cssi/SciSciGPT-SciSciNet huggingface dataset:
Capturing the regulatory agenda: An empirical study of agency responsiveness to rulemaking petitions

In environmental regulation as well as in other regulatory domains, a critical question is how outside interests shape the rulemaking agenda. A great deal of skepticism toward regulation stems from the widespread perception that agencies excessively or even exclusively cater to business interests. One answer to these concerns is administrative procedure, and in particular, rulemaking petitions, which are provided for in the Administrative Procedure Act and in many substantive environmental statutes. Although rulemaking petitions could in theory be used by business interests to strengthen their hold on regulatory agenda-setting, a growing number of scholars have pointed to the potential for rulemaking petitions to combat agency inaction and under-regulation, highlighting the critical role a rulemaking petition played in the Supreme Court’s 2007 decision forcing EPA action on climate change. Despite these warring descriptions, we actually have very little generalizable understanding of how rulemaking petitions operate in practice and to whom the benefits of the institution flow. In this Article, I take a close look at original data on all the rulemaking petitions submitted to three administrative agencies from 2000 to 2016, statistically tracing their fates from submission to resolution. I find that, although business interests may participate at a higher rate than public interest groups and individuals, there is little evidence of full-on regulatory capture via petitions. Even in a venue where it would be exceedingly easy to give business interests precisely what they want, agencies remain largely unmoved and even-handed. The pattern that does emerge—an agency preference for using petitions to inform incremental revision and softening of existing regulations to reflect changed circumstances or new technologies—probably does inure mostly to the benefit of regulated entities, but it is difficult to square these findings with theories of excessive influence or capture of the regulatory process by business interests. At the same time, the findings pour cold water on the more sanguine account of petitions as a tool to advance environmental regulation. Despite the allure of such an account after Massachusetts v. EPA, the reality is that petitions are rarely transformative and will remain so unless significant changes are made to the institution.
Exposure to sevoflurane results in changes of transcription factor occupancy in sperm and inheritance of autism

One in 54 children in the United States is diagnosed with autism spectrum disorder. De novo germline and somatic mutations cannot account for all cases of autism spectrum disorder, suggesting that epigenetic alterations triggered by environmental exposures may be responsible for a subset of autism spectrum disorder cases. Human and animal studies have shown that exposure of the developing brain to general anesthetic agents can trigger neurodegeneration and neurobehavioral abnormalities, but the effects of general anesthetics on the germline have not been explored in detail. We exposed pregnant mice to sevoflurane during the time of embryonic development when the germ cells undergo epigenetic reprogramming and found that more than 38% of the directly exposed F1 animals exhibit impairments in anxiety and social interactions. Strikingly, 44–47% of the F2 and F3 animals, which were not directly exposed to sevoflurane, show the same behavioral problems. We performed ATAC-seq and identified more than 1200 differentially accessible sites in the sperm of F1 animals, 69 of which are also present in the sperm of F2 animals. These sites are located in regulatory regions of genes strongly associated with autism spectrum disorder, including Arid1b, Ntrk2, and Stmn2. These findings suggest that epimutations caused by exposing germ cells to sevoflurane can lead to autism spectrum disorder in the offspring, and this effect can be transmitted through the male germline inter- and transgenerationally. Summary sentence Pregnant mouse F0 females exposed to sevoflurane give rise to F1 males with sociability and anxiety defects. These behaviors are transmitted to F2 and F3 males. Their sperm show changes in transcription factor occupancy in genes implicated in autism. Graphical Abstract
Actin-dependent membrane polarization reveals the mechanical nature of the neuroblast polarity cycle.

Summary The Par complex directs fate-determinant segregation from the apical membrane of asymmetrically dividing Drosophila neuroblasts. While the physical interactions that recruit the Par complex have been extensively studied, little is known about how the membrane itself behaves during polarization. We examined the membrane dynamics of neuroblasts and surrounding cells using a combination of super-resolution and time-lapse imaging, revealing cellular-scale movements of diverse membrane features during asymmetric division cycles. Membrane domains that are distributed across the neuroblast membrane in interphase become polarized in early mitosis, where they mediate formation of cortical patches of the Par protein atypical protein kinase C (aPKC). Membrane and protein polarity cycles are precisely synchronized and are generated by extensive actin-dependent forces that deform the surrounding tissue. In addition to suggesting a role for the membrane in asymmetric division, our results reveal the mechanical nature of the neuroblast polarity cycle.
Dynamical signatures of symmetry protected topology following symmetry breaking

The authors show that out of equilibrium, symmetry breaking and constrained dynamics can produce dynamical signatures of topology on single-particle and many-body levels.
On the capacity to absorb public investment: How much is too much?☆

