cssi/SciSciGPT-SciSciNet huggingface dataset:
The mode of expression divergence in Drosophila fat body is infection-specific

Transcription is controlled by interactions of cis-acting DNA elements with diffusible trans-acting factors. Changes in cis or trans factors can drive expression divergence within and between species, and their relative prevalence can reveal the evolutionary history and pressures that drive expression variation. Previous work delineating the mode of expression divergence in animals has largely used whole-body expression measurements in one condition. Because cis-acting elements often drive expression in a subset of cell types or conditions, these measurements may not capture the complete contribution of cis-acting changes. Here, we quantify the mode of expression divergence in the Drosophila fat body, the primary immune organ, in several conditions, using two geographically distinct lines of D. melanogaster and their F1 hybrids. We measured expression in the absence of infection and in infections with Gram-negative S. marcescens or Gram-positive E. faecalis bacteria, which trigger the two primary signaling pathways in the Drosophila innate immune response. The mode of expression divergence strongly depends on the condition, with trans-acting effects dominating in response to Gram-negative infection and cis-acting effects dominating in Gram-positive and preinfection conditions. Expression divergence in several receptor proteins may underlie the infection-specific trans effects. Before infection, when the fat body has a metabolic role, there are many compensatory effects, changes in cis and trans that counteract each other to maintain expression levels. This work shows that within a single tissue, the mode of expression divergence varies between conditions and suggests that these differences reflect the diverse evolutionary histories of host-pathogen interactions.
Merkle Trees Optimized for Stateless Clients in Bitcoin.

The ever-growing size of the Bitcoin UTXO state is a factor preventing nodes with limited storage capacity from validating transactions. Cryptographic accumulators, such as Merkle trees, offer a viable solution to the problem. Full nodes create a Merkle tree from the UTXO set, while stateless nodes merely store the root of the Merkle tree. When provided with a proof, stateless nodes can verify that a transaction’s inputs belong to the UTXO set. In this work, we present a systematic study of Merkle tree based accumulators, with a focus on factors that reduce the proof size. Based on the observation that UTXOs typically have a short lifetime, we propose that recent UTXOs be co-located in the tree. When proofs for different transactions are batched, such a design reduces the per-transaction proof size. We provide details of our implementation of this idea, describing certain optimizations that further reduce the proof size in practice. On Bitcoin data before August 2019, we show that our design achieves a 4.6x reduction in proof size vis-a-vis UTREEXO [10], which is a different Merkle-tree based system designed to support stateless nodes.
Market equilibrium in multi-tier supply chain networks

We consider a sequential decision model over multi-tier supply chain networks and show that in particular, for series parallel networks, there is a unique equilibrium. We provide a linear time algorithm to compute the equilibrium and study the impact and invariant of the network structure to the total trade flow and social welfare.
Boundedness of minimal partial du Val resolutions of canonical surface foliations

In this paper, we prove the boundedness of foliated surfaces $$(X,{{\mathscr {F}}})$$ which are minimal partial du Val resolutions of canonical models $$(X_c,{{\mathscr {F}}}_c)$$ of general type. For applications, we show the boundedness of non-cusp singularities on canonical models of foliated surfaces of general type and the effective generation on the complement of the cusp singularities.
Entropy production and chemical reactions in nonequilibrium plasma

In this work, methods based upon nonequilibrium thermodynamics are elucidated to predict stationary states of chemical reactions in nonequilibrium plasma, and limits for energy conversion efficiency. Two example reactions are used: CO2 splitting and NH3 synthesis, with emphasis on CO2 splitting. Expectations from the theoretical framework are compared to experimental results for both reactions, and reasonable agreement is obtained. The conclusion is that the probability of observing either reactants or products increases with the amount of energy dissipated by that side of the reaction as heat through collisions with hot electrons. The side of the reaction that dissipates more energy as heat has a higher probability of occurrence. Furthermore, endergonic chemical reactions in nonequilibrium plasma, such as CO2 splitting at low temperature, require an intrinsic energy dissipation to satisfy the 2nd law of thermodynamics – a sufficient and necessary waste. This intrinsic dissipation limits the maximum theoretical energy conversion efficiency
Non-classical probabilities invariant under symmetries

Classical real-valued probabilities come at a philosophical cost: in many infinite situations, they assign the same probability value—namely, zero—to cases that are impossible as well as to cases that are possible. There are three non-classical approaches to probability that can avoid this drawback: full conditional probabilities, qualitative probabilities and hyperreal probabilities. These approaches have been criticized for failing to preserve intuitive symmetries that can be preserved by the classical probability framework, but there has not been a systematic study of the conditions under which these symmetries can and cannot be preserved. This paper fills that gap by giving complete characterizations under which symmetries understood in a certain “strong” way can be preserved by these non-classical probabilities, as well as by offering some results to make it plausible that the strong notion of symmetry here is the right one. Philosophical implications are briefly discussed, but the main purpose of the paper is to offer technical results to help make further philosophical discussion more sophisticated.
Adversarially Robust Classification Based on GLRT

