cssi/SciSciGPT-SciSciNet huggingface dataset:
Interpretability Analysis for Named Entity Recognition to Understand System Predictions and How They Can Improve

Named Entity Recognition systems achieve remarkable performance on domains such as English news. It is natural to ask: What are these models actually learning to achieve this? Are they merely memorizing the names themselves? Or are they capable of interpreting the text and inferring the correct entity type from the linguistic context? We examine these questions by contrasting the performance of several variants of LSTM-CRF architectures for named entity recognition, with some provided only representations of the context as features. We also perform similar experiments for BERT. We find that context representations do contribute to system performance, but that the main factor driving high performance is learning the name tokens themselves. We enlist human annotators to evaluate the feasibility of inferring entity types from the context alone and find that, while people are not able to infer the entity type either for the majority of the errors made by the context-only system, there is some room for improvement. A system should be able to recognize any name in a predictive context correctly and our experiments indicate that current systems may be further improved by such capability.
Inherently confinable split-drive systems in Drosophila

CRISPR-based gene-drive systems, which copy themselves via gene conversion mediated by the homology-directed repair (HDR) pathway, have the potential to revolutionize vector control. However, mutant alleles generated by the competing non-homologous end-joining (NHEJ) pathway, resistant to Cas9 cleavage, can interrupt the spread of gene-drive elements. We hypothesized that drives targeting genes essential for viability or reproduction also carrying recoded sequences that restore endogenous gene functionality should benefit from dominantly-acting maternal clearance of NHEJ alleles combined with recessive Mendelian culling processes. Here, we test split gene-drive (sGD) systems in Drosophila melanogaster that are inserted into essential genes required for viability (rab5, rab11, prosalpha2) or fertility (spo11). In single generation crosses, sGDs copy with variable efficiencies and display sex-biased transmission. In multigenerational cage trials, sGDs follow distinct drive trajectories reflecting their differential tendencies to induce target chromosome damage and/or lethal/sterile mosaic Cas9-dependent phenotypes, leading to inherently confinable drive outcomes.
Does Disagreement Facilitate Informed Trading? Evidence from Activist Investors

Abstract We study the effects of investor disagreement on informed trading by activist investors using high-frequency disagreement data derived from the investor social network StockTwits. Greater investor disagreement leads to more trading activity on the subsequent day by privately informed activists. Disagreement leads to higher prices and improvements in measured liquidity, but these observed valuation and market liquidity differences do not explain the increase in activist trading. Instead, investor disagreement affects activist trading primarily by facilitating trading by non-activist investors. These findings suggest that investor disagreement not only affects trading by uninformed investors but also facilitates trading by informed market participants who often take actions aimed at changing corporate policies.
Global hyperbolicity and factorization in cosmological models

The subject of this paper is the geometry and topology of cosmological spacetimes and vector bundles thereon, which are used to model physical fields propagating in the universe. Global hyperbolicity and factorization properties of the spacetime and the vector bundle that are usually independently assumed to hold are now derived from a minimal set of assumptions based on the recent progress in differential geometry and topology.
Abstraction and subsumption in modular verification of C programs

Representation predicates enable data abstraction in separation logic, but when the same concrete implementation may need to be abstracted in different ways, one needs a notion of subsumption. We demonstrate function-specification subtyping, analogous to subtyping, with a subsumption rule: if \(\phi \) is a Open image in new window of \(\psi \), that is \(\phi <:\psi \), then \(x:\phi \) implies \(x:\psi \), meaning that any function satisfying specification \(\phi \) can be used wherever a function satisfying \(\psi \) is demanded. We extend previous notions of Hoare-logic sub-specification, which already included parameter adaption, to include framing (necessary for separation logic) and impredicative bifunctors (necessary for higher-order functions, i.e. function pointers). We show intersection specifications, with the expected relation to subtyping. We show how this enables compositional modular verification of the functional correctness of C programs, in Coq, with foundational machine-checked proofs of soundness.
Efficient management of replicated data

We propose in this paper a protocol aimed at managing replicated data with many copies more efficiently. Our protocol, called Majority Consensus Voting with Tokens generalizes the primary copy approach by allowing for more than one primary copy. Since primary copies are identified by the ownership of a token, the protocol allows for easy transfer of tokens from one copy to another to respond to changing access patterns. Tokens owned by failed primary copies can also be reassigned to some live copy to enhance data availability. Upper and lower bounds for the availability of replicated data objects managed by the MCVT protocol and derived under very general probabilistic hypotheses. We show that MCVT significantly improves upon the availability and reliability offered by the primary copy protocol without incurring the high overhead of most other extant protocols.
Fitting Laplacian regularized stratified Gaussian models

