cssi/SciSciGPT-SciSciNet huggingface dataset:
Prolonged anesthesia alters brain synaptic architecture

Prolonged medically induced coma (pMIC) is carried out routinely in intensive care medicine. pMIC leads to cognitive impairment, yet the underlying neuromorphological correlates are still unknown, as no direct studies of MIC exceeding ∼6 h on neural circuits exist. Here, we establish pMIC (up to 24 h) in adolescent and mature mice, and combine longitudinal two-photon imaging of cortical synapses with repeated behavioral object recognition assessments. We find that pMIC affects object recognition, and that it is associated with enhanced synaptic turnover, generated by enhanced synapse formation during pMIC, while the postanesthetic period is dominated by synaptic loss. Our results demonstrate major side effects of prolonged anesthesia on neural circuit structure.
On the nonlinear Dysthe equation

Abstract This work is dedicated to putting on a solid analytic ground the theory of local well-posedness for the two dimensional Dysthe equation. This equation can be derived from the incompressible Navier–Stokes equation after performing an asymptotic expansion of a wavetrain modulation to the fourth order. Recently, this equation has been used to numerically study rare phenomena on large water bodies such as rogue waves. In order to study well-posedness, we use Strichartz, and improved smoothing and maximal function estimates. We follow ideas from the pioneering work of Kenig, Ponce and Vega, but since the equation is highly anisotropic, several technical challenges had to be resolved. We conclude our work by also presenting an ill-posedness result.
Improvements on SCORE, Especially for Weak Signals

A network may have weak signals and severe degree heterogeneity, and may be very sparse in one occurrence but very dense in another. SCORE (Ann. Statist. 43, 57–89, 2015) is a recent approach to network community detection. It accommodates severe degree heterogeneity and is adaptive to different levels of sparsity, but its performance for networks with weak signals is unclear. In this paper, we show that in a broad class of network settings where we allow for weak signals, severe degree heterogeneity, and a wide range of network sparsity, SCORE achieves prefect clustering and has the so-called “exponential rate” in Hamming clustering errors. The proof uses the most recent advancement on entry-wise bounds for the leading eigenvectors of the network adjacency matrix. The theoretical analysis assures us that SCORE continues to work well in the weak signal settings, but it does not rule out the possibility that SCORE may be further improved to have better performance in real applications, especially for networks with weak signals. As a second contribution of the paper, we propose SCORE+ as an improved version of SCORE. We investigate SCORE+ with 8 network data sets and found that it outperforms several representative approaches. In particular, for the 6 data sets with relatively strong signals, SCORE+ has similar performance as that of SCORE, but for the 2 data sets (Simmons, Caltech) with possibly weak signals, SCORE+ has much lower error rates. SCORE+ proposes several changes to SCORE. We carefully explain the rationale underlying each of these changes, using a mixture of theoretical and numerical study.
PecanPy: a fast, efficient, and parallelized Python implementation of node2vec.

SUMMARY Learning low-dimensional representations (embeddings) of nodes in large graphs is key to applying machine learning on massive biological networks. Node2vec is the most widely used method for node embedding. However, its original Python and C ++ implementations scale poorly with network density, failing for dense biological networks with hundreds of millions of edges. We have developed PecanPy, a new Python implementation of node2vec that uses cache-optimized compact graph data structures and precomputing/parallelization to result in fast, high-quality node embeddings for biological networks of all sizes and densities. AVAILABILITY PecanPy software is freely available at https://github.com/krishnanlab/PecanPy. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
From Plants to Ants: Fungal Modification of Leaf Lipids for Nutrition and Communication in the Leaf-Cutter Ant Fungal Garden Ecosystem.

ABSTRACT Lipids are essential to all living organisms, as an energy source, as an important cellular structural component, and as a communication tool. In this study, we used global lipidomic methods to evaluate the lipids in leaf-cutter ant fungal gardens. Leaf-cutter ants and their coevolved fungal cultivar, Leucoagaricus gongylophorus, are a model mutualistic system. The fungus enzymatically digests fresh plant material that the ants cut and deliver, converting energy and nutrients from plants and providing them to the ants through specialized hyphal swellings called gongylidia. Using combined liquid chromatography, ion mobility spectrometry, and tandem mass spectrometry, we evaluated differences between the molecular species of lipids in the leaf-cutter ant fungal garden ecosystem. This lipidomic study characterized leaves that are fed to the gardens, gongylidia that are produced by the fungus to feed the ants, and spatially resolved regions of the fungal garden through stages of leaf degradation. Lipids containing alpha-linolenic acid (18:3) were enriched in leaves and the top of the gardens but not dominant in the middle or bottom regions. Gongylidia were dominated by lipids containing linoleic acid (18:2). To evaluate the communicative potential of the lipids in fungal gardens, we conducted a behavioral experiment that showed Atta leaf-cutter ants responded differently to 18:3 and 18:2 fatty acids, with aggression toward 18:3 and attraction for 18:2. This work demonstrates the role of lipids in both the transfer of energy and as an interkingdom communication tool in leaf-cutter ant fungal gardens. IMPORTANCE In this work, we examined the role of lipids in the mutualism between leaf-cutter ants and fungus. These ants cut fresh leaf material, which they provide to their fungal cultivar, that converts energy and nutrients from the plants and provides it to the ants in specialized hyphal swellings called gongylidia. This work constitutes the first example of a global lipidomics study of a symbiotic system and provides insights as to how the fungus modifies plant lipids into a usable source for the ants. Through a behavioral experiment, this work also demonstrates how lipids can be used as an interkingdom communication tool, in this case, as an attractant rather than as a repellant, which is more often seen. Author Video: An author video summary of this article is available.
Uniqueness of the measure of maximal entropy for the standard map