Abstract While expanding public investment can contribute to fill infrastructure gaps, scaling up too much and too fast often leads to inefficient outcomes. This paper rationalizes this outcome looking at the association between cost inflation and public investment in a large sample of road construction projects in developing countries. Consistent with the presence of absorptive capacity constraints, our results show a non-linear U-shaped relationship between public investment and project costs. Unit costs increase once public investment is close to 10% of GDP. This threshold is lower (about 7% of GDP) in countries with low investment efficiency and, in general, the effect of investment scaling up on costs is especially strong during investment booms.
Analysis of an Arched Outer-Race Ball Bearing Considering Centrifugal Forces

A Newton-Raphson method of iteration was used in evaluating the radial and axial projection of the distance between the ball center and the outer raceway groove curvature center (V and W). Fatigue life evaluations were made. The similar analysis of a conventional bearing can be directly obtained from the arched bearing analysis by simply letting the amount of arching be zero (g = 0) and not considering equations related to the unloaded half of the outer race. The analysis was applied to a 150-mm angular contact ball bearing. Results for life, contact loads, and angles are shown for a conventional bearing (g = 0) and two arched bearings (g = 0.127 mm (0.005 in.), and 0.254 mm (0.010 in.)). The results indicate that an arched bearing is highly desirable for high speed applications. In particular, for a DN value of 3 million (20,000 rpm) and an applied axial load of 4448 N (1000 lb), an arched bearing shows an improvement in life of 306 percent over that of a conventional bearing. At 4.2 million DN (28,000 rpm), the corresponding improvement is 340 percent. It was also found for low speeds, the arched bearing does not offer the advantages that it does for high speed applications.
Improving Factual Completeness and Consistency of Image-to-Text Radiology Report Generation

Neural image-to-text radiology report generation systems offer the potential to improve radiology reporting by reducing the repetitive process of report drafting and identifying possible medical errors. However, existing report generation systems, despite achieving high performances on natural language generation metrics such as CIDEr or BLEU, still suffer from incomplete and inconsistent generations. Here we introduce two new simple rewards to encourage the generation of factually complete and consistent radiology reports: one that encourages the system to generate radiology domain entities consistent with the reference, and one that uses natural language inference to encourage these entities to be described in inferentially consistent ways. We combine these with the novel use of an existing semantic equivalence metric (BERTScore). We further propose a report generation system that optimizes these rewards via reinforcement learning. On two open radiology report datasets, our system substantially improved the F1 score of a clinical information extraction performance by +22.1 (Delta +63.9%). We further show via a human evaluation and a qualitative analysis that our system leads to generations that are more factually complete and consistent compared to the baselines.
The Future of Work in Agriculture : Some Reflections

As countries develop (and food saturation takes hold), agriculture's role as domestic employer declines. But the broader agri-food system also expands, and the scope for agriculture-related job creation shifts beyond the farm. Historically, technological revolutions have shaped and been shaped by these dynamics. Today, a digital revolution is taking hold, affecting agricultural labor and skill demands. In this process, societies evolve from having a surplus to a shortage of domestic farm labor, typically met largely by foreign agricultural wage workers. Yet, anti-immigration sentiments are flying high in migrant-destination countries, and robots in the fields and packing plants offer an alternative. Agricultural trade may be similarly challenged. In the world's poorest countries, particularly in Africa, labor productivity in agriculture remains at historically low levels. So, what can the role of agriculture as a source of employment be in the future? This viewpoint elaborates on these trends and reviews several policy options, including inclusive value chain development, better immigration policies, social insurance schemes, and ramp up in agricultural education and extension.
Fine-Grained System Identification of Nonlinear Neural Circuits

We study the problem of sparse nonlinear model recovery of high dimensional compositional functions. Our study is motivated by emerging opportunities in neuroscience to recover fine-grained models of biological neural circuits using collected measurement data. Guided by available domain knowledge in neuroscience, we explore conditions under which one can recover the underlying biological circuit that generated the training data. Our results suggest insights of both theoretical and practical interests. Most notably, we find that a sign constraint on the weights is a necessary condition for system recovery, which we establish both theoretically with an identifiability guarantee and empirically on simulated biological circuits. We conclude with a case study on retinal ganglion cell circuits using data collected from mouse retina, showcasing the practical potential of this approach.
Robustness of Neural Network Emulations of Radiative Transfer Parameterizations in a State-of-the-Art General Circulation Model

Abstract. The ability of Machine-Learning (ML) based model components to generalize to the previously unseen inputs, and the resulting stability of the models that use these components, has been receiving a lot of recent attention, especially when it comes to ML-based parameterizations. At the same time, ML-based emulators of existing parameterizations can be stable, accurate, and fast when used in the model they were specifically designed for. In this work we show that shallow-neural-network-based emulators of radiative transfer parameterizations developed almost a decade ago for a state-of-the-art GCM are robust with respect to the substantial structural and parametric change in the host model: when used in two seven month-long experiments with the new model, they not only remain stable, but generate realistic output. Aspects of neural network architecture and training set design potentially contributing to stability of ML-based model components are discussed.
Jurisdictional Competition, Market Power, and the Compensation of Public Employees