We consider the problem of jointly estimating multiple related zero-mean Gaussian distributions from data. We propose to jointly estimate these covariance matrices using Laplacian regularized stratified model fitting, which includes loss and regularization terms for each covariance matrix, and also a term that encourages the different covariances matrices to be close. This method ‘borrows strength’ from the neighboring covariances, to improve its estimate. With well chosen hyper-parameters, such models can perform very well, especially in the low data regime. We propose a distributed method that scales to large problems, and illustrate the efficacy of the method with examples in finance, radar signal processing, and weather forecasting.
Role of pulsatility on particle dispersion in expiratory flows

Expiratory events, such as coughs, are often pulsatile in nature and result in vortical flow structures that transport respiratory particles. In this work, direct numerical simulation (DNS) of turbulent pulsatile jets, coupled with Lagrangian particle tracking of micron-sized droplets, is performed to investigate the role of secondary and tertiary expulsions on particle dispersion and penetration. Fully developed turbulence obtained from DNS of a turbulent pipe flow is provided at the jet orifice. The volumetric flow rate at the orifice is modulated in time according to a damped sine wave, thereby allowing for control of the number of pulses, duration, and peak amplitude. Thermodynamic effects, such as evaporation and buoyancy, are neglected in order to isolate the role of pulsatility on particle dispersion. The resulting vortex structures are analyzed for single-, two-, and three-pulse jets. The evolution of the particle cloud is then compared to existing single-pulse models. Particle dispersion and penetration of the entire cloud are found to be hindered by increased pulsatility. However, the penetration of particles emanating from a secondary or tertiary expulsion is enhanced due to acceleration downstream by vortex structures.
Philippine inequality across the twentieth century: slim evidence, but fat questions

In spite of persistent debates about income inequality and pro-poor policy in the Philippines, its history over the past century has been ignored, at least by economists. This is surprising given that the Philippines already had its first census in 1903, long before its neighbors, augmented by other relevant evidence embedded in official documents generated by the American insular government. It is also surprising given that we know that income distributions change only very slowly and must be examined over the long run to identify its drivers. This essay reviews the thin historical evidence and proposes explanations. There is no Kuznets Curve, and no Marxian, Pikettian, or other grand endogenous inequality theory at work, but there are dramatic episodes of change. It appears that there was an inequality rise up to World War 1, a fall between the world wars, a rise to high levels by the 1950s, and an almost certain rise up to the end of the century which, due to mismeasurement, looks instead like stasis. We need to collect better evidence to confirm these narratives and to assess competing hypotheses. JEL classification: D30, N15, N35, O15, O53
Experience-dependent inhibitory plasticity is mediated by CCK+ basket cells in the developing dentate gyrus.

Early postnatal experience shapes both inhibitory and excitatory networks in the hippocampus. However, the underlying circuit plasticity is unclear. Using an enriched environment (EE) paradigm during pre-weaning period in mice of either sex, we assessed the circuit plasticity of inhibitory cell-types in the hippocampus. We found that cholecystokinin (CCK)-expressing basket cells strongly increased somatic inhibition on the excitatory granular cells (GC) following EE, while another pivotal inhibitory cell-type, parvalbumin (PV)-expressing cells did not show changes. Using electrophysiological analysis and the use of cannabinoid receptor 1 (CB1R) agonist WIN 55,212-2, we demonstrate that the change in somatic inhibition from CCK+ neurons increases CB1R-mediated inhibition in the circuit. By inhibiting activity of the entorhinal cortex (EC) using a chemogenetic approach, we further demonstrate that the activity of the projections from the EC mediates the developmental assembly of CCK+ basket cell network. Altogether, our study places the experience-dependent remodeling of CCK+ basket cell innervation as a central process to adjust inhibition in the DG and shows that cortical inputs to the hippocampus play an instructional role in controlling the refinement of the synaptic connections during the pre-weaning period. Significance Brain plasticity is triggered by experience during postnatal brain development and shapes the maturing neural circuits. In humans, altered experience-dependent plasticity can have long-lasting detrimental effects on circuit function and lead to psychiatric disorders. Yet, the cellular mechanisms governing how early experience fine tune the maturing synaptic network is not fully understood. Here, taking advantage of an enrichment-housing paradigm we unravel a new plasticity mechanism involved in the maintenance of the inhibitory to excitatory balance in the hippocampus. Our findings demonstrate that cortical activity instructs the assembly of the cholecystokinin-expressing (CCK+) basket cell network. Considering the importance of this specific cell-type for learning and memory, experience-dependent remodeling of CCK+ cells may be a critical determinant for establishing appropriate neural networks.
A note on the price of bandit feedback for mistake-bounded online learning

