cssi/SciSciGPT-SciSciNet huggingface dataset:
Implicit rate-constrained optimization of non-decomposable objectives

We consider a popular family of constrained optimization problems arising in machine learning that involve optimizing a non-decomposable evaluation metric with a certain thresholded form, while constraining another metric of interest. Examples of such problems include optimizing the false negative rate at a fixed false positive rate, optimizing precision at a fixed recall, optimizing the area under the precision-recall or ROC curves, etc. Our key idea is to formulate a rate-constrained optimization that expresses the threshold parameter as a function of the model parameters via the Implicit Function theorem. We show how the resulting optimization problem can be solved using standard gradient based methods. Experiments on benchmark datasets demonstrate the effectiveness of our proposed method over existing state-of-the art approaches for these problems.
Sub-Seasonal Forecasting With a Large Ensemble of Deep-Learning Weather Prediction Models

We present an ensemble prediction system using a Deep Learning Weather Prediction (DLWP) model that recursively predicts key atmospheric variables with six-hour time resolution. This model uses con...
Evolution of mouse circadian enhancers from transposable elements

Transposable elements are increasingly recognized as a source of cis-regulatory variation. Previous studies have revealed that transposons are often bound by transcription factors and some have been co-opted into functional enhancers regulating host gene expression. However, the process by which transposons mature into complex regulatory elements, like enhancers, remains poorly understood. To investigate this process, we examined the contribution of transposons to the cis-regulatory network controlling circadian gene expression in the mouse liver, a well-characterized network serving an important physiological function. ChIP-seq analyses reveal that transposons and other repeats contribute ~ 14% of the binding sites for core circadian regulators (CRs) including BMAL1, CLOCK, PER1/2, and CRY1/2, in the mouse liver. RSINE1, an abundant murine-specific SINE, is the only transposon family enriched for CR binding sites across all datasets. Sequence analyses and reporter assays reveal that the circadian regulatory activity of RSINE1 stems from the presence of imperfect CR binding motifs in the ancestral RSINE1 sequence. These motifs matured into canonical motifs through point mutations after transposition. Furthermore, maturation occurred preferentially within elements inserted in the proximity of ancestral CR binding sites. RSINE1 also acquired motifs that recruit nuclear receptors known to cooperate with CRs to regulate circadian gene expression specifically in the liver. Our results suggest that the birth of enhancers from transposons is predicated both by the sequence of the transposon and by the cis-regulatory landscape surrounding their genomic integration site.
Exploring a quantum-information-relevant magnonic material: Ultralow damping at low temperature in the organic ferrimagnet V[TCNE] x

Quantum information science and engineering require novel low-loss magnetic materials for magnon-based quantum-coherent operations. The search for low-loss magnetic materials, traditionally driven by applications in microwave electronics near room temperature, has gained additional constraints from the need to operate at cryogenic temperatures for many applications in quantum information science and technology. Whereas yttrium iron garnet (YIG) has been the material of choice for decades, the emergence of molecule-based materials with robust magnetism and ultra-low damping has opened new avenues for exploration. Specifically, thin films of vanadium tetracyanoethylene (V[TCNE]x) can be patterned into the multiple, connected structures needed for hybrid quantum elements and have shown room-temperature Gilbert damping (α = 4 × 10−5) that rivals the intrinsic (bulk) damping otherwise seen only in highly polished YIG spheres (far more challenging to integrate into arrays). Here, the authors present a comprehensive and systematic study of the low-temperature magnetization dynamics for V[TCNE]x thin films, with implications for their application in quantum systems. These studies reveal a temperature-driven, strain-dependent magnetic anisotropy that compensates the thin-film shape anisotropy and the recovery of a magnetic resonance linewidth at 5 K that is comparable to room-temperature values (roughly 2 G at 9.4 GHz). The authors can account for these variations of the V[TCNE]x linewidth within the context of scattering from very dilute paramagnetic impurities and anticipate additional linewidth narrowing as the temperature is further reduced.
Optimal Pose and Shape Estimation for Category-level 3D Object Perception

We consider a category-level perception problem, where one is given 3D sensor data picturing an object of a given category (e.g., a car), and has to reconstruct the pose and shape of the object despite intra-class variability (i.e., different car models have different shapes). We consider an active shape model, where—for an object category— we are given a library of potential CAD models describing objects in that category, and we adopt a standard formulation where pose and shape estimation are formulated as a non-convex optimization. Our first contribution is to provide the first certifiably optimal solver for pose and shape estimation. In particular, we show that rotation estimation can be decoupled from the estimation of the object translation and shape, and we demonstrate that (i) the optimal object rotation can be computed via a tight (small-size) semidefinite relaxation, and (ii) the translation and shape parameters can be computed in closed-form given the rotation. Our second contribution is to add an outlier rejection layer to our solver, hence making it robust to a large number of misdetections. Towards this goal, we wrap our optimal solver in a robust estimation scheme based on graduated non-convexity. To further enhance robustness to outliers, we also develop the first graph-theoretic formulation to prune outliers in category-level perception, which removes outliers via convex hull and maximum clique computations; the resulting approach is robust to 70 − 90% outliers. Our third contribution is an extensive experimental evaluation. Besides providing an ablation study on a simulated dataset and on the PASCAL3D+ dataset, we combine our solver with a deep-learned keypoint detector, and show that the resulting approach improves over the state of the art in vehicle pose estimation in the ApolloScape driving datasets.
Finite Element Analysis for Bearingless Rotor Blade Aeroelasticity