Jurisdictional competition can encourage government efficiency but may also lead to collective action problems (e.g., bidding wars) that increase taxpayer costs. The net effect is particularly consequential for the cost of public employee compensation, which accounts for more than half of local government spending. Examining two decades of U.S. teacher salary data and changes in local teacher labor markets over time, I show that jurisdictional competition is associated with lower teacher salaries. One mechanism consistent with the evidence is that competitive markets help encourage lower spending through "yardstick competition.'' The findings may help explain why government consolidations designed to promote economies of scale and efforts to take advantage of coordinated buying power do not always produce the expected savings.
Social progress, business formation, and access to investment capital

Abstract The rate of business formation is higher in countries that rank better on dimensions of social progress. This creates a need for investment capital and helps drive a positive relation between social progress and IPO activity. For example, my results suggest that a one standard deviation improvement in a country's Social Progress Index is associated with an additional 0.372 IPOs and a $69.416 increase in cumulative IPO proceeds raised per million people. The relation is stronger for emerging markets than for developed markets. Social progress is also positively correlated with the proportion of IPO firms that receive venture capital funding.
Market Structure and Competition in Airline Markets

We provide an econometric framework for estimating a game of simultaneous entry and pricing decisions while allowing for correlations between unobserved cost and demand shocks. We use our framework...
Development of a parmetric estimating model for technology driven deployment projects [parmetric read as parametric]

The purpose of this paper is to define the process used to develop a parametric estimating model and to explain the use of the model as it allows a nonsubject matter expert to predict the cost of deployment of a technology-driven project with improved accuracy. The overall objective is to define criteria for organizations to use as a rule of thumb estimating model for determining potential resource requirements during the pre-concept and concept phase of a technology-driven project. The use of the model determines the complexity level of a project and then using a matrix simulating the task complexity knowledge of a deployment subject matter expert, the organization deployment resource required is estimated based upon responses to certain inquiries. The user of the model has the capability to modify the output to derive their specific deployment estimates.
LABRAT reveals association of alternative polyadenylation with transcript localization, RNA binding protein expression, transcription speed, and cancer survival.

BACKGROUND The sequence content of the 3' UTRs of many mRNA transcripts is regulated through alternative polyadenylation (APA). The study of this process using RNAseq data, though, has been historically challenging. RESULTS To combat this problem, we developed LABRAT, an APA isoform quantification method. LABRAT takes advantage of newly developed transcriptome quantification techniques to accurately determine relative APA site usage and how it varies across conditions. Using LABRAT, we found consistent relationships between gene-distal APA and subcellular RNA localization in multiple cell types. We also observed connections between transcription speed and APA site choice as well as tumor-specific transcriptome-wide shifts in APA isoform abundance in hundreds of patient-derived tumor samples that were associated with patient prognosis. We investigated the effects of APA on transcript expression and found a weak overall relationship, although many individual genes showed strong correlations between relative APA isoform abundance and overall gene expression. We interrogated the roles of 191 RNA-binding proteins in the regulation of APA isoforms, finding that dozens promote broad, directional shifts in relative APA isoform abundance both in vitro and in patient-derived samples. Finally, we find that APA site shifts in the two classes of APA, tandem UTRs and alternative last exons, are strongly correlated across many contexts, suggesting that they are coregulated. CONCLUSIONS We conclude that LABRAT has the ability to accurately quantify APA isoform ratios from RNAseq data across a variety of sample types. Further, LABRAT is able to derive biologically meaningful insights that connect APA isoform regulation to cellular and molecular phenotypes.
Moments and interpretations of the Cohen–Lenstra–Martinet heuristics

The goal of this paper is to prove theorems that elucidate the Cohen-Lenstra-Martinet conjectures for the distributions of class groups of number fields, and further the understanding of their implications. We start by giving a simpler statement of the conjectures. We show that the probabilities that arise are inversely proportional the to number of automorphisms of structures slightly larger than the class groups. We find the moments of the Cohen-Lenstra-Martinet distributions and prove that the distributions are determined by their moments. In order to apply these conjectures to class groups of non-Galois fields, we prove a new theorem on the capitulation kernel (of ideal classes that become trivial in a larger field) to relate the class groups of non-Galois fields to the class groups of Galois fields. We then construct an integral model of the Hecke algebra of a finite group, show that it acts naturally on class groups of non-Galois fields, and prove that the Cohen-Lenstra-Martinet conjectures predict a distribution for class groups of non-Galois fields that involves the inverse of the number of automorphisms of the class group as a Hecke-module.
De-Coding the Contributions of the Viral RNAs to Alphaviral Pathogenesis

