Miracle-dz/UniFAR-FTUs huggingface dataset:
A Model for Divergent, Allopatric Speciation of Polyploid Pteridophytes Resulting from Silencing of Duplicate-Gene Expression

We present a model for allopatric speciation at the polyploid level, based on different patterns of gene silencing that would occur in geographically isolated populations. Allopatric populations of a single allotetraploid species may experience silencing of the same gene but in different genomes (reciprocal silencing). Hybrids between such genotypes would experience a reduction in viability of gametophyte progeny to 0.75n, where n is the number of reciprocally silenced homoeologous gene pairs. A minimum value of n ≥ 8, representing a maximum hybrid fertility of 0.10, is shown to be virtually certain when the proportion of genes silenced reaches 0.2. Gene silencing would also lead to genetic divergence between the populations. Silencing of regulatory genes could lead to significant interpopulational differences in morphology and other complex traits. It is predicted that the combined effect of postzygotic hybrid sterility and genetic divergence resulting from gene silencing might lead to speciation of allopatric populations even in the absence of strong selection. It is also predicted that such speciation events would be relatively rapid, because they are driven by the most frequent class of mutations, and only a fraction of the genome needs to be silenced for speciation to occur. Criteria are provided for evaluating whether this mode of speciation has occurred in nature.
ERK1/2 activation by the C. elegans muscarinic acetylcholine receptor GAR-3 in cultured mammalian cells involves multiple signaling pathways

Abstract Extracellular signal-regulated kinases 1/2 (ERK1/2) play important roles in a variety of biological processes including cell growth and differentiation. We have previously reported that GAR-3 activates ERK1/2 via phospholipase C and protein kinase C, presumably through pertussis toxin (PTX)-insensitive Gq proteins, in Chinese hamster ovary (CHO) cells. Here we provide evidence that GAR-3 also activates ERK1/2 through PTX-sensitive G proteins, phosphatidylinositol 3-kinase (PI 3-kinase), and Src family kinases in CHO cells. We further show that in human embryonic kidney (HEK293) cells, epidermal growth factor receptor and Ras are required for efficient ERK1/2 activation by GAR-3. Taken together, our data indicate that GAR-3 evokes ERK1/2 activation through multiple signaling pathways in cultured mammalian cells.
The ASAS-SN catalogue of variable stars – VIII. ‘Dipper’ stars in the Lupus star-forming region

Some young stellar objects such as T Tauri-like "dipper" stars vary due to transient partial occultation by circumstellar dust, and observations of this phenomenon inform us of conditions in the planet-forming zones close to these stars. Although many dipper stars have been identified with space missions such as $Kepler$/$K2$, ground-based telescopes offer longer term and multi-wavelength perspectives. We identified 11 dipper stars in the Lupus star forming region in data from the All-Sky Automated Survey for SuperNovae (ASAS-SN), and further characterized these using observations by the Las Cumbres Global Observatory Telescope (LCOGT) and the Transiting Exoplanet Survey Satellite $TESS$, as well as archival data from other missions. Dipper stars were identified from a catalog of nearby young stars and selected based on the statistical significance, asymmetry, and quasi-periodicity or aperiodicity of variability in their ASAS-SN light curves. All 11 stars lie above or red-ward of the zero-age main sequence and have infrared excesses indicating the presence of full circumstellar disks. We obtain reddening-extinction relations for the variability of 7 stars using our combined ASAS-SN-$TESS$ and LCOGT photometry. In all cases the slopes are below the ISM value, suggesting larger grains, and we find a tentative relation between the slope (grain size) and the $\text{K}_\text{s}-[22\:\mu\text{m}]$ infrared color regarded as a proxy for disk evolutionary state.
Entanglement of local operators and the butterfly effect

We study the robustness of quantum and classical information to perturbations implemented by local operator insertions. We do this by computing multipartite entanglement measures in the Hilbert space of local operators in the Heisenberg picture. The sensitivity to initial conditions that we explore is an illuminating manifestation of the butterfly effect in quantum many-body systems. We derive a "membrane theory" in Haar random unitary circuits to compute the mutual information, logarithmic negativity, and reflected entropy in the local operator state by mapping to a classical statistical mechanics problem and find that any local operator insertion delocalizes information as fast as is allowed by causality. Identical behavior is found for conformal field theories admitting holographic duals where the bulk geometry is described by the eternal black hole with a local object situated at the horizon. In contrast to these maximal scramblers, only an $O(1)$ amount of information is found to be delocalized by local operators in integrable systems such as free fermions and Clifford circuits.
Constructing Human Motion Manifold With Sequential Networks

