cssi/SciSciGPT-SciSciNet huggingface dataset:
Second-order renormalization group flow of three-dimensional homogeneous geometries

We study the behavior of the second order Renormalization Group flow on locally homogeneous metrics on closed three-manifolds. In the cases $\mathbb R^3$ and $\text{SO}(3)\times \R$, the flow is qualitatively the same as the Ricci flow. In the cases $\text{H}(3)$ and $\text{H}(2)\times \R$, if the curvature is small, then the flow expands as in the Ricci flow case, while if the curvature is large, then the flow contracts and forms a singularity in finite time. The main focus of the paper is the flow on the $\text{SU}(2)$, $\text{Nil}$, $\text{Sol}$, and $\text{SL}(2,\R)$ 3-geometries, with two of the three principal directions set equal. The configuration spaces for these geometries are two dimensional, and we can consequently apply phase plane techniques to the study. For the $\text{SU}(2)$ case, the flow is everywhere qualitatively the same as Ricci flow. For the $\text{Nil}$, $\text{Sol}$, and $\text{SL}(2,\R)$ cases, we show that the configuration space is partitioned into two regions which are delineated by a solution curve of the flow that depends on the coupling parameter: in one of the regions, the flow develops cigar or pancake singularities characteristic of the Ricci flow, while in the other both directions shrink. In the $\text{Nil}$ case we obtain a characterization of the full 3-dimensional flow.
Volumes of polytopes without triangulations

The geometry of the dual amplituhedron is generally described in reference to a particular triangulation. A given triangulation manifests only certain aspects of the underlying space while obscuring others, therefore understanding this geometry without reference to a particular triangulation is desirable. In this note we introduce a new for-malism for computing the volumes of general polytopes in any dimension. We define new “vertex objects” and introduce a calculus for expressing volumes of polytopes in terms of them. These expressions are unique, independent of any triangulation, manifestly depend only on the vertices of the underlying polytope, and can be used to easily derive identities amongst different triangulations. As one application of this formalism, we obtain new expressions for the volume of the tree-level, n-point NMHV dual amplituhedron.
On order bounded subsets of locally solid Riesz spaces

AbstractIn a topological Riesz space there are two types of bounded subsets: order bounded subsets and topologically bounded subsets. It is natural to ask (1) whether an order bounded subset is topologically bounded and (2) whether a topologically bounded subset is order bounded. A classical result gives a partial answer to (1) by saying that an order bounded subset of a locally solid Riesz space is topologically bounded. This paper attempts to further investigate these two questions. In particular, we show that (i) there exists a non-locally solid topological Riesz space in which every order bounded subset is topologically bounded; (ii) if a topological Riesz space is not locally solid, an order bounded subset need not be topologically bounded; (iii) a topologically bounded subset need not be order bounded even in a locally convex-solid Riesz space. Next, we show that (iv) if a locally solid Riesz space has an order bounded topological neighborhood of zero, then every topologically bounded subset is orde...
Variational image compression with a scale hyperprior

We describe an end-to-end trainable model for image compression based on variational autoencoders. The model incorporates a hyperprior to effectively capture spatial dependencies in the latent representation. This hyperprior relates to side information, a concept universal to virtually all modern image codecs, but largely unexplored in image compression using artificial neural networks (ANNs). Unlike existing autoencoder compression methods, our model trains a complex prior jointly with the underlying autoencoder. We demonstrate that this model leads to state-of-the-art image compression when measuring visual quality using the popular MS-SSIM index, and yields rate-distortion performance surpassing published ANN-based methods when evaluated using a more traditional metric based on squared error (PSNR). Furthermore, we provide a qualitative comparison of models trained for different distortion metrics.
Sharp bounds for the cubic Parsell–Vinogradov system in two dimensions

Abstract We prove a sharp decoupling for a certain two dimensional surface in R 9 . As an application, we obtain the full range of expected estimates for the cubic Parsell–Vinogradov system in two dimensions.
Lazy Receding Horizon A* for Efficient Path Planning in Graphs with Expensive-to-Evaluate Edges

Motion-planning problems, such as manipulation in cluttered environments, often require a collision-free shortest path to be computed quickly given a roadmap graph. Typically, the computational cost of evaluating whether an edge of the roadmap graph is collision-free dominates the running time of search algorithms. Algorithms such as Lazy Weighted A* (LWA*) and LazySP have been proposed to reduce the number of edge evaluations by employing a lazy lookahead (one-step lookahead and infinite-step lookahead, respectively). However, this comes at the expense of additional graph operations: the larger the lookahead, the more the graph operations that are typically required. We propose Lazy Receding-Horizon A* (LRA*) to minimize the total planning time by balancing edge evaluations and graph operations. Endowed with a lazy lookahead, LRA* represents a family of lazy shortest-path graph-search algorithms that generalizes LWA* and LazySP. We analyze the theoretic properties of LRA* and demonstrate empirically that, in many cases, to minimize the total planning time, the algorithm requires an intermediate lazy lookahead. Namely, using an intermediate lazy lookahead, our algorithm outperforms both LWA* and LazySP. These experiments span simulated random worlds in R^2 and R^4, and manipulation problems using a 7-DOF manipulator.
Resistance distance in straight linear 2-trees