In this paper we prove that for sufficiently large parameters the standard map has a unique measure of maximal entropy (m.m.e.). Moreover, we prove: the m.m.e. is Bernoulli, and the periodic points with Lyapunov exponents bounded away from zero equidistribute with respect to the m.m.e. We prove some estimates regarding the Hausdorff dimension of the m.m.e. and about the density of the support of the measure on the manifold. For a generic large parameter, we prove that the support of the m.m.e. has Hausdorff dimension $2$. We also obtain the $C^2$-robustness of several of these properties.
Hurricane Sandy: Damages, Disruptions and Pathways to Recovery

Critical infrastructure and public utility systems are often severely damaged by natural disasters like hurricanes. Based on a framework of household disaster resilience, this paper focuses on the role of utility disruption on household-level recovery in the context of Hurricane Sandy. Using data collected through a two-stage household survey, it first confirms that the sample selection bias is not present, thus the responses can be estimated sequentially. Second, it quantitatively examines factors contributing to hurricane-induced property damages and household-level recovery. The finding suggests that respondents who suffered from a longer period of utility disruptions (e.g., electricity, water, gas, phone/cell phone, public transportation) are more likely to incur monetary losses and have more difficulty in recovering. Effective preparedness activities (e.g., installing window protections, having an electric generator) can have positive results in reducing adverse shocks. Respondents with past hurricane experiences and higher educational attainments are found to be more resilient compared to others. Finally, the paper discusses the implications of the findings on effective preparation and mitigation strategies for future disasters.
Rejoinder: Learning Optimal Distributionally Robust Individualized Treatment Rules.

We thank the opportunity offered by editors for this discussion and the discussants for their insightful comments and thoughtful contributions. We also want to congratulate Kallus (2020) for his in...
The final orbital separation in common envelope evolution

In the majority of population synthesis calculations of close binary stars, the common envelope (CE) phase is modeled using a standard prescription based upon the conservation of energy, known as the alpha prescription. In this prescription, the orbital separation of the secondary and giant core at the end of the CE phase is taken to be the orbital separation when the envelope becomes unbound. However, recent observations of planetary nebulae with binary cores (BPNe), believed to be the immediate products of CE evolution, indicate orbital periods that are significantly shorter than predicted by population synthesis models using the alpha prescription. We argue that unbinding the envelope provides a necessary, but not sufficient, condition to escape a merger during CE evolution. The spiral-in of the secondary must also be halted. This requires the additional dynamical constraint that the frictional torque on the secondary be reduced to approximately zero. In this paper, we undertake a preliminary examination of the effect of adding this dynamical constraint in population synthesis calculations of BPNe. We assume that the frictional torque will be sufficiently reduced when the secondary enters a region within the giant where the mass-radius profile is flat. We crudely estimate the location of this region as a function of the core mass based upon existing stellar models of AGB stars between 1 and 7 M ⊙ . We calculate a theoretical orbital period distribution of BPNe using a population synthesis code that incorporates this dynamical constraint along with the alpha prescription.
The Role of Stochasticity in Noise-Induced Tipping Point Cascades: A Master Equation Approach

Tipping points have been shown to be ubiquitous, both in models and empirically in a range of physical and biological systems. The question of how tipping points cascade through systems has been less explored and is an important one. A study of noise-induced tipping, in particular, could provide key insights into tipping cascades. Here, we consider a specific example of a simple model system that could have cascading tipping points. This model consists of two interacting populations with underlying Allee effects and stochastic dynamics, in separate patches connected by dispersal, which can generate bistability. From an ecological standpoint, we look for rescue effects whereby one population can prevent the collapse of a second population. As a way to investigate the stochastic dynamics, we use an individual-based modeling approach rooted in chemical reaction network theory. Then, using continuous-time Markov chains and the theory of first passage times, we essentially approximate, or emulate, the original high-dimensional model by a Markov chain with just three states, where each state corresponds to a combination of population thresholds. Analysis of this reduced model shows when the system is likely to recover, as well as when tipping cascades through the whole system.
Cyclic Sieving for cyclic codes

