Miracle-dz/UniFAR-FTUs huggingface dataset:
PML Body Component Sp100A Is a Cytosolic Responder to IFN and Activator of Antiviral ISGs

PML bodies sit at the center stage of various important biological processes; however, the signal transduction networks of these macromolecular protein complexes remain enigmatic. The present study illustrates, in detail and for the first time, the course of signal receiving, processing, and implementation by PML bodies in response to IFN and virus infection. ABSTRACT Promyelocytic leukemia protein (PML) bodies are implicated in one of the key pathways in the establishment of antiviral status in response to interferon (IFN), yet the molecular mechanisms bridging the cross talk remain elusive. Herein, we report that a major constitutive component of the PML body, Sp100A, is ubiquitously located in the cytosol of various cell types and is an immediate responder to multiple extracellular stimuli, including virus infection, IFN, epidermal growth factor (EGF), glial cell-derived nerve factor (GDNF), etc., signaling through the phosphatidylinositol 3-kinase (PI3K) pathway. IFN-β induces phosphorylation of Sp100A on Ser188, which fortifies the binding of Sp100A to pyruvate kinase 2 (PKM2) and facilitates its nuclear importation through the extracellular signal-regulated kinase 1/2 (ERK1/2)-PKM2-PIN1-importin axes. Blocking PI3K pathway signaling or interference with the ERK1/2-PKM2-PIN1-importin axes independently hampers nuclear translocation of Sp100A in response to IFN, reflecting a dual-regulation mechanism governing this event. In the nucleus, Sp100A is enriched in the promoter regions of essential antiviral interferon-stimulated genes (ISGs), such as those coding for IFI16, OAS2, and RIG-I, and activates their transcription. Importantly, nuclear importation of Sp100A, but not accumulation of a mutant Sp100A that failed to respond to IFN, during infection potently enhanced transcription of these antiviral ISGs and restricted virus propagation. These findings depict a novel IFN response mechanism by PML bodies in the cytosol and shed light on the complex sensing-regulatory network of PML bodies. IMPORTANCE PML bodies sit at the center stage of various important biological processes; however, the signal transduction networks of these macromolecular protein complexes remain enigmatic. The present study illustrates, in detail and for the first time, the course of signal receiving, processing, and implementation by PML bodies in response to IFN and virus infection. It shows that PML body constitutive component Sp100A was phosphorylated on Ser188 by IFN signaling through the PI3K pathway in the cytosol, cotranslocated into the nucleus with PKM2, enriched on the promoter regions of essential antiviral ISGs such as those coding for IFI16, RIG-I, OAS2, etc., and mediating their transcriptional activation.
Neutron Star Kicks by the Gravitational Tug-boat Mechanism in Asymmetric Supernova Explosions: Progenitor and Explosion Dependence

Asymmetric mass ejection in the early phase of supernova (SN) explosions can impart a kick velocity to the new-born neutron star (NS). For neutrino-driven explosions the NS acceleration has been shown to be mainly caused by the gravitational attraction of the anisotropically expelled inner ejecta, while hydrodynamic forces contribute on a subdominant level, and asymmetric neutrino emission plays only a secondary role. Two- and three-dimensional hydrodynamic simulations have demonstrated that this gravitational tug-boat mechanism can explain the observed space velocities of young NSs up to more than 1000 km s−1. Here, we discuss how the NS kick depends on the energy, ejecta mass, and asymmetry of the SN explosion, and what role the compactness of the pre-collapse stellar core plays for the momentum transfer to the NS. We also provide simple analytic expressions for the NS velocity in terms of these quantities. Referring to results of hydrodynamic simulations in the literature, we argue why, within the discussed scenario of NS acceleration, electron-capture SNe, low-mass Fe-core SNe, and ultra-stripped SNe can be expected to have considerably lower intrinsic NS kicks than core-collapse SNe of massive stellar cores. Our basic arguments also remain valid if progenitor stars possess large-scale asymmetries in their convective silicon and oxygen burning layers. Possible scenarios for spin-kick alignment are sketched. Much of our discussion stays on a conceptual and qualitative level, and more work is necessary on the numerical modeling side to determine the dependences of involved parameters, whose prescriptions will be needed for recipes that can be used to better describe NS kicks in binary evolution and population synthesis studies.
Surgical versus transcatheter aortic valve replacement: Impact of patient‐prosthesis mismatch on outcomes