Abstract The standard model and the bandit model are two generalizations of the mistake-bound model to online multiclass classification. In both models the learner guesses a classification in each round, but in the standard model the learner receives the correct classification after each guess, while in the bandit model the learner is only told whether or not their guess is correct in each round. For any set F of multiclass classifiers, define op t std ( F ) and op t bandit ( F ) to be the optimal worst-case number of prediction mistakes in the standard and bandit models respectively. Long (Theoretical Computer Science, 2020) claimed that for all M > 2 and infinitely many k, there exists a set F of functions from a set X to a set Y of size k such that op t std ( F ) = M and op t bandit ( F ) ≥ ( 1 − o ( 1 ) ) ( | Y | ln ⁡ | Y | ) op t std ( F ) . The proof of this result depended on the following lemma, which is false e.g. for all prime p ≥ 5 , s = 1 (the all 1 vector), t = 2 (the all 2 vector), and all z. Lemma: Fix n ≥ 2 and prime p, and let u be chosen uniformly at random from { 0 , … , p − 1 } n . For any s , t ∈ { 1 , … , p − 1 } n with s ≠ t and for any z ∈ { 0 , … , p − 1 } , we have Pr ⁡ ( t ⋅ u = z mod p | s ⋅ u = z mod p ) = 1 p . We show that this lemma is false precisely when s and t are multiples of each other mod p. Then using a new lemma, we fix Long's proof.
Non-asymptotic Results for Singular Values of Gaussian Matrix Products

This article provides a non-asymptotic analysis of the singular values (and Lyapunov exponents) of Gaussian matrix products in the regime where N, the number of terms in the product, is large and n, the size of the matrices, may be large or small and may depend on N. We obtain concentration estimates for sums of Lyapunov exponents, a quantitative rate for convergence of the empirical measure of the squared singular values to the uniform distribution on [0, 1], and results on the joint normality of Lyapunov exponents when N is sufficiently large as a function of n. Our technique consists of non-asymptotic versions of the ergodic theory approach at $$N=\infty $$ due originally to Furstenberg and Kesten (Ann Math Stat 31(2):457–469, 1960) in the 1960s, which were then further developed by Newman (Commun Math Phys 103(1):121–126, 1986) and Isopi and Newman (Commun Math Phys 143(3):591–598, 1992) as well as by a number of other authors in the 1980s. Our key technical idea is that small ball probabilities for volumes of random projections gives a way to quantify convergence in the multiplicative ergodic theorem for random matrices.
Development and analytical validation of a finite element model of fluid transport through osteochondral tissue

Abstract Fluid transport is critical to joint health. In this study we evaluate an unexplored component of joint fluid transport –fluid transport between cartilage and bone. Such transport across the cartilage-bone interface could potentially provide chondrocytes with an additional source of nutrients and signaling molecules. A biphasic viscoelastic model using an ellipsoidal fiber distribution was created with three distinct layers of cartilage (superficial zone, middle zone, and deep zone) along with a layer of subchondral bone. For stress-relaxation in unconfined compression, our results for compressive stress, radial stress, and effective fluid pressure were compared with established biphasic analytical solutions. Our model also shows the development of fluid pressure gradients at the cartilage-bone interface during loading. Fluid pressure gradients that develop at the cartilage-bone interface show consistently higher pressures in cartilage following the initial loading to 10% stain, followed by convergence of the pressures in cartilage and bone during the 400 s relaxation period. These results provide additional evidence that fluid is transported between cartilage and bone during loading and improves upon estimates of the magnitude of this effect through incorporating a realistic distribution and estimate of the collagen ultrastructure. Understanding fluid transport between cartilage and bone may be key to new insights about the mechanical and biological environment of both tissues in health and disease.
Minding the gap: academic outcomes from pre-college programs

This paper analyzes the effects of assignment to a one-year college preparatory program on academic performance in college. We use a large dataset of United States Naval Academy students from the 1...
Rapid and High-Fidelity Subsurface Exploration with Multiple Aerial Robots.

This paper develops a communication-efficient distributed mapping approach for rapid exploration of a cave by a multi-robot team. Subsurface planetary exploration is an unsolved problem challenged by communication, power, and compute constraints. Prior works have addressed the problems of rapid exploration and leveraging multiple systems to increase exploration rate; however, communication considerations have been left largely unaddressed. This paper bridges this gap in the state of the art by developing distributed perceptual modeling that enables high-fidelity mapping while remaining amenable to low-bandwidth communication channels. The approach yields significant gains in exploration rate for multi-robot teams as compared to state-of-the-art approaches. The work is evaluated through simulation studies and hardware experiments in a wild cave in West Virginia.
A Social Policy Theory of Everyday Borrowing: On the Role of Welfare States and Credit Regimes

Debt has become an essential part of many people's daily lives. This paper develops a new comparative political economy perspective on the relationship between welfare states and household borrowing. I argue that the ways in which welfare states distribute benefits and credit regimes provide access to credit affect how individuals address social risks and, as a consequence, shape patterns of indebtedness. Permissive credit regimes substitute for social policies in limited welfare states, pushing economically disadvantaged groups into debt. Alternatively, credit markets complement social policies in the provision of financial liquidity in comprehensive welfare states, protecting vulnerable groups through government benefits while allowing less-protected affluent groups to borrow money. In restrictive regimes, people instead rely on savings, expenditure cuts, and family support. I test these arguments using an original measure of credit regime permissiveness, cross-national survey data, and full-population administrative records from Denmark and panel data from the United States.
Outcomes of allogeneic hematopoietic cell transplantation in patients with myelofibrosis: A systematic review and meta-analysis.