Alphaviruses are positive-sense RNA arboviruses that are capable of causing severe disease in otherwise healthy individuals. There are many aspects of viral infection that determine pathogenesis and major efforts regarding the identification and characterization of virulence determinants have largely focused on the roles of the nonstructural and structural proteins. Nonetheless, the viral RNAs of the alphaviruses themselves play important roles in regard to virulence and pathogenesis. In particular, many sequences and secondary structures within the viral RNAs play an important part in the development of disease and may be considered important determinants of virulence. In this review article, we summarize the known RNA-based virulence traits and host:RNA interactions that influence alphaviral pathogenesis for each of the viral RNA species produced during infection. Overall, the viral RNAs produced during infection are important contributors to alphaviral pathogenesis and more research is needed to fully understand how each RNA species impacts the host response to infection as well as the development of disease.
Holographic quantum algorithms for simulating correlated spin systems

We present a suite of ``holographic'' quantum algorithms for efficient ground-state preparation and dynamical evolution of correlated spin systems, which require far fewer qubits than the number of spins being simulated. The algorithms exploit the equivalence between matrix-product states (MPS) and quantum channels, along with partial measurement and qubit reuse, in order to simulate a $D$-dimensional spin system using only a $(D\ensuremath{-}1)$-dimensional subset of qubits along with an ancillary qubit register whose size scales logarithmically in the amount of entanglement present in the simulated state. Ground states can either be directly prepared from a known MPS representation or obtained via a holographic variational quantum eigensolver (holoVQE). Dynamics of MPS under local Hamiltonians for time $t$ can also be simulated with an additional (multiplicative) $\mathrm{poly}(t)$ overhead in qubit resources. These techniques open the door to efficient quantum simulation of MPS with exponentially large bond dimension, including ground states of two- and three-dimensional systems, or thermalizing dynamics with rapid entanglement growth. As a demonstration of the potential resource savings, we implement a holoVQE simulation of the antiferromagnetic Heisenberg chain on a trapped-ion quantum computer, achieving within $10(3)%$ of the exact ground-state energy of an infinite chain using only a pair of qubits.
The effects of eccentricities on the fracture of off-axis fiber composites

Finite element analyses were performed to investigate theoretically the effects of in-plane and out-of-plane eccentricities, bending or twisting, and thickness nonuniformity on the axial stress and strain variations across the width of off-axis specimens. The results are compared with measured data and are also used to assess the effects of these eccentricities on the fracture stress of off-axis fiber composites. Guidelines for detecting and minimizing the presence of eccentricities are described.
Leptinergic Regulation of Vertebrate Communication Signals.

Animal communication signals are regulated by multiple hormonal axes that ensure appropriate signal targeting, timing, and information content. The regulatory roles of steroid hormones and many peptide hormones are well understood and documented across a wide range of vertebrate taxa. Two recent studies have reported a novel function for leptin, a peptide hormone central to energy balance regulation: regulating communication signals of weakly electric fish and singing mice. With only limited evidence available at this time, a key question is just how widespread leptinergic regulation of communication signals is within and across taxa. A second important question is what features of communication signals are subject to leptinergic regulation. Here we consider the functional significance of leptinergic regulation of animal communication signals in the context of both direct and indirect signal metabolic costs. Direct costs arise from metabolic investment in signal production, while indirect costs arise from the predation and social conflict consequences of the signal's information content. We propose a preliminary conceptual framework for predicting which species will exhibit leptinergic regulation of their communication signals and which signal features leptin will regulate. This framework suggests a number of directly testable predictions within and across taxa. Accounting for additional factors such as life history and the potential co-regulation of communication signals by leptin and glucocorticoids will likely require modification or elaboration of this model.
Sublinear Time Hypergraph Sparsification via Cut and Edge Sampling Queries.

The problem of sparsifying a graph or a hypergraph while approximately preserving its cut structure has been extensively studied and has many applications. In a seminal work, Benczur and Karger (1996) showed that given any $n$-vertex undirected weighted graph $G$ and a parameter $\varepsilon \in (0,1)$, there is a near-linear time algorithm that outputs a weighted subgraph $G'$ of $G$ of size $\tilde{O}(n/\varepsilon^2)$ such that the weight of every cut in $G$ is preserved to within a $(1 \pm \varepsilon)$-factor in $G'$. The graph $G'$ is referred to as a {\em $(1 \pm \varepsilon)$-approximate cut sparsifier} of $G$. Subsequent recent work has obtained a similar result for the more general problem of hypergraph cut sparsifiers. However, all known sparsification algorithms require $\Omega(n + m)$ time where $n$ denotes the number of vertices and $m$ denotes the number of hyperedges in the hypergraph. Since $m$ can be exponentially large in $n$, a natural question is if it is possible to create a hypergraph cut sparsifier in time polynomial in $n$, {\em independent of the number of edges}. We resolve this question in the affirmative, giving the first sublinear time algorithm for this problem, given appropriate query access to the hypergraph.
Theory and applications of electromagnetic levitation

