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In this country-comparative study, we ask to what extent differentiation in secondary education accounts for the association between social origins and social destinations in adult age. We go beyond the widely applied formal definitions of educational tracking and particularly pay attention to country-specific approaches to educational differentiation. Our main expectation is that once we factor in these particularities, the degree to which educational differentiation accounts for social reproduction is quite similar across countries. Our analyses are based on national individual-level life-course data from six European countries that span from secondary education to occupational maturity. Our findings show that educational differentiation mediates the association between social origins and social destinations to a substantial degree in all countries. However, we still find some differences between countries in the extent to which educational differentiation accounts for social reproduction.

While there is ample evidence showing that people who believe in one conspiracy theory are more likely to believe in other conspiracy theories, and many studies that show that some people have a stronger general propensity to believe in conspiracy theories – i.e., conspiracy mindset – than others, the empirical relationship between conspiracy mindset and beliefs in specific conspiracy theories is unclear. This paper thus aims to investigate this relationship using a unique three-wave panel study. Among other things, the findings suggest that a conspiracy mindset empirically can be distinguished from beliefs in specific conspiracy theories, and that conspiracy mindset is a stronger predictor of beliefs in specific conspiracy theories than the other way around.

"Everyone knows the moon is made of cheese..." This line, famously uttered by Wallace to his canine sidekick Gromit in the 80s classic, may be one of the most cruelly underappreciated movie quotations of our time. Indeed, while most scientists today would simply reject Wallace's claim as preposterous, we aim to revisit his theory on the composition of our natural satellite, revealing that it may not be as implausible as the scientific consensus would have it. Through a revelatory novel analysis of existing data, we will show that very simple cheese-based models can provide a convincing explanation of the Lunar surface's spectral characteristics in the near-infrared. Using the tried and tested PLS (Partial Least Squares) method, we efficiently and reliably retrieve the concentrations of various cheese types in different locations of the Lunar surface. Our results bring to light a bold and flavourful prediction about the Moon's composition, which lays the groundwork for an important paradigm shift in planetary sciences. We urge the scientific community to take a serious notice of this piquant novel interpretation, and strongly consider it in their future models of planetary composition and formation in our solar system and beyond.

The epidermal cells of petunia (Petunia × hybrida) flowers are the main site of volatile emission. However, the mechanisms underlying the release of volatiles into the environment are still being explored. Here, using cell-layer-specific transcriptomic analysis, reverse genetics by virus-induced gene silencing and clustered regularly interspaced short palindromic repeat (CRISPR), and metabolomics, we identified EPIDERMIS VOLATILE EMISSION REGULATOR (EVER)-a petal adaxial epidermis-specific MYB activator that affects the emission of volatiles. To generate ever knockout lines, we developed a viral-based CRISPR/Cas9 system for efficient gene editing in plants. These knockout lines, together with transient-suppression assays, revealed EVER's involvement in the repression of low-vapor-pressure volatiles. Internal pools and annotated scent-related genes involved in volatile production and emission were not affected by EVER. RNA-Seq analyses of petals of ever knockout lines and EVER-overexpressing flowers revealed enrichment in wax-related biosynthesis genes. Liquid chromatography/gas chromatography-MS analyses of petal epicuticular waxes revealed substantial reductions in wax loads in ever petals, particularly of monomers of fatty acids and wax esters. These results implicate EVER in the emission of volatiles by fine-tuning the composition of petal epicuticular waxes. We reveal a petunia MYB regulator that interlinks epicuticular wax composition and volatile emission, thus unraveling a regulatory layer in the scent-emission machinery in petunia flowers.

Starting from Robbins (1952), the literature on experimentation via multi-armed bandits has wed exploration and exploitation. Nonetheless, in many applications, agents' exploration and exploitation need not be intertwined: a policymaker may assess new policies different than the status quo; an investor may evaluate projects outside her portfolio. We characterize the optimal experimentation policy when exploration and exploitation are disentangled in the case of Poisson bandits, allowing for general news structures. The optimal policy features complete learning asymptotically, exhibits lots of persistence, but cannot be identified by an index a la Gittins. Disentanglement is particularly valuable for intermediate parameter values.

While increasing scholarly attention has been devoted to news avoidance, there are only few studies taking the distinction between intentional and unintentional news avoidance into consideration, and none that has investigated the linkage between the two types of news avoidance and knowledge about politics and society. To fill this void, this study explores this relationship while distinguishing between knowledge related to uncontested issues and knowledge related to issues that have been subject to public controversies (climate change, vaccination, genetically modified organisms, crime, and immigration). Relying on a large-scale survey among Swedish citizens conducted in 2020 (N = 2,160), we find that the relationship with patterns of news use is substantially different across these types of beliefs. Among other things, the results suggest that knowledge of uncontested issue domains is positively related to news use, but knowledge of contested issue domains is not. The intentional avoidance of news is only negatively related to knowledge of contested issues. Taken together, the results suggest that the mechanisms driving beliefs related to uncontested versus contested issues are substantially different.

