cssi/SciSciGPT-SciSciNet huggingface dataset:
Guarantees for Tuning the Step Size using a Learning-to-Learn Approach

Learning-to-learn---using optimization algorithms to learn a new optimizer---has successfully trained efficient optimizers in practice. This approach relies on meta-gradient descent on a meta-objective based on the trajectory that the optimizer generates. However, there were few theoretical guarantees on how to avoid meta-gradient explosion/vanishing problem, or how to train an optimizer with good generalization performance. In this paper, we study the learning-to-learn approach on a simple problem of tuning the step size for quadratic loss. Our results show that although there is a way to design the meta-objective so that the meta-gradient remains polynomially bounded, computing the meta-gradient directly using backpropagation leads to numerical issues that look similar to gradient explosion/vanishing problems. We also characterize when it is necessary to compute the meta-objective on a separate validation set instead of the original training set. Finally, we verify our results empirically and show that a similar phenomenon appears even for more complicated learned optimizers parametrized by neural networks.
Statistics for Building Synthetic Power System Cyber Models

A realistic communication system model is critical in power system studies emphasizing the cyber and physical intercoupling. In this paper, we provide characteristics that could be used in modeling the underlying cyber network for power grid models. A real utility communication network and a simplified inter-substation connectivity model are studied, and their statistics could be used to fulfill the requirements for different modeling resolutions.
Causality-aware counterfactual confounding adjustment as an alternative to linear residualization in anticausal prediction tasks based on linear learners

Linear residualization is a common practice for confounding adjustment in machine learning (ML) applications. Recently, causality-aware predictive modeling has been proposed as an alternative causality-inspired approach for adjusting for confounders. The basic idea is to simulate counterfactual data that is free from the spurious associations generated by the observed confounders. In this paper, we compare the linear residualization approach against the causality-aware confounding adjustment in anticausal prediction tasks, and show that the causality-aware approach tends to (asymptotically) outperform the residualization adjustment in terms of predictive performance in linear learners. Importantly, our results still holds even when the true model is not linear. We illustrate our results in both regression and classification tasks, where we compared the causality-aware and residualization approaches using mean squared errors and classification accuracy in synthetic data experiments where the linear regression model is mispecified, as well as, when the linear model is correctly specified. Furthermore, we illustrate how the causality-aware approach is more stable than residualization with respect to dataset shifts in the joint distribution of the confounders and outcome variables.
Global Solutions to the Compressible Euler Equations with Heat Transport by Convection Around Dyson's Isothermal Affine Solutions

Global solutions to the compressible Euler equations with heat transport by convection in the whole space are shown to exist through perturbations of Dyson’s isothermal affine solutions (Dyson in J Math Mech 18(1):91–101, 1968). This setting presents new difficulties because of the vacuum at infinity behavior of the density. In particular, the perturbation of isothermal motion introduces a Gaussian function into our stability analysis and a novel finite propagation result is proven to handle potentially unbounded terms arising from the presence of the Gaussian. Crucial stabilization-in-time effects of the background motion are mitigated through the use of this finite propagation result however and a careful use of the heat transport formulation in conjunction with new time weight manipulations are used to establish global existence. The heat transport by convection offers unique physical insights into the model and mathematically, we use a controlled spatial perturbation in the analysis of this feature of our system which leads us to exploit source term estimates as part of our techniques.
EfficientNetV2: Smaller Models and Faster Training

This paper introduces EfficientNetV2, a new family of convolutional networks that have faster training speed and better parameter efficiency than previous models. To develop this family of models, we use a combination of training-aware neural architecture search and scaling, to jointly optimize training speed and parameter efficiency. The models were searched from the search space enriched with new ops such as Fused-MBConv. Our experiments show that EfficientNetV2 models train much faster than state-of-the-art models while being up to 6.8x smaller. Our training can be further sped up by progressively increasing the image size during training, but it often causes a drop in accuracy. To compensate for this accuracy drop, we propose to adaptively adjust regularization (e.g., dropout and data augmentation) as well, such that we can achieve both fast training and good accuracy. With progressive learning, our EfficientNetV2 significantly outperforms previous models on ImageNet and CIFAR/Cars/Flowers datasets. By pretraining on the same ImageNet21k, our EfficientNetV2 achieves 87.3% top-1 accuracy on ImageNet ILSVRC2012, outperforming the recent ViT by 2.0% accuracy while training 5x-11x faster using the same computing resources. Code will be available at this https URL.
SGD-Net: Efficient Model-Based Deep Learning With Theoretical Guarantees