Machine learning models are vulnerable to adversarial attacks that can often cause misclassification by introducing small but well designed perturbations. In this paper, we explore, in the setting of classical composite hypothesis testing, a defense strategy based on the generalized likelihood ratio test (GLRT), which jointly estimates the class of interest and the adversarial perturbation. We evaluate the GLRT approach for the special case of binary hypothesis testing in white Gaussian noise under l ∞ norm-bounded adversarial perturbations, a setting for which a minimax strategy optimizing for the worst-case attack is known. We show that the GLRT approach yields performance competitive with that of the minimax approach under the worst-case attack, while yielding a better robustness-accuracy trade-off under weaker attacks. The GLRT defense is applicable in multi-class settings and generalizes naturally to more complex models for which optimal minimax classifiers are not known.
Sonothermogenetics for noninvasive and cell-type specific deep brain neuromodulation.

Abstract Background Critical advances in the investigation of brain functions and treatment of brain disorders are hindered by our inability to selectively target neurons in a noninvasive manner in the deep brain. Objective This study aimed to develop sonothermogenetics for noninvasive, deep-penetrating, and cell-type-specific neuromodulation by combining a thermosensitive ion channel TRPV1 with focused ultrasound (FUS)-induced brief, non-noxious thermal effect. Methods The sensitivity of TRPV1 to FUS sonication was evaluated in vitro. It was followed by in vivo assessment of sonothermogenetics in the activation of genetically defined neurons in the mouse brain by two-photon calcium imaging. Behavioral response evoked by sonothermogenetic stimulation at a deep brain target was recorded in freely moving mice. Immunohistochemistry staining of ex vivo brain slices was performed to evaluate the safety of FUS sonication. Results TRPV1 was found to be an ultrasound-sensitive ion channel. FUS sonication at the mouse brain in vivo selectively activated neurons that were genetically modified to express TRPV1. Temporally precise activation of TRPV1-expressing neurons was achieved with its success rate linearly correlated with the peak temperature within the FUS-targeted brain region as measured by in vivo magnetic resonance thermometry. FUS stimulation of TRPV1-expressing neurons at the striatum repeatedly evoked locomotor behavior in freely moving mice. FUS sonication was confirmed to be safe based on inspection of neuronal integrity, inflammation, and apoptosis markers. Conclusions This noninvasive and cell-type-specific neuromodulation approach with the capability to stimulate deep brain has the promise to advance the study of the intact nervous system and uncover new ways to treat neurological disorders.
Dualities in comparison theorems and bundle-valued generalized harmonic forms on noncompact manifolds

We observe, utilize dualities in differential equations and differential inequalities (see Theorem 2.1), dualities between comparison theorems in differential equations (see Theorems E and 2.2), and obtain dualities in ‘swapping’ comparison theorems in differential equations. These dualities generate comparison theorems on differential equations of mixed types I and II (see Theorems 2.3 and 2.4) and lead to comparison theorems in Riemannian geometry (see Theorems 2.5 and 2.8) with analytic, geometric, PDE’s and physical applications. In particular, we prove Hessian comparison theorems (see Theorems 3.1–3.5) and Laplacian comparison theorems (see Theorems 2.6, 2.7 and 3.1–3.5) under varied radial Ricci curvature, radial curvature, Ricci curvature and sectional curvature assumptions, generalizing and extending the work of Han-Li-Ren-Wei (2014) and Wei (2016). We also extend the notion of function or differential form growth to bundle-valued differential form growth of various types and discuss their interrelationship (see Theorem 5.4). These provide tools in extending the notion, integrability and decomposition of generalized harmonic forms to those of bundle-valued generalized harmonic forms, introducing Condition W for bundle-valued differential forms, and proving the duality theorem and the unity theorem, generalizing the work of Andreotti and Vesentini (1965) and Wei (2020). We then apply Hessian and Laplacian comparison theorems to obtain comparison theorems in mean curvature, generalized sharp Caffarelli-Kohn-Nirenberg type inequalities on Riemannian manifolds, the embedding theorem for weighted Sobolev spaces of functions on manifolds, geometric differential-integral inequalities, generalized sharp Hardy type inequalities on Riemannian manifolds, monotonicity formulas and vanishing theorems for differential forms of degree k with values in vector bundles, such as F-Yang-Mills fields (when F is the identity map, they are Yang-Mills fields), generalized Yang-Mills-Born-Infeld fields on manifolds, Liouville type theorems for F - harmonic maps (when $$F\left(t \right) = {1 \over p}{\left({2t} \right)^{{p \over 2}}},p > 1$$ , they become p-harmonic maps or harmonic maps if p = 2), and Dirichlet problems on starlike domains for vector bundle valued differential 1-forms and {tF}-harmonic maps (see Theorems 4.1, 7.3–7.7, 8.1, 9.1–9.3, 10.1, 11.2, 12.1 and 12.2), generalizing the work of Caffarelli et al. (1984) and Costa (2008), in which M = ℝn and its radial curvature K(r) = 0, the work of Wei and Li (2009), Chen et al. (2011, 2014), Dong and Wei (2011), Wei (2020) and Karcher and Wood (1984), etc. The boundary value problem for bundle-valued differential 1-forms is in contrast to the Dirichlet problem for p-harmonic maps to which the solution is due to Hamilton (1975) for the case p = 2 and RiemN ⩽ 0, and Wei (1998) for 1 < p < ∞.
Long-Term Regulation of Prolonged Epidemic Outbreaks in Large Populations via Adaptive Control: A Singular Perturbation Approach

