Miracle-dz/UniFAR-FTUs huggingface dataset:
Cloning of the Cytochrome b Gene From the Tomato Powdery Mildew Fungus Leveillula taurica Reveals High Levels of Allelic Variation and Heteroplasmy for the G143A Mutation

Leveillula taurica is a major pathogen of tomato and several other crops that can cause substantial yield losses in favorable conditions for the fungus. Quinone outside inhibitor fungicides (QoIs) are routinely used for the control of the pathogen in tomato fields across California, but their recurrent use could lead to the emergence of resistance against these compounds. Here, we partially cloned the cytochrome b gene from L. taurica (Lt cytb) and searched within populations of the fungus collected from tomato fields across California for mutations that confer resistance to QoIs. A total of 21 single nucleotide polymorphisms (SNPs) were identified within a 704 bp fragment of the Lt cytb gene analyzed, of which five were non-synonymous substitutions. Among the most frequent SNPs encountered within field populations of the pathogen was the G143A substitution that confers high levels of resistance against QoIs in several fungi. The other four amino acid substitutions were novel mutations, whose effect on QoI resistance is currently unknown. Sequencing of the Lt cytb gene from individual single-cell conidia of the fungus further revealed that most SNPs, including the one leading to the G143A substitution, were present in a heteroplasmic state, indicating the co-existence of multiple mitotypes in single cells. Analysis of the field samples showed that the G143A substitution is predominantly heteroplasmic also within field populations of L. taurica in California, suggesting that QoI resistance in this fungus is likely to be quantitative rather than qualitative.
A Risky Business? Habitat and Social Behavior Impact Skin and Gut Microbiomes in Caribbean Cleaning Gobies

The broadstripe cleaning goby Elacatinus prochilos has two alternative ecotypes: sponge-dwellers, which live in large groups and feed mainly upon nematode parasites; and coral-dwellers, that live in small groups or in solitude and behave as cleaners. Recent studies focusing on the skin and gut microbiomes of tropical fish showed that microbial communities are influenced mainly by diet and host species. Here, we compare the skin and gut microbiomes of the Caribbean broadstripe cleaning goby E. prochilos alternative ecotypes (cleaners and non-cleaners) from Barbados and predict that different habitat use and behavior (cleaning vs. non-cleaning) will translate in different bacterial profiles between the two ecotypes. We found significant differences in both alpha- and beta-diversity of skin and gut microbiomes belonging to different ecotypes. Importantly, the skin microbiome of obligate cleaners showed greater intra-sample diversity and harbored a significantly higher prevalence of potential fish pathogens. Likewise, potential pathogens were also more prevalent in the gut of obligate cleaners. We suggest that habitat use, diet, but also direct contact with potential diseased clientele during cleaning, could be the cause for these patterns.
Adversarial Learning of Disentangled and Generalizable Representations for Visual Attributes

Recently, a multitude of methods for image-to-image translation has demonstrated impressive results on problems such as multi-domain or multi-attribute transfer. The vast majority of such works leverages the strengths of adversarial learning in tandem with deep convolutional autoencoders to achieve realistic results by well-capturing the target data distribution. Nevertheless, the most prominent representatives of this class of methods do not facilitate semantic structure in the latent space, and usually rely on domain labels for test-time transfer. This leads to rigid models that are unable to capture the variance of each domain label. In this light, we propose a novel adversarial learning method that (i) facilitates latent structure by disentangling sources of variation based on a novel cost function and (ii) encourages learning generalizable, continuous and transferable latent codes that can be utilized for tasks such as unpaired multi-domain image transfer and synthesis, without requiring labelled test data. The resulting representations can be combined in arbitrary ways to generate novel hybrid imagery, as for example generating mixtures of identities. We demonstrate the merits of the proposed method by a set of qualitative and quantitative experiments on popular databases, where our method clearly outperforms other, state-of-the-art methods. Code for reproducing our results can be found at: this https URL
Correlation-Based Community Detection