Deep unfolding networks have recently gained popularity for solving imaging inverse problems. However, the computational and memory complexity of data-consistency layers within traditional deep unfolding networks scales with the number of measurements, limiting their applicability to large-scale imaging inverse problems. We propose SGD-Net as a new methodology for improving the efficiency of deep unfolding through stochastic approximations of the data-consistency layers. Our theoretical analysis shows that SGD-Net can be trained to approximate batch deep unfolding networks to an arbitrary precision. Our simulations on intensity diffraction tomography and sparse-view computed tomography show that SGD-Net can match the performance of the traditional batch network at a fraction of training and testing complexity.
Discrete-time model reduction in limited frequency ranges

A mathematical formulation for model reduction of discrete time systems such that the reduced order model represents the system in a particular frequency range is discussed. The algorithm transforms the full order system into balanced coordinates using frequency weighted discrete controllability and observability grammians. In this form a criterion is derived to guide truncation of states based on their contribution to the frequency range of interest. Minimization of the criterion is accomplished without need for numerical optimization. Balancing requires the computation of discrete frequency weighted grammians. Close form solutions for the computation of frequency weighted grammians are developed. Numerical examples are discussed to demonstrate the algorithm.
Estimating Spread of Contact-Based Contagions in a Population Through Sub-Sampling

Various phenomena such as viruses, gossips, and physical objects (e.g., packages and marketing pamphlets) can be spread through physical contacts. The spread depends on how people move, i.e., their...
Analysis of nonlinear panel flutter and response under random excitation or nonlinear aerodynamic loading

Nonlinear panel flutter analysis and response under random excitation or nonlinear aerodynamic loading, using Rayleigh-Ritz approximation to Hamilton variational principle
North Korea's trade and the integration of Northeast Asia

Abstract In addition to being one of the most isolated countries in the world, North Korea has been the target of sanctions and trade restrictions in recent years. This paper examines the trade patterns of North Korea with its 19 major trading partners over the period 1989–2016. Moreover, the paper quantifies the trade barriers between North Korea and the rest of the world by estimating border effects in a gravity model framework. The findings indicate that North Korea's trade with developed countries has been declining for almost two decades, while trade with emerging economies has been intensifying. Over the last decade, trade with almost all countries has collapsed with the exception of China. The regression results imply that despite being the second largest trading partner, South Korea faces the highest trade barriers with North Korea, after controlling for distance, contiguity, and economic size. Emerging economies recorded the lowest trade hurdles, while developed countries witnessed a dramatic rise in border effects since the early 2000s. China's trade costs vis-a-vis North Korea are higher than for the rest of the world but remained relatively stable over the past two decades.
BlackSheep: A Bioconductor and Bioconda Package for Differential Extreme Value Analysis

Unbiased assays such as shotgun proteomics and RNA-seq provide high-resolution molecular characterization of tumors. These assays measure molecules with highly varied distributions, making interpretation and hypothesis testing challenging. Samples with the most extreme measurements for a molecule can reveal the most interesting biological insights yet are often excluded from analysis. Furthermore, rare disease subtypes are, by definition, underrepresented in cancer cohorts. To provide a strategy for identifying molecules aberrantly enriched in small sample cohorts, we present BlackSheep, a package for nonparametric description and differential analysis of genome-wide data, available from Bioconductor (https://www.bioconductor.org/packages/release/bioc/html/blacksheepr.html) and Bioconda (https://bioconda.github.io/recipes/blksheep/README.html). BlackSheep is a complementary tool to other differential expression analysis methods, which is particularly useful when analyzing small subgroups in a larger cohort.
Analysis of Twitter Users' Lifestyle Choices using Joint Embedding Model.

Multiview representation learning of data can help construct coherent and contextualized users' representations on social media. This paper suggests a joint embedding model, incorporating users' social and textual information to learn contextualized user representations used for understanding their lifestyle choices. We apply our model to tweets related to two lifestyle activities, `Yoga' and `Keto diet' and use it to analyze users' activity type and motivation. We explain the data collection and annotation process in detail and provide an in-depth analysis of users from different classes based on their Twitter content. Our experiments show that our model results in performance improvements in both domains.
Immaterial Error Corrections and Financial Reporting Reliability

We provide evidence on two qualitative characteristics of financial reporting errors deemed immaterial by management (immaterial errors) and benchmark these characteristics against those of material errors corrected by restatements. With regard to relevance for equity valuation, our results suggest that investors find immaterial errors relevant, in particular those that are more severe, with valuation effects smaller than those associated with restatements disclosed in form 8-K. With regard to predictive ability for reporting quality, we find immaterial errors are informative about financial reporting reliability because they indicate increased propensities for three outcomes known to be associated with negative economic consequences: future material errors, immaterial errors and material weakness assessments. The predictive ability of immaterial errors for reporting quality is not qualitatively different from that of material errors. Viewed in the context of previous research on material errors, our results support the view that immaterial errors are informative about reporting quality; this qualitative characteristic is not part of the authoritative guidance management applies in assessing materiality of financial reporting errors. Our analysis informs debates about materiality assessments from an investor perspective.
A System for the Evolution of Protein-Protein Interaction Inducers.