In order to control highly-contagious and prolonged outbreaks, public health authorities intervene to institute social distancing, lock-down policies, and other Non-Pharmaceutical Interventions (NPIs). Given the high social, educational, psychological, and economic costs of NPIs, authorities tune them, alternatively tightening up or relaxing rules, with the result that, in effect, a relatively flat infection rate results. For example, during the summer of 2020 in parts of the United States, daily COVID-19 infection numbers dropped to a plateau. This letter approaches NPI tuning as a control-theoretic problem, starting from a simple dynamic model for social distancing based on the classical SIR epidemics model. Using a singular-perturbation approach, the plateau becomes a Quasi-Steady-State (QSS) of a reduced two-dimensional SIR model regulated by adaptive dynamic feedback. It is shown that the QSS can be assigned and it is globally asymptotically stable. Interestingly, the dynamic model for social distancing can be interpreted as a nonlinear integral controller. Problems of data fitting and parameter identifiability are also studied for this model. This letter also discusses how this simple model allows for a meaningful study of the effect of population size, vaccinations, and the emergence of second waves.
Comparison of Horizontal Difference Schemes for the Shallow Water Equations on a Sphere

The accuracy of horizontal difference schemes used in the hydrodynamics parts of General Circulation Models are compared by means of numerical experiments for the shallow water equations on a sphere. As expected, the phase lag of moving waves decreases as the order of accuracy of a scheme increases or as the grid resolution increases. Overall, Takano and Wurtele's partial fourth order energy and potential enstrophy conserving scheme on the C grid is most accurate. It is clearly superior to the other schemes for the Rossby-Haurwitz wave number 6 initial conditions for coarse grid resolution.
How Can We Know What Language Models Know

Recent work has presented intriguing results examining the knowledge contained in language models (LM) by having the LM fill in the blanks of prompts such as "Obama is a _ by profession". These prompts are usually manually created, and quite possibly sub-optimal; another prompt such as "Obama worked as a _" may result in more accurately predicting the correct profession. Because of this, given an inappropriate prompt, we might fail to retrieve facts that the LM does know, and thus any given prompt only provides a lower bound estimate of the knowledge contained in an LM. In this paper, we attempt to more accurately estimate the knowledge contained in LMs by automatically discovering better prompts to use in this querying process. Specifically, we propose mining-based and paraphrasing-based methods to automatically generate high-quality and diverse prompts, as well as ensemble methods to combine answers from different prompts. Extensive experiments on the LAMA benchmark for extracting relational knowledge from LMs demonstrate that our methods can improve accuracy from 31.1% to 39.6%, providing a tighter lower bound on what LMs know. We have released the code and the resulting LM Prompt And Query Archive (LPAQA) at https://github.com/jzbjyb/LPAQA.
Convalescent Plasma Therapy for COVID-19: A Graphical Mosaic of the Worldwide Evidence

Convalescent plasma has been used worldwide to treat patients hospitalized with coronavirus disease 2019 (COVID-19) and prevent disease progression. Despite global usage, uncertainty remains regarding plasma efficacy, as randomized controlled trials (RCTs) have provided divergent evidence regarding the survival benefit of convalescent plasma. Here, we argue that during a global health emergency, the mosaic of evidence originating from multiple levels of the epistemic hierarchy should inform contemporary policy and healthcare decisions. Indeed, worldwide matched-control studies have generally found convalescent plasma to improve COVID-19 patient survival, and RCTs have demonstrated a survival benefit when transfused early in the disease course but limited or no benefit later in the disease course when patients required greater supportive therapies. RCTs have also revealed that convalescent plasma transfusion contributes to improved symptomatology and viral clearance. To further investigate the effect of convalescent plasma on patient mortality, we performed a meta-analytical approach to pool daily survival data from all controlled studies that reported Kaplan-Meier survival plots. Qualitative inspection of all available Kaplan-Meier survival data and an aggregate Kaplan-Meier survival plot revealed a directionally consistent pattern among studies arising from multiple levels of the epistemic hierarchy, whereby convalescent plasma transfusion was generally associated with greater patient survival. Given that convalescent plasma has a similar safety profile as standard plasma, convalescent plasma should be implemented within weeks of the onset of future infectious disease outbreaks.
Diffusion Source Identification on Networks with Statistical Confidence