Abstract Prompted by a question of Jim Propp, this paper examines the cyclic sieving phenomenon (CSP) in certain cyclic codes. For example, it is shown that, among dual Hamming codes over F q , the generating function for codedwords according to the major index statistic (resp. the inversion statistic) gives rise to a CSP when q = 2 or q = 3 (resp. when q = 2 ). A byproduct is a curious characterization of the irreducible polynomials in F 2 [ x ] and F 3 [ x ] that are primitive.
Hessenberg varieties of parabolic type

This paper studies the geometry and combinatorics of three interrelated varieties: Springer fibers, Steinberg varieties, and parabolic Hessenberg varieties. We prove that each parabolic Hessenberg variety is the pullback of a Steinberg variety under the projection of the flag variety to an appropriate partial flag variety and we give three applications of this result. The first application constructs an explicit paving of all Steinberg varieties in Lie type A in terms of semistandard tableaux. As a result, we obtain an elementary proof of a theorem of Steinberg and Shimomura that the well-known Kostka numbers count the maximal-dimensional irreducible components of Steinberg varieties. The second application proves an open conjecture for certain parabolic Hessenberg varieties in Lie type A by showing that their Betti numbers equal those of a specific union of Schubert varieties. The third application proves that the irreducible components of parabolic Hessenberg varieties are in bijection with the irreducible components of the Steinberg variety. All three of these applications extend our geometric understanding of the three varieties at the heart of this paper, a full understanding of which is unknown even for Springer varieties, despite over forty years’ worth of work.
Electricity Consumption and Temperature: Evidence from Satellite Data

Past studies on the relationship between electricity consumption and temperature have primarily focused on individual countries. Many regions are understudied as a result of data constraint. This paper studies the relationship on a global scale, overcoming the data constraint by using grid-level night light and temperature data. Mostly generated by electricity and recorded by satellites, night light has a strong linear relationship with electricity consumption and is correlated with both its extensive and intensive margins. Using night light as a proxy for electricity consumption at the grid level, we find: (1) there is a U-shaped relationship between electricity consumption and temperature; (2) the critical point of temperature for minimum electricity consumption is around 14.6°C for the world and it is higher in urban and more industrial areas; and (3) the impact of temperature on electricity consumption is persistent. Sub-Saharan African countries, while facing a large electricity deficit already, are particularly vulnerable to climate change: a 1°C increase in temperature is estimated to increase their electricity demand by 6.7% on average.
Abridging Source Code

In this paper, we consider the problem of source code abridgment, where the goal is to remove statements from a source code in order to display the source code in a small space, while at the same time leaving the ``important'' parts of the source code intact, so that an engineer can read the code and quickly understand purpose of the code. To this end, we develop an algorithm that looks at a number of examples, human-created source code abridgments, and learns how to remove lines from the code in order to mimic the human abridger. The learning algorithm takes into account syntactic features of the code, as well as semantic features such as control flow and data dependencies. Through a comprehensive user study, we show that the abridgments that our system produces can decrease the time that a user must look at code in order to understand its functionality, as well as increase the accuracy of the assessment, while displaying the code in a greatly reduced area.
Neural synchrony predicts children's learning of novel words.

Abstract Social interactions, such as joint book reading, have a well-studied influence on early development and language learning. Recent work has begun to investigate the neural mechanisms that underlie shared representations of input, documenting neural synchrony (measured using intersubject temporal correlations of neural activity) between individuals exposed to the same stimulus. Neural synchrony has been found to predict the quality of engagement with a stimulus and with communicative cues, but studies have yet to address how neural synchrony among children may relate to real-time learning. Using functional near-infrared spectroscopy (fNIRS), we recorded the neural activity of 45 children (3.5–4.5 years) during joint book reading with an adult experimenter. The custom children's book contained four novel words and objects embedded in an unfolding story, as well as a range of narrative details about object functions and character roles. We observed synchronized neural activity between child participants during book reading and found a positive correlation between learning and intersubject neural synchronization in parietal cortex, an area implicated in narrative-level processing in adult research. Our findings suggest that signature patterns of neural engagement with the dynamics of stories facilitate children's learning.
Bifurcation to Instability Through the Lens of the Maslov Index

The Maslov index is a powerful tool for assessing the stability of solitary waves. Although it is difficult to calculate in general, a framework for doing so was recently established for singularly perturbed systems (Cornwell and Jones in SIAM J Appl Dyn Syst 17(1):754–787, 2018). In this paper, we apply this framework to standing wave solutions of a three-component activator-inhibitor model. These standing waves are known to become unstable as parameters vary. Our goal is to see how this established stability criterion manifests itself in the Maslov index calculation. In so doing, we obtain new insight into the mechanism for instability. We further suggest how this mechanism might be used to reveal new instabilities in singularly perturbed models.
Method for Increasing Wind Tunnel Mach Number for Large-Scale Inlet Testing