Mining community structures from the complex network is an important problem across a variety of fields. Many existing community detection methods detect communities through optimizing a community evaluation function. However, most of these functions even have high values on random graphs and may fail to detect small communities in the large-scale network (the so-called resolution limit problem). In this paper, we introduce two novel node-centric community evaluation functions by connecting correlation analysis with community detection. We will further show that the correlation analysis can provide a novel theoretical framework which unifies some existing evaluation functions in the context of a correlation-based optimization problem. In this framework, we can mitigate the resolution limit problem and eliminate the influence of random fluctuations by selecting the right correlation function. Furthermore, we introduce three key properties used in mining association rule into the context of community detection to help us choose the appropriate correlation function. Based on our introduced correlation functions, we propose a community detection algorithm called CBCD. Our proposed algorithm outperforms existing state-of-the-art algorithms on both synthetic benchmark networks and real-world networks.
Beyond CAR T Cells: Other Cell-Based Immunotherapeutic Strategies Against Cancer

Background: Chimeric antigen receptor (CAR)-modified T cells have successfully harnessed T cell immunity against malignancies, but they are by no means the only cell therapies in development for cancer. Main Text Summary: Systemic immunity is thought to play a key role in combatting neoplastic disease; in this vein, genetic modifications meant to explore other components of T cell immunity are being evaluated. In addition, other immune cells—from both the innate and adaptive compartments—are in various stages of clinical application. In this review, we focus on these non-CAR T cell immunotherapeutic approaches for malignancy. The first section describes engineering T cells to express non-CAR constructs, and the second section describes other gene-modified cells used to target malignancy. Conclusions: CAR T cell therapies have demonstrated the clinical benefits of harnessing our body's own defenses to combat tumor cells. Similar research is being conducted on lesser known modifications and gene-modified immune cells, which we highlight in this review.
From Primordial Seed Magnetic Fields to the Galactic Dynamo

The origin and maintenance of coherent magnetic fields in the Universe is reviewed with an emphasis on the possible challenges that arise in their theoretical understanding. We begin with the interesting possibility that magnetic fields originated at some level from the early universe. This could be during inflation, the electroweak, or the quark-hadron phase transitions. These mechanisms can give rise to fields which could be strong, but often with much smaller coherence scales than galactic scales. Their subsequent turbulent decay decreases their strength but increases their coherence. We then turn to astrophysical batteries which can generate seed magnetic fields. Here the coherence scale can be large, but the field strength is generally very small. These seed fields need to be further amplified and maintained by a dynamo to explain observed magnetic fields in galaxies. Basic ideas behind both small and large-scale turbulent dynamos are outlined. The small-scale dynamo may help to understand the first magnetization of young galaxies, while the large-scale dynamo is important for the generation of fields with scales larger than the stirring scale, as observed in nearby disk galaxies. The current theoretical challenges that turbulent dynamos encounter and their possible resolution are discussed.
SoDeep: A Sorting Deep Net to Learn Ranking Loss Surrogates

﻿Several tasks in machine learning are evaluated using non-differentiable metrics such as mean average precision or Spearman correlation. However, their non-differentiability prevents from using them as objective functions in a learning framework. Surrogate and relaxation methods exist but tend to be specific to a given metric. In the present work, we introduce a new method to learn approximations of such non-differentiable objective functions. Our approach is based on a deep architecture that approximates the sorting of arbitrary sets of scores. It is trained virtually for free using synthetic data. This sorting deep (SoDeep) net can then be combined in a plug-and-play manner with existing deep architectures. We demonstrate the interest of our approach in three different tasks that require ranking: Cross-modal text-image retrieval, multi-label image classification and visual memorability ranking. Our approach yields very competitive results on these three tasks, which validates the merit and the flexibility of SoDeep as a proxy for sorting operation in ranking-based losses.
Convergence in Hölder norms with applications to Monte Carlo methods in infinite dimensions