7045Background: Allogeneic hematopoietic cell transplant (allo-HCT) remains the only potentially curative therapeutic modality for patients with primary or secondary myelofibrosis (MF). However, ma...
Aerodynamic Performance and Acoustic Measurements of a High-Lift Propeller in an Isolated Configuration

A series of aerodynamic performance and acoustic measurements has been made on a high-lift propeller intended for utilization on a distributed electric propulsion (DEP) aircraft. Tests were performed in the NASA Langley Low Speed Aeroacoustic Wind Tunnel (LSAWT), which has recently undergone a capability enhancement for the testing of small propellers/rotors and small unmanned aircraft system (UAS) platforms. The objectives of this testing campaign are two-fold: first to demonstrate the facility capabilities for performing small propeller aeroacoustic testing, and second to compare experimental measurements with computational fluid dynamic (CFD) predictions and CFD-based acoustic predictions of the tested propeller configurations for tool development and validation purposes.
Evidence for a Role for Sestrin1 in Mediating Leucine‐Induced Activation of mTORC1 in Skeletal Muscle

Previous studies established that leucine stimulates protein synthesis in skeletal muscle to the same extent as a complete mixture of amino acids, and the effect occurs through activation of the me...
Numerical Investigation and Optimization of a Flushwall Injector for Scramjet Applications at Hypervelocity Flow Conditions

An investigation utilizing Reynolds-averaged simulations (RAS) was performed in order to demonstrate the use of design and analysis of computer experiments (DACE) methods in Sandia’s DAKOTA software package for surrogate modeling and optimization. These methods were applied to a flow- path fueled with an interdigitated flushwall injector suitable for scramjet applications at hyper- velocity conditions and ascending along a constant dynamic pressure flight trajectory. The flight Mach number, duct height, spanwise width, and injection angle were the design variables selected to maximize two objective functions: the thrust potential and combustion efficiency. Because the RAS of this case are computationally expensive, surrogate models are used for optimization. To build a surrogate model a RAS database is created. The sequence of the design variables comprising the database were generated using a Latin hypercube sampling (LHS) method. A methodology was also developed to automatically build geometries and generate structured grids for each design point. The ensuing RAS analysis generated the simulation database from which the two objective functions were computed using a one-dimensionalization (1D) of the three-dimensional simulation data. The data were fitted using four surrogate models: an artificial neural network (ANN), a cubic polynomial, a quadratic polynomial, and a Kriging model. Variance-based decomposition showed that both objective functions were primarily driven by changes in the duct height. Multiobjective design optimization was performed for all four surrogate models via a genetic algorithm method. Optimal solutions were obtained at the upper and lower bounds of the flight Mach number range. The Kriging model predicted an optimal solution set that exhibited high values for both objective functions. Additionally, three challenge points were selected to assess the designs on the Pareto fronts. Further sampling among the designs of the Pareto fronts may be required to lower the surrogate model errors and perform more accurate surrogate-model-based optimization.
Nde1 is Required for Heterochromatin Compaction and Stability in Neocortical Neurons

The NDE1 gene encodes a scaffold protein essential for brain development. While biallelic NDE1 loss of function (LOF) causes microcephaly with profound mental retardation, NDE1 missense mutations and copy number variations are associated with multiple neuropsychiatric disorders. However, the etiology of the diverse phenotypes resulting from NDE1 aberrations remains elusive. Here we show Nde1 controls neurogenesis through heterochromatin compaction via histone H4K20 trimethylation. This mechanism patterns diverse chromatin landscapes and stabilizes constitutive heterochromatin of neocortical neurons. We show NDE1 undergoes dynamic liquid-liquid phase separation, partitioning to the nucleus and interacting with pericentromeric and centromeric satellite repeats. Nde1 LOF results in nuclear architecture aberrations, DNA double strand breaks, as well as instability and derepression of pericentromeric satellite repeats in neocortical neurons. These findings uncover a pivotal role of NDE1/Nde1 in establishing and maintaining neuronal heterochromatin. They suggest that heterochromatin impairments underlie a wide range of brain dysfunction.
High-Dimensional Uncertainty Quantification via Tensor Regression With Rank Determination and Adaptive Sampling