Molecules that induce interactions between proteins, often referred to as "molecular glues", are increasingly recognized as important therapeutic modalities and as entry points for rewiring cellular signaling networks. Here, we report a new PACE-based method to rapidly select and evolve molecules that mediate interactions between otherwise noninteracting proteins: rapid evolution of protein-protein interaction glues (rePPI-G). By leveraging proximity-dependent split RNA polymerase-based biosensors, we developed E. coli-based detection and selection systems that drive gene expression outputs only when interactions between target proteins are induced. We then validated the system using engineered bivalent molecular glues, showing that rePPI-G robustly selects for molecules that induce the target interaction. Proof-of-concept evolutions demonstrated that rePPI-G reduces the "hook effect" of the engineered molecular glues, due at least in part to tuning the interaction affinities of each individual component of the bifunctional molecule. Altogether, this work validates rePPI-G as a continuous, phage-based evolutionary technology for optimizing molecular glues, providing a strategy for developing molecules that reprogram protein-protein interactions.
Exploring the Sub-state Intergovernmental Game

Cooperative budgeting arrangements between state and local governments jointly fund local programs. The mix of state and local revenues contributed to these arrangements can change as budget priorities shift over time. This study examines the strategic choices public officials make as they determine their contributions to jointly-funded programs. Using a game theory model known as the “Diner’s Dilemma”, the analysis explores how each level of government considers how much revenue to contribute and whether it can induce its partner to increase their level of support. This analysis applies primarily to local programs with regional externalities. The model illustrates how local efforts to aggressively try to shift costs to the state government can backfire, particularly during periods of state fiscal stress.
Energy Non-conservation in Quantum Mechanics

We study the conservation of energy, or lack thereof, when measurements are performed in quantum mechanics. The expectation value of the Hamiltonian of a system changes when wave functions collapse in accordance with the standard textbook (Copenhagen) treatment of quantum measurement, but one might imagine that the change in energy is compensated by the measuring apparatus or environment. We show that this is not true; the change in the energy of a state after measurement can be arbitrarily large, independent of the physical measurement process. In Everettian quantum theory, while the expectation value of the Hamiltonian is conserved for the wave function of the universe (including all the branches), it is not constant within individual worlds. It should therefore be possible to experimentally measure violations of conservation of energy, and we suggest an experimental protocol for doing so.
Multiscale Domain Adaptive Yolo For Cross-Domain Object Detection

The area of domain adaptation has been instrumental in addressing the domain shift problem encountered by many applications. This problem arises due to the difference between the distributions of source data used for training in comparison with target data used during realistic testing scenarios. In this paper, we introduce a novel MultiScale Domain Adaptive YOLO (MS-DAYOLO) framework that employs multiple domain adaptation paths and corresponding domain classifiers at different scales of the recently introduced YOLOv4 object detector to generate domain-invariant features. We train and test our proposed method using popular datasets. Our experiments show significant improvements in object detection performance when training YOLOv4 using the proposed MS-DAYOLO and when tested on target data representing challenging weather conditions for autonomous driving applications.
Legislative Organization and Administrative Redundancy

Congress regularly enacts legislation providing for redundant administrative programs. For example, there are more than 100 federal programs for surface transportation, 82 programs to ensure teacher quality, 80 programs to promote domestic economic development, and 47 programs to provide employment and job-training services. Recent high-profile legislation – such as the financial-industry reform measure and the health-care reform measure – add new programs without repealing existing ones directed at the same policy goals. Prior academic analyses generally have not considered why Congress pursues redundancy. This Article addresses that question through both theoretical and institutional analysis. The Article first constructs an organizational theory that attributes redundancy in administrative programs to the congressional committee system. Specifically, the Article demonstrates that two critical components of the existing committee system – fragmented jurisdictions and parliamentary prerogatives – systematically bias legislative outcomes in favor of redundancy. Building on leading theoretical accounts of congressional committees from political science, the Article then presents a novel cost-benefit analysis of this tendency toward redundancy. It shows that redundancy allows legislators to increase distributive favors for constituents and interest groups but that redundancy is also linked to the desirable pursuit of informational efficiency. Thus, the institutional structures facilitating redundancy have mixed effects. Consequently, the Article describes and analyzes specific institutional reforms that trade off the distributive costs and the informational benefits associated with redundancy. One approach would subject more legislative decisions to external advisory processes such as that used to close unneeded military facilities. A second and more promising approach would preserve existing committee jurisdictions but would scale back committees’ parliamentary prerogatives, thereby encouraging redundancy in program design but discouraging redundancy in program implementation.
Sequential Procurement with Contractual and Experimental Learning

We study the design of sequential procurement strategies that integrate stochastic and strategic information. We consider a buyer who repeatedly demands a certain good and is unable to commit to lo...
Alternative State Remedies in Constitutional Torts