The hemodynamics of most prosthetic valves are often inferior to that of the normal native valve, and a significant proportion of patients undergoing surgical (SAVR) or transcatheter aortic valve replacement (TAVR) have high residual transaortic pressure gradients due to prosthesis–patient mismatch (PPM). As the experience with TAVR has increased and long‐term outcomes are reported, a close look at the PPM literature is required in light of new evidence.
Flower-fruit dynamics, visitor-predator patterns and chemical preferences in the tropical bamboo, Melocanna baccifera

Mast seeding and associated events in Melocanna baccifera, the largest fruit producing bamboo, is an enigma. So far there are no comprehensive accounts on its flowering phenology, fruiting dynamics and animal interactions. In this study, spanning over 13 years (2009 to 2022), we observed eight M. baccifera clumps in JNTBGRI Bambusetum from flowering initiation, fruiting to eventual death. Flowering phenology, floral characteristics, floret types, breeding system, bee visitation, pollination, fruit production and predators were recorded; predation patterns were correlated with fruit chemistry. Flowering duration of clumps ranged from 20 (March 2009—October 2010) to 120 (September 2012—August 2022) months. Bisexual florets are dichogamous and protogynous; and female duration (22–72 h) is many times higher than male duration (2–6 h). The highest ever fruit production for an individual bamboo clump (456.67 Kg) was recorded. Of the total fallen fruits (38371), 38.11% were predated, 43.80% good fruits (no predator hits) and 18.09% immature fruits. A positive correlation between reward (fruits) versus predation was observed, especially in short intervals of high fruit production. Pollen predators (Apis cerana indica, Halictus taprabonae, Braunsapis cupulifera, Trigona iridipennis), fruit predators, ranging from arthropods to mammals, viz., millipede (Spinotarsus colosseus), slug (Mariaella dussumieri), snails (Cryptozona bistrialis, Macrochlamys sp.), borers (Achroia grisella, Blattella germanica), mammals (monkeys Macaca radiata, rats Rattus rattus, porcupine Hystrix indica, wild boar Sus scrofa, palm civet Paradoxurus hermaphroditus), seedling predators (rabbit Lepus nigricollis, deer Axis axis), and insect/pest predators (ants Crematogaster biroi, Oecophylla smaragdina, mantis Euchomenella indica) were identified. Fruit predation is linked to its age and chemistry. Apart from new insights on flowering phenology, breeding system, pollination and fruiting dynamics, this study demonstrates the vibrant interaction between M. baccifera flowers/fruits and visitors/predators, and provides significant leads towards elucidating the cause of rat multiplication and other events associated with its gregarious flowering.
Improving Interpretability via Regularization of Neural Activation Sensitivity

State-of-the-art deep neural networks (DNNs) are highly effective at tackling many real-world tasks. However, their wide adoption in mission-critical contexts is hampered by two major weaknesses - their susceptibility to adversarial attacks and their opaqueness. The former raises concerns about the security and generalization of DNNs in real-world conditions, whereas the latter impedes users' trust in their output. In this research, we (1) examine the effect of adversarial robustness on interpretability and (2) present a novel approach for improving the interpretability of DNNs that is based on regularization of neural activation sensitivity. We evaluate the interpretability of models trained using our method to that of standard models and models trained using state-of-the-art adversarial robustness techniques. Our results show that adversarially robust models are superior to standard models and that models trained using our proposed method are even better than adversarially robust models in terms of interpretability.
Comparing Explanation Methods for Traditional Machine Learning Models Part 1: An Overview of Current Methods and Quantifying Their Disagreement