We show that if a sequence of piecewise affine linear processes converges in the strong sense with a positive rate to a stochastic process that is strongly Hölder continuous in time, then this sequence converges in the strong sense even with respect to much stronger Hölder norms and the convergence rate is essentially reduced by the Hölder exponent. Our first application hereof establishes pathwise convergence rates for spectral Galerkin approximations of stochastic partial differential equations. Our second application derives strong convergence rates of multilevel Monte Carlo approximations of expectations of Banach-space-valued stochastic processes.
Polymerized 4-Fold Coordinated Carbonate Melts in the Deep Mantle

Our understanding of the deep carbon cycle has witnessed amazing advances in the last decade, including the discovery of tetrahedrally coordinated high pressure (P) carbonate phases. However, little is known on the properties of molten carbonates at depth, while their properties at lower mantle conditions are unknown. Here, we report the structure and density of FeCO3 melts and glasses from 44 GPa to 110 GPa by means of in situ x-ray synchrotron diffraction, and ex situ Raman and x-ray Raman spectroscopies. Carbon is fully transformed to 4-fold coordination, a bond change recoverable at ambient P. While low P melts react with silica, resulting in the formation of silico-carbonate glasses, high P melts are not contaminated but still quench as glasses. Carbonate melts are therefore polymerized, highly viscous and poorly reacting with silicates in the lower mantle, in stark opposition with their low P properties.
Holistic and Comprehensive Annotation of Clinically Significant Findings on Diverse CT Images: Learning From Radiology Reports and Label Ontology

In radiologists' routine work, one major task is to read a medical image, e.g., a CT scan, find significant lesions, and describe them in the radiology report. In this paper, we study the lesion description or annotation problem. Given a lesion image, our aim is to predict a comprehensive set of relevant labels, such as the lesion's body part, type, and attributes, which may assist downstream fine-grained diagnosis. To address this task, we first design a deep learning module to extract relevant semantic labels from the radiology reports associated with the lesion images. With the images and text-mined labels, we propose a lesion annotation network (LesaNet) based on a multilabel convolutional neural network (CNN) to learn all labels holistically. Hierarchical relations and mutually exclusive relations between the labels are leveraged to improve the label prediction accuracy. The relations are utilized in a label expansion strategy and a reliable hard example mining algorithm. We also attach a simple score propagation layer on LesaNet to enhance recall and explore implicit relation between labels. Multilabel metric learning is combined with classification to enable interpretable prediction. We evaluated LesaNet on the public DeepLesion dataset, which contains over 32K diverse lesion images. Experiments show that LesaNet can precisely annotate the lesions using an ontology of 171 fine-grained labels with an average AUC of 0.9344.
Purely Quantum Polar Codes

We provide a purely quantum version of polar codes, achieving the coherent information of any quantum channel. Our scheme relies on a recursive channel combining and splitting construction, where random two-qubit Clifford gates are used to combine two single-qubit channels. The inputs to the synthesized bad channels are frozen by sharing EPR pairs between the sender and the receiver, so our scheme is entanglement assisted. We further show that a Pauli channel polarizes if and only if a specific classical channel over a four-symbol input set polarizes. We exploit this equivalence to prove fast polarization for Pauli channels, and to devise an efficient successive cancellation based decoding algorithm for such channels.
Graphonomy: Universal Human Parsing via Graph Transfer Learning