Abstract We consider the graph G n with vertex set V ( G n ) = { 1 , 2 , … , n } and { i , j } ∈ E ( G n ) if and only if 0 | i − j | ≤ 2 . We call G n the straight linear 2-tree on n vertices. Using Δ –Y transformations and identities for the Fibonacci and Lucas numbers we obtain explicit formulae for the resistance distance r G n ( i , j ) between any two vertices i and j of G n . To our knowledge { G n } n = 3 ∞ is the first nontrivial family with diameter going to ∞ for which all resistance distances have been explicitly calculated. Our result also gives formulae for the number of spanning trees and 2-forests in a straight linear 2-tree. We show that the maximal resistance distance in G n occurs between vertices 1 and n and the minimal resistance distance occurs between vertices n ∕ 2 and n ∕ 2 + 1 for n even (with a similar result for n odd). It follows that r n ( 1 , n ) → ∞ as n → ∞ . Moreover, our explicit formula makes it possible to order the non-edges of G n exactly according to resistance distance, and this ordering agrees with the intuitive notion of distance on a graph. Consequently, G n is a geometric graph with entirely different properties than the random geometric graphs investigated in Luxburg, Radl and Hein (2010). These results for straight linear 2-trees along with an example of a bent linear 2-tree and empirical results for additional graph classes convincingly demonstrate that resistance distance should not be discounted as a viable method for link prediction in geometric graphs.
Quantum graph spectra of a graphyne structure

We study the dispersion relations and spectra of in- variant Schrodinger operators on a graphyne structure (lithographite). In particular, description of dierent parts of the spectrum, band- gap structure, and Dirac points are provided.
A Deep Multi-task Learning Approach to Skin Lesion Classification

Skin lesion identification is a key step toward dermatological diagnosis. When describing a skin lesion, it is very important to note its body site distribution as many skin diseases commonly affect particular parts of the body. To exploit the correlation between skin lesions and their body site distributions, in this study, we investigate the possibility of improving skin lesion classification using the additional context information provided by body location. Specifically, we build a deep multi-task learning (MTL) framework to jointly optimize skin lesion classification and body location classification (the latter is used as an inductive bias). Our MTL framework uses the state-of-the-art ImageNet pretrained model with specialized loss functions for the two related tasks. Our experiments show that the proposed MTL based method performs more robustly than its standalone (single-task) counterpart.
Super Atiyah Classes and Obstructions to Splitting of Supermoduli Space

The first obstruction to splitting a supermanifold S is one of the three components of its super Atiyah class, the two other components being the ordinary Atiyah classes on the reduced space M of the even and odd tangent bundles of S. We evaluate these classes explicitly for the moduli space of super Riemann surfaces ("super moduli space") and its reduced space, the moduli space of spin curves. These classes are interpreted in terms of certain extensions arising from line bundles on the square of the varying (super) Riemann surface. These results are used to give a new proof of the non- projectedness of Mg,1, the moduli space of super Riemann surfaces with one puncture.
Disagreement-Based Combinatorial Pure Exploration: Sample Complexity Bounds and an Efficient Algorithm

We design new algorithms for the combinatorial pure exploration problem in the multi-arm bandit framework. In this problem, we are given K distributions and a collection of subsets $\mathcal{V}\subset 2^{[K])$ of these distributions, and we would like to find the subset $v \in \mathcal{V}$ that has largest mean, while collecting, in a sequential fashion, as few samples from the distributions as possible. In both the fixed budget and fixed confidence settings, our algorithms achieve new sample-complexity bounds that provide polynomial improvements on previous results in some settings. Via an information-theoretic lower bound, we show that no approach based on uniform sampling can improve on ours in any regime, yielding the first interactive algorithms for this problem with this basic property. Computationally, we show how to efficiently implement our fixed confidence algorithm whenever $\mathcal{V}$ supports efficient linear optimization. Our results involve precise concentration-of-measure arguments and a new algorithm for linear programming with exponentially many constraints.
A New Algorithm for Non-stationary Contextual Bandits: Efficient, Optimal and Parameter-free

We propose the first contextual bandit algorithm that is parameter-free, efficient, and optimal in terms of dynamic regret. Specifically, our algorithm achieves dynamic regret $\mathcal{O}(\min\{\sqrt{ST}, \Delta^{\frac{1}{3}}T^{\frac{2}{3}}\})$ for a contextual bandit problem with $T$ rounds, $S$ switches and $\Delta$ total variation in data distributions. Importantly, our algorithm is adaptive and does not need to know $S$ or $\Delta$ ahead of time, and can be implemented efficiently assuming access to an ERM oracle. Our results strictly improve the $\mathcal{O}(\min \{S^{\frac{1}{4}}T^{\frac{3}{4}}, \Delta^{\frac{1}{5}}T^{\frac{4}{5}}\})$ bound of (Luo et al., 2018), and greatly generalize and improve the $\mathcal{O}(\sqrt{ST})$ result of (Auer et al, 2018) that holds only for the two-armed bandit problem without contextual information. The key novelty of our algorithm is to introduce replay phases, in which the algorithm acts according to its previous decisions for a certain amount of time in order to detect non-stationarity while maintaining a good balance between exploration and exploitation.
Theoretical investigation of two-ends-open free spontaneous imbibition