Diffusion source identification on networks is a problem of fundamental importance in a broad class of applications, including rumor controlling and virus identification. Though this problem has received significant recent attention, most studies have focused only on very restrictive settings and lack theoretical guarantees for more realistic networks. We introduce a statistical framework for the study of diffusion source identification and develop a confidence set inference approach inspired by hypothesis testing. Our method efficiently produces a small subset of nodes, which provably covers the source node with any prespecified confidence level without restrictive assumptions on network structures. Moreover, we propose multiple Monte Carlo strategies for the inference procedure based on network topology and the probabilistic properties that significantly improve the scalability. To our knowledge, this is the first diffusion source identification method with a practically useful theoretical guarantee on general networks. We demonstrate our approach via extensive synthetic experiments on well-known random network models, a large data set of hundreds of real-world networks, as well as a mobility network between cities concerning the COVID-19 spreading.
Attractive Characteristics of Mirrors

A summary of the attractive characteristics of mirror devices is presented. Recent progress in development of axisymmetric mirror devices is described. Potentialities of mirrors as a basis for D{sup 3}He fusion power generators and high flux neutron sources for fusion material tests are discussed. 16 refs., 2 figs.
Efficiently Answering Durability Prediction Queries

We consider a class of queries called durability prediction queries that arise commonly in predictive analytics, where we use a given predictive model to answer questions about possible futures to inform our decisions. Examples of durability prediction queries include "what is the probability that this financial product will keep losing money over the next 12 quarters before turning in any profit?" and "what is the chance for our proposed server cluster to fail the required service-level agreement before its term ends?" We devise a general method called Multi-Level Splitting Sampling (MLSS) that can efficiently handle complex queries and complex models---including those involving black-box functions---as long as the models allow us to simulate possible futures step by step. Our method addresses the inefficiency of standard Monte Carlo (MC) methods by applying the idea of importance splitting to let one "promising" sample path prefix generate multiple "offspring" paths, thereby directing simulation efforts toward more promising paths. We propose practical techniques for designing splitting strategies, freeing users from manual tuning. Experiments show that our approach is able to achieve unbiased estimates and the same error guarantees as standard MC while offering an order-of-magnitude cost reduction.
Queueing Dynamics and State Space Collapse in Fragmented Limit Order Book Markets

In modern equity markets, participants have a choice of many exchanges at which to trade, each of which typically operates as an electronic limit order book. This paper analyzes this setting as a m...
How Does Online Lending Influence Bankruptcy Filings

By providing quick and easy access to credit, online lending platforms may help borrowers overcome financial setbacks and/or refinance high-interest debt, thereby decreasing bankruptcy filings. On ...
Implicit Representations of Meaning in Neural Language Models

Does the effectiveness of neural language models derive entirely from accurate modeling of surface word co-occurrence statistics, or do these models represent and reason about the world they describe? In BART and T5 transformer language models, we identify contextual word representations that function as *models of entities and situations* as they evolve throughout a discourse. These neural representations have functional similarities to linguistic models of dynamic semantics: they support a linear readout of each entity’s current properties and relations, and can be manipulated with predictable effects on language generation. Our results indicate that prediction in pretrained neural language models is supported, at least in part, by dynamic representations of meaning and implicit simulation of entity state, and that this behavior can be learned with only text as training data.
Manifestly Phased Communication via Shared Session Types

Session types denote message protocols between concurrent processes, allowing a type-safe expression of inter-process communication. Although previous work demonstrate a well-defined notion of subtyping where processes have different perceptions of the protocol, these formulations were limited to linear session types where each channel of communication has a unique provider and client. In this paper, we extend subtyping to shared session types where channels can now have multiple clients instead of a single client. We demonstrate that this generalization can statically capture protocol requirements that span multiple phases of interactions of a client with a shared service provider, something not possible in prior proposals. Moreover, the phases are manifest in the type of the client.
Kernel Thinning

We introduce kernel thinning, a new procedure for compressing a distribution $\mathbb{P}$ more effectively than i.i.d. sampling or standard thinning. Given a suitable reproducing kernel $\mathbf{k}$ and $\mathcal{O}(n^2)$ time, kernel thinning compresses an $n$-point approximation to $\mathbb{P}$ into a $\sqrt{n}$-point approximation with comparable worst-case integration error across the associated reproducing kernel Hilbert space. With high probability, the maximum discrepancy in integration error is $\mathcal{O}_d(n^{-\frac{1}{2}}\sqrt{\log n})$ for compactly supported $\mathbb{P}$ and $\mathcal{O}_d(n^{-\frac{1}{2}} \sqrt{(\log n)^{d+1}\log\log n})$ for sub-exponential $\mathbb{P}$ on $\mathbb{R}^d$. In contrast, an equal-sized i.i.d. sample from $\mathbb{P}$ suffers $\Omega(n^{-\frac14})$ integration error. Our sub-exponential guarantees resemble the classical quasi-Monte Carlo error rates for uniform $\mathbb{P}$ on $[0,1]^d$ but apply to general distributions on $\mathbb{R}^d$ and a wide range of common kernels. We use our results to derive explicit non-asymptotic maximum mean discrepancy bounds for Gaussian, Mat\'ern, and B-spline kernels and present two vignettes illustrating the practical benefits of kernel thinning over i.i.d. sampling and standard Markov chain Monte Carlo thinning, in dimensions $d=2$ through $100$.
Battle between Rate and Error in Minimizing Age of Information