A conventional articulated rotor blade has mechanical flap and lag hinges, a lag damper, and a pitch bearing. In connection with an interest in designs of greater mechanical simplicity and increased maintainability, hingeless and bearingless rotors have been developed. A hingeless blade lacks the hinges and is cantilevered at the hub. It does have a pitch bearing for pitch control. A bearingless design eliminates the hinges and the pitch bearing as well. In the present investigation of bearingless rotor blade characteristics, finite element analysis has been successfully applied to determine the solutions of the nonlinear trim equations and the linearized flutter equations of multiple-load-path blades. The employed formulation is based on Hamilton's principle. The spatial dependence of the equations of motion is discretized by dividing the flexbeams, the torque tube, and the outboard into a number of elements.
Optimization of Jet Mixing into a Rich, Reacting Crossflow

Radial jet mixing of pure air into a fuel-rich, reacting crossflow confined to a cylindrical geometry is addressed with a focus on establishing an optimal number of jet orifices. Note that the optimum would not be expected to be universal. That is, the optimum mixer that one would identify will depend on both the momentum-flux ratio and an axial distance appropriate to the application. The number of round holes that most efficiently mixes the jets with the mainstream flow, and thereby minimizes the residence time of near-stoichiometric and unreacted packets, was determined. Such a condition might reduce pollutant formation in axially staged, gas turbine combustor systems. Five different configurations consisting of 8, 10, 12, 14, and 18 round holes are reported. An optimum number of jet orifices is found for a jet to mainstream momentum-flux ratio J of 57 and a mass-flow ratio (MR) of 2.5. For this condition, the 14-orifice case produces the lowest spatial unmixedness and the most uniformly distributed species concentration and temperature profiles at a plane located an axial distance equal to one duct diameter from the jet orifice inlet. Note that this is the same configuration that would be identified from non-reacting experiments.
RalA and PLD1 promote lipid droplet growth in response to nutrient withdrawal

Lipid droplets (LDs) are dynamic organelles that undergo dynamic changes in response to changing cellular conditions. During nutrient depletion, LD numbers increase to protect cells against toxic fatty acids generated through autophagy and provide fuel for beta-oxidation. However, the precise mechanisms through which these changes are regulated have remained unclear. Here, we show that the small GTPase RalA acts downstream of autophagy to directly facilitate LD growth during nutrient depletion. Mechanistically, RalA performs this function through phospholipase D1 (PLD1), an enzyme that converts phosphatidylcholine (PC) to phosphatidic acid (PA) and that is recruited to lysosomes during nutrient stress in a RalA-dependent fashion. RalA inhibition prevents recruitment of the LD-associated protein perilipin 3, which is required for LD growth. Our data support a model in which RalA recruits PLD1 to lysosomes during nutrient deprivation to promote the localized production of PA and the recruitment of perilipin 3 to expanding LDs.
Do high electricity bills undermine public support for renewables? Evidence from the European Union

Abstract The clean energy transition has long been framed in terms of its technological and economic feasibility. An increasingly salient constraint lies in its political feasibility. The transition requires vigorous public support to be completed. Yet increased consumer costs associated with the deployment of renewable electricity could make voters - and, by extension, governments - less supportive of it. As a result, overly aggressive government support for renewables could lead to its own downfall. To examine this threat, I document two stylized facts. First, the expansion of renewable electricity capacity has been followed by an increase in household electricity bills, and this has mostly happened because of energy-specific taxes. An increase of renewable electricity capacity by one within-country standard deviation raises a typical household's bill by €5.7 per MWh (95% CI: [3, 8.3]), most of which comes from an increase in non-VAT taxes (+€3.8/MWh [2.6; 5.1]). Second, these taxes have hurt popular support for aggressive renewable energy policy. An increase of non-VAT taxes by one standard deviation increases the share of people who find renewable energy too ambitious by 0.7 percentage points (95% CI: [0.1; 1.3]). Climbing costs could therefore undermine further political support toward renewable electricity deployment and threaten its contribution to greenhouse gas reductions.
All Models Are Useful: Bayesian Ensembling for Robust High Resolution COVID-19 Forecasting