FOR the development of efficient mixed-compression inlet systems for supersonic aircraft, relatively large'-scale models are required to accurately assess the inlet performance. Large-scale models are particularly essential as design inlet Mach numbers increase above about M°° = 2.2. That is, for mixed-compression inlets the throat contraction ratio increases, resulting in small throat heights and large negative pressure gradients; under these conditions accurate measurements of the inlet performance parameters would be difficult without large models. For inlet tests above Mach numbers of 3.5, few wind tunnels exist that are suitable for large-scale (50 cm capture diam) models. To overcome this problem, a simple and inexpensive technique to extend the Mach number range in existing wind tunnels for large-scale inlet testing has been investigated. The technique involves an expansion plate, wherein flow is expanded two-dimensionally around the sharp leading-edge to create a local uniform flow field with higher than freestream Mach numbers. Previously, flow expansion plates have been used to vary the Mach number in supersonic tunnels,' and occasionally they have been used to achieve special test conditions. The objective of the investigation was to show the feasibility of using locally expanded flow to extend the Mach number range of supersonic wind tunnels for large-scale inlet testing. The primary considerations of this investigation were to determine the Mach number increase possible and to define the uniformity of the expanded flowfield.
KO codes: inventing nonlinear encoding and decoding for reliable wireless communication via deep-learning

Landmark codes underpin reliable physical layer communication, e.g., Reed-Muller, BCH, Convolution, Turbo, LDPC and Polar codes: each is a linear code and represents a mathematical breakthrough. The impact on humanity is huge: each of these codes has been used in global wireless communication standards (satellite, WiFi, cellular). Reliability of communication over the classical additive white Gaussian noise (AWGN) channel enables benchmarking and ranking of the different codes. In this paper, we construct KO codes, a computationaly efficient family of deep-learning driven (encoder, decoder) pairs that outperform the state-of-the-art reliability performance on the standardized AWGN channel. KO codes beat state-of-the-art Reed-Muller and Polar codes, under the low-complexity successive cancellation decoding, in the challenging short-to-medium block length regime on the AWGN channel. We show that the gains of KO codes are primarily due to the nonlinear mapping of information bits directly to transmit real symbols (bypassing modulation) and yet possess an efficient, high performance decoder. The key technical innovation that renders this possible is design of a novel family of neural architectures inspired by the computation tree of the {\bf K}ronecker {\bf O}peration (KO) central to Reed-Muller and Polar codes. These architectures pave way for the discovery of a much richer class of hitherto unexplored nonlinear algebraic structures. The code is available at \href{this https URL}{this https URL}
Autotuning Plug-and-Play Algorithms for MRI

For magnetic resonance imaging (MRI), recently proposed "plug-and-play" (PnP) image recovery algorithms have shown remarkable performance. These PnP algorithms are similar to traditional iterative algorithms like FISTA, ADMM, or primal-dual splitting (PDS), but differ in that the proximal update is replaced by a call to an application-specific image denoiser, such as BM3D or DnCNN. The fixed-points of PnP algorithms depend upon an algorithmic stepsize parameter, however, which must be tuned for optimal performance. In this work, we propose a fast and robust auto-tuning PnP-PDS algorithm that exploits knowledge of the measurement-noise variance that is available from a pre-scan in MRI. Experimental results show that our algorithm converges very close to genie-tuned performance, and does so significantly faster than existing autotuning approaches.
Persistence of hubs in growing random networks

We consider models of evolving networks $$\left\{ {\mathcal {G}}_n:n\ge 0\right\} $$ modulated by two parameters: an attachment function $$f:{\mathbb {N}}_0 \rightarrow {\mathbb {R}}_+$$ and a (possibly random) attachment sequence $$\left\{ m_i:i\ge 1\right\} $$ . Starting with a single vertex, at each discrete step $$i\ge 1$$ a new vertex $$v_i$$ enters the system with $$m_i\ge 1$$ edges which it sequentially connects to a pre-existing vertex $$v\in {\mathcal {G}}_{i-1}$$ with probability proportional to $$f(\text{ degree }(v))$$ . We consider the problem of emergence of persistent hubs: existence of a finite (a.s.) time $$n^*$$ such that for all $$n\ge n^*$$ the identity of the maximal degree vertex (or in general the K largest degree vertices for $$K\ge 1$$ ) does not change. We obtain general conditions on f and $$\left\{ m_i:i\ge 1\right\} $$ under which a persistent hub emerges, and also those under which a persistent hub fails to emerge. In the case of lack of persistence, for the specific case of trees ( $$m_i\equiv 1$$ for all i), we derive asymptotics for the maximal degree and the index of the maximal deg ree vertex (time at which the vertex with current maximal degree entered the system) to understand the movement of the maximal degree vertex as the network evolves. A key role in the analysis is played by an inverse rate weighted martingale constructed from a continuous time embedding of the discrete time model. Asymptotics for this martingale, including concentration inequalities and moderate deviations form the technical foundations for the main results.
High-Order Entropy Stable Formulations for Computational Fluid Dynamics