The phenylpropanoid pathway of flavonoid biosynthesis has been the subject of considerable research attention. By contrast, the proposed polyketide pathway, also known as the acetate pathway, which provides malonyl-CoA moieties for the C2 elongation reaction catalyzed by chalcone synthase, is less well studied. Here, we identified four genes as candidates for involvement in the supply of cytosolic malonyl-CoA from the catabolism of acyl-CoA, based on coexpression analysis with other flavonoid-related genes. Two of these genes, ACC and KAT5, have been previously characterized with respect to their involvement in lipid metabolism, but no information concerning their relationship to flavonoid biosynthesis is available. To assess the occurrence and importance of the acetate pathway, we characterized the metabolomes of two mutant or transgenic Arabidopsis lines for each of the four enzymes of this putative pathway using a hierarchical approach covering primary and secondary metabolites as well as lipids. Intriguingly, not only flavonoid content but also glucosinolate content was altered in lines deficient in the acetate pathway, as were levels of lipids and most primary metabolites. We discuss these data in the context of our current understanding of flavonoids and lipid metabolism as well as with regard to improving human nutrition.

To explore the genetic robustness (canalization) of metabolism, we examined the levels of fruit metabolites in multiple harvests of a tomato introgression line (IL) population. The IL partitions the whole genome of the wild species Solanum pennellii in the background of the cultivated tomato (Solanum lycopersicum). We identified several metabolite quantitative trait loci that reduce variability for both primary and secondary metabolites, which we named canalization metabolite quantitative trait loci (cmQTL). We validated nine cmQTL using an independent population of backcross inbred lines, derived from the same parents, which allows increased resolution in mapping the QTL previously identified in the ILs. These cmQTL showed little overlap with QTL for the metabolite levels themselves. Moreover, the intervals they mapped to harbored few metabolism-associated genes, suggesting that the canalization of metabolism is largely controlled by regulatory genes.

The temperate climax tree species Fagus sylvatica and the floodplain tree species Populus × canescens possess contrasting phosphorus (P) nutrition strategies. While F. sylvatica has been documented to display P storage and mobilization (Netzer et al., 2017), this was not observed for Populus × canescens (Netzer et al., 2018b). Nevertheless, changes in the abundance of organic bound P in gray poplar trees indicated adaptation of the P nutrition to different needs during annual growth. The present study aimed at characterizing seasonal changes in metabolite and lipid abundances in gray poplar and uncovering differences in metabolite requirement due to specific needs depending on the season. Seasonal variations in the abundance of (i) sugar-Ps and phospholipids, (ii) amino acids, (iii) sulfur compounds, and (iv) carbon metabolites were expected. It was hypothesized that seasonal changes in metabolite levels relate to N, S, and C storage and mobilization. Changes in organic metabolites binding Pi (Porg) are supposed to support these processes. Variation in triacylglycerols, in sugar-phosphates, in metabolites of the TCA cycle and in the amino acid abundance of poplar twig buds, leaves, bark, and wood were found to be linked to changes in metabolite abundances as well as to C, N, and S storage and mobilization processes. The observed changes support the view of a lack of any P storage in poplar. Yet, during dormancy, contents of phospholipids in twig bark and wood were highest probably due to frost-hardening and to its function in extra-plastidic membranes such as amyloplasts, oleosomes, and protein bodies. Consistent with this assumption, in spring sugar-Ps increased when phospholipids declined and poplar plants entering the vegetative growth period and, hence, metabolic activity increases. These results indicate that poplar trees adopt a policy of P nutrition without P storage and mobilization that is different from their N- and S-nutrition strategies.

Background:Flavonoids constitute a diverse class of secondary metabolites which exhibit potent bioactivities for human health and have been indicated to play an important role in plant development and defense. However, accumulation and variation of flavonoid content in diverse maize lines and the genes responsible for their biosynthesis in this important crop remain largely unknown. In this study, we combine genetic mapping, metabolite profiling and gene regulatory network analysis to further enhance understanding of the maize flavonoid pathway.Results:We repeatedly detected 25 QTL corresponding to 23 distinct flavonoids across different environments or populations. In addition, a total of 39 genes were revealed both by an expression based network analysis and genetic mapping. Finally, the function of three candidate genes, including two UDP-glycosyltransferases (UGT) and an oxygenase which belongs to the flavone synthase super family, was revealed via preliminary molecular functional characterization.Conclusion:We explored the genetic influences on the flavonoid biosynthesis based on integrating the genomic, transcriptomic and metabolomic information which provided a rich source of potential candidate genes. The integrated genomics based genetic mapping strategy is highly efficient for defining the complexity of functional genetic variants and their respective regulatory networks as well as in helping to select candidate genes and allelic variance before embarking on laborious transgenic validations.

In most studies on news repertoires and the linkage between media use and (mis)perceptions of social and political matters, the use of political alternative media has not been included. In this paper, we therefore investigate how people combine both traditional and political alternative media into different media repertoires, and how these media repertoires are related to misperceptions. We rely on a two-wave panel survey, conducted in 2020 and 2021, with a probability-recruited sample of the Swedish population. Measures on the use of a wide range of media, including political alternative ones, are used to conduct a Latent Profile Analysis. This analysis distinguishes five media repertoires in the data. The results show that these repertoires coincide with different levels of misperceptions, with those dominated by TV use showing higher levels of misperceptions and those with higher newspaper and alternative media use showing lower levels of misperceptions. Results also show that media repertoires do not affect over-time changes in misperceptions., In most studies on news repertoires and the linkage between media use and (mis)perceptions of social and political matters, the use of political alternative media has not been included. In this paper, we therefore investigate how people combine both traditional and political alternative media into different media repertoires, and how these media repertoires are related to misperceptions. We rely on a two-wave panel survey, conducted in 2020 and 2021, with a probability-recruited sample of the Swedish population. Measures on the use of a wide range of media, including political alternative ones, are used to conduct a Latent Profile Analysis. This analysis distinguishes five media repertoires in the data. The results show that these repertoires coincide with different levels of misperceptions, with those dominated by TV use showing higher levels of misperceptions and those with higher newspaper and alternative media use showing lower levels of misperceptions. Results also show that media repertoires do not affect over-time changes in misperceptions.