Timely, high-resolution forecasts of infectious disease incidence are useful for policy makers in deciding intervention measures and estimating healthcare resource burden. In this paper, we consider the task of forecasting COVID-19 confirmed cases at the county level for the United States. Although multiple methods have been explored for this task, their performance has varied across space and time due to noisy data and the inherent dynamic nature of the pandemic. We present a forecasting pipeline which incorporates probabilistic forecasts from multiple statistical, machine learning and mechanistic methods through a Bayesian ensembling scheme, and has been operational for nearly 6 months serving local, state and federal policymakers in the United States. While showing that the Bayesian ensemble is at least as good as the individual methods, we also show that each individual method contributes significantly for different spatial regions and time points. We compare our model's performance with other similar models being integrated into CDC-initiated COVID-19 Forecast Hub, and show better performance at longer forecast horizons. Finally, we also describe how such forecasts are used to increase lead time for training mechanistic scenario projections. Our work demonstrates that such a real-time high resolution forecasting pipeline can be developed by integrating multiple methods within a performance-based ensemble to support pandemic response.
A 3D Printing Hexacopter: Design and Demonstration

3D printing using robots has garnered significant interest in manufacturing and construction in recent years. A robot's versatility paired with the design freedom of 3D printing offers promising opportunities for how parts and structures are built in the future. However, 3D printed objects are still limited in size and location due to a lack of vertical mobility of ground robots. These limitations severely restrict the potential of the 3D printing process. To overcome these limitations, we develop a hexacopter testbed that can print via fused deposition modeling during flight. We discuss the design of this testbed and develop a simple control strategy for initial print tests. By successfully performing these initial print tests, we demonstrate the feasibility of this approach and lay the groundwork for printing 3D parts and structures with drones. A video for one of the initial print tests can be found at https://youtu.be/tEooDpE2TyE.
Robust asymptotic growth in stochastic portfolio theory under long-only constraints

We consider the problem of maximizing the asymptotic growth rate of an investor under drift uncertainty in the setting of stochastic portfolio theory (SPT). As in the work of Kardaras and Robertson we take as inputs (i) a Markovian volatility matrix $c(x)$ and (ii) an invariant density $p(x)$ for the market weights, but we additionally impose long-only constraints on the investor. Our principal contribution is proving a uniqueness and existence result for the class of concave functionally generated portfolios and developing a finite dimensional approximation, which can be used to numerically find the optimum. In addition to the general results outlined above, we propose the use of a broad class of models for the volatility matrix $c(x)$, which can be calibrated to data and, under which, we obtain explicit formulas of the optimal unconstrained portfolio for any invariant density.
APPLE: Adaptive Planner Parameter Learning From Evaluative Feedback

Classical autonomous navigation systems can control robots in a collision-free manner, oftentimes with verifiable safety and explainability. When facing new environments, however, fine-tuning of the system parameters by an expert is typically required before the system can navigate as expected. To alleviate this requirement, the recently-proposed Adaptive Planner Parameter Learning paradigm allows robots to learn how to dynamically adjust planner parameters using a teleoperated demonstration or corrective interventions from non-expert users. However, these interaction modalities require users to take full control of the moving robot, which requires the users to be familiar with robot teleoperation. As an alternative, we introduce apple , Adaptive Planner Parameter Learning from Evaluative Feedback (real-time, scalar-valued assessments of behavior), which represents a less-demanding modality of interaction. Simulated and physical experiments show apple can achieve better performance compared to the planner with static default parameters and even yield improvement over learned parameters from richer interaction modalities.
Emergence of structure in columns of grains and elastic loops.

It is possible to build free-standing, load-bearing structures using only rocks and loops of elastic material. We investigate how these structures emerge, and find that the necessary maximum loop spacing (the critical spacing) is a function of the frictional properties of the grains and the elasticity of the confining material. We derive a model to understand both of these relationships, which depends on a simplification of the behavior of the grains at the edge of a structure. We find that higher friction leads to larger stable grain–grain and grain–loop contact angles resulting in a simple function for the frictional critical spacing, which depends linearly on friction to first order. On the other hand, a higher bending rigidity enables the loops to better contain the hydrostatic pressure of the grains, which we understand using a hydroelastic scale. These findings will illuminate the stabilization of dirt by plant roots, and potentially enable the construction of simple adhesion-less structures using only granular material and fiber.
Visible Rank and Codes with Locality