In this paper, we consider a status update system, in which update packets are sent to the destination via a wireless medium that allows for multiple rates, where a higher rate also naturally corresponds to a higher error probability. The data freshness is measured using age of information, which is defined as the age of the recent update at the destination. A packet that is transmitted with a higher rate, will encounter a shorter delay and a higher error probability. Thus, the choice of the transmission rate affects the age at the destination. In this paper, we design a low-complexity scheduler that selects between two different transmission rate and error probability pairs to be used at each transmission epoch. This problem can be cast as a Markov Decision Process. We show that there exists a threshold-type policy that is age-optimal. More importantly, we show that the objective function is quasi-convex or non-decreasing in the threshold, based on the system parameters values. This enables us to devise a low-complexity algorithm to minimize the age. These results reveal an interesting phenomenon: While choosing the rate with minimum mean delay is delay-optimal, this does not necessarily minimize the age.
Fuel-optimal propulsive reboost of flexible spacecraft

This paper presents for the first time an exact solution to the fuel optimal propulsive reboost problem for flexible spacecraft. The spacecraft undergoes rigid-body motion and flexible-body motion and reboost is achieved propulsively through the use of reaction control jets. The exact fuel optimal solution to the associated minimization problem is found numerically based on an adaptive grid bisection search. The reboost of the floating harmonic oscillator reveals properties of the fuel optimal solution. Nondimensional plots of minimum fuel versus maneuver time expose the nature of the solution classes depending on the maneuver time. Some very interesting properties are observed, among them, the shifting of impulses to the smaller oscillator mass, and comparisons are made with near fuel optimal solutions obtained by other investigators.
Health education telecommunications experiment

The Health/Education Telecommunications Experiment (HET) was conducted jointly by NASA and HEW on NASA's ATS-6 communications satellite. This experiment actually consisted of six experiments testing health and education applications of a communication spacecraft producing a broadcast of color television directly from space to over 120 low-cost receivers located in remote rural areas throughout the U.S. (including Alaska). The experiments were conducted over the period from 2 July 1974 to 20 May 1975 and operated on an almost daily basis. The overall telecommunications system to support these experiments consisted of many elements: The ATS-6 spacecraft; five different types of earth stations consisting of 120 video receive terminals, 51 telephony tranceivers and eight video originating terminals of three different types. Actual performance of the equipment as measured in the field was shown to equal or exceed predicted values.
Imaging-guided cardiac resynchronization therapy: A meta-analysis of randomized controlled trials

BACKGROUND Among patients with heart failure and left ventricular (LV) dysfunction despite guideline directed medical therapy, cardiac resynchronization (CRT) is an effective technology to reverse LV remodeling. Given that a large portion of patients are non-responders, alternatives to traditional LV-lead placement have been explored. A promising alternative is image targeted placement of an LV-lead to latest mechanically activated segment without scar. METHODS Electronic database search for randomized controlled trials (RCTs) that evaluated the imaging-guided LV-lead placement on clinical, echocardiographic, and functional outcomes. The primary outcome was a composite of mortality and heart failure hospitalization. The secondary outcomes included CRT responders, New York Heart Association (NYHA), 6-minute walk test, Minnesota Living with Heart Failure Questionnaire (MLHFQ), and ejection fraction (EF) changes. RESULTS Analysis included 4 RCTs of 691 patients with an average follow-up of 2 years (age 69.5 ± 10.3 years, 76% males, 54% ischemic cardiomyopathy, 81% with NYHA classes III/IV, and EF of 24.4% ± 8). The most common site for LV-lead paced segment was the anterolateral segment (45%) and at mid-LV (49%). Compared with the control, imaging-guided LV-lead placement was associated with a significant reduction of the primary outcome (hazard ratio [HR] = 0.60; 95% CI = 0.40-0.88; p = .01), higher CRT responders (odd ratio [OR] = 2.10; p < .01), more NYHA improvements by ≥1 (OR = 1.89; p = .01), increased 6MWT (mean difference [MD] = 25.78 feet; p < .01), and lower MLHFQ (MD = -4.04; p = .04), without significant differences in the LVEF (p = .08). CONCLUSIONS In patients undergoing CRT, imaging-guided LV-lead placement was associated with improved clinical, echocardiographic, and functional status.
Optimization of Reconfigurable Intelligent Surfaces Through Trace Maximization

Reconfigurable Intelligent Surfaces (RIS) have received significant attention recently as an innovation for enhanced connectivity, capacity, and energy efficiency in future wireless networks. Recent works indicate that such RIS-augmented communications can significantly enhance performance by intelligently shaping the characteristics of the multipath propagation environment to focus the energy in a desired direction and to circumvent impediments such as blockage, especially for communication at millimeter-wave (mmW), Terahertz (THz) and higher frequencies. In this paper, we investigate optimized (amplitude and phase) RIS design in a point-to-point multipath MIMO link and study the impact on link capacity under the assumption of perfect channel state information at the transmitter (TX), receiver (RX) and RIS. Specifically, we propose RIS design based on the maximization of the trace of the composite TX-RIS-RX link matrix which is a measure of the average power at the RX. We propose two RIS designs: a diagonal RIS matrix, and a general RIS matrix representing a more advanced architecture. The optimum design, in both cases, corresponds to calculating the dominant eigenvector of certain Hermitian matrices induced by the component channel matrices. We illustrate the capacity performance of the optimized RIS designs and compare them to a baseline design (random amplitudes and phases) and a recently proposed low-complexity phase-only design. We present results for sparse and rich multipath, and also consider the impact of line-of-sight paths. Our results show that while all designs offer comparable capacity at high signal-to-noise ratios (SNRs), the proposed optimum designs offer substantial gains at lower SNRs.
Elemental Abundances in M31: Gradients in the Giant Stellar Stream