Two-ends-open free spontaneous imbibition refers to a laboratory core experiment, with one end face exposed to the wetting phase and the other end exposed to the non-wetting phase. Spontaneous imbibition leads to the production of non-wetting phase both co-currently and counter-currently. This paper extends previous work on systems of infinite length and presents the exact one-dimensional semi-analytic solution for such a system (Lagrangian approach) and validates the solution with numerical simulation (Eulerian approach). The methodology solves the partial differential equation of unsteady state immiscible, incompressible flow with arbitrary relative permeability and capillary pressure functions using a fractional flow concept. The solution strategy uses a Lagrangian discretization in both the temporal variable and the water saturation to solve for the instantaneous and time averaged normalized water fluxes. The approach avoids the evaluation of implicit integral solutions but instead combines an explicit calculation for the total fluid flux along with a shooting algorithm for the water imbibition to satisfy both the flow and pressure boundary conditions. The wetting phase is spontaneously imbibed into the core with the initial water inlet flux being close to infinite. As the imbibition front propagates, and before it reaches the system outlet, the ratio of the co-current non-wetting phase (oil) flux to the inlet water flux increases from zero to a finite value below one which is dependent on the intrinsic properties of the system. This indicates that the production of non-wetting phase at the inlet will not cease before the front reaches the outlet, irrespective of the length of the system. The time for the front to reach the outlet, the final ratio of co-current oil flux to inlet water flux, and the variation of total produced volume from both ends, along with their sensitivities with respect to the absolute permeability, system length, and free water saturation, are analyzed in this study as well. The results also indicate that the solution is independent of system length and permeability when expressed in terms of dimensionless time. Unlike previous studies, we have not assumed self-similar solutions or treated the flow as purely co-current or counter-current. The boundary conditions for the system analyzed here are easily achievable in the lab and have been discussed in the literature. The results from this study could be used to serve as a benchmark for numerical simulations, or in applications such as the interpretation of laboratory data for relative permeability and/or capillary pressure, or improved interpretation of laboratory to field relationships through scaling group analysis.
Balancing Safety and Traffic Throughput in Cooperative Vehicle Platooning

In this paper we propose a distributed model predictive control architecture to coordinate the longitudinal motion of a vehicle platoon at a signalized intersection. Our control approach is cooperative; we use vehicle-to-vehicle (V2V) communication in order to maintain small inter-vehicle distances and correspondingly achieve large vehicle throughput at an intersection. We study the trade-off between safety and road throughput for this problem. In particular, we present the link between traffic efficiency gains in terms of throughput, and safety of the connected platoon measured as trust on the predicted speed profile of other vehicles in the network.
Making Sense of Vision and Touch: Self-Supervised Learning of Multimodal Representations for Contact-Rich Tasks

Contact-rich manipulation tasks in unstructured environments often require both haptic and visual feedback. However, it is non-trivial to manually design a robot controller that combines modalities with very different characteristics. While deep reinforcement learning has shown success in learning control policies for high-dimensional inputs, these algorithms are generally intractable to deploy on real robots due to sample complexity. We use self-supervision to learn a compact and multimodal representation of our sensory inputs, which can then be used to improve the sample efficiency of our policy learning. We evaluate our method on a peg insertion task, generalizing over different geometry, configurations, and clearances, while being robust to external perturbations. We present results in simulation and on a real robot.
WHICH COMMUNITIES COMPLAIN TO POLICYMAKERS? EVIDENCE FROM CONSUMER SENTINEL

Consumer complaints provide a signal of the problems that different American communities face. I use a large database of millions of complaints to examine how per capita complaint rates vary across communities, as well as heterogeneity in who complains to different agencies and about different consumer protection issues. I find higher complaint rates in more heavily Black, more educated, higher income, older, and more urban communities and lower complaint rates in more heavily Hispanic and higher household size communities. The demographics of complaints are quite different for the Consumer Financial Protection Bureau, with much higher rates of complaints from Black and college educated areas compared to the Federal Trade Commission or Better Business Bureaus. I also find much higher rates of finance related complaints from Black communities across all reporting agencies. (JEL D18, H50, J10)
Near Neighbor: Who is the Fairest of Them All?