We propose a framework to study the effect of local recovery requirements of codeword symbols on the dimension of linear codes, based on a combinatorial proxy that we call visible rank. The locality constraints of a linear code are stipulated by a matrix H of ⋆’s and 0’s (which we call a "stencil"), whose rows correspond to the local parity checks (with the ⋆’s indicating the support of the check). The visible rank of H is the largest r for which there is a r × r submatrix in H with a unique generalized diagonal of ⋆’s. The visible rank yields a field-independent combinatorial lower bound on the rank of H and thus the co-dimension of the code. We point out connections of the visible rank to other notions in the literature such as unique restricted graph matchings, matroids, spanoids, and min-rank. In particular, we prove a rank-nullity type theorem relating visible rank to the rank of an associated construct called symmetric spanoid, which was introduced by Dvir, Gopi, Gu, and Wigderson [Zeev Dvir et al., 2020]. Using this connection and a construction of appropriate stencils, we answer a question posed in [Zeev Dvir et al., 2020] and demonstrate that symmetric spanoid rank cannot improve the currently best known O(n^{(q-2)/(q-1)}) upper bound on the dimension of q-query locally correctable codes (LCCs) of length n. This also pins down the efficacy of visible rank as a proxy for the dimension of LCCs. We also study the t-Disjoint Repair Group Property (t-DRGP) of codes where each codeword symbol must belong to t disjoint check equations. It is known that linear codes with 2-DRGP must have co-dimension Ω(√n) (which is matched by a simple product code construction). We show that there are stencils corresponding to 2-DRGP with visible rank as small as O(log n). However, we show the second tensor of any 2-DRGP stencil has visible rank Ω(n), thus recovering the Ω(√n) lower bound for 2-DRGP. For q-LCC, however, the k'th tensor power for k ⩽ n^{o(1)} is unable to improve the O(n^{(q-2)/(q-1)}) upper bound on the dimension of q-LCCs by a polynomial factor.Inspired by this and as a notion of intrinsic interest, we define the notion of visible capacity of a stencil as the limiting visible rank of high tensor powers, analogous to Shannon capacity, and pose the question whether there can be large gaps between visible capacity and algebraic rank.
Wave Dark Matter

We review the physics and phenomenology of wave dark matter: a bosonic dark matter candidate lighter than about 30 eV. Such particles have a de Broglie wavelength exceeding the average interparticl...
Developmental genetics of color pattern establishment in cats

Intricate color patterns are a defining aspect of morphological diversity in the Felidae. We applied morphological and single-cell gene expression analysis to fetal skin of domestic cats to identify when, where, and how, during fetal development, felid color patterns are established. Early in development, we identify stripe-like alterations in epidermal thickness preceded by a gene expression pre-pattern. The secreted Wnt inhibitor encoded by Dickkopf 4 plays a central role in this process, and is mutated in cats with the Ticked pattern type. Our results bring molecular understanding to how the leopard got its spots, suggest that similar mechanisms underlie periodic color pattern and periodic hair follicle spacing, and identify targets for diverse pattern variation in other mammals.
Uniform Potential Density for Rational Points on Algebraic Groups and Elliptic K3 Surfaces

A collection of varieties satisfies uniform potential density if each of them contains a dense subset of rational points after extending its ground field by a bounded degree. In this paper, we prove that uniform potential density holds for connected algebraic groups of a fixed dimension over fields of characteristic zero as well as elliptic K3 surfaces over number fields.
Gut bacterial aggregates as living gels.

The human gut is home to vast numbers of bacteria that grow, compete and cooperate in a dynamic, densely packed space. The spatial arrangement of organisms – for example, if they are clumped together or broadly dispersed – plays a major role in all ecosystems; but how bacteria are organized in the human gut remains mysterious and difficult to investigate. Zebrafish larvae provide a powerful tool for studying microbes in the gut, as they are optically transparent and anatomically similar to other vertebrates, including humans. Furthermore, zebrafish can be easily manipulated so that one species of bacteria can be studied at a time. To investigate whether individual bacterial species are arranged in similar ways, Scholmann and Parthasarathy exposed zebrafish with no gut bacteria to one of eight different strains. Each species was then monitored using three-dimensional microscopy to see how the population shaped itself into clusters (or colonies). Schlomann and Parthasarathy used this data to build a mathematical model that can predict the size of the clusters formed by different gut bacteria. This revealed that the spatial arrangement of each species depended on the same biological processes: bacterial growth, aggregation and fragmentation of clusters, and expulsion from the gut. These new details about how bacteria are organized in zebrafish may help scientists learn more about gut health in humans. Although it is not possible to peer into the human gut and watch how bacteria behave, scientists could use the same analysis method to study the size of bacterial colonies in fecal samples. This may provide further clues about how microbes are spatially arranged in the human gut and the biological processes underlying this formation.
High-dimensional Precision Matrix Estimation with a Known Graphical Structure

A precision matrix is the inverse of a covariance matrix. In this paper, we study the problem of estimating the precision matrix with a known graphical structure under high-dimensional settings. We propose a simple estimator of the precision matrix based on the connection between the known graphical structure and the precision matrix. We obtain the rates of convergence of the proposed estimators and derive the asymptotic normality of the proposed estimator in the high-dimensional setting when the data dimension grows with the sample size. Numerical simulations are conducted to demonstrate the performance of the proposed method. We also show that the proposed method outperforms some existing methods that do not utilize the graphical structure information.
Physics-informed machine learning of the Lagrangian dynamics of velocity gradient tensor

Reduced models describing the Lagrangian dynamics of the Velocity Gradient Tensor (VGT) in Homogeneous Isotropic Turbulence (HIT) are developed under the Physics-Informed Machine Learning (PIML) framework. We construct the pressure Hessian and sub-filter contributions using the integrity bases and invariants of VGT and express them with extended Tensor Basis Neural Network (TBNN). We observe that the PIML model provides an improved representation for the magnitude and orientation of the small-scale pressure Hessian contributions. Statistics of the flow, as indicated by the joint PDF of second and third invariants of the VGT, show good agreement with the ``ground-truth'' DNS data.
Near-field imaging of plasmonic nanopatch antennas with integrated semiconductor quantum dots