Fabrication process variations can significantly influence the performance and yield of nanoscale electronic and photonic circuits. Stochastic spectral methods have achieved great success in quantifying the impact of process variations, but they suffer from the curse of dimensionality. Recently, low-rank tensor methods have been developed to mitigate this issue, but two fundamental challenges remain open: how to automatically determine the tensor rank and how to adaptively pick the informative simulation samples. This article proposes a novel tensor-regression method to address these two challenges. We use an $\ell _{q}/ \ell _{2}$ group-sparsity regularization to determine the tensor rank. The resulting optimization problem can be efficiently solved via an alternating minimization solver. We also propose a two-stage adaptive sampling method to reduce the simulation cost. Our method considers both exploration and exploitation via the estimated Voronoi cell volume and nonlinearity measurement, respectively. The proposed model is verified with synthetic and some realistic circuit benchmarks, on which our method can well capture the uncertainty caused by 19–100 random variables with only 100–600 simulation samples.
Active and machine learning-based approaches to rapidly enhance microbial chemical production.

Abstract In order to make renewable fuels and chemicals from microbes, new methods are required to engineer microbes more intelligently. Computational approaches, to engineer strains for enhanced chemical production typically rely on detailed mechanistic models (e.g., kinetic/stoichiometric models of metabolism)—requiring many experimental datasets for their parameterization—while experimental methods may require screening large mutant libraries to explore the design space for the few mutants with desired behaviors. To address these limitations, we developed an active and machine learning approach (ActiveOpt) to intelligently guide experiments to arrive at an optimal phenotype with minimal measured datasets. ActiveOpt was applied to two separate case studies to evaluate its potential to increase valine yields and neurosporene productivity in Escherichia coli. In both the cases, ActiveOpt identified the best performing strain in fewer experiments than the case studies used. This work demonstrates that machine and active learning approaches have the potential to greatly facilitate metabolic engineering efforts to rapidly achieve its objectives.
Competitive Harm from Vertical Mergers

The antitrust enforcement Agencies’ 2020 Vertical Merger Guidelines (VMGs) introduce a nontechnical application of bargaining theory into the competitive assessment of vertical acquisitions. This bargaining theory has much in common with the theory of unilateral effects that is applied to horizontal mergers. The VMGs focus on post-merger price increases requires consideration of a vertical merger’s role in eliminating double marginalization (EDM). The problem occurs when two bargaining firms both have market power but are unable to coordinate their output. Assessing EDM bundles two themes that Ronald Coase developed in his two most well-known articles: “The Nature of the Firm” and “The Problem of Social Cost”. The first argued that the boundaries of a firm are determined by the firm’s continuous search to minimize costs. The second argued that two traders in a well-functioning market will achieve the joint-maximizing solution. Anti-interventionists rely heavily on Coasean arguments that unless high transaction costs get in the way firms will bargain to joint maximizing results. If that is true, then double marginalization will rarely provide a defense to a vertical merger. The law of vertical mergers deals largely with firms that transact with one another routinely, in legally enforceable buy-sell relationships. In a well-functioning vertical market durable double marginalization should be rare.
Sustaining Research Software via Research Software Engineers and Professional Associations

Research software is a class of software developed to support research. Today a wealth of such software is created daily in universities, government, and commercial research enterprises worldwide. The sustainability of this software faces particular challenges due, at least in part, to the type of people who develop it. These Research Software Engineers (RSEs) face challenges in developing and sustaining software that differ from those faced by the developers of traditional software. As a result, professional associations have begun to provide support, advocacy, and resources for RSEs. These benefits are critical to sustaining RSEs, especially in environments where their contributions are often undervalued and not rewarded. This paper focuses on how professional associations, such as the United States Research Software Engineer Association (US-RSE), can provide this support.
The Geometry of Domain-General Performance Monitoring in the Human Medial Frontal Cortex

Flexibly controlling behavior to achieve a desired goal depends on the ability to monitor one’s own performance. A key open question is how performance monitoring can be both flexible to support multiple tasks and specialized to support specific tasks. We characterized performance monitoring representations by recording single neurons in the human medial frontal cortex (MFC). Subjects performed two tasks that involve three types of cognitive conflict. Neurons encoding error, conflict and predicted control demand in one or both tasks coexisted in the same population. Collectively, they gave rise to a representational geometry that simultaneously allowed task specialization and generalization. Representations of conflicts were compositional. Neurons encoding conflict retrospectively served to update estimates of control demand as predicted by a Bayesian model. These findings reveal how the MFC representation of evaluative signals are both abstract and specific, suggesting a mechanism for computing and maintaining control demand estimates across trials and tasks.
Comparison and Analysis of Deep Audio Embeddings for Music Emotion Recognition.