In this work we study a "fair" variant of the near neighbor problem. Namely, given a set of $n$ points $P$ and a parameter $r$, the goal is to preprocess the points, such that given a query point $q$, any point in the $r$-neighborhood of the query, i.e., $B(q,r)$, have the same probability of being reported as the near neighbor. We show that LSH based algorithms can be made fair, without a significant loss in efficiency. Specifically, we show an algorithm that reports a point $p$ in the $r$-neighborhood of a query $q$ with almost uniform probability. The time to report such a point is proportional to $O(\dns(q.r) Q(n,c))$, and its space is $O(S(n,c))$, where $Q(n,c)$ and $S(n,c)$ are the query time and space of an LSH algorithm for $c$-approximate near neighbor, and $\dns(q,r)$ is a function of the local density around $q$. Our approach works more generally for sampling uniformly from a sub-collection of sets of a given collection and can be used in a few other applications. Finally, we run experiments to show performance of our approach on real data.
Efficient sentence embedding using discrete cosine transform

Vector averaging remains one of the most popular sentence embedding methods in spite of its obvious disregard for syntactic structure. While more complex sequential or convolutional networks potentially yield superior classification performance, the improvements in classification accuracy are typically mediocre compared to the simple vector averaging. As an efficient alternative, we propose the use of discrete cosine transform (DCT) to compress word sequences in an order-preserving manner. The lower order DCT coefficients represent the overall feature patterns in sentences, which results in suitable embeddings for tasks that could benefit from syntactic features. Our results in semantic probing tasks demonstrate that DCT embeddings indeed preserve more syntactic information compared with vector averaging. With practically equivalent complexity, the model yields better overall performance in downstream classification tasks that correlate with syntactic features, which illustrates the capacity of DCT to preserve word order information.
Tax and Nontax Incentives in Income Shifting: Evidence from Shadow Insurers

Using the shadow insurance setting, we study the interplay between tax and nontax incentives in income shifting. Shadow insurance involves intercompany transactions designed to help firms meet regu...
Binary Ensemble Neural Network: More Bits per Network or More Networks per Bit?

Binary neural networks (BNN) have been studied extensively since they run dramatically faster at lower memory and power consumption than floating-point networks, thanks to the efficiency of bit operations. However, contemporary BNNs whose weights and activations are both single bits suffer from severe accuracy degradation. To understand why, we investigate the representation ability, speed and bias/variance of BNNs through extensive experiments. We conclude that the error of BNNs is predominantly caused by intrinsic instability (training time) and non-robustness (train & test time). Inspired by this investigation, we propose the Binary Ensemble Neural Network (BENN) which leverages ensemble methods to improve the performance of BNNs with limited efficiency cost. While ensemble techniques have been broadly believed to be only marginally helpful for strong classifiers such as deep neural networks, our analysis and experiments show that they are naturally a perfect fit to boost BNNs. We find that our BENN, which is faster and more robust than state-of-the-art binary networks, can even surpass the accuracy of the full-precision floating number network with the same architecture.
Nanomechanics and co-transcriptional folding of Spinach and Mango

Recent advances in fluorogen-binding “light-up” RNA aptamers have enabled protein-free detection of RNA in cells. Detailed biophysical characterization of folding of G-Quadruplex (GQ)-based light-up aptamers such as Spinach, Mango and Corn is still lacking despite the potential implications on their folding and function. In this work we employ single-molecule fluorescence-force spectroscopy to examine mechanical responses of Spinach2, iMangoIII and MangoIV. Spinach2 unfolds in four discrete steps as force is increased to 7 pN and refolds in reciprocal steps upon force relaxation. In contrast, GQ-core unfolding in iMangoIII and MangoIV occurs in one discrete step at forces >10 pN and refolding occurred at lower forces showing hysteresis. Co-transcriptional folding using superhelicases shows reduced misfolding propensity and allowed a folding pathway different from refolding. Under physiologically relevant pico-Newton levels of force, these aptamers may unfold in vivo and subsequently misfold. Understanding of the dynamics of RNA aptamers will aid engineering of improved fluorogenic modules for cellular applications. Light-up aptamers are widely used for fluorescence visualization of non-coding RNA in vivo. Here the authors employ single-molecule fluorescence-force spectroscopy to characterize the mechanical responses of the G-Quadruplex based light-up aptamers Spinach2, iMangoIII and MangoIV, which is of interest for the development of improved fluorogenic modules for imaging applications.
Calibration between trigger and color: Neutralization of a genetically encoded coulombic switch and dynamic arrest precisely tune reflectin assembly.

Reflectin proteins are widely distributed in reflective structures in cephalopods. However, only in loliginid squids are they and the subwavelength photonic structures they control dynamically tunable, driving changes in skin color for camouflage and communication. The reflectins are block copolymers with repeated canonical domains interspersed with cationic linkers. Neurotransmitter-activated signal transduction culminates in catalytic phosphorylation of the tunable reflectins' cationic linkers; the resulting charge neutralization overcomes coulombic repulsion to progressively allow condensation, folding, and assembly into multimeric spheres of tunable well-defined size and low polydispersity. Here, we used dynamic light scattering, transmission EM, CD, atomic force microscopy, and fluorimetry to analyze the structural transitions of reflectins A1 and A2. We also analyzed the assembly behavior of phosphomimetic, deletion, and other mutants in conjunction with pH titration as an in vitro surrogate of phosphorylation. Our experiments uncovered a previously unsuspected, precisely predictive relationship between the extent of neutralization of a reflectin's net charge density and the size of resulting multimeric protein assemblies of narrow polydispersity. Comparisons of mutants revealed that this sensitivity to neutralization resides in the linkers and is spatially distributed along the protein. Imaging of large particles and analysis of sequence composition suggested that assembly may proceed through a dynamically arrested liquid–liquid phase-separated intermediate. Intriguingly, it is this dynamic arrest that enables the observed fine-tuning by charge and the resulting calibration between neuronal trigger and color in the squid. These results offer insights into the basis of reflectin-based biophotonics, opening paths for the design of new materials with tunable properties.
The European Union's authoritarian equilibrium