In recent years, a subtle shift in constitutional tort doctrine has quietly begun to take root. In Bivens actions, the Supreme Court has recently implied that constitutional tort plaintiffs must seek relief under state law when it is available, rather than invoke their federal constitutional rights. This marks a dramatic change from past practices. For much of the twentieth century, a central premise in the constitutional tort field has been that the federal remedy is "supplementary" to the state remedy; constitutional tort plaintiffs have therefore been permitted to seek a remedy under federal law without regard to the availability of state remedies. Underlying this premise has been the belief that state law?usually tort law?might be "inconsistent with, or even hostile" to federal rights, or that state courts, "by reason of prejudice, passion, neglect, intolerance or otherwise," would fail to enforce state law. In this Article, I first demonstrate that these two beliefs are no longer sound or certain. Though I find the beliefs insupportable, I find the traditional rule remains justified. State remedies should be ignored, not because state tort law will be hostile to federal rights, or because state courts will be prejudiced towards litigants, but because state tort law?having developed over centuries to address interactions between private individuals?cannot reliably capture, through doctrine or theory, interactions between private citizens and government officials. Given this inherent nature of tort law, attempts to assess the availability of alternative state remedies will often be confounding and result in incorrect conclusions. Moreover, this problem is likely to be exacerbated by the sparse record available to the court at the moment these issues arise, which is typically during a motion to dismiss. Finally, relying on state remedies in constitutional tort actions is likely to have deleterious effects on state law, effects that are not offset by any federalist benefits. I conclude by considering whether certification?which would allow state courts to resolve the vexing state law questions?would resolve the problems identified. I find certification wanting and thus recommend that state remedies play no role in constitutional tort actions.
Statistics of a family of piecewise linear maps

Abstract We study statistical properties of a family of piecewise linear monotone circle maps f t ( x ) related to the angle doubling map x → 2 x mod 1. In particular, we investigate whether for large n , the deviations ∑ i = 0 n − 1 f t i ( x 0 ) − 1 2 upon rescaling satisfies a Q -Gaussian distribution if x 0 and t are both independently and uniformly distributed on the unit circle. This was motivated by the fact that if f t is the rotation by t , then it was recently found that in this case the rescaled deviations are distributed as a Q -Gaussian with Q = 2 (a Cauchy distribution). This is the only case where a non-trivial (i.e. Q ≠ 1 ) Q -Gaussian has been analytically established in a conservative dynamical system. In this note, however, we prove that for the family considered here, lim n S n / n converges to a random variable with a curious distribution which is clearly not a Q -Gaussian or any other standard smooth distribution.
Self-Supervised Claim Identification for Automated Fact Checking

We propose a novel, attention-based self-supervised approach to identify "claim-worthy" sentences in a fake news article, an important first step in automated fact-checking. We leverage "aboutness" of headline and content using attention mechanism for this task. The identified claims can be used for downstream task of claim verification for which we are releasing a benchmark dataset of manually selected compelling articles with veracity labels and associated evidence. This work goes beyond stylistic analysis to identifying content that influences reader belief. Experiments with three datasets show the strength of our model. Data and code available at https://github.com/architapathak/Self-Supervised-ClaimIdentification
Nucleic Acid Sensing in the Tumor Vasculature

Endothelial cells form a powerful interface between tissues and immune cells. In fact, one of the underappreciated roles of endothelial cells is to orchestrate immune attention to specific sites. Tumor endothelial cells have a unique ability to dampen immune responses and thereby maintain an immunosuppressive microenvironment. Recent approaches to trigger immune responses in cancers have focused on activating nucleic acid sensors, such as cGAS-STING, in combination with immunotherapies. In this review, we present a case for targeting nucleic acid-sensing pathways within the tumor vasculature to invigorate tumor-immune responses. We introduce two specific nucleic acid sensors-the DNA sensor TREX1 and the RNA sensor RIG-I-and discuss their functional roles in the vasculature. Finally, we present perspectives on how these nucleic acid sensors in the tumor endothelium can be targeted in an antiangiogenic and immune activation context. We believe understanding the role of nucleic acid-sensing in the tumor vasculature can enhance our ability to design more effective therapies targeting the tumor microenvironment by co-opting both vascular and immune cell types.
Silence is safest: Information disclosure when the audience’s preferences are uncertain

Abstract We examine voluntary disclosure decisions when firms are uncertain about audience preferences and are risk averse. In contrast to classic “unraveling” results, some firms remain silent in equilibrium. Silence is safer than disclosure; silence reduces the sensitivity of a firm’s payoff to audience preferences. Increases in firm (audience) risk-aversion reduce (increase) disclosure. Our model explains why some firms do not disclose earnings breakdowns, executive compensation, or Environmental, Social, and Governance (ESG) performance when they face diverse audiences, and why they disclose less under regulatory rules mandating that disclosure be entirely public.
Corporate innovation and future earnings: does early patent disclosure matter?