This paper presents a novel recurrent neural network‐based method to construct a latent motion manifold that can represent a wide range of human motions in a long sequence. We introduce several new components to increase the spatial and temporal coverage in motion space while retaining the details of motion capture data. These include new regularization terms for the motion manifold, combination of two complementary decoders for predicting joint rotations and joint velocities and the addition of the forward kinematics layer to consider both joint rotation and position errors. In addition, we propose a set of loss terms that improve the overall quality of the motion manifold from various aspects, such as the capability of reconstructing not only the motion but also the latent manifold vector, and the naturalness of the motion through adversarial loss. These components contribute to creating compact and versatile motion manifold that allows for creating new motions by performing random sampling and algebraic operations, such as interpolation and analogy, in the latent motion manifold.
Chook - A comprehensive suite for generating binary optimization problems with planted solutions

We present Chook, an open-source Python-based tool to generate discrete optimization problems of tunable complexity with a priori known solutions. Chook provides a cross-platform unified environment for solution planting using a number of techniques, such as tile planting, Wishart planting, equation planting, and deceptive cluster loop planting. Chook also incorporates planted solutions for higher-order (beyond quadratic) binary optimization problems. The support for various planting schemes and the tunable hardness allows the user to generate problems with a wide range of complexity on different graph topologies ranging from hypercubic lattices to fully-connected graphs.
A Current-Flow Centrality With Adjustable Reach

Centrality, which quantifies the "importance" of individual nodes, is among the most essential concepts in modern network theory. Most prominent centrality measures can be expressed as an aggregation of influence flows between pairs of nodes. As there are many ways in which influence can be defined, many different centrality measures are in use. Parametrized centralities allow further flexibility and utility by tuning the centrality calculation to the regime most appropriate for a given network. Here, we identify two categories of centrality parameters. Reach parameters control the attenuation of influence flows between distant nodes. Grasp parameters control the centrality's potential to send influence flows along multiple, often nongeodesic paths. Combining these categories with Borgatti's centrality types [S. P. Borgatti, Social Networks 27, 55-71 (2005)], we arrive at a novel classification system for parametrized centralities. Using this classification, we identify the notable absence of any centrality measures that are radial, reach parametrized, and based on acyclic, conservative flows of influence. We therefore introduce the ground-current centrality, which is a measure of precisely this type. Because of its unique position in the taxonomy, the ground-current centrality has significant advantages over similar centralities. We demonstrate that, compared to other conserved-flow centralities, it has a simpler mathematical description. Compared to other reach centralities, it robustly preserves an intuitive rank ordering across a wide range of network architectures. We also show that it produces a consistent distribution of centrality values among the nodes, neither trivially equally spread (delocalization), nor overly focused on a few nodes (localization). Other reach centralities exhibit both of these behaviors on regular networks and hub networks, respectively.
Active Brownian particle in harmonic trap: exact computation of moments, and re-entrant transition

We consider an active Brownian particle in a d-dimensional harmonic trap, in the presence of translational diffusion. While the Fokker–Planck equation cannot in general be solved to obtain a closed form solution of the joint distribution of positions and orientations, as we show, it can be utilized to evaluate the exact time dependence of all moments, using a Laplace transform approach. We present an explicit calculation of several such moments at arbitrary times and their evolution to the steady state. In particular we compute the kurtosis of the displacement, a quantity which clearly shows the difference of the active steady state properties from the equilibrium Gaussian form. We find that it increases with activity to asymptotic saturation, but varies non-monotonically with the trap-stiffness, thereby capturing recently observed active-to-passive re-entrant behavior.
The physics of streamer discharge phenomena

In this review we describe a transient type of gas discharge which is commonly called a streamer discharge, as well as a few related phenomena in pulsed discharges. Streamers are propagating ionization fronts with self-organized field enhancement at their tips that can appear in atmospheric air, or more generally in gases over distances larger than order 1 cm times N 0/N, where N is gas density and N 0 is gas density under ambient conditions. Streamers are the precursors of other discharges like sparks and lightning, but they also occur in for example corona reactors or plasma jets which are used for a variety of plasma chemical purposes. When enough space is available, streamers can also form at much lower pressures, like in the case of sprite discharges high up in the atmosphere. We explain the structure and basic underlying physics of streamer discharges, and how they scale with gas density. We discuss the chemistry and applications of streamers, and describe their two main stages in detail: inception and propagation. We also look at some other topics, like interaction with flow and heat, related pulsed discharges, and electron runaway and high energy radiation. Finally, we discuss streamer simulations and diagnostics in quite some detail. This review is written with two purposes in mind: first, we describe recent results on the physics of streamer discharges, with a focus on the work performed in our groups. We also describe recent developments in diagnostics and simulations of streamers. Second, we provide background information on the above-mentioned aspects of streamers. This review can therefore be used as a tutorial by researchers starting to work in the field of streamer physics.
Dynamics of photoinduced phonon entanglement generation between remote electron-phonon systems