: With increasing interest in explaining machine learning (ML) models, the ﬁrst part of this two-part study synthesizes recent research on methods for explaining global and local aspects of ML models. This study distinguishes explainability from interpretability, local from global explainability, and feature importance versus feature relevance. We demonstrate and visualize diﬀerent explanation methods, how to interpret them, and provide a complete Python package (scikit-explain) to allow future researchers to explore these products. We also highlight the frequent disagreement between explanation methods for feature rankings and feature eﬀects and provide practical advice for dealing with these disagreements. We used ML models developed for severe weather prediction and sub-freezing road surface temperature prediction to generalize the behavior of the diﬀerent explanation methods. For feature rankings, there is substantially more agreement on the set of top features (e.g., on average, two methods agree on 6 of the top 10 features) than on speciﬁc rankings (on average, two methods only agree on the ranks of 2-3 features in the set of top 10 features). On the other hand, two feature eﬀect curves from diﬀerent methods are in high agreement as long as the phase space is well sampled. Finally, a lesser-known method, tree interpreter, was found comparable to SHAP for feature eﬀects, and with the widespread use of random forests in geosciences and computational ease of tree interpreter, we recommend it be explored in future research. make the atmospheric science community aware of recently developed explainability methods for traditional ML models and demonstrate their use in a software package developed by the authors (scikit-explain; Flora and Handler 2022). Another important goal is to highlight that these methods tend to disagree with each other, which can aﬀect the trust in these products. Like forecast veriﬁcation metrics that describe diﬀerent aspects of forecast quality, some disagreement is to be expected as explainability methods are designed for diﬀerent tasks. Finally, we oﬀer some practical advice on explaining the behavior of ML models when diﬀerent explanation methods disagree with each other. We hope that this will motivate future work to explore the quality of explainability methods. For example, in Part II of this study, we ﬁnd that knowing the relative faithfulness of feature ranking methods can objec-tively improve explainability.
The rainbow saturation number is linear

Given a graph $H$, we say that an edge-coloured graph $G$ is $H$-rainbow saturated if it does not contain a rainbow copy of $H$, but the addition of any non-edge in any colour creates a rainbow copy of $H$. The rainbow saturation number $\text{rsat}(n,H)$ is the minimum number of edges among all $H$-rainbow saturated edge-coloured graphs on $n$ vertices. We prove that for any non-empty graph $H$, the rainbow saturation number is linear in $n$, thus proving a conjecture of Gir\~{a}o, Lewis, and Popielarz. In addition, we also give an improved upper bound on the rainbow saturation number of the complete graph, disproving a second conjecture of Gir\~{a}o, Lewis, and Popielarz.
The pivot algorithm and polygons: results on the FCC lattice

The authors consider the pivot algorithm for polygons on the face-centred cubic lattice and prove ergodicity. A numerical study of the algorithm is carried out; in particular, the authors look at the acceptance fraction of the elementary moves and find that it goes only slowly to zero as the length of the polygons is increased. The numerical properties of polygons on the FCC lattice are also considered. They calculate the exponent nu (by considering the mean square radius of gyration of the polygons) and compare the result to the expected value (from field theory). They also consider the mean span of the polygons and discuss corrections to scaling and their influence on the numerical calculation of critical exponents.
ChartParser: Automatic Chart Parsing for Print-Impaired

Infographics are often an integral component of scientiﬁc documents for reporting qualitative or quantitative ﬁndings as they make it much simpler to comprehend the underly- ing complex information. However, their interpretation con-tinues to be a challenge for the blind, low-vision, and other print-impaired (BLV) individuals. In this paper, we propose ChartParser, a fully automated pipeline that leverages deep learning, OCR, and image processing techniques to extract all ﬁgures from a research paper, classify them into various chart categories (bar chart, line chart, etc.) and obtain relevant information from them, speciﬁcally bar charts (includ- ing horizontal, vertical, stacked horizontal and stacked vertical charts) which already have several exciting challenges. Fi- nally, we present the retrieved content in a tabular format that is screen-reader friendly and accessible to the BLV users. We present a thorough evaluation of our approach by applying our pipeline to sample real-world annotated bar charts from research papers.
Learning linear operators: Infinite-dimensional regression as a well-behaved non-compact inverse problem