We present a method for reconstructing high-quality meshes of large unbounded real-world scenes suitable for photorealistic novel view synthesis. We first optimize a hybrid neural volume-surface scene representation designed to have well-behaved level sets that correspond to surfaces in the scene. We then bake this representation into a high-quality triangle mesh, which we equip with a simple and fast view-dependent appearance model based on spherical Gaussians. Finally, we optimize this baked representation to best reproduce the captured viewpoints, resulting in a model that can leverage accelerated polygon rasterization pipelines for real-time view synthesis on commodity hardware. Our approach outperforms previous scene representations for real-time rendering in terms of accuracy, speed, and power consumption, and produces high quality meshes that enable applications such as appearance editing and physical simulation., Comment: Video and interactive web demo available at https://bakedsdf.github.io

Recent advances in text-to-image generation with diffusion models present transformative capabilities in image quality. However, user controllability of the generated image, and fast adaptation to new tasks still remains an open challenge, currently mostly addressed by costly and long re-training and fine-tuning or ad-hoc adaptations to specific image generation tasks. In this work, we present MultiDiffusion, a unified framework that enables versatile and controllable image generation, using a pre-trained text-to-image diffusion model, without any further training or finetuning. At the center of our approach is a new generation process, based on an optimization task that binds together multiple diffusion generation processes with a shared set of parameters or constraints. We show that MultiDiffusion can be readily applied to generate high quality and diverse images that adhere to user-provided controls, such as desired aspect ratio (e.g., panorama), and spatial guiding signals, ranging from tight segmentation masks to bounding boxes. Project webpage: https://multidiffusion.github.io

In recent years, image generation has shown a great leap in performance, where diffusion models play a central role. Although generating high-quality images, such models are mainly conditioned on textual descriptions. This begs the question: "how can we adopt such models to be conditioned on other modalities?". In this paper, we propose a novel method utilizing latent diffusion models trained for text-to-image-generation to generate images conditioned on audio recordings. Using a pre-trained audio encoding model, the proposed method encodes audio into a new token, which can be considered as an adaptation layer between the audio and text representations. Such a modeling paradigm requires a small number of trainable parameters, making the proposed approach appealing for lightweight optimization. Results suggest the proposed method is superior to the evaluated baseline methods, considering objective and subjective metrics. Code and samples are available at: https://pages.cs.huji.ac.il/adiyoss-lab/AudioToken., Comment: Accepted to INTERSPEECH 2023

The recently introduced Quantum Lego framework provides a powerful method for generating complex quantum error correcting codes (QECCs) out of simple ones. We gamify this process and unlock a new avenue for code design and discovery using reinforcement learning (RL). One benefit of RL is that we can specify \textit{arbitrary} properties of the code to be optimized. We train on two such properties, maximizing the code distance, and minimizing the probability of logical error under biased Pauli noise. For the first, we show that the trained agent identifies ways to increase code distance beyond naive concatenation, saturating the linear programming bound for CSS codes on 13 qubits. With a learning objective to minimize the logical error probability under biased Pauli noise, we find the best known CSS code at this task for $\lesssim 20$ qubits. Compared to other (locally deformed) CSS codes, including Surface, XZZX, and 2D Color codes, our $[[17,1,3]]$ code construction actually has \textit{lower} adversarial distance, yet better protects the logical information, highlighting the importance of QECC desiderata. Lastly, we comment on how this RL framework can be used in conjunction with physical quantum devices to tailor a code without explicit characterization of the noise model., Comment: 10 pages + appendices; v2 figure updated and note added

Surface reconstruction has been seeing a lot of progress lately by utilizing Implicit Neural Representations (INRs). Despite their success, INRs often introduce hard to control inductive bias (i.e., the solution surface can exhibit unexplainable behaviours), have costly inference, and are slow to train. The goal of this work is to show that replacing neural networks with simple grid functions, along with two novel geometric priors achieve comparable results to INRs, with instant inference, and improved training times. To that end we introduce VisCo Grids: a grid-based surface reconstruction method incorporating Viscosity and Coarea priors. Intuitively, the Viscosity prior replaces the smoothness inductive bias of INRs, while the Coarea favors a minimal area solution. Experimenting with VisCo Grids on a standard reconstruction baseline provided comparable results to the best performing INRs on this dataset., Comment: Published in NeurIPS 2022

One implication of the transition to high-choice media environments is that what information people are exposed to depend less on journalistic curation and more on social, algorithmic, and personal curation. This has sparked a renewed interest in the concept of political opinion leaders, who are often assumed to be more interested in and informed about politics and society. Theoretically, political opinion leaders could hence help disseminate information to less interested and informed. At the same time, there are theoretical reasons for why political opinion leaders may be more prone to politically motivated reasoning, which may lead them to believe in and disseminate misinformation. Thus far, there is only limited research on whether political opinion leaders are more informed than others that also takes into consideration that some facts are contested, whereas other facts are uncontested. Hence, this paper seeks to investigate the relationship between political opinion leadership and knowledge, distinguishing between (a) uncontested and (b) contested facts. Among other things, findings show that those who score high in political opinion leadership traits in general are not more knowledgeable about contested and uncontested facts.