The generation of phonon entanglement between photoirradiated remote systems is numerically studied. Analysis by the quantum mutual information reveals that laser pulse irradiation induces phonon entanglement with a certain incubation period, which is determined by the creation time of phonons. This behavior is consistent with the motion of composite modes of the system, which are derived from the Heisenberg equation of motion for the amplitude operator of the Stokes light. These results show that the dynamics of the phonon entanglement can be observed by time-resolved spectroscopy.
Noise-robust Named Entity Understanding for Virtual Assistants

Named Entity Understanding (NEU) plays an essential role in interactions between users and voice assistants, since successfully identifying entities and correctly linking them to their standard forms is crucial to understanding the user's intent. NEU is a challenging task in voice assistants due to the ambiguous nature of natural language and because noise introduced by speech transcription and user errors occur frequently in spoken natural language queries. In this paper, we propose an architecture with novel features that jointly solves the recognition of named entities (a.k.a. Named Entity Recognition, or NER) and the resolution to their canonical forms (a.k.a. Entity Linking, or EL). We show that by combining NER and EL information in a joint reranking module, our proposed framework improves accuracy in both tasks. This improved performance and the features that enable it, also lead to better accuracy in downstream tasks, such as domain classification and semantic parsing.
Infinite dimensional pathwise Volterra processes driven by Gaussian noise – Probabilistic properties and applications –

We investigate the probabilistic and analytic properties of Volterra processes constructed as pathwise integrals of deterministic kernels with respect to the H\"older continuous trajectories of Hilbert-valued Gaussian processes. To this end, we extend the Volterra sewing lemma from \cite{HarangTindel} to the two dimensional case, in order to construct two dimensional operator-valued Volterra integrals of Young type. We prove that the covariance operator associated to infinite dimensional Volterra processes can be represented by such a two dimensional integral, which extends the current notion of representation for such covariance operators. We then discuss a series of applications of these results, including the construction of a rough path associated to a Volterra process driven by Gaussian noise with possibly irregular covariance structures, as well as a description of the irregular covariance structure arising from Gaussian processes time-shifted along irregular trajectories. Furthermore, we consider an infinite dimensional fractional Ornstein-Uhlenbeck process driven by Gaussian noise, which can be seen as an extension of the volatility model proposed by Rosenbaum et al. in \cite{ElEuchRosenbaum}.
Fairness-Aware Link Analysis

Algorithmic fairness has attracted significant attention in the past years. Surprisingly, there is little work on fairness in networks. In this work, we consider fairness for link analysis algorithms and in particular for the celebrated PageRank algorithm. We provide definitions for fairness, and propose two approaches for achieving fairness. The first modifies the jump vector of the Pagerank algorithm to enfonce fairness, and the second imposes a fair behavior per node. We also consider the problem of achieving fairness while minimizing the utility loss with respect to the original algorithm. We present experiments with real and synthetic graphs that examine the fairness of Pagerank and demonstrate qualitatively and quantitatively the properties of our algorithms.
Intelligent Residential Energy Management System Using Deep Reinforcement Learning

The rising demand for electricity and its essential nature in today's world call for intelligent home energy management systems that can reduce energy usage. This article aims a novel way to develop a learning system that can learn from experience to shift loads from one time instance to another and achieve the goal of minimizing the aggregate peak load. Specifically, this article proposes a deep reinforcement learning model for demand response where the virtual agent learns the task like humans learns a task. The agent gets feedback for every action it takes in the environment; these feedbacks will drive the agent to learn about the environment and take much smarter steps later in its learning stages. The proposed approach outperformed the state of the art mixed integer linear programming for load peak reduction. Other key contribution is the design of an agent to minimize both consumer electricity bills and system peak load demand simultaneously. The proposed model was analyzed with loads from five different residential consumers; the method increases the monthly savings of each consumer by reducing their electricity bill drastically along with minimizing the peak load of the grid.
Generative Network Complex for the Automated Generation of Drug-like Molecules

Current drug discovery is expensive and time-consuming. It remains a challenging task to create a wide variety of novel compounds with desirable pharmacological properties and cheaply available to low-income people. In this work, we develop a generative network complex (GNC) to generate new drug-like molecules based on the multi-property optimization via the gradient descent in the latent space of an autoencoder. In our GNC, both multiple chemical properties and similarity scores are optimized to generate and predict drug-like molecules with desired chemical properties. To further validate the reliability of the predictions, these molecules are reevaluated and screened by independent 2D fingerprint-based predictors to come up with a few hundreds of new drug candidates. As a demonstration, we apply our GNC to generate a large number of new BACE1 inhibitors, as well as thousands of novel alternative drug candidates for eight existing market drugs, including Ceritinib, Ribociclib, Acalabrutinib, Idelalisib, Dabrafenib, Macimorelin, Enzalutamide, and Panobinostat.
Changes in gene-expression in response to sucrose accumulation in leaf tissue of Lolium temulentum L.