Prior highly-tuned human parsing models tend to fit towards each dataset in a specific domain or with discrepant label granularity, and can hardly be adapted to other human parsing tasks without extensive re-training. In this paper, we aim to learn a single universal human parsing model that can tackle all kinds of human parsing needs by unifying label annotations from different domains or at various levels of granularity. This poses many fundamental learning challenges, e.g. discovering underlying semantic structures among different label granularity, performing proper transfer learning across different image domains, and identifying and utilizing label redundancies across related tasks. To address these challenges, we propose a new universal human parsing agent, named ``Graphonomy", which incorporates hierarchical graph transfer learning upon the conventional parsing network to encode the underlying label semantic structures and propagate relevant semantic information. In particular, Graphonomy first learns and propagates compact high-level graph representation among the labels within one dataset via Intra-Graph Reasoning, and then transfers semantic information across multiple datasets via Inter-Graph Transfer. Various graph transfer dependencies (e.g., similarity, linguistic knowledge) between different datasets are analyzed and encoded to enhance graph transfer capability. By distilling universal semantic graph representation to each specific task, Graphonomy is able to predict all levels of parsing labels in one system without piling up the complexity. Experimental results show Graphonomy effectively achieves the state-of-the-art results on three human parsing benchmarks as well as advantageous universal human parsing performance.
NuSTAR Measurement of Coronal Temperature in Two Luminous, High-redshift Quasars

X-ray emission from the active galactic nucleus (AGN) is believed to be produced via Comptonization of optical/ultraviolet seed photons emitted by the accretion disk, upscattered by hot electrons in a corona surrounding the black hole. A critical compactness versus temperature threshold is predicted above which any increase in the source luminosity, for a fixed size, would then generate positron–electron pairs rather than continue heating the coronal plasma. Current observations seem to confirm that all AGNs populate the region below this critical line. These models, however, have never been probed by observations in the high-luminosity regime, where the critical line is expected to reach low temperatures. To fill this observational gap, we selected two luminous (log(LBol) > 47.5 erg s−1) quasars, 2MASSJ1614346+470420 (z = 1.86) and B1422+231 (z = 3.62), and obtained XMM–Newton and NuSTAR deep observations for them. We performed detailed spectral analysis of their quasisimultaneous soft and hard X-ray data, in order to constrain the parameters of their coronae. Using a phenomenological cutoff power-law model, with the inclusion of a reflection component, we derived rest-frame values of the high-energy cutoff of Ecut = keV and Ecut = keV, respectively. Comptonization models consistently give as best-fit parameters electron temperatures of ∼45 keV and ∼28 keV, respectively, and optically thick coronae (τ > 1). These low coronal temperatures fall in the limited region allowed at these luminosities to avoid runaway pair production.
Approximation in $L^p(\mu)$ with deep ReLU neural networks.

We discuss the expressive power of neural networks which use the non-smooth ReLU activation function $\varrho(x) = \max\{0,x\}$ by analyzing the approximation theoretic properties of such networks. The existing results mainly fall into two categories: approximation using ReLU networks with a fixed depth, or using ReLU networks whose depth increases with the approximation accuracy. After reviewing these findings, we show that the results concerning networks with fixed depth--- which up to now only consider approximation in $L^p(\lambda)$ for the Lebesgue measure $\lambda$--- can be generalized to approximation in $L^p(\mu)$, for any finite Borel measure $\mu$. In particular, the generalized results apply in the usual setting of statistical learning theory, where one is interested in approximation in $L^2(\mathbb{P})$, with the probability measure $\mathbb{P}$ describing the distribution of the data.
Deep Learning the EEG Manifold for Phonological Categorization from Active Thoughts

Speech-related Brain Computer Interfaces (BCI) aim primarily at finding an alternative vocal communication pathway for people with speaking disabilities. As a step towards full decoding of imagined speech from active thoughts, we present a BCI system for subject-independent classification of phonological categories exploiting a novel deep learning based hierarchical feature extraction scheme. To better capture the complex representation of high-dimensional electroencephalography (EEG) data, we compute the joint variability of EEG electrodes into a channel cross-covariance matrix. We then extract the spatio-temporal information encoded within the matrix using a mixed deep neural network strategy. Our model framework is composed of a convolutional neural network (CNN), a long-short term network (LSTM), and a deep autoencoder. We train the individual networks hierarchically, feeding their combined outputs in a final gradient boosting classification step. Our best models achieve an average accuracy of 77.9% across five different binary classification tasks, providing a significant 22.5% improvement over previous methods. As we also show visually, our work demonstrates that the speech imagery EEG possesses significant discriminative information about the intended articulatory movements responsible for natural speech synthesis.
A Survey on Software-Defined VANETs: Benefits, Challenges, and Future Directions