Current diffusion or flow-based generative models for 3D shapes divide to two: distilling pre-trained 2D image diffusion models, and training directly on 3D shapes. When training a diffusion or flow models on 3D shapes a crucial design choice is the shape representation. An effective shape representation needs to adhere three design principles: it should allow an efficient conversion of large 3D datasets to the representation form; it should provide a good tradeoff of approximation power versus number of parameters; and it should have a simple tensorial form that is compatible with existing powerful neural architectures. While standard 3D shape representations such as volumetric grids and point clouds do not adhere to all these principles simultaneously, we advocate in this paper a new representation that does. We introduce Mosaic-SDF (M-SDF): a simple 3D shape representation that approximates the Signed Distance Function (SDF) of a given shape by using a set of local grids spread near the shape's boundary. The M-SDF representation is fast to compute for each shape individually making it readily parallelizable; it is parameter efficient as it only covers the space around the shape's boundary; and it has a simple matrix form, compatible with Transformer-based architectures. We demonstrate the efficacy of the M-SDF representation by using it to train a 3D generative flow model including class-conditioned generation with the 3D Warehouse dataset, and text-to-3D generation using a dataset of about 600k caption-shape pairs., Comment: More results and details can be found at https://lioryariv.github.io/msdf

We consider the task of generating diverse and realistic videos guided by natural audio samples from a wide variety of semantic classes. For this task, the videos are required to be aligned both globally and temporally with the input audio: globally, the input audio is semantically associated with the entire output video, and temporally, each segment of the input audio is associated with a corresponding segment of that video. We utilize an existing text-conditioned video generation model and a pre-trained audio encoder model. The proposed method is based on a lightweight adaptor network, which learns to map the audio-based representation to the input representation expected by the text-to-video generation model. As such, it also enables video generation conditioned on text, audio, and, for the first time as far as we can ascertain, on both text and audio. We validate our method extensively on three datasets demonstrating significant semantic diversity of audio-video samples and further propose a novel evaluation metric (AV-Align) to assess the alignment of generated videos with input audio samples. AV-Align is based on the detection and comparison of energy peaks in both modalities. In comparison to recent state-of-the-art approaches, our method generates videos that are better aligned with the input sound, both with respect to content and temporal axis. We also show that videos produced by our method present higher visual quality and are more diverse., Comment: 9 pages, 6 figures

Scholars have raised concerns that on many issues, citizens are reluctant to trust factual evidence and statistics. One factor that has been shown to impact the perceived truth in statistics is how they are presented, where negatively framed statistics are perceived as truer than positive. This study explores when this bias applies and not. Results from a survey experiment confirm the presence of a negativity bias in truth perceptions, but also that effects are heterogeneous and moderated by, in particular, the recipients’ preexisting opinions. These findings provide valuable information to public actors responsible for disseminating factual information to diverse publics.

Hepatic cysts are fluid-filled lesions in the liver that are estimated to occur in 5% of the population. They may cause hepatomegaly and abdominal pain. Progression to secondary fibrosis, cirrhosis, or cholangiocarcinoma can lead to morbidity and mortality. Previous studies of patients and rodent models have associated hepatic cyst formation with increased proliferation and fluid secretion in cholangiocytes, which are partially due to impaired primary cilia. Congenital hepatic cysts are thought to originate from faulty bile duct development, but the underlying mechanisms are not fully understood. In a forward genetic screen, we identified a zebrafish mutant that developed hepatic cysts during larval stages. The cyst formation was not due to changes in biliary cell proliferation, bile secretion, or impairment of primary cilia. Instead, time-lapse live imaging data showed that the mutant biliary cells failed to form interconnecting bile ducts because of defects in motility and protrusive activity. Accordingly, immunostaining revealed a disorganized actin and microtubule cytoskeleton in the mutant biliary cells. By whole-genome sequencing, we determined that the cystic phenotype in the mutant was caused by a missense mutation in the furinb gene, which encodes a proprotein convertase. The mutation altered Furinb localization and caused endoplasmic reticulum (ER) stress. The cystic phenotype could be suppressed by treatment with the ER stress inhibitor 4-phenylbutyric acid and exacerbated by treatment with the ER stress inducer tunicamycin. The mutant liver also exhibited increased mammalian target of rapamycin (mTOR) signaling. Treatment with mTOR inhibitors halted cyst formation at least partially through reducing ER stress. Conclusion: Our study has established a vertebrate model for studying hepatic cystogenesis and illustrated the contribution of ER stress in the disease pathogenesis., SCOPUS: ar.j, info:eu-repo/semantics/published

We consider mediated interactions in an array of floating transmons, where each qubit capacitor consists of two superconducting pads galvanically isolated from ground. Each such pair contributes two quantum degrees of freedom, one of which is used as a qubit, while the other remains fixed. However, these extraneous modes can generate coupling between the qubit modes that extends beyond the nearest neighbor. We present a general formalism describing the formation of this coupling and calculate it for a one-dimensional chain of transmons. We show that the strength of coupling and its range (that is, the exponential falloff) can be tuned independently via circuit design to realize a continuum from nearest-neighbor-only interactions to interactions that extend across the length of the chain. We present designs with capacitance and microwave simulations showing that various interaction configurations can be achieved in realistic circuits. Such coupling could be used in analog simulation of different quantum regimes or to increase connectivity in digital quantum systems. Thus mechanism must also be taken into account in other types of qubits with extraneous modes.