A simple treatment of the electromagnetic levitation problem is presented, with emphasis placed on approximate formulas useful in planning and interpreting laboratory measurements. Consideration is also given to numerical solutions for fields, eddy currents, and Lorentz forces for rapidly varying applied fields, with particular reference made to traveling wave levitation experiments. Applications of levitation processing are briefly reviewed, including thermophysical property measurements, undercooling studies, containerless crystal growth, and continuous casting of cylinders.
Adaptive Federated Dropout: Improving Communication Efficiency and Generalization for Federated Learning

To exploit the wealth of data generated and located at distributed entities such as mobile phones, a revolutionary decentralized machine learning setting, known as federated learning, enables multiple clients to collaboratively learn a machine learning model while keeping all their data on-device. However, the scale and decentralization of federated learning present new challenges. Communication between the clients and the server is considered a main bottleneck in the convergence time of federated learning because of a very large number of model’s weights that need to be exchanged in each training round. In this paper, we propose and study Adaptive Federated Dropout (AFD), a novel technique to reduce the communication costs associated with federated learning. It optimizes both server-client communications and computation costs by allowing clients to train locally on a selected subset of the global model. We empirically show that this strategy, combined with existing compression methods, collectively provides up to 57× reduction in convergence time. It also outperforms the state-of-the-art solutions for communication efficiency. Furthermore, it improves model generalization by up to 1.7%.
Data Tracking under Competition

We explore the welfare implications of data tracking technologies that enable firms to collect consumer data and potentially use it for price discrimination. The model we develop centers around two features: first, competition between firms and, second, consumers' level of sophistication. Our baseline environment features a firm that can collect information about the consumers it transacts with in a duopoly market, which it can then use in a second monopoly market. We characterize and compare the equilibrium outcomes in three settings of interest: (i) an economy with myopic consumers, who, when making purchase decisions, do not internalize the fact that firms have the ability to track their behavior and use this information in future transactions, (ii) an economy with forward-looking consumers, who take into account the implications of data tracking when determining their actions, and (iii) an economy where no data tracking technologies are used either due to technological or regulatory constraints. We find that the absence of data tracking may lead to a decrease in consumer surplus, even when consumers are myopic. Importantly, this result relies critically on competition: consumer surplus may be higher when data tracking technologies are used in the marketplace only when multiple firms offer substitutable products to consumers. Our results contribute to the debate of whether to regulate firms' use of data tracking technologies by illustrating that their effect on consumers depends not only on their level of sophistication, i.e., the extent to which they internalize how their data may be used, but also on the degree of competition in the market. Finally, in contrast to earlier work, we show that firms may have no incentive to self-regulate their use of consumer data even when consumers fully internalize and anticipate how their data may be used. The full paper is available at: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3808228
Interpolation and Model Checking for Nonlinear Arithmetic.

We present a new model-based interpolation procedure for satisfiability modulo theories (SMT). The procedure uses a new mode of interaction with the SMT solver that we call solving modulo a model. This either extends a given partial model into a full model for a set of assertions or returns an explanation (a model interpolant) when no solution exists. This mode of interaction fits well into the model-constructing satisfiability (MCSAT) framework of SMT. We use it to develop an interpolation procedure for any MCSAT-supported theory. In particular, this method leads to an effective interpolation procedure for nonlinear real arithmetic. We evaluate the new procedure by integrating it into a model checker and comparing it with state-of-art model-checking tools for nonlinear arithmetic.
Care Strategies for Reducing Hospital Readmissions Using Stochastic Programming.

A hospital readmission occurs when a patient has an unplanned admission to a hospital within a specific time period of discharge from an earlier or initial hospital stay. Preventable readmissions have turned into a critical challenge for the healthcare system globally, and hospitals seek care strategies that reduce the readmission burden. Some countries have developed hospital readmission reduction policies, and in some cases, these policies impose financial penalties for hospitals with high readmission rates. Decision models are needed to help hospitals identify care strategies that avoid financial penalties, yet maintain balance among quality of care, the cost of care, and the hospital’s readmission reduction goals. We develop a multi-condition care strategy model to help hospitals prioritize treatment plans and allocate resources. The stochastic programming model has probabilistic constraints to control the expected readmission probability for a set of patients. The model determines which care strategies will be the most cost-effective and the extent to which resources should be allocated to those initiatives to reach the desired readmission reduction targets and maintain high quality of care. A sensitivity analysis was conducted to explore the value of the model for low- and high-performing hospitals and multiple health conditions. Model outputs are valuable to hospitals as they examine the expected cost of hitting its target and the expected improvement to its readmission rates.
On Stability Analysis of Power Grids with Synchronous Generators and Grid-Forming Converters under DC-side Current Limitation