We consider the problem of learning a linear operator $\theta$ between two Hilbert spaces from empirical observations, which we interpret as least squares regression in infinite dimensions. We show that this goal can be reformulated as an inverse problem for $\theta$ with the undesirable feature that its forward operator is generally non-compact (even if $\theta$ is assumed to be compact or of $p$-Schatten class). However, we prove that, in terms of spectral properties and regularisation theory, this inverse problem is equivalent to the known compact inverse problem associated with scalar response regression. Our framework allows for the elegant derivation of dimension-free rates for generic learning algorithms under H\"older-type source conditions. The proofs rely on the combination of techniques from kernel regression with recent results on concentration of measure for sub-exponential Hilbertian random variables. The obtained rates hold for a variety of practically-relevant scenarios in functional regression as well as nonlinear regression with operator-valued kernels and match those of classical kernel regression with scalar response.
Two-Sample Test for Stochastic Block Models via Maximum Entry-wise Deviation

The stochastic block model is a popular tool for detecting community structures in network data. Detecting the difference between two community structures is an important issue for stochastic block models. However, the two-sample test has been a largely under-explored domain, and too little work has been devoted to it. In this article, based on the maximum entry--wise deviation of the two centered and rescaled adjacency matrices, we propose a novel test statistic to test two samples of stochastic block models. We prove that the null distribution of the proposed test statistic converges in distribution to a Gumbel distribution, and we show the change of the two samples from stochastic block models can be tested via the proposed method. Then, we show that the proposed test has an asymptotic power guarantee against alternative models. One noticeable advantage of the proposed test statistic is that the number of communities can be allowed to grow linearly up to a logarithmic factor. Further, we extend the proposed method to the degree-corrected stochastic block model. Both simulation studies and real-world data examples indicate that the proposed method works well.
PrivacyProber: Assessment and Detection of Soft-Biometric Privacy-Enhancing Techniques

Soft-biometric privacy-enhancing techniques represent machine learning methods that aim to: (i) mitigate privacy concerns associated with face recognition technology by suppressing selected soft-biometric attributes in facial images (e.g., gender, age, ethnicity) and (ii) make unsolicited extraction of sensitive personal information infeasible. Because such techniques are increasingly used in real-world applications, it is imperative to understand to what extent the privacy enhancement can be inverted and how much attribute information can be recovered from privacy-enhanced images. While these aspects are critical, they have not been investigated in the literature. We, therefore, study the robustness of several state-of-the-art soft-biometric privacy-enhancing techniques to attribute recovery attempts. We propose PrivacyProber, a high-level framework for restoring soft-biometric information from privacy-enhanced facial images, and apply it for attribute recovery in comprehensive experiments on three public face datasets, i.e., LFW, MUCT and Adience. Our experiments show that the proposed framework is able to restore a considerable amount of suppressed information, regardless of the privacy-enhancing technique used, but also that there are significant differences between the considered privacy models. These results point to the need for novel mechanisms that can improve the robustness of existing privacy-enhancing techniques and secure them against potential adversaries trying to restore suppressed information.
Rotation Period Evolution in Low-mass Binary Stars: The Impact of Tidal Torques and Magnetic Braking

We examine how tides, stellar evolution, and magnetic braking shape the rotation period (Prot) evolution of low-mass stellar binaries up to orbital periods (Porb) of 100 days across a wide range of tidal dissipation parameters using two common equilibrium tidal models. We find that many binaries with Porb ≲ 20 days tidally lock, and most with Porb ≲ 4 days tidally lock into synchronous rotation on circularized orbits. At short Porb, tidal torques produce a population of fast rotators that single-star-only models of magnetic braking fail to produce. In many cases, we show that the competition between magnetic braking and tides produces a population of subsynchronous rotators that persists for 1 Gyr, even in short-Porb binaries, qualitatively reproducing the subsynchronous eclipsing binaries discovered in the Kepler field by Lurie et al. Both equilibrium tidal models predict that binaries can tidally interact out to Porb ≈ 80 days, while the constant phase lag tidal model predicts that binaries can tidally lock out to Porb ≈ 100 days. Tidal torques often force the Prot evolution of stellar binaries to depart from the long-term magnetic-braking-driven spin-down experienced by single stars, revealing that Prot is not a valid proxy for age in all cases, i.e., gyrochronology can underpredict ages by up to 300% unless one accounts for binarity. We suggest that accurate determinations of orbital eccentricties and Prot can be used to discriminate between which equilibrium tidal models best describe tidal interactions in low-mass binary stars.
The impact of stochastic modeling on the predictive power of galaxy formation simulations