We analyze existing measurements of [Fe/H] and [$\alpha$/Fe] for individual red giant branch (RGB) stars in the Giant Stellar Stream (GSS) of M31 to determine whether spatial abundance gradients are present. These measurements were obtained from low- ($R \sim 3000$) and moderate- ($R \sim 6000$) resolution Keck/DEIMOS spectroscopy using spectral synthesis techniques as part of the Elemental Abundances in M31 survey. From a sample of 62 RGB stars spanning the GSS at 17, 22, and 33 projected kpc, we measure a [Fe/H] gradient of $-$0.018 $\pm$ 0.003 dex kpc$^{-1}$ and negligible [$\alpha$/Fe] gradient with M31-centric radius. We investigate GSS abundance patterns in the outer halo using additional [Fe/H] and [$\alpha$/Fe] measurements for 6 RGB stars located along the stream at 45 and 58 projected kpc. These abundances provide tentative evidence that the trends in [Fe/H] and [$\alpha$/Fe] beyond 40 kpc in the GSS are consistent with those within 33 kpc. We also compare the GSS abundances to 65 RGB stars located along the possibly related Southeast (SE) shelf substructure at 12 and 18 projected kpc. The abundances of the GSS and SE shelf are consistent, supporting a common origin hypothesis, although this interpretation may be complicated by the presence of [Fe/H] gradients in the GSS. We discuss the abundance patterns in the context of photometric studies from the literature and explore implications for the properties of the GSS progenitor, suggesting that the high $\langle$[$\alpha$/Fe]$\rangle$ of the GSS (+0.40 $\pm$ 0.05 dex) favors a major merger scenario for its formation.
SANM: a symbolic asymptotic numerical solver with applications in mesh deformation

Solving nonlinear systems is an important problem. Numerical continuation methods efficiently solve certain nonlinear systems. The Asymptotic Numerical Method (ANM) is a powerful continuation method that usually converges faster than Newtonian methods. ANM explores the landscape of the function by following a parameterized solution curve approximated with a high-order power series. Although ANM has successfully solved a few graphics and engineering problems, prior to our work, applying ANM to new problems required significant effort because the standard ANM assumes quadratic functions, while manually deriving the power series expansion for nonquadratic systems is a tedious and challenging task. This paper presents a novel solver, SANM, that applies ANM to solve symbolically represented nonlinear systems. SANM solves such systems in a fully automated manner. SANM also extends ANM to support many nonquadratic operators, including intricate ones such as singular value decomposition. Furthermore, SANM generalizes ANM to support the implicit homotopy form. Moreover, SANM achieves high computing performance via optimized system design and implementation. We deploy SANM to solve forward and inverse elastic force equilibrium problems and controlled mesh deformation problems with a few constitutive models. Our results show that SANM converges faster than Newtonian solvers, requires little programming effort for new problems, and delivers comparable or better performance than a hand-coded, specialized ANM solver. While we demonstrate on mesh deformation problems, SANM is generic and potentially applicable to many tasks.
Effects of resolution inhomogeneity in large-eddy simulation

Discretizations with inhomogeneous resolution affect large-eddy simulations (LES) through the commutator of the filtering and differentiation operators. We employ a multiscale asymptotic analysis to investigate the statistical characteristics of this commutator, which can serve as a target for commutation models. Further, we demonstrate how the neglect of this commutator manifests numerically, drawing a connection between the so-called commutation error and the dispersion relation of the underlying numerics. A modeling approach for the commutator is proposed that is based on the numerical properties of the LES evolution equation.
Additive Tensor Decomposition Considering Structural Data Information

Tensor data with rich structural information become increasingly important in process modeling, monitoring, and diagnosis in manufacturing medical and other applications. Here structural information is referred to the information of tensor components such as sparsity, smoothness, low-rank, and piecewise constancy. To reveal useful information from tensor data, we propose to decompose the tensor into the summation of multiple components based on their different structural information. In this article, we provide a new definition of structural information in tensor data. We then propose an additive tensor decomposition (ATD) framework to extract useful information from tensor data. This framework specifies a high dimensional optimization problem to obtain the components with distinct structural information. An alternating direction method of multipliers (ADMM) algorithm is proposed to solve it, which is highly parallelable and thus suitable for the proposed optimization problem. Two simulation examples and a real case study in medical image analysis illustrate the versatility and effectiveness of the ATD framework.
Quantifying the impact of state mixing on the Rydberg excitation blockade