Emotion is a complicated notion present in music that is hard to capture even with fine-tuned feature engineering. In this paper, we investigate the utility of state-of-the-art pre-trained deep audio embedding methods to be used in the Music Emotion Recognition (MER) task. Deep audio embedding methods allow us to efficiently capture the high dimensional features into a compact representation. We implement several multi-class classifiers with deep audio embeddings to predict emotion semantics in music. We investigate the effectiveness of L3-Net and VGGish deep audio embedding methods for music emotion inference over four music datasets. The experiments with several classifiers on the task show that the deep audio embedding solutions can improve the performances of the previous baseline MER models. We conclude that deep audio embeddings represent musical emotion semantics for the MER task without expert human engineering.
An invertible transform for efficient string matching in Labeled Digraphs

Let $G = (V, E)$ be a digraph where each vertex is unlabeled, each edge is labeled by a character in some alphabet $\Omega$, and any two edges with both the same head and the same tail have different labels. The powerset construction gives a transform of $G$ into a weakly connected digraph $G' = (V', E')$ that enables solving the decision problem of whether there is a walk in $G$ matching an arbitrarily long query string $q$ in time linear in $|q|$ and independent of $|E|$ and $|V|$. We show $G$ is uniquely determined by $G'$ when for every $v_\ell \in V$, there is some distinct string $s_\ell$ on $\Omega$ such that $v_\ell$ is the origin of a closed walk in $G$ matching $s_\ell$, and no other walk in $G$ matches $s_\ell$ unless it starts and ends at $v_\ell$. We then exploit this invertibility condition to strategically alter any $G$ so its transform $G'$ enables retrieval of all $t$ terminal vertices of walks in the unaltered $G$ matching $q$ in $O(|q| + t \log |V|)$ time. We conclude by proposing two defining properties of a class of transforms that includes the Burrows-Wheeler transform and the transform presented here.
High Aversion to Stochastic Time Preference Shocks and Counterfactual Long-Run Risk in the Albuquerque et al., Valuation Risk Model

High Aversion to Stochastic Time Preference Shocks and Counterfactual Long-Run Risk in the Albuquerque et al., Valuation Risk Model
Discovery of a Wide Variety of Linear Complexions in Face Centered Cubic Alloys

Linear complexions are defect states that have been recently discovered along dislocations in body centered cubic Fe-based alloys. In this work, we use atomistic simulations to extend this concept and explore segregation-driven structural transitions at dislocations in face centered cubic alloys. We discovered a variety of stable, nanoscale-size structural and chemical states, which are confined near dislocations and can be classified as linear complexions. Depending on the alloy system and thermodynamic conditions, such new states can preserve, partially modify, or completely replace the original defects they were born at. By considering different temperatures and compositions, we construct linear complexion diagrams that are similar to bulk phase diagrams, defining the important conditions for complexion formation while also specifying an expected complexion size and type. Several notable new complexion types were discovered here: (1) nanoparticle arrays comprised of L12 phases in Ni-Fe, Ni-Al, and Al-Zr, (2) replacement of stacking faults with layered complexions comprised of (111) planes from the Cu5Zr intermetallic phase in Cu-Zr, (3) platelet arrays comprised of two-dimensional Guinier-Preston zones in Al-Cu, and finally (4) coexistence of multiple linear complexions containing both Guinier-Preston zones and L12 phases in ternary Al-Cu-Zr. All of these new complexion states are expected to alter material properties and affect the stability of the dislocations themselves, offering a unique opportunity for future materials design.
A Generic Complementary Sequence Construction and Associated Encoder/Decoder Design

In this study, we propose a flexible construction of complementary sequences (CSs) that can contain zero-valued elements. To derive the construction, we use Boolean functions to represent a polynomial generated with a recursion. By applying this representation to recursive CS constructions, we show the impact of construction parameters such as sign, amplitude, phase rotation used in the recursion on the elements of the synthesized CS. As a result, we extend Davis and Jedwab’s CS construction by obtaining independent functions for the amplitude and phase of each element of the CS, and the seed sequence positions in the CS. The proposed construction shows that a set of distinct CSs compatible with non-contiguous resource allocations for orthogonal frequency-division multiplexing (OFDM) and various constellations can be synthesized systematically. It also leads to a low peak-to-mean-envelope-power ratio (PMEPR) multiple accessing scheme in the uplink and a low-complexity recursive decoder. We demonstrate the performance of the proposed encoder and decoder through comprehensive simulations.
Adaptive Online Distributed Optimal Control of Very-Large-Scale Robotic Systems