While the European Union (EU) professes a commitment to liberal democracy, in recent years it has allowed some member governments to backslide toward competitive authoritarianism. The EU has become...
Analyzing and Evaluating the 1:1 Learning Model: What Would Dewey Do?.

This paper explores and evaluates 1:1 computing models in K-12 learning environments through the lens of educational philosopher John Dewey. Using some of John Dewey's most salient beliefs on education, the paper provides a framework that analyzes and evaluates this instructional model. The paper also serves as a guideline to provide suggestions to any educators who may be interested in or are currently employing ubiquitous computing in their schools. Recommendations for 1:1 computing models are offered around the central principles of access, support, and use.
Individual alpha frequency increases during a task but is unchanged by alpha-band flicker.

Visual perception fluctuates in sync with ongoing neural oscillations in the delta, theta, and alpha frequency bands of the human EEG. Supporting the relationship between alpha and perceptual sampling, recent work has demonstrated that variations in individual alpha frequency (IAF) correlate with the ability to discriminate one from two stimuli presented briefly in the same location. Other studies have found that, after being presented with a flickering stimulus at alpha frequencies, perception of near-threshold stimuli fluctuates for a short time at the same frequency. Motivated by previous work, we were interested in whether this alpha entrainment involves shifts in IAF. While recording EEG, we tested whether two-flash discrimination (a behavioral correlate of IAF) can be influenced by ~1 s of rhythmic visual stimulation at two different alpha frequencies (8.3 and 12.5 Hz). Speaking against the bottom-up malleability of IAF, we found no change in IAF during stimulation and no change in two-flash discrimination immediately afterward. We also found synchronous activity that persisted after 12.5 Hz stimulation, which suggests that a separate source of alpha was entrained. Importantly, we replicated the correlation between IAF and two-flash discrimination in a no-stimulation condition, demonstrating the sensitivity of our behavioral measure. We additionally found that IAF increased during the task compared to rest, which demonstrates that IAF is influenced by top-down factors but is not involved in entrainment.
Functions on Antipower Prefix Lengths of the Thue-Morse Word

Abstract We say that a word w of length k n is a k -antipower if it can be written in the form w 1 ⋯ w k , where each w i is a distinct word of length n . We analyze prefixes of the Thue–Morse word , t and lengths of antipowers occurring in them. Define Γ ( k ) to be the largest odd n such that the prefix of , t of length k n is not a k -antipower, and γ ( k ) to be the smallest odd n such that the corresponding prefix is a k -antipower. We provide strong bounds on the asymptotic values of γ ( k ) and Γ ( k ) − γ ( k ) . Our bounds on γ ( k ) affirmatively answer one conjecture of Defant and make substantial progress towards answering a second conjecture of Defant. It was previously known that Γ ( k ) and γ ( k ) grow linearly in k , but our bounds on Γ ( k ) − γ ( k ) prove that Γ ( k ) − γ ( k ) also grows linearly in k .
Real Time Attitude Independent GPS Integer Ambiguity Resolution

In this paper, a new motion-based approach for Global Positioning System (GPS) integer ambiguity resolution is derived. The approach first represents the GPS sightline vectors in the body frame or the baseline vectors in the reference frame. The solution to this problem is always available as long as at least three non-coplanar baseline or sightline vectors exist. The body-frame sightline or reference-frame baseline observations are the sum of two vectors, one depending on the phase measurements and the other on the unknown integers. The vector containing the integer phases is then converted into an attitude independent observation using scalar checking, which leads to a problem that treats the integers as to-be-determined biases. The bias estimation problem is typically solved by using a batch process. In this paper, simple real time algorithms are developed based on both the extended Kalman filter and Unscented filter. Simulation results indicate that both algorithms provide accurate integer resolution in real time, but the Unscented filter is more robust to large initial condition errors and slow vehicle motions than the extended Kalman filter.
Screening mechanism when online users have privacy concerns