All modern galaxy formation models employ stochastic elements in their sub-grid prescriptions to discretise continuous equations across the time domain. In this paper, we investigate how the stochastic nature of these models, notably star formation, black hole accretion, and their associated feedback, that act on small ($<$ kpc) scales, can back-react on macroscopic galaxy properties (e.g. stellar mass and size) across long ($>$ Gyr) timescales. We find that the scatter in scaling relations predicted by the EAGLE model implemented in the SWIFT code can be significantly impacted by random variability between re-simulations of the same object, even when galaxies are resolved by tens of thousands of particles. We then illustrate how re-simulations of the same object can be used to better understand the underlying model, by showing how correlations between galaxy stellar mass and black hole mass disappear at the highest black hole masses ($M_{\rm BH}>10^8$ M$_\odot$), indicating that the feedback cycle may be interrupted by external processes. We find that although properties that are collected cumulatively over many objects are relatively robust against random variability (e.g. the median of a scaling relation), the properties of individual galaxies (such as galaxy stellar mass) can vary by up to 25\%, even far into the well-resolved regime, driven by bursty physics (black hole feedback) and mergers between galaxies. We suggest that studies of individual objects within cosmological simulations be treated with caution, and that any studies aiming to closely investigate such objects must account for random variability within their results.
Secure SWIPT in STAR-RIS Aided Downlink MISO Rate-Splitting Multiple Access Networks

Recently, simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) have emerged as a novel technology that facilitates sustainable communication by providing 360 coverage and new degrees-of-freedom (DoF) for manipulating signal propagation as well as simultaneous wireless information and power transfer (SWIPT). Inspired by these applications, this paper presents a novel STAR-RIS-aided secure SWIPT system for downlink multiple input single output (MISO) Rate-Splitting multiple access (RSMA) networks. The transmitter concurrently communicates with the information receivers (IRs) and sends energy to untrusted energy receivers (UERs). UERs are also able to wiretap the IR streams. The paper assumes that the channel state information (CSI) of the IRs is known at the transmitter. However, only imperfect CSI (ICSI) for the UERs is available at the transmitter. The paper aims to maximize the achievable worst-case sum secrecy rate (WCSSR) of the IRs under a total transmit power constraint, a sum energy constraint for the UERs, and constraints on the transmission and reflection coefficients by jointly optimizing the precoders and the transmission and reflection beamforming at the STAR-RIS. The formulated problem is non-convex with intricately coupled variables, and to tackle this challenge a suboptimal two-step iterative algorithm based on the sequential parametric convex approximation (SPCA) method is proposed. Specifically, the precoders and the transmission and reflection beamforming vectors are optimized alternatingly. Simulations are conducted to show that the proposed RSMA-based algorithm in a STAR-RIS aided network can improve the secrecy of the confidential information and the overall spectral efficiency.
TSMind: Alibaba and Soochow University’s Submission to the WMT22 Translation Suggestion Task

This paper describes the joint submission of Alibaba and Soochow University to the WMT 2022 Shared Task on Translation Suggestion (TS). We participate in the English to/from German and English to/from Chinese tasks. Basically, we utilize the model paradigm fine-tuning on the downstream tasks based on large-scale pre-trained models, which has recently achieved great success. We choose FAIR’s WMT19 English to/from German news translation system and MBART50 for English to/from Chinese as our pre-trained models. Considering the task’s condition of limited use of training data, we follow the data augmentation strategies provided by Yang to boost our TS model performance. And we further involve the dual conditional cross-entropy model and GPT-2 language model to filter augmented data. The leader board finally shows that our submissions are ranked first in three of four language directions in the Naive TS task of the WMT22 Translation Suggestion task.