AbstractParticle transport and localization phenomena in condensed-matter systems can be modeled using a tight-binding lattice Hamiltonian. The ideal experimental emulation of such a model utilizes simultaneous, high-fidelity control and readout of each lattice site in a highly coherent quantum system. Here, we experimentally study quantum transport in one-dimensional and two-dimensional tight-binding lattices, emulated by a fully controllable 3 × 3 array of superconducting qubits. We probe the propagation of entanglement throughout the lattice and extract the degree of localization in the Anderson and Wannier-Stark regimes in the presence of site-tunable disorder strengths and gradients. Our results are in quantitative agreement with numerical simulations and match theoretical predictions based on the tight-binding model. The demonstrated level of experimental control and accuracy in extracting the system observables of interest will enable the exploration of larger, interacting lattices where numerical simulations become intractable.

Interacting many-body quantum systems show a rich array of physical phenomena and dynamical properties, but are notoriously difficult to study: they are challenging analytically and exponentially difficult to simulate on classical computers. Small-scale quantum information processors hold the promise to efficiently emulate these systems, but characterizing their dynamics is experimentally challenging, requiring probes beyond simple correlation functions and multi-body tomographic methods. Here, we demonstrate the measurement of out-of-time-ordered correlators (OTOCs), one of the most effective tools for studying quantum system evolution and processes like quantum thermalization. We implement a 3x3 two-dimensional hard-core Bose-Hubbard lattice with a superconducting circuit, study its time-reversibility by performing a Loschmidt echo, and measure OTOCs that enable us to observe the propagation of quantum information. A central requirement for our experiments is the ability to coherently reverse time evolution, which we achieve with a digital-analog simulation scheme. In the presence of frequency disorder, we observe that localization can partially be overcome with more particles present, a possible signature of many-body localization in two dimensions.

A key problem with research on news media trust is that it has mostly focused on general media trust and that there is limited research on how media trust might vary across levels of analysis. In this paper, we seek to remedy this by investigating whether news media trust differs depending on the topic of news coverage and whether topical trust can be distinguished from general media trust. We also investigate the antecedents of trust in news coverage of different topics and the effects of topical trust on issue (mis)perceptions. Among other things, findings show that topical media trust can be distinguished from general media trust and is a better predictor of correct perceptions on political matters.

Anomalies (or outliers) are prevalent in real-world empirical observations and potentially mask important underlying structures. Accurate identification of anomalous samples is crucial for the success of downstream data analysis tasks. To automatically identify anomalies, we propose Probabilistic Robust AutoEncoder (PRAE). PRAE aims to simultaneously remove outliers and identify a low-dimensional representation for the inlier samples. We first present the Robust AutoEncoder (RAE) objective as a minimization problem for splitting the data into inliers and outliers. Our objective is designed to exclude outliers while including a subset of samples (inliers) that can be effectively reconstructed using an AutoEncoder (AE). RAE minimizes the autoencoder's reconstruction error while incorporating as many samples as possible. This could be formulated via regularization by subtracting an $\ell_0$ norm counting the number of selected samples from the reconstruction term. Unfortunately, this leads to an intractable combinatorial problem. Therefore, we propose two probabilistic relaxations of RAE, which are differentiable and alleviate the need for a combinatorial search. We prove that the solution to the PRAE problem is equivalent to the solution of RAE. We use synthetic data to show that PRAE can accurately remove outliers in a wide range of contamination levels. Finally, we demonstrate that using PRAE for anomaly detection leads to state-of-the-art results on various benchmark datasets.

©2019 American Physical Society. The effects of quenched disorder on a single and many active run-and-tumble particles are studied in one dimension. For a single particle, we consider both the steady-state distribution and the particle's dynamics subject to disorder in three parameters: a bounded external potential, the particle's speed, and its tumbling rate. We show that in the case of a disordered potential, the behavior is like an equilibrium particle diffusing on a random force landscape, implying a dynamics that is logarithmically slow in time. In the situations of disorder in the speed or tumbling rate, we find that the particle generically exhibits diffusive motion, although particular choices of the disorder may lead to anomalous diffusion. Based on the single-particle results, we find that in a system with many interacting particles, disorder in the potential leads to strong clustering. We characterize the clustering in two different regimes depending on the system size and show that the mean cluster size scales with the system size, in contrast to nondisordered systems.

© 2020, The Author(s). The pursuit of superconducting-based quantum computers has advanced the fabrication of and experimentation with custom lattices of qubits and resonators. Here, we describe a roadmap to use present experimental capabilities to simulate an interacting many-body system of bosons and measure quantities that are exponentially difficult to calculate numerically. We focus on the two-dimensional hard-core Bose–Hubbard model implemented as an array of floating transmon qubits. We describe a control scheme for such a lattice that can perform individual qubit readout and show how the scheme enables the preparation of a highly excited many-body state, in contrast with atomic implementations restricted to the ground state or thermal equilibrium. We discuss what observables could be accessed and how they could be used to better understand the properties of many-body systems, including the observation of the transition of eigenstate entanglement entropy scaling from area-law behavior to volume-law behavior.