This study investigates how technology disclosure through patent filings affects the value of corporate innovation. We first examine how different proxies of firm innovation relate to future earnings, and find that patent citations and originality contribute more strongly to future earnings than patent counts. We next study how early technology disclosure affects the relationship between innovation outputs and future earnings by exploiting the regulatory shock caused by the implementation of the American Inventors Protection Act (AIPA). We find that the accelerated disclosure of patents required by the AIPA strengthens the contributions of patent originality and citations to future earnings ― but does not strengthen the contribution of patent counts. The asymmetric effect of patent disclosure on the value of innovation implies that the AIPA helps protect and promote high-quality innovation. We further support this conjecture by demonstrating that firms with high-quality innovation are more likely to voluntarily disclose their innovations before the AIPA’s implementation.
Protocell arrays for simultaneous detection of diverse analytes.

Simultaneous detection of multiple analytes from a single sample (multiplexing), particularly when done at the point of need, can guide complex decision-making without increasing the required sample volume or cost per test. Despite recent advances, multiplexed analyte sensing still typically faces the critical limitation of measuring only one type of molecule (e.g., small molecules or nucleic acids) per assay platform. Here, we address this bottleneck with a customizable platform that integrates cell-free expression (CFE) with a polymer-based aqueous two-phase system (ATPS), producing membrane-less protocells containing transcription and translation machinery used for detection. We show that multiple protocells, each performing a distinct sensing reaction, can be arrayed in the same microwell to detect chemically diverse targets from the same sample. Furthermore, these protocell arrays are compatible with human biofluids, maintain function after lyophilization and rehydration, and can produce visually interpretable readouts, illustrating this platform’s potential as a minimal-equipment, field-deployable, multi-analyte detection tool. Simultaneous detection of multiple analytes from a sample is currently difficult. Here the authors present protocell arrays in a customisable platform integrating cell-free expression with a polymer-based aqueous two-phase system; they use this for detection of chemically diverse targets from biofluids.
Resolution-enhanced OCT and expanded framework of information capacity and resolution in coherent imaging.

Spatial resolution in conventional optical microscopy has traditionally been treated as a fixed parameter of the optical system. Here, we present an approach to enhance transverse resolution in beam-scanned optical coherence tomography (OCT) beyond its aberration-free resolution limit, without any modification to the optical system. Based on the theorem of invariance of information capacity, resolution-enhanced (RE)-OCT navigates the exchange of information between resolution and signal-to-noise ratio (SNR) by exploiting efficient noise suppression via coherent averaging and a simple computational bandwidth expansion procedure. We demonstrate a resolution enhancement of 1.5 × relative to the aberration-free limit while maintaining comparable SNR in silicone phantom. We show that RE-OCT can significantly enhance the visualization of fine microstructural features in collagen gel and ex vivo mouse brain. Beyond RE-OCT, our analysis in the spatial-frequency domain leads to an expanded framework of information capacity and resolution in coherent imaging that contributes new implications to the theory of coherent imaging. RE-OCT can be readily implemented on most OCT systems worldwide, immediately unlocking information that is beyond their current imaging capabilities, and so has the potential for widespread impact in the numerous areas in which OCT is utilized, including the basic sciences and translational medicine.
Flavivirus Persistence in Wildlife Populations.

A substantial number of humans are at risk for infection by vector-borne flaviviruses, resulting in considerable morbidity and mortality worldwide. These viruses also infect wildlife at a considerable rate, persistently cycling between ticks/mosquitoes and small mammals and reptiles and non-human primates and humans. Substantially increasing evidence of viral persistence in wildlife continues to be reported. In addition to in humans, viral persistence has been shown to establish in mammalian, reptile, arachnid, and mosquito systems, as well as insect cell lines. Although a considerable amount of research has centered on the potential roles of defective virus particles, autophagy and/or apoptosis-induced evasion of the immune response, and the precise mechanism of these features in flavivirus persistence have yet to be elucidated. In this review, we present findings that aid in understanding how vector-borne flavivirus persistence is established in wildlife. Research studies to be discussed include determining the critical roles universal flavivirus non-structural proteins played in flaviviral persistence, the advancement of animal models of viral persistence, and studying host factors that allow vector-borne flavivirus replication without destructive effects on infected cells. These findings underscore the viral–host relationships in wildlife animals and could be used to elucidate the underlying mechanisms responsible for the establishment of viral persistence in these animals.
The importance of environmentally-acquired bacterial symbionts for the squash bug (Anasa tristis), a significant agricultural pest