In consumer-to-consumer online platforms that enable selling (e.g., eBay, Taobao) or sharing (e.g., Airbnb, Uber) of goods and services, information asymmetry between providers (e.g., sellers, hosts, drivers) and consumers (e.g., buyers, guests, passengers) pose challenges. Such platforms facilitate transactions between users (providers and consumers), who are often strangers. Stricter screening, background cheeks, and identity verification requirements may reduce the probability of bad users entering the platform. However, users are reluctant to share personal information on the internet. We design a matching mechanism to maximise platform profit when users are heterogeneous with some more likely to be good than others, but the platform does not know who. We argue that in some cases, the platform increases its profit by allowing users with a higher probability of being bad to join as well.
Leveraging the bfloat16 Artificial Intelligence Datatype For Higher-Precision Computations

In recent years fused-multiply-add (FMA) units with lower-precision multiplications and higher-precision accumulation have proven useful in machine learning/artificial intelligence applications, most notably in training deep neural networks due to their extreme computational intensity. Compared to classical IEEE-754 32 bit (FP32) and 64 bit (FP64) arithmetic, these reduced precision arithmetic can naturally be sped up disproportional to their shortened width. The common strategy of all major hardware vendors is to aggressively further enhance their performance disproportionately. One particular FMA operation that multiplies two BF16 numbers while accumulating in FP32 has been found useful in deep learning, where BF16 is the 16-bit floating point datatype with IEEE FP32 numerical range but 8 significant bits of precision. In this paper, we examine the use this FMA unit to implement higher-precision matrix routines in terms of potential performance gain and implications on accuracy. We demonstrate how a decomposition into multiple smaller datatypes can be used to assemble a high-precision result, leveraging the higher precision accumulation of the FMA unit. We first demonstrate that computations of vector inner products and by natural extension, matrix-matrix products can be achieved by decomposing FP32 numbers in several BF16 numbers followed by appropriate computations that can accommodate the dynamic range and preserve accuracy compared to standard FP32 computations, while projecting up to 5.2x speed-up. Furthermore, we examine solution of linear equations formulated in the residual form that allows for iterative refinement. We demonstrate that the solution obtained to be comparable to those offered by FP64 under a large range of linear system condition numbers.
At what spatial scales are alternative stable states relevant in highly interconnected ecosystems

Whether ecosystems recover from disturbance depends on the presence of alternative stable states, which are theoretically possible in simple models of many systems. However, definitive empirical evidence for this phenomenon remains limited to demographically closed ecosystems such as lakes. In more interconnected systems such as temperate rocky reefs, the local relevance of alternative stable states might erode as immigration overwhelms local feedbacks and produces a single stable state. At larger spatial scales, dispersal might counter localized disturbance and feedbacks to synchronize states throughout a region. Here, we quantify how interconnectedness affects the relevance of alternative stable states using dynamical models of California rocky reef communities that incorporate observed environmental stochasticity and feedback loops in kelp-urchin-predator interactions. Our models demonstrate the potential for localized alternative states despite high interconnectedness likely due to feedbacks affecting dispersers as they settle into local communities. Regionally, such feedbacks affecting settlement can produce a mosaic of alternative stable states that span local (10-20 km) scales despite the synchronizing effect of long-distance dispersal. The specific spatial scale and duration of each state predominantly depend on the scales of environmental variation and on local dynamics (here, fishing). Model predictions reflect observed scales of community states in California rocky reefs and suggest how alternative states co-occur in the wide array of marine and terrestrial systems with settlement feedbacks.
Visual Font Pairing

This paper introduces the problem of automatic font pairing . Font pairing is an important design task that is difficult for novices. Given a font selection for one part of a document (e.g., header), our goal is to recommend a font to be used in another part (e.g., body) such that the two fonts used together look visually pleasing. There are three main challenges in font pairing. First, this is a fine-grained problem, in which the subtle distinctions between fonts may be important. Second, rules and conventions of font pairing given by human experts are difficult to formalize. Third, font pairing is an asymmetric problem in that the roles played by header and body fonts are not interchangeable. To address these challenges, we propose automatic font pairing through learning visual relationships from large-scale human-generated font pairs. We introduce a new database for font pairing constructed from millions of PDF documents available on the Internet. We propose two font pairing algorithms: dual-space $k$ -NN and asymmetric similarity metric learning (ASML). These two methods automatically learn fine-grained relationships from large-scale data. We also investigate several baseline methods based on the rules from professional designers. Experiments and user studies demonstrate the effectiveness of our proposed dataset and methods.
Revisionist History? Responding to Gun Violence Under Historical Limitations.

In the D.C. Circuit case Heller v. District of Columbia (Heller II), Judge Kavanaugh wrote that “Heller and McDonald leave little doubt that courts are to assess gun bans and regulations based on text, history, and tradition, not by a balancing test such as strict or intermediate scrutiny.” Now Justice Kavanaugh, will he find support on the highest court for what was then a dissenting view? Chief Justice Roberts, during oral arguments for Heller I, asked “Isn’t it enough to…look at the various regulations that were available at the time…and determine how these—how this restriction and the scope of this right looks in relation to those?” Justice Thomas, in cert denial dissents for recent Second Amendment cases, has referenced the importance of history in determining what the Framers understood the Second Amendment to protect. There is an open question as to what role history should play in evaluating gun control regulations and, given present circuit splits, it is only a matter of time before this debate finds its way to the highest court. Absent in these debates on history, and Second Amendment discussions more generally, is police power doctrine in the area of public health. The judiciary must recognize that police power enables the state to recognize the changes in firearm technology and the continued growth of gun violence plaguing this country. Looking to relevant public health law cases, particularly Jacobson v. Massachusetts, helps to illustrate that history has not, and cannot, be the sole focus in determining the scope of the state’s ability to tackle public health problems, including gun violence.
Effect of Prenatal EPA and DHA on Maternal and Cord Blood Insulin Sensitivity: A secondary analysis of the mothers, omega 3, and mental health study