Autonomous systems comprised of many cooperative agents have the potential for enabling long-duration tasks and data collection critical to the understanding of a wide range of phenomena in spatially and temporally variable environments. The adaptive distributed optimal control approach presented in this article extends online approximate dynamic programming to very-large-scale robotics (VLSR) systems that must operate and adapt to highly uncertain and variable environments. Optimal mass transport theory is used to show that, in the Wasserstein–Gaussian mixture model space, the VLSR system's cost to go can be represented by a value functional of the robot distribution and dynamic environmental maps. The approach is demonstrated on a cooperative path planning problem in which knowledge of the obstacles in the environment changes incrementally over time based on in situ measurements. Numerical simulations show that the proposed approach significantly outperforms existing methods by finding an approximately optimal solution that avoids obstacles and meets a desired final robot distribution using minimum energy.
The Hob proteins are novel and conserved lipid binding proteins at ER-PM contact sites

Membrane contact sites are critical junctures for organelle signaling and communication. Endoplasmic reticulum-plasma membrane (ER-PM) contact sites were the first membrane contact sites to be described; however, the protein composition and molecular function of these sites is still emerging. Here, we leverage yeast and Drosophila model systems to uncover a novel role for the Hobbit (Hob) proteins at ER-PM contact sites. We find that Hobbit localizes to ER-PM contact sites in both yeast cells and the Drosophila larval salivary glands, and this localization is mediated by an N-terminal ER membrane anchor and conserved C-terminal sequences. The C-terminus of Hobbit binds to plasma membrane phosphatidylinositols, and the distribution of these lipids is altered in hobbit mutant cells. Notably, the Hobbit protein is essential for viability in Drosophila, providing one of the first examples of a membrane contact site-localized lipid binding protein that is required for development.
Keeping it in the family: Female inheritance, inmarriage, and the status of women

Abstract While female property ownership is associated with positive outcomes for women, their right to inherit property in patrilineal societies may also result in more constraining marriage norms. I test the following hypothesis: Where a woman inherits property, her male relatives are more likely to arrange her marriage to a cousin in order to keep her share of property within the male lineage. The increase in unearned income due to female inheritance also reduces women’s economic participation, especially in blue-collar jobs where women’s work is subject to social stigmas. Using a difference-in-differences design that exploits exogenous variation induced by a reform of inheritance laws in India in 2005, the study finds that women exposed to the female inheritance law are more likely to marry their paternal cousins and less likely to work, especially in agriculture. The paper also discusses possible implications for the evolution of marriage and gender norms in Islamic societies, where female inheritance is mandated by Islamic law.
An Edge-Based Resource Allocation Optimization for the Internet of Medical Things (IoMT)

As the number of Internet of Medical Things (IoMT) increases, the need for performing on-premises tasks within hospitals or medical centers also increases. Many healthcare organizations are progressively embracing or adopting an edge computing paradigm such that computationally intensive tasks can be processed at the edge of the network in order to avoid latency and network reliability issues associated with offloading tasks to the cloud. The problem, however, hospitals or medical centers may not be equipped with sufficient computing resources that can process advanced ML or AI tasks efficiently. In addition, some tasks may not be easily offloadable or contain sensitive patient healthcare data which increases the risks of having malicious attacks. In this paper, we extend our Edgify resource provisioning framework to consider the task offloading of healthcare applications' involving patients' data as a multiple criteria decision making (MCDM) process that often involves multiple conflicting criteria (e.g. data privacy risks, costs, latency, network reliability, among others). We evaluate our proposed framework through a number of experiments which demonstrate the usefulness and effectiveness of employing our optimization approach within hospitals or medical centers.
"Pennies on the Dollar": Reallocating Risk and Deficiency Judgment Liability

Many homeowners are unaware that they face the prospect of crushing personal financial liability if they default on their mortgage loans. While owners may appreciate that they can lose their homes to the lender if they fail to make payments in accordance with their loan terms, many do not fully comprehend that the exposure they have under such circumstances does not end with relinquishing the financed property. In what are known as recourse states, if the lender forecloses and the foreclosure sale does not yield an amount sufficient to cover the borrower’s outstanding debt balance, the lender may file for a deficiency judgment against the borrower to make-up the difference. Whereas in the past, in many jurisdictions, lenders have resorted to this remedy, sparingly, there are signs that this lax approach is being abandoned. First and second mortgagees and private insurance companies are increasingly opting to aggressively pursue foreclosed homeowners for fear of leaving money on the table. To make matters worse, even in those situations where lenders determine that it is not economical for them to follow-up on collecting the debt from mortgagors where a deficiency exists, they are selling the deficiency judgment or the claim to debt collectors for pennies on the dollar. Looking at a representative sample of mortgage laws and practices in California, Illinois, and Florida, this paper argues for the prohibition of deficiency judgments in the residential mortgage loan context. The article also offers a proposal for anti-deficiency legislation. Homebuyers and lenders are not equal players in the mortgage loan transaction. The disadvantages of homeowners are particularly apparent in times of severe economic crisis, like the current great recession. Excising the option of deficiency judgments from the loan negotiation will help to address the glaring inequities between parties.
Fast model-based clustering of partial records