Plasmonic nanopatch antennas that incorporate dielectric gaps hundreds of picometers to several nanometers thick have drawn increasing attention over the past decade because they confine electromagnetic fields to grossly sub-diffraction-limited volumes. Substantial control over the optical properties of excitons and color centers confined within these plasmonic cavities has already been demonstrated with far-field optical spectroscopies, but near-field optical spectroscopies are essential for an improved understanding of the plasmon–emitter interaction at the nanoscale. Here, we characterize the intensity and phase-resolved plasmonic response of isolated nanopatch antennas by cathodoluminescence microscopy. Furthermore, we explore the distinction between optical and electron beam spectroscopies of coupled plasmon–exciton heterostructures to identify constraints and opportunities for future nanoscale characterization and control of hybrid nanophotonic structures. While we observe substantial Purcell enhancement in time-resolved photoluminescence spectroscopies, negligible Purcell enhancement is observed in cathodoluminescence spectroscopies of hybrid nanophotonic structures. The substantial differences in measured Purcell enhancement for electron beam and laser excitation can be understood as a result of the different selection rules for these complementary experiments. These results provide a fundamentally new understanding of near-field plasmon–exciton interactions in nanopatch antennas, which is essential for myriad emerging quantum photonic devices.
A Graph-Based Ant Algorithm for the Winner Determination Problem in Combinatorial Auctions

Iterative combinatorial auctions are known to resolve bidder preference elicitation problems. However, winner determination is a known key bottleneck that has prevented widespread adoption of such ...
Voltage-Controlled Energy-Efficient Domain Wall Synapses With Stochastic Distribution of Quantized Weights in the Presence of Thermal Noise and Edge Roughness

We propose energy-efficient voltage-induced strain control of a domain wall (DW) in a perpendicularly magnetized nanoscale racetrack on a piezoelectric substrate that can implement a multistate synapse to be utilized in neuromorphic computing platforms. Here, strain generated in the piezoelectric is mechanically transferred to the racetrack and modulates the perpendicular magnetic anisotropy (PMA) in a system that has significant interfacial Dzyaloshinskii-Moriya interaction (DMI). When different voltages are applied (i.e., different strains are generated) in conjunction with spin-orbit torque (SOT) due to a fixed current flowing in the heavy metal layer for a fixed time, DWs are translated to different distances and implement different synaptic weights. We have shown using micromagnetic simulations that five-state and three-state synapses can be implemented in a racetrack that is modeled with the inclusion of natural edge roughness and room temperature thermal noise. These simulations show interesting dynamics of DWs due to interaction with roughness-induced pinning sites. Thus, notches need not be fabricated to implement multistate nonvolatile synapses. Such a strain-controlled synapse has an energy consumption of ~1 fJ and could thus be very attractive to implement energy-efficient quantized neural networks, which has been shown recently to achieve near equivalent classification accuracy to the full-precision neural networks.
Differential Impacts of COVID-19 Lockdowns on PM2.5 across the United States

Abstract The COVID-19 pandemic has induced large-scale behavioral changes, presenting a unique opportunity to study how air pollution is affected by societal shifts. At 455 PM 2.5 monitoring sites across the United States, we conduct a causal inference analysis to determine the impacts of COVID-19 lockdowns on PM 2.5 . Our approach allows for rigorous confounding adjustment with highly spatio-temporally resolved effect estimates. We find that, with the exception of the Southwest, most of the US experienced increases in PM 2.5 compared to concentrations expected under business-as-usual. To investigate possible drivers of this phenomenon, we use a regression model to characterize the relationship of various factors with the observed impacts. Our findings have immense environmental policy relevance, suggesting that mobility reductions alone may be insufficient to substantially and uniformly reduce PM 2.5 .
Robot Navigation in Constrained Pedestrian Environments using Reinforcement Learning

Navigating fluently around pedestrians is a necessary capability for mobile robots deployed in human environments, such as buildings and homes. While research on social navigation has focused mainly on the scalability with the number of pedestrians in open spaces, typical indoor environments present the additional challenge of constrained spaces such as corridors and doorways that limit maneuverability and influence patterns of pedestrian interaction. We present an approach based on reinforcement learning (RL) to learn policies capable of dynamic adaptation to the presence of moving pedestrians while navigating between desired locations in constrained environments. The policy network receives guidance from a motion planner that provides waypoints to follow a globally planned trajectory, whereas RL handles the local interactions. We explore a compositional principle for multi-layout training and find that policies trained in a small set of geometrically simple layouts successfully generalize to more complex unseen layouts that exhibit composition of the structural elements available during training. Going beyond walls-world like domains, we show transfer of the learned policy to unseen 3D reconstructions of two real environments. These results support the applicability of the compositional principle to navigation in real-world buildings and indicate promising usage of multi-agent simulation within reconstructed environments for tasks that involve interaction. https://ai.stanford.edu/∼cdarpino/socialnavconstrained/
Secular Stagnation, Financial Frictions, and Land Prices