A systematic approach is presented for developing entropy stable (SS) formulations of any order for the Navier-Stokes equations. These SS formulations discretely conserve mass, momentum, energy and satisfy a mathematical entropy inequality. They are valid for smooth as well as discontinuous flows provided sufficient dissipation is added at shocks and discontinuities. Entropy stable formulations exist for all diagonal norm, summation-by-parts (SBP) operators, including all centered finite-difference operators, Legendre collocation finite-element operators, and certain finite-volume operators. Examples are presented using various entropy stable formulations that demonstrate the current state-of-the-art of these schemes.
Genetic disruption of guanylyl cyclase/natriuretic peptide receptor‐A upregulates renal (pro)renin receptor expression in Npr1 null mutant mice

The objective of the present study was to determine whether targeted-disruption of Npr1 gene (encoding for guanylyl cyclase/natriuretic peptide receptor-A; GC-A/NPRA) upregulates pro(renin) receptor (P)RR expression and leads to the activation of MAPKs in Npr1 gene-knockout mice. The Npr1 homozygous (Npr1-/-; 0-copy), heterozygous (Npr1+/-; 1-copy), wild-type (Npr1+/+; 2-copy), and gene-duplicated (Npr1++/++; 4-copy) mice were utilized. To identify the canonical pathway of (P)RR, we administered ACE-1 inhibitor (captopril), AT1R blocker (losartan), and MAPKs inhibitors (U0126 and SB203580) to all Npr1 mice genotypes. The renal expression of (P)RR mRNA was increased by 3-fold in 0-copy mice and 2-fold in 1-copy mice compared with 2-copy mice, which was also associated with significantly increased expression of ACE-1 and AT1R mRNA levels. Similarly, the phosphorylation of MAPKs (Erk1/2 and p-p38) was enhanced by 3.5-fold and 3.2-fold, respectively, in 0-copy mice with significant increases in 1-copy mice compared with 2-copy mice. The kidney and plasma levels of proinflammatory cytokines were significantly elevated in 0-copy and 1-copy mice. Treatment with captopril and losartan did not alter the expression of (P)RR in any of the Npr1 mice genotypes. Interestingly, losartan significantly reduced the phosphorylation of Erk1/2 and p38 in Npr1 mice. The present results suggest that the ablation of Npr1 upregulates (P)RR, MAPKs (Erk1/2 and p38), and proinflammatory cytokines in 0-copy and 1-copy mice. In contrast, the duplication of Npr1 exhibits the anti-inflammatory and antihypertensive effects by reducing the activation of MAPKs and inhibiting the expression levels of RAAS components and proinflammatory cytokines.
Discovering Hidden Physics Behind Transport Dynamics

Transport processes are ubiquitous. They are, for example, at the heart of optical flow approaches; or of perfusion imaging, where blood transport is assessed, most commonly by injecting a tracer. An advection-diffusion equation is widely used to describe these transport phenomena. Our goal is estimating the underlying physics of advection-diffusion equations, expressed as velocity and diffusion tensor fields. We propose a learning framework (YETI) building on an auto-encoder structure between 2D and 3D image time-series, which incorporates the advection-diffusion model. To help with identifiability, we develop an advection-diffusion simulator which allows pre-training of our model by supervised learning using the velocity and diffusion tensor fields. Instead of directly learning these velocity and diffusion tensor fields, we introduce representations that assure incompressible flow and symmetric positive semi-definite diffusion fields and demonstrate the additional benefits of these representations on improving estimation accuracy. We further use transfer learning to apply YETI on a public brain magnetic resonance (MR) perfusion dataset of stroke patients and show its ability to successfully distinguish stroke lesions from normal brain regions via the estimated velocity and diffusion tensor fields.
Data Augmentation for Abstractive Query-Focused Multi-Document Summarization.

The progress in Query-focused Multi-Document Summarization (QMDS) has been limited by the lack of sufficient largescale high-quality training datasets. We present two QMDS training datasets, which we construct using two data augmentation methods: (1) transferring the commonly used single-document CNN/Daily Mail summarization dataset to create the QMDSCNN dataset, and (2) mining search-query logs to create the QMDSIR dataset. These two datasets have complementary properties, i.e., QMDSCNN has real summaries but queries are simulated, while QMDSIR has real queries but simulated summaries. To cover both these real summary and query aspects, we build abstractive end-to-end neural network models on the combined datasets that yield new state-of-the-art transfer results on DUC datasets. We also introduce new hierarchical encoders that enable a more efficient encoding of the query together with multiple documents. Empirical results demonstrate that our data augmentation and encoding methods outperform baseline models on automatic metrics, as well as on human evaluations along multiple attributes.
Redistribution under general decision rules

We study the political economy of redistribution over a broad class of decision rules. Since the core is generically non-unique, we suggest a simple and elegant procedure to select a robust equilibrium. Our selected policy depends on the full income profile, and in particular, on the preferences of two decisive voters. The effect of increasing inequality on redistribution depends on the decision rule and the shape of the income distribution; redistribution will increase if both decisive voters are 'relatively poor', and decrease if at least one is sufficiently 'rich'. Additionally, redistribution decreases as the polity adopts increasingly stringent super-majority rules.
On Magnetic Forces and Work

