future works need to explore the impact on student practice in clinical settings and resident health.
A Comparison of Different Immune Activation Strategies to Reverse HIV-1 Latency

Abstract Resting CD4+ T cells are the best characterized component of the latent reservoir. Activation of these CD4+ T cells is needed to optimize transcription and viral replication, and this strategy has been used to measure the inducible reservoir. There are several methods that can be used to activate CD4+ T cells, and in this study, we compared 3 different strategies: the combination of the lectin phytohaemagglutinin (PHA) and irradiated allogeneic feeders, a combination of PHA and a superagonistic anti-CD28 antibody, and the combination of the protein kinase C agonist phorbol 12-myristate 13-acetate and the calcium ionophore ionomycin. We show that each strategy induces a different pattern of expression of activation markers on CD4+ T cells. However, the different activation strategies induced similar frequencies of latently infected CD4+ T cells from people living with HIV on suppressive antiretroviral therapy regimens to produce replication-competent virus. Furthermore, the frequency of infectious units per million induced by each regimen was positively correlated with the copies of intact proviral DNA per million CD4+ T cells. Our results suggest that no single pattern of activation marker expression is most associated with latency reversal and demonstrate that different immune activation strategies reverse latency in a low frequency of CD4+ T cells that harbor intact proviral DNA.
Methanotrophic bacterial symbionts fuel dense populations of deep-sea feather duster worms (Sabellida, Annelida) and extend the spatial influence of methane seepage

Hidden in plain sight: Methanotrophic feather duster worms redefine the methane seep sphere of influence. Deep-sea cold seeps are dynamic sources of methane release and unique habitats supporting ocean biodiversity and productivity. Here, we describe newly discovered animal-bacterial symbioses fueled by methane, between two species of annelid (a serpulid Laminatubus and sabellid Bispira) and distinct aerobic methane-oxidizing bacteria belonging to the Methylococcales, localized to the host respiratory crown. Worm tissue δ13C of −44 to −58‰ are consistent with methane-fueled nutrition for both species, and shipboard stable isotope labeling experiments revealed active assimilation of 13C-labeled methane into animal biomass, which occurs via the engulfment of methanotrophic bacteria across the crown epidermal surface. These worms represent a new addition to the few animals known to intimately associate with methane-oxidizing bacteria and may further explain their enigmatic mass occurrence at 150–million year–old fossil seeps. High-resolution seafloor surveys document significant coverage by these symbioses, beyond typical obligate seep fauna. These findings uncover novel consumers of methane in the deep sea and, by expanding the known spatial extent of methane seeps, may have important implications for deep-sea conservation.
Una luz frente a la pérdida de visión por diabetes

Encontrar novedosas estrategias terapeuticas para atender el edema macular por diabetes, tambien llamado edema macular diabetico ( EMD ), es un reto importante en la actualidad ya que esta complicacion de la diabetes mellitus de tipo 1 y 2 es la principal causante de perdida de vision en personas con diabetes. El EMD es una acumulacion de liquido en la retina que se debe a un aumento excesivo de la permeabilidad retiniana y la formacion de depositos en la retina central o macula. La magnitud del problema puede ser apreciada al considerar que 6.4 millones de mexicanos han sido diagnosticados con diabetes y que el 29% de ellos presenta EMD . En este articulo se revisaran las opciones terapeuticas actuales para el EMD y sus limitantes, y se presentaran las evidencias que apoyan el uso potencial de farmacos bloqueadores de los canales ionicos Transient Receptor Potential Vanilloid 4 ( TRPV 4) como alternativa terapeutica novedosa para el EMD . Un estudio recien realizado permitio comprobar que el bloqueo de TRPV 4 elimina el aumento en la permeabilidad retiniana inducido por diabetes, alteracion que promueve el EMD . Por lo anterior y en vista de que antagonistas para TRPV 4 se usan actualmente para mitigar el edema pulmonar en pacientes, se espera que proximamente los farmacos bloqueadores de TRPV 4 se puedan usar como agentes curativos para el EMD .
The cohesin release factor Wapl interacts with Bub3 to govern SAC activity in female meiosis I

Cohesin release factor functions as a spindle assembly checkpoint regulator to prevent aneuploidy in oocyte meiosis I. During mitotic prophase, cohesins are removed from chromosome arms by Wapl to ensure faithful sister chromatid separation. However, during female meiosis I, the resolution of chiasmata requires the proteolytic cleavage of cohesin subunit Rec8 along chromosome arms by Separase to separate homologs, and thus the role of Wapl remained unknown. Here, we report that Wapl functions as a regulator of spindle assembly checkpoint (SAC) to prevent aneuploidy in meiosis I. Depletion of Wapl accelerates meiotic progression, inactivates SAC, and causes meiotic defects such as aberrant spindle/chromosome structure and incorrect kinetochore-microtubule (K-MT) attachment, consequently leading to aneuploid eggs. Notably, we identify Bub3 as a binding partner of Wapl by immunoprecipitation and mass spectrometry analysis. We further determine that Wapl controls the SAC activity by maintaining Bub3 protein level and document that exogenous Bub3 restores the normal meiosis in Wapl-depleted oocytes. Together, our findings uncover unique, noncanonical roles for Wapl in mediating control of the SAC in female meiosis I.