Stability of power grids with synchronous generators (SGs) and renewable generation interfaced with grid-forming converters (GFCs) under dc-side current limitation is studied. To that end, we first consider a simple 2-bus test system and reduced-order models to highlight the fundamental difference between two classes of GFC controls - (A) droop, dispatchable virtual oscillator control (dVOC) and virtual synchronous machine (VSM), and (B) matching control. Next, we study Lyapunov stability and input-output stability of the dc voltage dynamics of class-A GFCs for the simple system and extend it to a generic system. Next, we provide a sufficiency condition for input-to-state stability of the 2-bus system with a class-B GFC and extend it for a generic system. Finally, time-domain simulations from a reduced-order averaged model of the simple test system and a detailed switched model of the GFC validate the proposed conditions.
Impingement of an Oblique Shock Wave on a Cylinder

This paper presents a numerical simulation of impingement of an oblique shock wave on a cylinder. The study was undertaken to understand the three-dimensional shock wave and boundary-layer interactions occurring on problems such as the impingement of the bow shock wave from the Shuttle nose on an external tank, and store-carriage interference on a supersonic tactical aircraft. A thin-layer approximation of the Navier-Stokes equations is solved by a mixed explicit-implicit scheme for a supersonic Mach 4 flow with an incident shock angle of 25 deg. Both laminar and turbulent flows are investigated. Surface pressures show large extents of upstream influence, except near windward plane. Based on plots of velocity at the first mesh points above the cylinder body, a conjectured limiting-streamline pattern is constructed. Three-dimensional separation is studied, and the role of crossflow is discussed.
The DNA-to-cytoplasm ratio broadly activates zygotic gene expression in Xenopus.

Summary In multicellular animals, the first major event after fertilization is the switch from maternal to zygotic control of development. During this transition, zygotic gene transcription is broadly activated in an otherwise quiescent genome in a process known as zygotic genome activation (ZGA). In fast-developing embryos, ZGA often overlaps with the slowing of initially synchronous cell divisions at the mid-blastula transition (MBT). Initial studies of the MBT led to the nuclear-to-cytoplasmic ratio model where MBT timing is regulated by the exponentially increasing amounts of some nuclear component "N" titrated against a fixed cytoplasmic component "C." However, more recent experiments have been interpreted to suggest that ZGA is independent of the N/C ratio. To determine the role of the N/C ratio in ZGA, we generated Xenopus frog embryos with ∼3-fold differences in genomic DNA (i.e., N) by using X. tropicalis sperm to fertilize X. laevis eggs with or without their maternal genome. Resulting embryos have otherwise identical X. tropicalis genome template amounts, embryo sizes, and X. laevis maternal environments. We generated transcriptomic time series across the MBT in both conditions and used X. tropicalis paternally derived mRNA to identify a high-confidence set of exclusively zygotic transcripts. Both ZGA and the increase in cell-cycle duration are delayed in embryos with ∼3-fold less DNA per cell. Thus, DNA is an important component of the N/C ratio, which is a critical regulator of zygotic genome activation in Xenopus embryos.
Rational Elliptic Surfaces and the Trigonometry of Tetrahedra

We study the trigonometry of non-Euclidean tetrahedra using tools from algebraic geometry. We establish a bijection between non-Euclidean tetrahedra and certain rational elliptic surfaces. We interpret the edge lengths and the dihedral angles of a tetrahedron as values of period maps for the corresponding surface. As a corollary we show that the cross-ratio of the exponents of the solid angles of a tetrahedron is equal to the cross-ratio of the exponents of the perimeters of its faces. The Regge symmetries of a tetrahedron are related to the action of the Weyl group $$W(D_6)$$ on the Picard lattice of the corresponding surface.
Model-based multi-sensor fusion for reconstructing wall-bounded turbulence

Wall-bounded turbulent flows can be challenging to measure within experiments due to the breadth of spatial and temporal scales inherent in such flows. Instrumentation capable of obtaining time-resolved data (e.g., hot-wire anemometers) tends to be restricted to spatially localized point measurements; likewise, instrumentation capable of achieving spatially resolved field measurements (e.g., particle image velocimetry) tends to lack the sampling rates needed to attain time resolution in many such flows. In this study, we propose to fuse measurements from multi-rate and multi-fidelity sensors with predictions from a physics-based model to reconstruct the spatiotemporal evolution of a wall-bounded turbulent flow. A “fast” filter is formulated to assimilate high-rate point measurements with estimates from a linear model derived from the Navier–Stokes equations. Additionally, a “slow” filter is used to update the reconstruction every time a new field measurement becomes available. By marching through the data both forward and backward in time, we are able to reconstruct the turbulent flow with greater spatiotemporal resolution than either sensing modality alone. We demonstrate the approach using direct numerical simulations of a turbulent channel flow from the Johns Hopkins Turbulence Database. A statistical analysis of the model-based multi-sensor fusion approach is also conducted.
High-performance, compact optical standard.