We address a long-standing debate over whether classical magnetic forces can do work, ultimately answering the question in the affirmative. In detail, we couple a classical particle with intrinsic spin and elementary dipole moments to the electromagnetic field, derive the appropriate generalization of the Lorentz force law, show that the particle’s dipole moments must be collinear with its spin axis, and argue that the magnetic field does mechanical work on the particle’s elementary magnetic dipole moment. As consistency checks, we calculate the overall system’s energy-momentum and angular momentum, and show that their local conservation equations lead to the same force law and therefore the same conclusions about magnetic forces and work. We also compute the system’s Belinfante–Rosenfeld energy–momentum tensor.
Creating Economies on Indian Reservations

Creating sustainable economies is crucial to the future viability of Indian reservations and communities and is the most important issue that tribal governments face today. The exercise of sovereignty and jurisdiction, and the preservation of tribal cultures are largely dependent on improving the often dire economic conditions on reservations. This short article suggests several ideas that tribal leaders and communities should consider implementing to help sustain their reservations, governments, and cultures.
Is there a racial gap in CEO compensation

Abstract Racial gap in corporate leadership has prompted continuous and intense discussions, motivating research into the conditions minorities face after they reach top management positions. We contribute to the ongoing debate in this area by examining the association between CEO race and compensation. We do not find evidence for a significant racial wage gap at the CEO level across various econometric specifications, including total-sample OLS, firm-fixed effects to capture CEO transitions within firm, propensity score matched sample, and instrumental variable analysis. The insignificant results hold for total compensation, cash compensation, and non-cash compensation. Further, there is no consistent evidence of differences in CEO compensation for any of the major racial groups (Blacks, Hispanics, and Asians). Based on our results, we conclude that racial minorities who make it to the CEO position in Corporate America are compensated at similar levels to their Caucasian counterparts.
Visual Object Recognition in Indoor Environments Using Topologically Persistent Features

Object recognition in unseen indoor environments remains a challenging problem for visual perception of mobile robots. In this letter, we propose the use of topologically persistent features, which rely on the objects’ shape information, to address this challenge. In particular, we extract two kinds of features, namely, sparse persistence image (PI) and amplitude, by applying persistent homology to multi-directional height function-based filtrations of the cubical complexes representing the object segmentation maps. The features are then used to train a fully connected network for recognition. For performance evaluation, in addition to a benchmark dataset, we collect a new dataset, comprising scene images from two different environments, namely, a living room and a mock warehouse. The scenes are captured using varying camera poses under different illumination conditions and include up to five different objects from a given set of fourteen objects. On the benchmark dataset, sparse PI features show better recognition performance in unseen environments than the features learned using the widely used ResNetV2-56 and EfficientNet-B4 models. Further, they provide slightly higher recall and accuracy values than Faster R-CNN, an end-to-end object detection method, and its state-of-the-art variant, Domain Adaptive Faster R-CNN. The performance of our methods also remains relatively unchanged from the training environment (living room) to the unseen environment (mock warehouse) in the new dataset. In contrast, the performance of the object detection methods drops substantially. We also implement the proposed method on a real-world robot to demonstrate its usefulness.
Effect of Substrate Stiffness on Human Intestinal Enteroids' Infectivity by Enteroaggregative Escherichia coli

Abstract Human intestinal enteroids (HIE) models have contributed significantly to our understanding of diarrheal diseases and other intestinal infections, but their routine culture conditions fail to mimic the mechanical environment of the native intestinal wall. Because the mechanical characteristics of the intestine significantly alter how pathogens interact with the intestinal epithelium, we used different concentrations of polyethylene glycol (PEG) to generate soft (~2 kPa), medium (~10 kPa), and stiff (~100 kPa) hydrogel biomaterial scaffolds. The height of HIEs cultured in monolayers atop these hydrogels was 18 µm whereas HIEs grown on rigid tissue culture surfaces (with stiffness in the GPa range) were 10 µm. Substrate stiffness also influenced the amount of enteroaggregative E. coli (EAEC strain 042) adhered to the HIEs. We quantified a striking difference in adherence pattern; on the medium and soft gels, the bacteria formed clusters of > 100 and even > 1000 on both duodenal and jejunal HIEs (such as would be found in biofilms), but did not on glass slides and stiff hydrogels. All hydrogel cultured HIEs showed significant enrichment for gene and signaling pathways related to epithelial differentiation, cell junctions and adhesions, extracellular matrix, mucins, and cell signaling compared to the HIEs cultured on rigid tissue culture surfaces. Collectively, these results indicate that the HIE monolayers cultured on the hydrogels are primed for a robust engagement with their mechanical environment, and that the soft hydrogels promote the formation of larger EAEC aggregates, likely through an indirect differential effect on mucus. Statement of significance Enteroids are a form of in vitro experimental mini-guts created from intestinal stem cells. Enteroids are usually cultured in 3D within Matrigel atop rigid glass or plastic substrates, which fail to mimic the native intestinal mechanical environment. Because intestinal mechanics significantly alter how pathogens interact with the intestinal epithelium, we grew human intestinal enteroids in 2D atop polyethylene glycol (PEG) hydrogel scaffolds that were soft, medium, or stiff. Compared with enteroids grown in 2D atop glass or plastic, the enteroids grown on hydrogels were taller and more enriched in mechanobiology-related gene signaling pathways. Additionally, enteroids on the softest hydrogels supported adhesion of large aggregates of enteroaggregative E. coli. Thus, this platform offers a more biomimetic model for studying enteric diseases.
An Atmospheric Variability Model for Venus Aerobraking Missions