This paper demonstrates a new technique to quantify the negative impact of state-mixing interactions on the Rydberg excitation blockade near the Forster resonance. By observing state-mixing events on a shot-by-shot basis, the authors identify the mechanisms responsible for large rates of mixing, and determine the extent to which mixing breaks the blockade in a mesoscopic ensemble.
Price Leadership and Uncertainty About Future Costs

Does uncertainty about future wholesale prices facilitate coordination? We address this question in the context of the Chilean retail-gasoline industry, where a policy intervention (Mepco) limited the week-to-week variation of wholesale prices. First, we show that Mepco caused a decrease in retail-gasoline margins in Chile. Second, using price leadership intensity as a proxy for the strength of coordination in a market, we show that margins decreased more in markets with higher leadership intensity. We rationalize these findings in a repeated-game framework, showing that a reduction in uncertainty about future wholesale prices hinders price coordination incentives, and has a greater impact in more coordinated markets.
Discovering single nucleotide variants and indels from bulk and single-cell ATAC-seq

Genetic variants and de novo mutations in regulatory regions of the genome are typically discovered by whole-genome sequencing (WGS), however WGS is expensive and most WGS reads come from non-regulatory regions. The Assay for Transposase-Accessible Chromatin (ATAC-seq) generates reads from regulatory sequences and could potentially be used as a low-cost 'capture' method for regulatory variant discovery, but its use for this purpose has not been systematically evaluated. Here we apply seven variant callers to bulk and single-cell ATAC-seq data and evaluate their ability to identify single nucleotide variants (SNVs) and insertions/deletions (indels). In addition, we develop an ensemble classifier, VarCA, which combines features from individual variant callers to predict variants. The Genome Analysis Toolkit (GATK) is the best-performing individual caller with precision/recall on a bulk ATAC test dataset of 0.92/0.97 for SNVs and 0.87/0.82 for indels within ATAC-seq peak regions with at least 10 reads. On bulk ATAC-seq reads, VarCA achieves superior performance with precision/recall of 0.99/0.95 for SNVs and 0.93/0.80 for indels. On single-cell ATAC-seq reads, VarCA attains precision/recall of 0.98/0.94 for SNVs and 0.82/0.82 for indels. In summary, ATAC-seq reads can be used to accurately discover non-coding regulatory variants in the absence of whole-genome sequencing data and our ensemble method, VarCA, has the best overall performance.
Mach limits in analytic spaces

Abstract We address the Mach limit problem for the Euler equations in the analytic spaces. We prove that, given analytic data, the solutions to the compressible Euler equations are uniformly bounded in a suitable analytic norm and then show that the convergence toward the incompressible Euler solution holds in the analytic norm. We also show that the same results hold more generally for Gevrey data with the convergence in the Gevrey norms.
Disturbance structures canopy and understory productivity along an environmental gradient.

Disturbances often disproportionately impact different vegetation layers in forests and other vertically stratified ecosystems, shaping community structure and ecosystem function. However, disturbance-driven changes may be mediated by environmental conditions that affect habitat quality and species interactions. In a decade-long field experiment, we tested how kelp forest net primary productivity (NPP) responds to repeated canopy loss along a gradient in grazing and substrate suitability. We discovered that habitat quality can mediate the effects of intensified disturbance on canopy and understory NPP. Experimental annual and quarterly disturbances suppressed total macroalgal NPP, but effects were strongest in high-quality habitats that supported dense kelp canopies that were removed by disturbance. Understory macroalgae partly compensated for canopy NPP losses and this effect magnified with increasing habitat quality. Disturbance-driven increases in understory NPP were still rising after 5-10 years of disturbance, demonstrating the value of long-term experimentation for understanding ecosystem responses to changing disturbance regimes.
On the nature of Hawking's incompleteness for the Einstein-vacuum equations: The regime of moderately spatially anisotropic initial data

In the mathematical physics literature, there are heuristic arguments, going back three decades, suggesting that for an open set of initially smooth solutions to the Einstein-vacuum equations in high dimensions, stable, approximately monotonic curvature singularities can dynamically form along a spacelike hypersurface. In this article, we study the Cauchy problem and give a rigorous proof of this phenomenon in sufficiently high dimensions, thereby providing the first constructive proof of stable curvature blowup (without symmetry assumptions) along a spacelike hypersurface as an effect of pure gravity. Our proof applies to an open subset of regular initial data satisfying the assumptions of Hawking's celebrated "singularity" theorem, which shows that the solution is geodesically incomplete but does not reveal the nature of the incompleteness. Specifically, our main result is a proof of the dynamic stability of the Kasner curvature singularity for a subset of Kasner solutions whose metrics exhibit only moderately (as opposed to severely) spatially anisotropic behavior. Of independent interest is our method of proof, which is more robust than earlier approaches in that i) it does not rely on approximate monotonicity identities and ii) it accommodates the possibility that the solution develops very singular high-order spatial derivatives, whose blowup rates are allowed to be, within the scope of our bootstrap argument, much worse than those of the base-level quantities driving the fundamental blowup. For these reasons, our approach could be used to obtain similar blowup results for various Einstein-matter systems in any number of spatial dimensions for solutions corresponding to an open set of moderately spatially anisotropic initial data, thus going beyond the nearly spatially isotropic regime treated in earlier works.
Is tax aggressiveness associated with tax litigation risk? Evidence from D&O Insurance