We describe a high-performance, compact optical frequency standard based on a microfabricated Rb vapor cell and a low-noise, external cavity diode laser operating on the Rb two-photon transition at 778 nm. The optical standard achieves an instability of 1.8×10−13τ−1/2 for times less than 100 s and a flicker noise floor of 1×10−14 out to 6000 s. At long integration times, the instability is limited by variations in optical probe power and the ac Stark shift. The retrace was measured to 5.7×10−13 after 30 h of dormancy. Such a simple, yet high-performance optical standard could be suitable as an accurate realization of the meter or, if coupled with an optical frequency comb, as a compact atomic clock comparable to a hydrogen maser.
Effects of Task Demands on Neural Correlates of Acoustic and Semantic Processing in Challenging Listening Conditions

Purpose Listeners shift their listening strategies between lower level acoustic information and higher level semantic information to prioritize maximum speech intelligibility in challenging listeni...
Processive dynamics of the usher assembly platform during uropathogenic Escherichia coli P pilus biogenesis.

Uropathogenic Escherichia coli assemble surface structures termed pili or fimbriae to initiate infection of the urinary tract. P pili facilitate bacterial colonization of the kidney and pyelonephritis. P pili are assembled through the conserved chaperone-usher pathway. Much of the structural and functional understanding of the chaperone-usher pathway has been gained through investigations of type 1 pili, which promote binding to the bladder and cystitis. In contrast, the structural basis for P pilus biogenesis at the usher has remained elusive. This is in part due to the flexible and variable-length P pilus tip fiber, creating structural heterogeneity, and difficulties isolating stable P pilus assembly intermediates. Here, we circumvent these hindrances and determine cryo-electron microscopy structures of the activated PapC usher in the process of secreting two- and three-subunit P pilus assembly intermediates, revealing processive steps in P pilus biogenesis and capturing new conformational dynamics of the usher assembly machine. Escherichia coli form pili structures in order to initiate infection of the urinary tract. Here, Thanassi et al., have solved the structures of pili assembly intermediates and provided insights into their biogenesis and assembly.
Wav2vec-C: A self-supervised model for speech representation learning

Wav2vec-C introduces a novel representation learning technique combining elements from wav2vec 2.0 and VQ-VAE. Our model learns to reproduce quantized representations from partially masked speech encoding using a contrastive loss in a way similar to Wav2vec 2.0. However, the quantization process is regularized by an additional consistency network that learns to reconstruct the input features to the wav2vec 2.0 network from the quantized representations in a way similar to a VQ-VAE model. The proposed self-supervised model is trained on 10k hours of unlabeled data and subsequently used as the speech encoder in a RNN-T ASR model and fine-tuned with 1k hours of labeled data. This work is one of only a few studies of self-supervised learning on speech tasks with a large volume of real far-field labeled data. The Wav2vec-C encoded representations achieves, on average, twice the error reduction over baseline and a higher codebook utilization in comparison to wav2vec 2.0
Reverse Shortest Path Problem for Unit-Disk Graphs

Given a set P of n points in the plane, a unit-disk graph \(G_{r}(P)\) with respect to a radius r is an undirected graph whose vertex set is P such that an edge connects two points \(p, q \in P\) if the Euclidean distance between p and q is at most r. The length of any path in \(G_r(P)\) is the number of edges of the path. Given a value \(\lambda >0\) and two points s and t of P, we consider the following reverse shortest path problem: finding the smallest r such that the shortest path length between s and t in \(G_r(P)\) is at most \(\lambda \). It was known previously that the problem can be solved in \(O(n^{4/3} \log ^3 n)\) time. In this paper, we present an algorithm of \(O(\lfloor \lambda \rfloor \cdot n \log n)\) time and another algorithm of \(O(n^{{5}/{4}} \log ^2 n)\) time.
Convergent Usage of Amino Acids in Human Cancers as A Reversed Process of Tissue Development.

Genome- and transcriptome-wide amino acid usage preference across different species is a well-studied phenomenon in molecular evolution, but its characteristics and implication in cancer evolution and therapy remain largely unexplored. Here, we analyzed large-scale transcriptome/proteome profiles, such as The Cancer Genome Atlas (TCGA), the Genotype-Tissue Expression (GTEx), and the Clinical Proteomic Tumor Analysis Consortium (CPTAC), and found that compared to normal tissues, different cancer types showed a convergent pattern toward using biosynthetically low-cost amino acids. Such a pattern can be accurately captured by a single index based on the average biosynthetic energy cost of amino acids, termed energy cost per amino acid (ECPA). With this index, we further compared the trends of amino acid usage and the contributing genes in cancer and tissue development, and revealed their reversed patterns. Finally, focusing on the liver, a tissue with a dramatic increase in ECPA during development, we found that EPCA represents a powerful biomarker that could distinguish liver tumors from normal liver samples consistently across 11 independent patient cohorts and outperforms any index based on single genes. Our study reveals an important principle underlying cancer evolution and suggests the global amino acid usage as a system-level biomarker for cancer diagnosis.
Entropy production given constraints on the energy functions.