Abstract We explore a model in which large transitory financial shocks can generate persistent slumps in output, land prices, and interest rate. The propagation channel works through a high sensitivity of land prices with respect to fundamental, achieved by a high complementarity between land services and consumption in households’ preference. When this complementarity is disciplined by micro-level evidence, the equilibrium features non-linear dynamics between two steady states. Large transitory financial shocks push the economy into a constrained region in which low interest rate makes firm unwilling to save out of the financial friction, leading to a secular stagnation. (JEL codes: E13, E32, E44)
Odd Diffusivity of Chiral Random Motion

Diffusive transport is characterized by a diffusivity tensor which may, in general, contain both a symmetric and an antisymmetric component. Although the latter is often neglected, we derive Green-Kubo relations showing it to be a general characteristic of random motion breaking time-reversal and parity symmetries, as encountered in chiral active matter. In analogy with the odd viscosity appearing in chiral active fluids, we term this component the odd diffusivity. We show how odd diffusivity emerges in a chiral random walk model, and demonstrate the applicability of the Green-Kubo relations through molecular dynamics simulations of a passive tracer particle diffusing in a chiral active bath.
Quasiballistic electron transport in cryogenic SiGe HBTs studied using an exact, semi-analytic solution to the Boltzmann equation

Silicon–germanium heterojunction bipolar transistors (HBTs) are of interest as low-noise microwave amplifiers due to their competitive noise performance and low cost relative to III–V devices. The fundamental noise performance limits of HBTs are thus of interest, and several studies report that quasiballistic electron transport across the base is a mechanism leading to cryogenic non-ideal IV characteristics that affect these limits. However, this conclusion has not been rigorously tested against theoretical predictions because prior studies modeled electron transport with empirical approaches or approximate solutions of the Boltzmann equation. Here, we study non-diffusive transport in narrow-base SiGe HBTs using an exact, semi-analytic solution of the Boltzmann equation based on an asymptotic expansion approach. We find that the computed transport characteristics are inconsistent with experiments, implying that quasiballistic electron transport is unlikely to be the origin of cryogenic non-ideal IV characteristics. Our work helps to identify the mechanisms governing the lower limits of the microwave noise figure of cryogenic HBT amplifiers.
The Diguanylate Cyclase YfiN of Pseudomonas aeruginosa Regulates Biofilm Maintenance in Response to Peroxide.

Pseudomonas aeruginosa forms surface-attached communities that persist in the face of antimicrobial agents and environmental perturbation. Published work has found extracellular polysaccharide (EPS) production, regulation of motility and induction of stress response pathways as contributing to biofilm tolerance during such insults. However, little is known regarding the mechanism(s) whereby biofilm maintenance is regulated when exposed to such environmental challenges. Here, we provide evidence that the diguanylate cyclase YfiN is important for the regulation of biofilm maintenance when exposed to peroxide. We find that, compared to the wild type (WT), static biofilms of the ΔyfiN mutant exhibit a maintenance defect, which can be further exacerbated by exposure to peroxide (H2O2); this defect can be rescued through genetic complementation. Additionally, we found that the ΔyfiN mutant biofilms produce less c-di-GMP than WT, and that H2O2 treatment enhanced motility of surface-associated bacteria and increased cell death for the ΔyfiN mutant grown as a biofilm compared to WT biofilms. These data provide evidence that YfiN is required for biofilm maintenance by P. aeruginosa, via c-di-GMP signaling, to limit motility and protect viability in response to peroxide stress. These findings add to the growing recognition that biofilm maintenance by P. aeruginosa is an actively regulated process that is controlled, at least in part, by the wide array of c-di-GMP metabolizing enzymes found in this microbe. Importance We build on previous findings that suggest that P. aeruginosa utilizes c-di-GMP metabolizing enzymes to actively maintain a mature biofilm. Here, we explore how the diguanylate cyclase YfiN contributes to the regulation of biofilm maintenance during peroxide exposure. We find that mature P. aeruginosa biofilms require YfiN to synthesize c-di-GMP, regulate motility and to insure viability during peroxide stress. These findings provide further evidence that the modulation of c-di-GMP in response to environmental signals is an important mechanism by which biofilms are maintained.
Ranking Methodology to Evaluate the Severity of a Quality Gap Using a National EHR Database

Selecting quality improvement projects can often be a reactive process. In order to demonstrate a data-driven strategy, we used multi-site, de-identified electronic health record (EHR) data to prioritize the severity of a quality concern: inappropriate A1c test orders for sickle cell disease patients in two randomly chosen facilities (Facility A & B). The best linear unbiased predictions (BLUP) generated from Generalized Linear Mixed Model (GLMM) was estimated for all 393 facilities with 37,151 SCD patients in the Cerner Health FactsTM (HF) data warehouse based on the ratio of inappropriate A1c orders. Ranking the BLUP after applying the GLMM indicates that the facility A being in the second quartile may not have a quality gap as significant as facility B in the top quartile for this quality concern. This study illustrates the utility of multisite EHR data for evaluating QI projects and the utility of GLMM to enable this analysis.
Beyond the Tip of the Iceberg: Assessing Coherence of Text Classifiers