This study uses directors’ and officers’ (D&O) insurance data to examine the relation between tax aggressiveness and tax litigation risk. D&O insurance covers litigation costs for tax-related cases. Thus D&O insurance premiums provide an independent and direct assessment of the risk in a firm’s tax aggressive strategies, which mitigates some of the challenges in studying tax risk. Based on pricing decisions, D&O insurers appear to view tax aggressiveness, as measured by industry- and size-adjusted cash effective tax rates (a measure where higher rates are associated with more aggressiveness), as increasing tax-related litigation risk. Regarding tax uncertainty, premiums increase (decrease) as unrecognized tax benefits (UTB-related settlements with tax authorities) increase. Finally, D&O insurers focus on firms with outbound tax haven activity when pricing tax aggressiveness. Overall, this suggests D&O insurers include aspects of both low taxes and tax uncertainty when pricing tax litigation risk.
Split-enzyme immunoassay to monitor EGFR-HER2 heterodimerization on cell surfaces.

Abstract Over 30,000 protein-protein interactions with pathological implications have been identified; yet, discovering and investigating drugs that target these specific interactions is greatly limited by the inability to monitor native protein-protein interactions (PPIs) efficiently. The two most frequently used tools to monitor PPIs, resonance-energy transfer (RET) assays and protein complementation assays (PCA), face significant limitations. RET assays have a narrow working range of 10 to 50 A, while PCA require permanent attachment of a reporter probe to a protein of interest by chemical conjugation or genetic engineering. We developed a non-invasive assay platform to measure PPIs without modifications to the proteins of interest and is functional at a greater working range than RET assays. We demonstrate our approach by monitoring the EGFR-HER2 heterodimerization on relevant cell surfaces, utilizing various EGFR- and HER2-specific binders (e.g., Fab, DARPin, and VHH) fused with small fragments of a tri-part split-luciferase derived from NanoLuc®. Following independent binding of the binder fusions to their respective targets, the dimerization of EGFR and HER2 induces complementation of the luciferase fragments into a functional native structure, producing glow-type luminescence. We have confirmed the functionality of the platform to monitor EGFR-HER2 dimerization induction and inhibition. Statement of Significance We describe a platform technology for rapid monitoring of protein-protein interactions (PPIs). Our approach is uses a luciferase split into three parts – two short peptide “tags” and a large third fragment. Each of the short peptides can be fused to antibodies which bind to domains of a target antigens which orients the two tags and facilitates refolding of an active enzyme. To our knowledge this is the first example of a split-enzyme used to monitor PPIs without requiring any modification of the target proteins. We demonstrate our approach on the important PPI of HER2 and EGFR. Significantly, we quantify stimulation and inhibition of these partners, opening the possibility of using our approach to assess potential drugs without engineering cells.
Geometric Perspectives on Fundamental Solutions in the Linearized Satellite Relative Motion Problem

Abstract Understanding natural relative motion trajectories is critical to enable fuel-efficient multi-satellite missions operating in complex environments. This paper studies the problem of computing and efficiently parameterizing satellite relative motion solutions for linearization about a closed chief orbit. By identifying the analytic relationship between Lyapunov–Floquet transformations of the relative motion dynamics in different coordinate systems, new means are provided for rapid computation and exploration of the types of close-proximity natural relative motion available in various applications. The approach is demonstrated for the Keplerian relative motion problem with general eccentricities in multiple coordinate representations. The Keplerian assumption enables an analytic approach, leads to new geometric insights, and allows for comparison to prior linearized relative motion solutions.
Subspace Clustering Based Analysis of Neural Networks

Tools to analyze the latent space of deep neural networks provide a step towards better understanding them. In this work, we motivate sparse subspace clustering (SSC) with an aim to learn affinity graphs from the latent structure of a given neural network layer trained over a set of inputs. We then use tools from Community Detection to quantify structures present in the input. These experiments reveal that as we go deeper in a network, inputs tend to have an increasing affinity to other inputs of the same class. Subsequently, we utilise matrix similarity measures to perform layer-wise comparisons between affinity graphs. In doing so we first demonstrate that when comparing a given layer currently under training to its final state, the shallower the layer of the network, the quicker it is to converge than the deeper layers. When performing a pairwise analysis of the entire network architecture, we observe that, as the network increases in size, it reorganises from a state where each layer is moderately similar to its neighbours, to a state where layers within a block have high similarity than to layers in other blocks. Finally, we analyze the learned affinity graphs of the final convolutional layer of the network and demonstrate how an input’s local neighbourhood affects its classification by the network.
Improved Revenue Bounds for Posted-Price and Second-Price Mechanisms

AbstractHow to optimize posted price mechanisms?The sequential posted-price (SPP) mechanism is one of the widely used selling mechanisms in practice. In this mechanism, the seller presents each buy...