Most insects maintain associations with microbes that shape their ecology and evolution. Such symbioses have important applied implications when the associated insects are pests or vectors of disease. The squash bug, Anasa tristis (Coreoidea: Coreidae), is a significant pest of human agriculture in its own right and also causes damage to crops due to its capacity to transmit a bacterial plant pathogen. Here, we demonstrate that complete understanding of these insects requires consideration of their association with bacterial symbionts in the family Burkholderiaceae. Isolation and sequencing of bacteria housed in the insects' midgut crypts indicates that these bacteria are consistent and dominant members of the crypt-associated bacterial communities. These symbionts are closely related to Caballeronia spp. associated with other true bugs in the superfamilies Lygaeoidea and Coreoidea. Fitness assays with representative Burkholderiaceae strains indicate that the association can significantly increase survival and decrease development time, though strains do vary in the benefits that they confer to their hosts, with Caballeronia spp. providing the greatest benefit. Experiments designed to assess transmission mode indicate that, unlike many other beneficial insect symbionts, the bacteria are not acquired from parents before or after hatching but are instead acquired from the environment after molting to a later developmental stage. The bacteria do, however, have the capacity to escape adults to be transmitted to later generations, leaving the possibility for a combination of indirect vertical and horizontal transmission.
The sRNA MicC downregulates hilD translation to control the SPI1 T3SS in Salmonella enterica serovar Typhimurium.

Salmonella enterica serovar Typhimurium invades the intestinal epithelium and induces inflammatory diarrhea using the Salmonella pathogenicity island 1 (SPI1) type III secretion system (T3SS). Expression of the SPI1 T3SS is controlled by three AraC-like regulators, HilD, HilC and RtsA, which form a feed-forward regulatory loop that leads to activation of hilA, encoding the main transcriptional regulator of the T3SS structural genes. This complex system is affected by numerous regulatory proteins and environmental signals, many of which act at the level of hilD mRNA translation or HilD protein function. Here, we show that the sRNA MicC blocks translation of the hilD mRNA by base pairing near the ribosome binding site. MicC does not induce degradation of the hilD message. Our data indicate that micC is transcriptionally activated by SlyA, and SlyA feeds into the SPI1 regulatory network solely through MicC. Transcription of micC is negatively regulated by the OmpR/EnvZ two-component system, but this regulation is dependent on SlyA. OmpR/EnvZ control SPI1 expression partially through MicC, but also affect expression through other pathways, including an EnvZ-dependent, OmpR-independent mechanism. MicC-mediated regulation plays a role during infection, as evidenced by a SPI1 T3SS-dependent increase in Salmonella fitness in the intestine in the micC deletion mutant. These results further elucidate the complex regulatory network controlling SPI1 expression and add to the list of sRNAs that control this primary virulence factor. IMPORTANCE The Salmonella SPI1 T3SS is the primary virulence factor required for causing intestinal disease and initiating systemic infection. The system is regulated in response to a large variety of environmental and physiological factors such that the T3SS is expressed at only the appropriate time and place in the host during infection. Here we show how the sRNA MicC affects expression of the system. This work adds to our detailed mechanistic studies aimed at a complete understanding of the regulatory circuit.
Generalization Bounds for Meta-Learning via PAC-Bayes and Uniform Stability

We are motivated by the problem of providing strong generalization guarantees in the context of meta-learning. Existing generalization bounds are either challenging to evaluate or provide vacuous guarantees in even relatively simple settings. We derive a probably approximately correct (PAC) bound for gradient-based meta-learning using two different generalization frameworks in order to deal with the qualitatively different challenges of generalization at the "base" and "meta" levels. We employ bounds for uniformly stable algorithms at the base level and bounds from the PAC-Bayes framework at the meta level. The result of this approach is a novel PAC bound that is tighter when the base learner adapts quickly, which is precisely the goal of meta-learning. We show that our bound provides a tighter guarantee than other bounds on a toy non-convex problem on the unit sphere and a text-based classification example. We also present a practical regularization scheme motivated by the bound in settings where the bound is loose and demonstrate improved performance over baseline techniques.
Configuration spaces of hard spheres

Hard sphere systems are often used to model simple fluids. The configuration spaces of hard spheres in a three-dimensional torus modulo various symmetry groups are comparatively simple and could provide valuable information about the nature of phase transitions. Specifically, the topological changes in the configuration space as a function of packing fraction have been conjectured to be related to the onset of first-order phase transitions. The critical configurations for 1 to 12 spheres are sampled using a Morse-theoretic approach, and they are available in an online, interactive database. Explicit triangulations are constructed for the configuration spaces of the two sphere system, and their topological and geometric properties are studied. The critical configurations are found to be associated with geometric changes to the configuration space that connect previously distant regions and reduce the configuration space diameter as measured by the commute time and diffusion distances. The number of such critical configurations around the packing fraction of the solid-liquid phase transition increases exponentially with the number of spheres, suggesting that the onset of the first-order phase transition in the thermodynamic limit is associated with a discontinuity in the configuration space diameter.
When Do We Need Massive Computations to Perform Detailed COVID-19 Simulations?