Neural volume rendering became increasingly popular recently due to its success in synthesizing novel views of a scene from a sparse set of input images. So far, the geometry learned by neural volume rendering techniques was modeled using a generic density function. Furthermore, the geometry itself was extracted using an arbitrary level set of the density function leading to a noisy, often low fidelity reconstruction. The goal of this paper is to improve geometry representation and reconstruction in neural volume rendering. We achieve that by modeling the volume density as a function of the geometry. This is in contrast to previous work modeling the geometry as a function of the volume density. In more detail, we define the volume density function as Laplace's cumulative distribution function (CDF) applied to a signed distance function (SDF) representation. This simple density representation has three benefits: (i) it provides a useful inductive bias to the geometry learned in the neural volume rendering process; (ii) it facilitates a bound on the opacity approximation error, leading to an accurate sampling of the viewing ray. Accurate sampling is important to provide a precise coupling of geometry and radiance; and (iii) it allows efficient unsupervised disentanglement of shape and appearance in volume rendering. Applying this new density representation to challenging scene multiview datasets produced high quality geometry reconstructions, outperforming relevant baselines. Furthermore, switching shape and appearance between scenes is possible due to the disentanglement of the two.

A common observation in data-driven applications is that high dimensional data has a low intrinsic dimension, at least locally. In this work, we consider the problem of estimating a $d$ dimensional sub-manifold of $\mathbb{R}^D$ from a finite set of noisy samples. Assuming that the data was sampled uniformly from a tubular neighborhood of $\mathcal{M}\in \mathcal{C}^k$, a compact manifold without boundary, we present an algorithm that takes a point $r$ from the tubular neighborhood and outputs $\hat p_n\in \mathbb{R}^D$, and $\widehat{T_{\hat p_n}\mathcal{M}}$ an element in the Grassmanian $Gr(d, D)$. We prove that as the number of samples $n\to\infty$ the point $\hat p_n$ converges to $p\in \mathcal{M}$ and $\widehat{T_{\hat p_n}\mathcal{M}}$ converges to $T_p\mathcal{M}$ (the tangent space at that point) with high probability. Furthermore, we show that the estimation yields asymptotic rates of convergence of $n^{-\frac{k}{2k + d}}$ for the point estimation and $n^{-\frac{k-1}{2k + d}}$ for the estimation of the tangent space. These rates are known to be optimal for the case of function estimation.

Modeling the distribution of high dimensional data by a latent tree graphical model is a prevalent approach in multiple scientific domains. A common task is to infer the underlying tree structure, given only observations of its terminal nodes. Many algorithms for tree recovery are computationally intensive, which limits their applicability to trees of moderate size. For large trees, a common approach, termed divide-and-conquer, is to recover the tree structure in two steps. First, recover the structure separately of multiple, possibly random subsets of the terminal nodes. Second, merge the resulting subtrees to form a full tree. Here, we develop Spectral Top-Down Recovery (STDR), a deterministic divide-and-conquer approach to infer large latent tree models. Unlike previous methods, STDR partitions the terminal nodes in a non random way, based on the Fiedler vector of a suitable Laplacian matrix related to the observed nodes. We prove that under certain conditions, this partitioning is consistent with the tree structure. This, in turn, leads to a significantly simpler merging procedure of the small subtrees. We prove that STDR is statistically consistent and bound the number of samples required to accurately recover the tree with high probability. Using simulated data from several common tree models in phylogenetics, we demonstrate that STDR has a significant advantage in terms of runtime, with improved or similar accuracy.

Perennial grasses will account for approximately 16 billion gallons of renewable fuels by the year 2022, contributing significantly to carbon and nitrogen sequestration. However, perennial grasses productivity can be limited by severe freezing conditions in some geographical areas, although these risks could decrease with the advance of climate warming, the possibility of unpredictable early cold events cannot be discarded. We conducted a study on the model perennial grass Brachypodium sylvaticum to investigate the molecular mechanisms that contribute to cold and freezing adaption. The study was performed on two different B. sylvaticum accessions, Ain1 and Osl1, typical to warm and cold climates, respectively. Both accessions were grown under controlled conditions with subsequent cold acclimation followed by freezing stress. For each treatment a set of morphological parameters, transcription, metabolite, and lipid profiles were measured. State-of-the-art algorithms were employed to analyze cross-component relationships. Phenotypic analysis revealed higher adaption of Osl1 to freezing stress. Our analysis highlighted the differential regulation of the TCA cycle and the GABA shunt between Ain1 and Osl1. Osl1 adapted to freezing stress by repressing the GABA shunt activity, avoiding the detrimental reduction in fatty acid biosynthesis and the concomitant detrimental effects on membrane integrity.