As large-scale, pre-trained language models achieve human-level and superhuman accuracy on existing language understanding tasks, statistical bias in benchmark data and probing studies have recently called into question their true capabilities. For a more informative evaluation than accuracy on text classification tasks can offer, we propose evaluating systems through a novel measure of prediction coherence. We apply our framework to two existing language understanding benchmarks with different properties to demonstrate its versatility. Our experimental results show that this evaluation framework, although simple in ideas and implementation, is a quick, effective, and versatile measure to provide insight into the coherence of machines’ predictions.
Weapons Radiochemistry: Trinity and Beyond

On July 16, 1945, the Trinity nuclear test exploded in the desert near Alamogordo, NM. A variety of new diagnostic experiments were fielded in an effort to understand the detailed performance of the nuclear device. This article describes a series of radiochemical experiments that were designed to measure the efficiency and neutron fluence of the test. These experiments, and the scientists who led them, laid the foundation of weapons radiochemistry for decades to come.
Experimental realization of negative refraction and subwavelength imaging for flexural waves in phononic crystal plates

Abstract In this paper, we numerically and experimentally demonstrate negative refraction of flexural waves in phononic crystal (PC) plates which is employed for designing flat elastic lenses. We propose a thickness contrast-based plate design to achieve refractive index equal to -1 at the interface of the PC and host plate. The thickness contrast between the PC and host plate enables matching their wave numbers in the all angle negative refraction (AANR) frequency regime. The PC-lens design is then numerically and experimentally verified to achieve the image of an omnidirectional subwavelength excitation source. By changing the thickness contrast between the plate and PC, the PC-lens can be tuned for wave focusing and subwavelength imaging at a desired frequency.
LM-Critic: Language Models for Unsupervised Grammatical Error Correction.

Grammatical error correction (GEC) requires a set of labeled ungrammatical / grammatical sentence pairs for training, but obtaining such annotation can be prohibitively expensive. Recently, the Break-It-Fix-It (BIFI) framework has demonstrated strong results on learning to repair a broken program without any labeled examples, but this relies on a perfect critic (e.g., a compiler) that returns whether an example is valid or not, which does not exist for the GEC task. In this work, we show how to leverage a pretrained language model (LM) in defining an LM-Critic, which judges a sentence to be grammatical if the LM assigns it a higher probability than its local perturbations. We apply this LM-Critic and BIFI along with a large set of unlabeled sentences to bootstrap realistic ungrammatical / grammatical pairs for training a corrector. We evaluate our approach on GEC datasets on multiple domains (CoNLL-2014, BEA-2019, GMEG-wiki and GMEG-yahoo) and show that it outperforms existing methods in both the unsupervised setting (+7.7 F0.5) and the supervised setting (+0.5 F0.5).
Interaction Energy Analysis of Monovalent Inorganic Anions in Bulk Water Versus Air/Water Interface.

Soft anions exhibit surface activity at the air/water interface that can be probed using surface-sensitive vibrational spectroscopy, but the structural implications of this surface activity remain a matter of debate. Here, we examine the nature of anion–water interactions at the air/water interface using a combination of molecular dynamics simulations and quantum-mechanical energy decomposition analysis based on symmetry-adapted perturbation theory. Results are presented for a set of monovalent anions, including Cl−, Br−, I−, CN−, OCN−, SCN−, NO2−, NO3−, and ClOn− (n=1,2,3,4), several of which are archetypal examples of surface-active species. In all cases, we find that average anion–water interaction energies are systematically larger in bulk water although the difference (with respect to the same quantity computed in the interfacial environment) is well within the magnitude of the instantaneous fluctuations. Specifically for the surface-active species Br−(aq), I−(aq), ClO4−(aq), and SCN−(aq), and also for ClO−(aq), the charge-transfer (CT) energy is found to be larger at the interface than it is in bulk water, by an amount that is greater than the standard deviation of the fluctuations. The Cl−(aq) ion has a slightly larger CT energy at the interface, but NO3−(aq) does not; these two species are borderline cases where consensus is lacking regarding their surface activity. However, CT stabilization amounts to <20% of the total induction energy for each of the ions considered here, and CT-free polarization energies are systematically larger in bulk water in all cases. As such, the role of these effects in the surface activity of soft anions remains unclear. This analysis complements our recent work suggesting that the short-range solvation structure around these ions is scarcely different at the air/water interface from what it is in bulk water. Together, these observations suggest that changes in first-shell hydration structure around soft anions cannot explain observed surface activities.
Estimation of blade forces in wind turbines using blade root strain measurements with OpenFAST verification