The COVID-19 pandemic has infected over 250 million people worldwide and killed more than 5 million as of November 2021. Many intervention strategies are utilized (e.g., masks, social distancing, vaccinations), but officials making decisions have a limited time to act. Computer simulations can aid them by predicting future disease outcomes, but they also require significant processing power or time. It is examined whether a machine learning model can be trained on a small subset of simulation runs to inexpensively predict future disease trajectories resembling the original simulation results. Using four previously published agent-based models (ABMs) for COVID-19, a decision tree regression for each ABM is built and its predictions are compared to the corresponding ABM. Accurate machine learning meta-models are generated from ABMs without strong interventions (e.g., vaccines, lockdowns) using small amounts of simulation data: the root-mean-square error (RMSE) with 25% of the data is close to the RMSE for the full dataset (0.15 vs 0.14 in one model;0.07 vs 0.06 in another). However, meta-models for ABMs employing strong interventions require much more training data (at least 60%) to achieve a similar accuracy. In conclusion, machine learning meta-models can be used in some scenarios to assist in faster decision-making. © 2021 Wiley-VCH GmbH
Factors of Influence for Transfer Learning across Diverse Appearance Domains and Task Types.

Transfer learning enables to re-use knowledge learned on a source task to help learning a target task. A simple form of transfer learning is common in current state-of-the-art computer vision models, i.e. pre-training a model for image classification on the ILSVRC dataset, and then fine-tune on any target task. However, previous systematic studies of transfer learning have been limited and the circumstances in which it is expected to work are not fully understood. In this paper we carry out an extensive experimental exploration of transfer learning across vastly different image domains (consumer photos, autonomous driving, aerial imagery, underwater, indoor scenes, synthetic, close-ups) and task types (semantic segmentation, object detection, depth estimation, keypoint detection). Importantly, these are all complex, structured output tasks types relevant to modern computer vision applications. In total we carry out over 2000 transfer learning experiments, including many where the source and target come from different image domains, task types, or both. We systematically analyze these experiments to understand the impact of image domain, task type, and dataset size on transfer learning performance. Our study leads to several insights and concrete recommendations for practitioners.
Projection-based model reduction of dynamical systems using space–time subspace and machine learning

Abstract This paper considers the creation of parametric surrogate models for applications in science and engineering where the goal is to predict high-dimensional spatiotemporal output quantities of interest, such as pressure, temperature and displacement fields. The proposed methodology develops a low-dimensional parametrization of these quantities of interest using space–time bases combined with machine learning methods. In particular, the space–time solutions are sought in a low-dimensional space–time linear trial subspace that can be obtained by computing tensor decompositions of usual state-snapshots data. The mapping between the input parameters and the basis expansion coefficients (or generalized coordinates) is approximated using four different machine learning techniques : multivariate polynomial regression , k -nearest-neighbors, random forests and neural networks . The relative costs and effectiveness of the four machine learning techniques are explored through three engineering problems: steady heat conduction , unsteady heat conduction and unsteady advective–diffusive–reactive system. Numerical results demonstrate that the proposed method performs well in terms of both accuracy and computational cost, and highlights the important point that the amount of model training data available in an engineering setting is often much less than it is in other machine learning applications, making it essential to incorporate knowledge from physical models.
Association of the Rad9-Rad1-Hus1 checkpoint clamp with MYH DNA glycosylase and DNA.

Abstract Cell cycle checkpoints provide surveillance mechanisms to activate the DNA damage response, thus preserving genomic integrity. The heterotrimeric Rad9–Rad1–Hus1 (9–1–1) clamp is a DNA damage response sensor and can be loaded onto DNA. 9–1–1 is involved in base excision repair (BER) by interacting with nearly every enzyme in BER. Here, we show that individual 9–1–1 components play distinct roles in BER directed by MYH DNA glycosylase. Analyses of Hus1 deletion mutants revealed that the interdomain connecting loop (residues 134–155) is a key determinant of MYH binding. Both the N-(residues 1–146) and C-terminal (residues 147–280) halves of Hus1, which share structural similarity, can interact with and stimulate MYH. The Hus1K136A mutant retains physical interaction with MYH but cannot stimulate MYH glycosylase activity. The N-terminal domain, but not the C-terminal half of Hus1 can also bind DNA with moderate affinity. Intact Rad9 expressed in bacteria binds to and stimulates MYH weakly. However, Rad91−266 (C-terminal truncated Rad9) can stimulate MYH activity and bind DNA with high affinity, close to that displayed by heterotrimeric 91−266–1–1 complexes. Conversely, Rad1 has minimal roles in stimulating MYH activity or binding to DNA. Finally, we show that preferential recruitment of 91−266–1–1 to 5′-recessed DNA substrates is an intrinsic property of this complex and is dependent on complex formation. Together, our findings provide a mechanistic rationale for unique contributions by individual 9–1–1 subunits to MYH-directed BER based on subunit asymmetry in protein–protein interactions and DNA binding events.
Changes in Endoglucanase Patterns during Growth of Thermomonospora curvata on Cellulose