We consider the problem of driving a finite-state classical system from some initial distribution $p$ to some final distribution ${p}^{\ensuremath{'}}$ with vanishing entropy production (EP), under the constraint that the driving protocols can only use some limited set of energy functions $\mathcal{E}$. Assuming no other constraints on the driving protocol, we derive a simple condition that guarantees that such a transformation can be carried out, which is stated in terms of the smallest probabilities in ${p,{p}^{\ensuremath{'}}}$ and a graph-theoretic property defined in terms of $\mathcal{E}$. Our results imply that a surprisingly small amount of control over the energy function is sufficient (in particular, any transformation $p\ensuremath{\rightarrow}{p}^{\ensuremath{'}}$ can be carried out as soon as one can control some one-dimensional parameter of the energy function, e.g., the energy of a single state). We also derive a lower bound on the EP under more general constraints on the transition rates, which is formulated in terms of a convex optimization problem.
Low cost cloud based remote microscopy for biological sciences

Author(s): Baudin, Pierre V; Ly, Victoria T; Pansodtee, Pattawong; Jung, Erik A; Currie, Robert; Hoffman, Ryan; Willsey, Helen Rankin; Pollen, Alex A; Nowakowski, Tomasz J; Haussler, David; Mostajo-Radji, Mohammed Andres; Salama, Sofie; Teodorescu, Mircea | Abstract: A low cost remote imaging platform for biological applications was developed. The "Picroscope" is a device that allows the user to perform longitudinal imaging studies on multi-well cell culture plates. Here we present the network architecture and software used to facilitate communication between modules within the device as well as external cloud services. A web based console was created to control the device and view experiment results. Post processing tools were developed to analyze captured data in the cloud. The result is a platform for controlling biological experiments from outside the lab.
ViNG: Learning Open-World Navigation with Visual Goals

We propose a learning-based navigation system for reaching visually indicated goals and demonstrate this system on a real mobile robot platform. Learning provides an appealing alternative to conventional methods for robotic navigation: instead of reasoning about environments in terms of geometry and maps, learning can enable a robot to learn about navigational affordances, understand what types of obstacles are traversable (e.g., tall grass) or not (e.g., walls), and generalize over patterns in the environment. However, unlike conventional planning algorithms, it is harder to change the goal for a learned policy during deployment. We propose a method for learning to navigate towards a goal image of the desired destination. By combining a learned policy with a topological graph constructed out of previously observed data, our system can determine how to reach this visually indicated goal even in the presence of variable appearance and lighting. Three key insights, waypoint proposal, graph pruning and negative mining, enable our method to learn to navigate in real-world environments using only offline data, a setting where prior methods struggle. We instantiate our method on a real outdoor ground robot and show that our system, which we call ViNG, outperforms previously-proposed methods for goal-conditioned reinforcement learning, including other methods that incorporate reinforcement learning and search. We also study how ViNG generalizes to unseen environments and evaluate its ability to adapt to such an environment with growing experience. Finally, we demonstrate ViNG on a number of real-world applications, such as last-mile delivery and warehouse inspection. We encourage the reader to visit the project website for videos of our experiments and demonstrations 1.
Toward witnessing molecular exciton entanglement from spectroscopy

Entanglement is both fragile and challenging to quantify experimentally, since the state density matrix is not readily available. Here, the authors propose an experimental measure of multipartite entanglement of excitons in some chemical systems using nonlinear optical spectroscopy.
The dynamic effect of mechanical losses of transmissions on the equation of motion of legged robots

Industrial manipulators do not collapse under their own weight when powered off due to the friction in their joints. Although these mechanism are effective for stiff position control of pick-and-place, they are inappropriate for legged robots that must rapidly regulate compliant interactions with the environment. However, no metric exists to quantify the robot’s performance degradation due to mechanical losses in the actuators and transmissions. This paper provides a fundamental formulation that uses the mechanical efficiency of transmissions to quantify the effect of power losses in the mechanical transmissions on the dynamics of a whole robotic system. We quantitatively demonstrate the intuitive fact that the apparent inertia of the robots increase in the presence of joint friction. We also show that robots that employ high gear ratio and low efficiency transmissions can statically sustain more substantial external loads. We expect that the framework presented here will provide the fundamental tools for designing the next generation of legged robots that can effectively interact with the world.