Abstract This paper introduces an inference method for computing the forces and bending moments on operating wind turbine blades using strain measurements and supervisory control and data acquisition (SCADA) data. Operational data from four months of a Clipper Liberty C96 2.5 MW turbine instrumented with interferometric strain sensors at the blade roots as well as SCADA data such as wind speed, rotor hub speed, and blade pitch angle allow for accurate calculation of blade forces and moments. To perform such calculations, certain structural properties of the turbine blades must be inferred in the absence of detailed, proprietary information. This is done by inferring missing information from the National Renewable Energy Laboratory (NREL) 3 MW WindPACT reference wind turbine specifications. The derived forces and moments computed on the blades of the Clipper turbine are compared with the behavior of the NREL 3 MW reference turbine according to OpenFAST simulation outputs. Comparison of blade root reaction forces to OpenFAST outputs match closely, demonstrating that this inference method can be used to successfully estimate the internal forces and bending moments acting on the blades. These methods are useful on turbines for which all the structural information is not available.
Role of heparan sulfate proteoglycans in cell signaling and cancer.

Publisher Summary Heparan sulfate proteoglycans (HSPGs) are composed of a protein core modified on specific serine residues by the addition of heparan sulfate (HS) glucosaminoglycans (GAGs) synthesized in the Golgi. Three distinct types of proteins can serve as the HSPG cores: the transmembrane proteins encoded by the syndecan genes, the glycosylphosphatidylinositol (GPI) membrane-bound glypicans, and the extracellular matrix secreted perlecan proteins. The chapter presents the two major advances made in understanding the role of HSPGs in development and cancer. First, studies in Drosophila have identified many mutations in either the biosynthetic enzymes for example, Sugarless (Sgl), Sulfateless (Sfl), Tout velu (Ttv); or the protein cores for example, syndecans and glypicans. Analysis of the mutant phenotypes has revealed the critical roles of HSPGs in modulating various growth factor signaling pathways. Second, many mutations linked to human cancers have been isolated and shown to correspond to defects in the biosynthesis of HSPGs. The chapter explains the importance of HSPGs in cell signaling and provides insights into their functions. Modulating the activity of HSPGs could influence the development of cancers caused by aberrant cell signaling. The chapter concludes that HSPGs constitute important targets for therapeutics to treat some human tumors.
On the origin of hyperglycaemia in the obese-hyperglycaemic mouse (obob): effect of diet on blood glucose and serum insulin in obob and gold-thioglucose obese mice.

Obese-hyperglycaemic mice (obob), goldthioglucose obese (GTG-obese) and normal control mice were fed a high-carbohydrate and a carbohydrate-free diet. Blood glucose and serum insulin levels were influenced by the diet in all animals studied, but more so in theobob and GTG-obese. Blood glucose levels were equally raised in both types of obesity. Serum insulin of GTG-obese showed a marked elevation above normal but failed to reach the serum insulin levels of theobob. The blood glucose ofobob and lean controls remained constant when fed the carbohydrate-free diet. Under these conditions, a trace amount of U-14C-glucose was injected intravenously. The rate of14C-glucose disappearance from the blood followed first-order kinetics and did not differ betweenobob and lean mice, suggesting a similar fractional rate of glucose uptake by tissues. The presence of a normal fractional rate of glucose extraction by the tissues ofobob, together with their hyperglycaemia and a normal volume of glucose distribution suggests an increased absolute rate of tissue glucose uptake. This increase is likely to be due to the expanded mass of adipose tissue in these animals.
A PLACE FOR CASE1

Chomsky's configurational definition of grammatical functions is incorrect as a representation of surface grammatical relations and inappropriate and clumsy as a means for representing grammatical functions in deep structure. Fillmore's case grammar framework is a vast improvement, but it still requires a deep– surface distinction and thus does not contribute to a solution of the problem of the excessive power of transformational grammars. By representing ‘case’ as a feature of lexical items, the deep– surface distinction can be eliminated, and the resulting ‘lexicase’ framework looks promising as a basis for the design of language teaching materials which is much less abstract than Fillmore's approach, and which avoids the theoretical and practical deficiencies of the other transformational models.
Nanocubes and Nanoboxes

Nanotechnology applications will require highly accurate, reproducible methods for making nanoparticles of specific size and shape. In her Perspective, Murphy highlights the report by Sun and Xia, who report a promising synthesis route that yields silver nanocubes and gold nanoboxes with well-defined crystallographic surfaces. The method allows the sizes of the structures to be tuned.
Pulmonary talc granulomas, pulmonary fibrosis, and pulmonary hypertension resulting from intravenous injection of talc-containing drugs intended for oral use.

Talcosis or talc pneumoconiosis is an uncommon form of pulmonary dust disease first recognized over 100 years ago. Talcosis may follow inhalation of relatively low concentrations of talc dust over a long period or exposure to very high concentrations of the dust over a short time (1, 2). In an infant, death occurred within 20 hours of accidental inhalation of talcum powder containing approximately 93% talc (3). Baby powder, consisting mainly of talc powder, is insoluble and, if inhaled in large amounts, dries the tracheobronchial mucous membrane and prevents normal ciliary function. The small air passages then fill with retained mucus.