The endoglucanases of the thermophilic actinomycete Thermomonospora curvata were characterized. Early-exponential-phase culture fluid contained at least three endoglucanases, with molecular weights of 23,000, 46,000, and 146,000 and Km values of 1.54, 3.60, and 1.32% carboxymethyl cellulose, respectively. The stationary-phase pattern was altered to include three enzymes with molecular weights of 52,000, 114,000, and 106,000, with respective Km values of 1.77, 8.30, and 1.91%.
A comparison of force sensing techniques for planetary manipulation

Five techniques for sensing forces with a manipulator are compared analytically and experimentally. The techniques compared are: a six-axis wrist force/torque sensor, joint torque sensors, link strain gauges, motor current sensors, and flexibility modeling. The accuracy and repeatability of each technique is quantified and compared. The relative complexity and the impact on flight design of each technique are also compared. The results presented can be used in a trade study for missions requiring manipulator force sensing capabilities.
In Situ Tem Studies of Crack Tip Deformation in Molybdenum

The dislocation model of plasticity ahead of a propagating crack was first treated by Bilby, Cottrell, and Swinden (BCS) (1). They considered a plastic zone consisting of an inverse pileup of dislocations with an infinite density of dislocations at the immediate tip of a shear crack. Physically, this density is not possible since dislocations cannot be located less than a unit Burgers vector apart (2). A series of direct observations by TEM (3–5) have shown that crack propagation in many materials occurs through the emission of dislocations from the crack tip. These dislocations are driven rapidly out of the crack tip area and pile up in a plastic zone, creating a dislocation-free zone (DFZ) between the crack and the plastic zone. This crack geometry has been treated theoretically by extending the BCS model (6–8). The results show that the DFZ is a manifestation of the difficulty associated with the emission of dislocations from the crack tip, and this can be expressed as a critical stress intensity factor for dislocation emission “ke”. ke is zero in the BCS model. The DFZ model has since been shown to be very useful in understanding the ductile vs. brittle behavior of various materials.
Simulation Studies of Protein-Ligand Interactions

Protein-ligand interactions control a majority of cellular processes and are the basis of many drug therapies. First, this paper summarizes experimental approaches used to characterize the interactions between proteins and small molecules: equilibrium measurement of binding constant and standard free energy of binding and the dynamic approach of ligand extraction via atomic force microscopy. Next, the paper reviews ideas about the origin of different component terms that contribute to the the stability of protein-ligand complexes. Then, theoretical approaches to studying protein-small molecule interactions are addressed, including forced extraction of ligand and perturbation methods for calculating potentials of mean force and free energies for molecular transformation. Last, these approaches are illustrated with several recent studies from our laboratory: (1) binding of water in cavities inside proteins, (2) calculation of binding free energy from “first principles” by a new application of molecular transformation, and (3) extraction of a small ligand (xenon) from a hydrophobic cavity in mutant T4-lysozyme L99A.
Leucyl-Transfer Ribonucleic Acid Synthetase from a Wild-Type and Temperature-Sensitive Mutant of Salmonella typhimurium

Leucyl-transfer ribonucleic acid (tRNA) synthetase was purified 100-fold from extracts of Salmonella typhimurium. The partially purified enzyme had the following K(m) values: leucine, 1.1 x 10(-5)m; adenosine triphosphate, 6.5 x 10(-4)m; tRNA(I) (Leu), 4.1 x 10(-8)m; tRNA(II) (Leu), 4.3 x 10(-8)m; tRNA(III) (Leu), 5.3 x 10(-8)m; and tRNA(IV) (Leu), 2.9 x 10(-8)m. The tRNA(Leu) fractions were isolated from Salmonella bulk tRNA by chromatography on reversed-phase columns and benzoylated diethylaminoethyl cellulose. The enzyme had a pH optimum of 8.5 and an activation energy of 10,400 cal per mole, and was inactivated exponentially at 49.5 C with a first-order rate constant of 0.064 min(-1). Strain CV356 (leuS3 leuABCD702 ara-9 gal-205) was isolated as a mutant resistant to dl-4-azaleucine and able to grow at 27 C but not at 37 C. Extracts of strain CV356 had no leucyl-tRNA synthetase activity (charging assay) when assayed at 27 or 37 C. Temperature sensitivity and enzyme deficiency were caused by mutation in the structural gene locus specifying leucyl-tRNA synthetase. A prototrophic derivative of strain CV356 (CV357) excreted branched-chain amino acids and had high pathway-specific enzyme levels when grown at temperatures where its doubling time was near normal. At growth-restricting temperatures, both amino acid excretion and enzyme levels were further elevated. The properties of strain CV357 indicate that there is only a single leucyl-tRNA synthetase in S. typhimurium.