Aerobraking has proven to be an enabling technology for planetary missions to Mars and has been proposed to enable low cost missions to Venus. Aerobraking saves a significant amount of propulsion fuel mass by exploiting atmospheric drag to reduce the eccentricity of the initial orbit. The solar arrays have been used as the primary drag surface and only minor modifications have been made in the vehicle design to accommodate the relatively modest aerothermal loads. However, if atmospheric density is highly variable from orbit to orbit, the mission must either accept higher aerothermal risk, a slower pace for aerobraking, or a tighter corridor likely with increased propulsive cost. Hence, knowledge of atmospheric variability is of great interest for the design of aerobraking missions. The first planetary aerobraking was at Venus during the Magellan mission. After the primary Magellan science mission was completed, aerobraking was used to provide a more circular orbit to enhance gravity field recovery. Magellan aerobraking took place between local solar times of 1100 and 1800 hrs, and it was found that the Venusian atmospheric density during the aerobraking phase had less than 10% 1 sigma orbit to orbit variability. On the other hand, at some latitudes and seasons, Martian variability can be as high as 40% 1 sigmaFrom both the MGN and PVO mission it was known that the atmosphere, above aerobraking altitudes, showed greater variability at night, but this variability was never quantified in a systematic manner. This paper proposes a model for atmospheric variability that can be used for aerobraking mission design until more complete data sets become available.
Neural-Swarm2: Planning and Control of Heterogeneous Multirotor Swarms Using Learned Interactions

We present Neural-Swarm2, a learning-based method for motion planning and control that allows heterogeneous multirotors in a swarm to safely fly in close proximity. Such operation for drones is challenging due to complex aerodynamic interaction forces, such as downwash generated by nearby drones and ground effect. Conventional planning and control methods neglect capturing these interaction forces, resulting in sparse swarm configuration during flight. Our approach combines a physics-based nominal dynamics model with learned deep neural networks with strong Lipschitz properties. We make use of two techniques to accurately predict the aerodynamic interactions between heterogeneous multirotors: 1) Spectral normalization for stability and generalization guarantees of unseen data and 2) heterogeneous deep sets for supporting any number of heterogeneous neighbors in a permutation-invariant manner without reducing expressiveness. The learned residual dynamics benefit both the proposed interaction-aware multirobot motion planning and the nonlinear tracking control design because the learned interaction forces reduce the modelling errors. Experimental results demonstrate that Neural-Swarm2 is able to generalize to larger swarms beyond training cases and significantly outperforms a baseline nonlinear tracking controller with up to three times reduction in worst-case tracking errors.
Inference for High-Dimensional Censored Quantile Regression

With the availability of high-dimensional genetic biomarkers, it is of interest to identify heterogeneous effects of these predictors on patients’ survival, along with proper statistical inference....
Prevention as a Giffen good

Abstract A criterion is provided for prevention to be a Giffen good. Under decreasing absolute risk aversion, prevention can only be Giffen if the loss probability is below an endogenous threshold value. For iso-elastic utility, this is possible but not empirically plausible.
Facial expressions can detect Parkinson's disease: preliminary evidence from videos collected online.

A prevalent symptom of Parkinson's disease (PD) is hypomimia - reduced facial expressions. In this paper, we present a method for diagnosing PD that utilizes the study of micro-expressions. We analyzed the facial action units (AU) from 1812 videos of 604 individuals (61 with PD and 543 without PD, with a mean age 63.9 y/o, sd. 7.8) collected online through a web-based tool ( www.parktest.net ). In these videos, participants were asked to make three facial expressions (a smiling, disgusted, and surprised face) followed by a neutral face. Using techniques from computer vision and machine learning, we objectively measured the variance of the facial muscle movements and used it to distinguish between individuals with and without PD. The prediction accuracy using the facial micro-expressions was comparable to methodologies that utilize motor symptoms. Logistic regression analysis revealed that participants with PD had less variance in AU6 (cheek raiser), AU12 (lip corner puller), and AU4 (brow lowerer) than non-PD individuals. An automated classifier using Support Vector Machine was trained on the variances and achieved 95.6% accuracy. Using facial expressions as a future digital biomarker for PD could be potentially transformative for patients in need of remote diagnoses due to physical separation (e.g., due to COVID) or immobility.
On the Round Complexity of Secure Quantum Computation