Most doctors in the NRMP are matched to one of their most-preferred internship programs. Since various surveys indicate similarities across doctors' preferences, this suggests a puzzle. How can nearly everyone get a position in a highly-desirable program when positions in each program are scarce? We provide one possible explanation for this puzzle. We show that the patterns observed in the NRMP data may be an artifact of the interview process that precedes the match. Our analysis highlights the importance of interactions occurring outside of a matching clearinghouse for resulting outcomes, and casts doubts on analysis of clearinghouses that take reported preferences at face value.

In contemporary high-choice media environments, the issue of media trust and its impact on people's media use has taken on new importance. At the same time, the extent to which people trust the news media and how much it matters for their use of different types of media is not clear. To lay the groundwork for future research, in this article we offer a focused review of (a) how news media trust has been conceptualized and operationalized in previous research and (b) research on the extent to which news media trust influences media use, and (c) offer a theoretically derived framework for future research on news media trust and its influence on media use., This work is part of the research project ‘Knowledge Resistance: Causes, Consequences, Cures,’ funded by Riksbankens Jubileumsfond for the Advancement of the Humanities and Social Sciences.

We use laboratory experiments to test for one of the foundations of the rational voter paradigm - that voters respond to probabilities of being pivotal. We exploit a setup that entails stark theoretical effects of information concerning the preference distribution (as revealed through polls) on costly participation decisions. We find that voting propensity increases systematically with subjects' predictions of their preferred alternative's advantage. Consequently, pre-election polls do not exhibit the detrimental welfare effects that extant theoretical work predicts. They lead to more participation by the expected majority and generate more landslide elections.

Perennial grasses will account for approximately 16 billion gallons of renewable fuels by the year 2022, contributing significantly to carbon and nitrogen sequestration. However, perennial grasses productivity can be limited by severe freezing conditions in some geographical areas, although these risks could decrease with the advance of climate warming, the possibility of unpredictable early cold events cannot be discarded. We conducted a study on the model perennial grass Brachypodium sylvaticum to investigate the molecular mechanisms that contribute to cold and freezing adaption. The study was performed on two different B. sylvaticum accessions, Ain1 and Osl1, typical to warm and cold climates, respectively. Both accessions were grown under controlled conditions with subsequent cold acclimation followed by freezing stress. For each treatment a set of morphological parameters, transcription, metabolite, and lipid profiles were measured. State-of-the-art algorithms were employed to analyze cross-component relationships. Phenotypic analysis revealed higher adaption of Osl1 to freezing stress. Our analysis highlighted the differential regulation of the TCA cycle and the GABA shunt between Ain1 and Osl1. Osl1 adapted to freezing stress by repressing the GABA shunt activity, avoiding the detrimental reduction in fatty acid biosynthesis and the concomitant detrimental effects on membrane integrity.

Peronospora belbahrii is one of the most destructive downy mildew diseases that has emerged throughout the past two decades. Due to the lack of quarantine regulations and its possible seed-borne nature, it has spread globally and is now present in most areas in which basil is produced. While most obligate biotrophic, plant parasitic oomycetes are highly host-specific, there are a few that have a wider host range, e.g. Albugo candida, Bremia tulasnei, and Pseudoperonospora cubensis. Recently, it was shown that Peronospora belbahrii is able to infect Rosmarinus, Nepetia, and Micromeria in Israel in cross-infection trials, hinting an extended host range for also this pathogen. In this study, a newly occurring downy mildew pathogen on lavender was investigated with respect to its morphology and phylogeny, and it is shown that it belongs to Peronospora belbahrii as well. Thus, it seems that Peronospora belbahrii is currently extending its host range to additional members of the tribe Mentheae and Ocimeae. Therefore, it seems advisable to scrutinise all commonly used members of these tribes in order to avoid further spread of virulent genotypes.

In this work we address the challenging problem of multiview 3D surface reconstruction. We introduce a neural network architecture that simultaneously learns the unknown geometry, camera parameters, and a neural renderer that approximates the light reflected from the surface towards the camera. The geometry is represented as a zero level-set of a neural network, while the neural renderer, derived from the rendering equation, is capable of (implicitly) modeling a wide set of lighting conditions and materials. We trained our network on real world 2D images of objects with different material properties, lighting conditions, and noisy camera initializations from the DTU MVS dataset. We found our model to produce state of the art 3D surface reconstructions with high fidelity, resolution and detail.

A common assumption in multiple scientific applications is that the distribution of observed data can be modeled by a latent tree graphical model. An important example is phylogenetics, where the tree models the evolutionary lineages of a set of observed organisms. Given a set of independent realizations of the random variables at the leaves of the tree, a key challenge is to infer the underlying tree topology. In this work we develop Spectral Neighbor Joining (SNJ), a novel method to recover the structure of latent tree graphical models. Given a matrix that contains a measure of similarity between all pairs of observed variables, SNJ computes a spectral measure of cohesion between groups of observed variables. We prove that SNJ is consistent, and derive a sufficient condition for correct tree recovery from an estimated similarity matrix. Combining this condition with a concentration of measure result on the similarity matrix, we bound the number of samples required to recover the tree with high probability. We illustrate via extensive simulations that in comparison to several other reconstruction methods, SNJ requires fewer samples to accurately recover trees with a large number of leaves or long edges.