We sought to determine whether prenatal supplementation with the omega-3 fatty acids eicosapentaenoic acid (EPA) or docosahexaenoic acid (DHA) would increase markers of insulin sensitivity in maternal or cord blood compared with placebo supplementation. A secondary aim was to evaluate the association of serum EPA and DHA fractions with adiponectin, leptin and the adiponectin:leptin ratio (ALR). We hypothesized that omega-3 fatty acid supplementation would increase markers of insulin sensitivity in maternal and umbilical cord plasma. We analyzed stored plasma samples collected from a prior 3-arm prospective, double-blinded, randomized controlled trial in which 126 women with singleton pregnancies between 12- and 20-weeks’ gestation were randomized to receive: 1) an EPA-rich fish oil supplement, 2) a DHA-rich fish oil supplement, or 3) a soy oil placebo. Maternal venous blood samples were collected at 12–20 weeks gestation (before supplementation) and at 34–36 weeks gestation. At delivery, cord blood was collected. Samples were analyzed using sandwich enzyme-linked immunosorbent assay kits to quantify leptin and adiponectin levels which were utilized to calculate the ALR, a proxy measure for insulin sensitivity. We found no difference in adiponectin, leptin, and the ALR between the treatment and placebo groups at baseline, after supplementation, or in umbilical cord blood. In regression analyses, higher maternal serum DHA fraction was associated with increased ALR before (p = 0.01) and after (p = 0.04) DHA supplementation. There was no association of EPA fraction with any measure of insulin sensitivity. Cord blood DHA fraction was significantly associated with cord plasma leptin (p = 0.02). Early pregnancy BMI was significantly associated with maternal leptin levels at baseline and in late pregnancy (p < 0.001) and was inversely associated with the ALR (p < 0.001). The ALR decreased significantly between the early and late pregnancy visits (p < 0.001). Pregnancy weight gain was inversely associated with the ALR (P. < 0.02). EPA- and DHA- rich fish oil supplementation had no effect on plasma markers of insulin sensitivity. However, maternal serum DHA fraction was significantly associated with markers of insulin sensitivity. https://clinicaltrials.gov/, registration number NCT00711971, 7/7/2008.
Selection of Solar Simulator for Solar Dynamic Ground Test

The 2 kWe Solar Dynamic (SD) Ground Test Demonstration (GTD) experiment will be conducted in 1995 at NASA Lewis Research Center (LeRC). This solar dynamic power system test will be conducted in a simulated space environment and will require an artificial sun. To address the solar simulator requirements for the GTD, Arnold Engineering Development Center (AEDC) was hired under contract to review and visit four existing solar simulator facilities. The four facilities included, AEDC's Mark 1 Chamber, NASA-JSC Chamber A, AEDC's 12V Chamber, and NASA-JPL Space Simulator Chamber. Two design concepts were considered following several months of evaluating existing solar simulator facilities throughout the United States. To satisfy system requirements for the SD GTD experiment the solar simulator needs to provide a uniform light flux to the SD concentrator, provide the light within a subtense angle of one degree, and provide an intensity of one solar constant (1.37 kW/sq m) at airmass zero. Most solar simulators are designed for supplying heat loads to spacecraft where a cone angle as large as 3 degrees is acceptable. It was also concluded that a solar simulator, such like these considered in the AEDC study, would require major facility modifications for NASA LeRC and result in significant impacts to the program. The advanced solar simulator concept developed by NASA LeRC will meet the system requirements for the SD GTD experiment Since SD GTD solar simulator requirements could not be addressed by existing simulator, an advanced concept was considered.
Dashing: fast and accurate genomic distances with HyperLogLog

Dashing is a fast and accurate software tool for estimating similarities of genomes or sequencing datasets. It uses the HyperLogLog sketch together with cardinality estimation methods that are specialized for set unions and intersections. Dashing summarizes genomes more rapidly than previous MinHash-based methods while providing greater accuracy across a wide range of input sizes and sketch sizes. It can sketch and calculate pairwise distances for over 87K genomes in 6 minutes. Dashing is open source and available at https://github.com/dnbaker/dashing.
Optimal Information Updating based on Value of Information