The evolving of Fifth Generation (5G) networks isbecoming more readily available as a major driver of the growthof new applications and business models. Vehicular Ad hocNetworks (VANETs) and Software Defined Networking (SDN)represent the key enablers of 5G technology with the developmentof next generation intelligent vehicular networks and applica-tions. In recent years, researchers have focused on the integrationof SDN and VANET, and look at different topics related to thearchitecture, the benefits of software-defined VANET servicesand the new functionalities to adapt them. However, securityand robustness of the complete architecture is still questionableand have been largely negleted. Moreover, the deployment andintegration of novel entities and several architectural componentsdrive new security threats and this http URL this paper, first we survey the state-of-the-art SDN basedVehicular ad-hoc Network (SDVN) architectures for their net-working infrastructure design, functionalities, benefits, and chal-lenges. Then we discuss these SDVN architectures against majorsecurity threats that violate the key security services such asavailability, confidentiality, authentication, and data integrity.We also propose different countermeasures to these threats.Finally, we discuss the lessons learned with the directions offuture research work towards provisioning stringent security andprivacy solutions in future SDVN architectures. To the best of ourknowledge, this is the first comprehensive work that presents sucha survey and analysis on SDVNs in the era of future generationnetworks (e.g., 5G, and Information centric networking) andapplications (e.g., intelligent transportation system, and IoT-enabled advertising in VANETs).
Quadratic Chabauty for (bi)elliptic curves and
Kim’s conjecture

We explore a number of problems related to the quadratic Chabauty method for determining integral points on hyperbolic curves. We remove the assumption of semistability in the description of the quadratic Chabauty sets $\mathcal{X}(\mathbb{Z}_p)_2$ containing the integral points $\mathcal{X}(\mathbb{Z})$ of an elliptic curve of rank at most $1$. Motivated by a conjecture of Kim, we then investigate theoretically and computationally the set-theoretic difference $\mathcal{X}(\mathbb{Z}_p)_2\setminus \mathcal{X}(\mathbb{Z})$. We also consider some algorithmic questions arising from Balakrishnan--Dogra's explicit quadratic Chabauty for the rational points of a genus-two bielliptic curve. As an example, we provide a new solution to a problem of Diophantus which was first solved by Wetherell. Computationally, the main difference from the previous approach to quadratic Chabauty is the use of the $p$-adic sigma function in place of a double Coleman integral.
Tor Users Contributing to Wikipedia: Just Like Everybody Else?

User-generated content on sites like Wikipedia is produced by volunteers who not only create content, but invest time and effort in quality control by reviewing others' contributions. Because of a perception that privacy enhancing tools are a source of vandalism, spam, and abuse, many user-generated sites like Wikipedia block contributions from anonymity-seeking editors who use proxies like Tor. While blocking anonymity seeking editors is perceived to be effective in stemming abuse, collateral damage in the form of unrealized valuable contributions from anonymity seeking editors is typically invisible. Although Wikipedia has taken steps to block contributions from Tor users since as early as 2005, we demonstrate that these blocks have been imperfect and that tens of thousands of attempts to edit on Wikipedia through Tor have been successful. We draw upon several data sources to measure and describe the history of Tor editing on Wikipedia over time and to compare contributions of Tor users to other groups of Wikipedia users. Our analysis suggests that the Tor users who manage to slip through Wikipedia's ban contribute content that is similar in quality to unregistered Wikipedia contributors and to the initial contributions of registered users.