Representing shapes as level sets of neural networks has been recently proved to be useful for different shape analysis and reconstruction tasks. So far, such representations were computed using either: (i) pre-computed implicit shape representations; or (ii) loss functions explicitly defined over the neural level sets. In this paper we offer a new paradigm for computing high fidelity implicit neural representations directly from raw data (i.e., point clouds, with or without normal information). We observe that a rather simple loss function, encouraging the neural network to vanish on the input point cloud and to have a unit norm gradient, possesses an implicit geometric regularization property that favors smooth and natural zero level set surfaces, avoiding bad zero-loss solutions. We provide a theoretical analysis of this property for the linear case, and show that, in practice, our method leads to state of the art implicit neural representations with higher level-of-details and fidelity compared to previous methods., Comment: 37th International Conference on Machine Learning, Vienna, Austria, 2020

Network tomography aims at estimating source-destination traffic rates from link traffic measurements. This inverse problem was formulated by Vardi in 1996 for Poisson traffic over networks operating under deterministic as well as random routing regimes. In this paper we expand Vardi's second-order moment matching rate estimation approach to higher-order cumulant matching with the goal of increasing the column rank of the mapping and consequently improving the rate estimation accuracy. We develop a systematic set of linear cumulant matching equations and express them compactly in terms of the Khatri-Rao product. Both least squares estimation and iterative minimum I-divergence estimation are considered. We develop an upper bound on the mean squared error (MSE) in least squares rate estimation from empirical cumulants. We demonstrate for the NSFnet that supplementing Vardi's approach with third-order empirical cumulant reduces its averaged normalized MSE relative to the theoretical minimum of the second-order moment matching approach by about 12%-18%. This minimum MSE is obtained when Vardi's second-order moment matching approach is based on the theoretical rather than the empirical moments., Comment: 10 pages, 2 figures, 6 tables

The Hippo pathway and its nuclear effector Yap regulate organ size and cancer formation. While many modulators of Hippo activity have been identified, little is known about the Yap target genes that mediate these growth effects. Here, we show that yap−/− mutant zebrafish exhibit defects in hepatic progenitor potential and liver growth due to impaired glucose transport and nucleotide biosynthesis. Transcriptomic and metabolomic analyses reveal that Yap regulates expression of glucose transporter glut1, causing decreased glucose uptake and use for nucleotide biosynthesis in yap−/− mutants, and impaired glucose tolerance in adults. Nucleotide supplementation improves Yap deficiency phenotypes, indicating functional importance of glucose-fueled nucleotide biosynthesis. Yap-regulated glut1 expression and glucose uptake are conserved in mammals, suggesting that stimulation of anabolic glucose metabolism is an evolutionarily conserved mechanism by which the Hippo pathway controls organ growth. Together, our results reveal a central role for Hippo signaling in glucose metabolic homeostasis., National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) (Grant P30DK034854), National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) (Grant 1R01DK090311), National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) (Grant R24OD017870), National Institutes of Health (U.S.) (Grant P30DK034854), National Institutes of Health (U.S.) (Grant 1R01DK090311), National Institutes of Health (U.S.) (Grant 1R01DK105198), National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) (Grant 1R01DK105198), National Institutes of Health (U.S.) (Grant R24OD017870), National Institute of General Medical Sciences (NIGMS) (Grant T32GM007753), NHMRC (Grant 1146558), National Cancer Institute (U.S.) (Grant 5P01CA120964), National Cancer Institute (U.S.) (Grant 5P30CA006516)

The phenylpropanoid pathway of flavonoid biosynthesis has been the subject of considerable research attention. By contrast, the proposed polyketide pathway, also known as the acetate pathway, which provides malonyl-CoA moieties for the C2 elongation reaction catalyzed by chalcone synthase, is less well studied. Here, we identified four genes as candidates for involvement in the supply of cytosolic malonyl-CoA from the catabolism of acyl-CoA, based on coexpression analysis with other flavonoid-related genes. Two of these genes, ACC and KAT5, have been previously characterized with respect to their involvement in lipid metabolism, but no information concerning their relationship to flavonoid biosynthesis is available. To assess the occurrence and importance of the acetate pathway, we characterized the metabolomes of two mutant or transgenic Arabidopsis lines for each of the four enzymes of this putative pathway using a hierarchical approach covering primary and secondary metabolites as well as lipids. Intriguingly, not only flavonoid content but also glucosinolate content was altered in lines deficient in the acetate pathway, as were levels of lipids and most primary metabolites. We discuss these data in the context of our current understanding of flavonoids and lipid metabolism as well as with regard to improving human nutrition., identifier:1532-2548

A celebrated result in social choice is May's Theorem (1952), providing the foundation for majority rule. May's crucial assumption of symmetry, often thought of as a procedural equity requirement, is violated by many choice procedures that grant voters identical roles. We show that a modification of May's symmetry assumption allows for a far richer set of rules that still treat voters equally, but have minimal winning coalitions comprising a vanishing fraction of the population. We conclude that procedural fairness can coexist with the empowerment of a small minority of individuals. Methodologically, we introduce techniques from discrete mathematics and illustrate their usefulness for the analysis of social choice questions.

We have demonstrated the use of a hybrid Si/III-V semiconductor laser to carry 20-Gbaud 16- and 64-QAM data signals over an 80-km fiber. The high coherence of the laser enables below FEC threshold performance after transmission.

We demonstrate that high-coherence Si/III-V hybrid lasers are much more robust than conventional III-V DFB lasers against both coherent and incoherent optical feedback by examining the frequency noise power spectral density of the lasers.