We address the problem of how to optimally schedule data packets over an unreliable channel in order to minimize the estimation error of a simple-to-implement remote linear estimator using a constant “Kalman” gain to track the state of a Gauss Markov process. The remote estimator receives time-stamped data packets which contain noisy observations of the process. Additionally, they also contain the information about the “quality” of the sensor/source, i.e., the variance of the observation noise that was used to generate the packet. In order to minimize the estimation error, the scheduler needs to use both while prioritizing packet transmissions. It is shown that a simple index rule that calculates the value of information (VoI) of each packet, and then schedules the packet with the largest current value of VoI, is optimal. The VoI of a packet decreases with its age, and increases with the precision of the source. Thus, we conclude that, for constant filter gains, a policy which minimizes the age of information does not necessarily maximize the estimator performance.
The human-specific paralogs SRGAP2B and SRGAP2C differentially modulate SRGAP2A-dependent synaptic development

Human-specific gene duplications (HSGDs) have recently emerged as key modifiers of brain development and evolution. However, the molecular mechanisms underlying the function of HSGDs remain often poorly understood. In humans, a truncated duplication of SRGAP2A led to the emergence of two human-specific paralogs: SRGAP2B and SRGAP2C. The ancestral copy SRGAP2A limits synaptic density and promotes maturation of both excitatory (E) and inhibitory (I) synapses received by cortical pyramidal neurons (PNs). SRGAP2C binds to and inhibits all known functions of SRGAP2A leading to an increase in E and I synapse density and protracted synapse maturation, traits characterizing human cortical neurons. Here, we demonstrate how the evolutionary changes that led to the emergence of SRGAP2 HSGDs generated proteins that, in neurons, are intrinsically unstable and, upon hetero-dimerization with SRGAP2A, reduce SRGAP2A levels in a proteasome-dependent manner. Moreover, we show that, despite only a few non-synonymous mutations specifically targeting arginine residues, SRGAP2C is unique compared to SRGAP2B in its ability to induce long-lasting changes in synaptic density throughout adulthood. These mutations led to the ability of SRGAP2C to inhibit SRGAP2A function and thereby contribute to the emergence of human-specific features of synaptic development during evolution.
Semiconducting SiGeSn high-entropy alloy: A density functional theory study

High-entropy alloys (HEAs), which have been intensely studied due to their excellent mechanical properties, generally refer to alloys with multiple equimolar or nearly equimolar elements. According to this definition, Si-Ge-Sn alloys with equal or comparable concentrations of the three group IV elements belong to the category of HEAs. As a result, the equimolar elements of Si-Ge-Sn alloys likely cause their atomic structures to exhibit the same core effects of metallic HEAs such as lattice distortion. Here, we apply density functional theory (DFT) calculations to show that the SiGeSn HEA indeed exhibits a large local distortion effect. Unlike metallic HEAs, our Monte Carlo and DFT calculations show that the SiGeSn HEA exhibits no chemical short-range order due to the similar electronegativity of the constituent elements, thereby increasing the configurational entropy of the SiGeSn HEA. Hybrid density functional calculations show that the SiGeSn HEA remains semiconducting with a bandgap of 0.38 eV, promising for economical and compatible midinfrared optoelectronic applications. We then study the energetics of neutral single Si, Ge, and Sn vacancies and (expectedly) find wide distributions of vacancy formation energies, similar to those found in metallic HEAs. However, we also find anomalously small lower bounds (e.g., 0.04 eV for a Si vacancy) in the energy distributions, which arise from the bond reformation near the vacancy. Such small vacancy formation energies and their associated bond reformations retain the semiconducting behavior of the SiGeSn HEA, which may be a signature feature of a semiconducting HEA that differentiates from metallic HEAs.
Cusp-artifacts in high order superresolution optical fluctuation imaging

Superresolution optical fluctuation imaging (SOFI) is a simple and affordable super-resolution imaging technique, and attracted a growing community over the past decade. However, the theoretical resolution enhancement of high order SOFI is still not fulfilled. In this study, we identify “cusp artifacts” in high order SOFI images, and show that the high-order cumulants, odd-order moments and balanced-cumulants (bSOFI) are highly vulnerable to cusp artifacts. Our study provides guidelines for developing and screening for fluorescence probes, and improving data acquisition for SOFI. The new insight is important to inspire positive utilization of the cusp artifacts.
Predicting AC Optimal Power Flows: Combining Deep Learning and Lagrangian Dual Methods.

The Optimal Power Flow (OPF) problem is a fundamental building block for the optimization of electrical power systems. It is nonlinear and nonconvex and computes the generator setpoints for power and voltage, given a set of load demands. It is often solved repeatedly under various conditions, either in real-time or in large-scale studies. This need is further exacerbated by the increasing stochasticity of power systems due to renewable energy sources in front and behind the meter. To address these challenges, this paper presents a deep learning approach to the OPF. The learning model exploits the information available in the similar states of the system (which is commonly available in practical applications), as well as a dual Lagrangian method to satisfy the physical and engineering constraints present in the OPF. The proposed model is evaluated on a large collection of realistic medium-sized power systems. The experimental results show that its predictions are highly accurate with average errors as low as 0.2%. Additionally, the proposed approach is shown to improve the accuracy of the widely adopted linear DC approximation by at least two orders of magnitude.