We construct the first constant-round protocols for secure quantum computation in the two-party (2PQC) and multi-party (MPQC) settings with security against malicious adversaries. Our protocols are in the common random string (CRS) model.
Stochastic-Adversarial Channels: Online Adversaries With Feedback Snooping

The growing need for reliable communication over untrusted networks has caused a renewed interest in adversarial channel models, which often behave much differently than traditional stochastic channel models. Of particular practical use is the assumption of a causal or online adversary who is limited to causal knowledge of the transmitted codeword. In this work, we consider stochastic-adversarial mixed noise models. In the setup considered, a transmit node (Alice) attempts to communicate with a receive node (Bob) over a binary erasure channel (BEC) or binary symmetric channel (BSC) in the presence of an online adversary (Calvin) who can erase or flip up to a certain number of bits at the input of the channel. Calvin knows the encoding scheme and has strict causal access to Bob's reception through feedback snooping. For erasures, we provide a complete capacity characterization with and without transmitter feedback. For bit-flips, we provide converse and achievability bounds.
C. elegans LIN-28 controls temporal cell fate progression by regulating LIN-46 expression via the 5' UTR of lin-46 mRNA.

Summary Lin28/LIN-28 is a conserved RNA-binding protein that promotes proliferation and pluripotency and can be oncogenic in mammals. Mammalian Lin28 and C. elegans LIN-28 have been shown to inhibit biogenesis of the conserved cellular differentiation-promoting microRNA let-7 by directly binding to unprocessed let-7 transcripts. Lin28/LIN-28 also bind and regulate many mRNAs in diverse cell types. However, the determinants and consequences of LIN-28-mRNA interactions are not well understood. Here, we report that C. elegans LIN-28 represses the expression of LIN-46, a downstream protein in the heterochronic pathway. We find that lin-28 and sequences within the lin-46 5′ UTR are required to prevent LIN-46 expression at early larval stages. Moreover, we find that precocious LIN-46 expression caused by mutations in the lin-46 5′ UTR is sufficient to cause precocious heterochronic defects similar to those of lin-28(lf) animals. Thus, our findings demonstrate the biological importance of the regulation of individual target mRNAs by LIN-28.
Bethe Ansatz Solutions for Certain Periodic Quantum Circuits

Abstract I derived Bethe Ansatz equations for two model Periodic Quantum Circuits: (1) XXZ model; (2) Chiral Hubbard Model. I obtained explicit expressions for the spectra of the strings of any length. These analytic results may be useful for calibration and error mitigations in modern engineered quantum platforms.
Consistent signatures of urban adaptation in a native, urban invader ant Tapinoma sessile

Biological invasions are becoming more prevalent due to the rise of global trade and expansion of urban areas. Ants are among the most prolific invaders, with many exhibiting a multi-queen colony structure, dependent colony foundation and reduced inter-nest aggression. Although these characteristics are generally associated with the invasions of exotic ants, they may also facilitate the spread of native ants into novel habitats. Native to diverse habitats across North America, the odorous house ant Tapinoma sessile has become abundant in urban environments throughout the United States. Natural colonies typically have a small workforce, inhabit a single nest, and are headed by a single queen, whereas urban colonies tend to be several orders of magnitude larger, inhabit multiple nests (i.e., polydomy) and are headed by multiple queens (i.e., polygyny). Here, we explore and compare the population genetic and breeding structure of T. sessile within and between urban and natural environments in several localities across its distribution range. We found the social structure of a colony to be a plastic trait in both habitats, although extreme polygyny was confined to urban habitats. Additionally, polydomous colonies were only present in urban habitats, suggesting T. sessile can only achieve supercoloniality within urbanized areas. Finally, we identified strong differentiation between urban and natural populations in each locality and continent-wide, indicating cities may restrict gene flow and exert intense selection pressure. Overall, our study highlights urbanization's influence in charting the evolutionary course for species.
Interim monitoring in sequential multiple assignment randomized trials.

A sequential multiple assignment randomized trial (SMART) facilitates the comparison of multiple adaptive treatment strategies (ATSs) simultaneously. Previous studies have established a framework to test the homogeneity of multiple ATSs by a global Wald test through inverse probability weighting. SMARTs are generally lengthier than classical clinical trials due to the sequential nature of treatment randomization in multiple stages. Thus, it would be beneficial to add interim analyses allowing for an early stop if overwhelming efficacy is observed. We introduce group sequential methods to SMARTs to facilitate interim monitoring based on the multivariate chi-square distribution. Simulation studies demonstrate that the proposed interim monitoring in SMART (IM-SMART) maintains the desired type I error and power with reduced expected sample size compared to the classical SMART. Finally, we illustrate our method by reanalyzing a SMART assessing the effects of cognitive behavioral and physical therapies in patients with knee osteoarthritis and comorbid subsyndromal depressive symptoms.