SUMMARY Sucrose accumulation and subsequenr fructan synthesis was initiated in detached, mature leaves of Loliumtemulentum by incubating them either in the light or with 200 mM sucrose in the dark. Fructan synthesis was alsoinduced in intact seedlings by lowering root and shoot meristem temperature to 5 °C while maintaining aerial partsof the plant at 20 °C in the light. In this system, subsequent breakdown of fructans was induced by raising rootand nneristem temperature to 20 "C in the dark. Products of iv vitro translations showed that transcripts encodingpolypeptides of 17 and 35 kDa increased in . sucrose accumulating-tissue. Transcripts encoding 2S and 32 kDapolypeptide products appeared to be associated with leaf tissue actively exporting carbohydrate. Labelling studiesin vivo in mature leaf tissue showed transient increases in the synthesis of polypeptides with MR 59, 60 and 70 kDaduring the first 6 h of sucrose accumulation. Polypeptides with an MR of 74 and 27 kDa showed increasedamounts later in induction.Cordycepin and cycloheximide were fed to mature leaf tissue at different stages during induction of sucroseaccumulation in the light. Both control and cordycepin-treated tissue showed higher levels of tnonosaccharideaccumulation compared with cyclohexitnide-treated tissue. Cordycepin applied during the first 2 h after excisionblocked subsequent synthesis of all fructans. When applied at two-hour-stages between 2 and 8 h, subsequentaccumulation of 1-kestose occurred without accumulation of higher fructans. Application after 8 h had no efFectupon the pattern of fructan accumulation when compared to untreated controls, . application of cycloheximideduring the first 8 h following excision blocked all fructan accumulation. Subsequent administration had no effecton the pattern of fructan accumulation. The results are discussed in relation to the changes in gene expressionassociated with induction of fructan synthesis.Key words: Sucrose, fructan, gene expression, source leaves, transcription.INTRODUCTION
Predicting Dynamics on Networks Hardly Depends on the Topology

Processes on networks consist of two interdependent parts: the network topology, consisting of the links between nodes, and the dynamics, specified by some governing equations. This work considers the prediction of the future dynamics on an unknown network, based on past observations of the dynamics. For a general class of governing equations, we propose a prediction algorithm which infers the network as an intermediate step. Inferring the network is impossible in practice, due to a dramatically ill-conditioned linear system. Surprisingly, a highly accurate prediction of the dynamics is possible nonetheless: Even though the inferred network has no topological similarity with the true network, both networks result in practically the same future dynamics.
Snr-Based Teachers-Student Technique For Speech Enhancement

It is very challenging for speech enhancement methods to achieves robust performance under both high signal-to-noise ratio (SNR) and low SNR simultaneously. In this paper, we propose a method that integrates an SNR-based teachersstudent technique and time-domain U-Net to deal with this problem. Specifically, this method consists of multiple teacher models and a student model. We first train the teacher models under multiple small-range SNRs that do not coincide with each other so that they can perform speech enhancement well within the specific SNR range. Then, we choose different teacher models to supervise the training of the student model according to the SNR of the training data. Eventually, the student model can perform speech enhancement under both high SNR and low SNR. To evaluate the proposed method, we constructed a dataset with an SNR ranging from -20dB to 20dB based on the public dataset. We experimentally analyzed the effectiveness of the SNR-based teachers-student technique and compared the proposed method with several state-of-the-art methods.
t-structures on stable infinity-categories

The present work re-enacts the classical theory of t-structures reducing the classical definition given in *Faisceaux Pervers* to a rather primitive categorical gadget: suitable reflective factorization systems. This translation is only possible due to the virtues of the infinity-categorical setting. Once the basic theory of t-structure has been developed, a natural direction of research is to develop a comprehensive treatment of t-structure in the "torsio-centric" perspective: we apply the technology of factorization systems to the theory of Postnikov towers, to the proof of the abelianity of the heart of a t-structure, and to the theory of Bridgeland's stability conditions.