Partially recorded data are frequently encountered in many applications and usually clustered by first removing incomplete cases or features with missing values, or by imputing missing values, followed by application of a clustering algorithm to the resulting altered dataset. Here, we develop clustering methodology through a model-based approach using the marginal density for the observed values, assuming a finite mixture model of multivariate $t$ distributions. We compare our approximate algorithm to the corresponding full expectation-maximization (EM) approach that considers the missing values in the incomplete data set and makes a missing at random (MAR) assumption, as well as case deletion and imputation methods. Since only the observed values are utilized, our approach is computationally more efficient than imputation or full EM. Simulation studies demonstrate that our approach has favorable recovery of the true cluster partition compared to case deletion and imputation under various missingness mechanisms, and is at least competitive with the full EM approach, even when MAR assumptions are violated. Our methodology is demonstrated on a problem of clustering gamma-ray bursts and is implemented at this https URL.
An exact integral-to-sum relation for products of Bessel functions

A useful identity relating the infinite sum of two Bessel functions to their infinite integral was discovered in Dominici et al. (Dominici et al. 2012 Proc. R. Soc. A 468, 26672681). Here, we exten...
Dynamic Distribution of Edge Intelligence at the Node Level for Internet of Things

In this paper, dynamic deployment of Convolutional Neural Network (CNN) architecture is proposed utilizing only IoT-level devices. By partitioning and pipelining the CNN, it horizontally distributes the computation load among resource-constrained devices (called as horizontal collaboration), which in turn increases the throughput. Through partitioning, we can decrease the computation and energy consumption on individual IoT devices and increase the throughput without sacrificing accuracy. Also, by processing the data at the generation point, data privacy can be achieved. The results show that throughput can be increased by 1.55x to 1.75x for sharing the CNN into two and three resource-constrained devices, respectively.
In-hospital respiratory viral infections for patients with established BPD in the SARS-CoV-2 era.

Objective Our objective was to test the hypothesis that in-hospital respiratory viral infections (RVI) would be significantly lower in a cohort of patients with established bronchopulmonary dysplasia exposed to a SARS-CoV-2 infection prevention protocol when compared to historical controls. Study design On April 1, 2020, we implemented a universal infection prevention protocol to minimize the risk of nosocomial SARS-CoV-2 transmission in a dedicated BPD intensive care unit. We performed a retrospective cohort study and included patients with established BPD, as defined by the 2019 Neonatal Research Network criteria, admitted to our center who underwent real-time polymerase-chain-reaction RVI testing between January 1, 2015 and March 31, 2021. We excluded patients re-admitted from home. We compared the proportion of positive tests to the number of tests performed and the distribution of viral respiratory pathogens in the pre- and post-SARS-CoV-2 eras. Results Among 176 patients included in the study, 663 RVI tests were performed and 172 (26%) tests were positive. The median number of tests performed, measured in tests per patient per month, in the SARS-CoV-2 era was not significantly different compared to the pre-SARS-CoV-2 era (0.45 vs 0.34 tests per patient per month, P = 0.07). The proportion of positive RVI tests was significantly lower in the SARS-CoV-2 era when compared to the pre-SARS-CoV-2 era (0.06 vs 0.30, P Conclusions Infection prevention measures developed in response to the SARS-CoV-2 pandemic may reduce the risk of RVIs in hospitalized patients with established BPD. This article is protected by copyright. All rights reserved.
ECNNs: Ensemble Learning Methods for Improving Planar Grasp Quality Estimation

We present an ensemble learning methodology that combines multiple existing robotic grasp synthesis algorithms and obtain a success rate that is significantly better than the individual algorithms. The methodology treats the grasping algorithms as "experts" providing grasp "opinions". An Ensemble Convolutional Neural Network (ECNN) is trained using a Mixture of Experts (MOE) model that integrates these opinions and determines the final grasping decision. The ECNN introduces minimal computational cost overhead, and the network can virtually run as fast as the slowest expert. We test this architecture using open-source algorithms in the literature by adopting GQCNN 4.0, GGCNN and a custom variation of GGCNN as experts and obtained a 6% increase in the grasp success on the Cornell Dataset compared to the best-performing individual algorithm. The performance of the method is also demonstrated using a Franka Emika Panda arm.
LiDARNet: A Boundary-Aware Domain Adaptation Model for Point Cloud Semantic Segmentation

We present a boundary-aware domain adaptation model for LiDAR scan full-scene semantic segmentation (LiDARNet). Our model can extract both the domain private features and the domain shared features with a two branch structure. We embedded Gated-SCNN into the segmentor component of LiDARNet to learn boundary information while learning to predict full-scene semantic segmentation labels. Moreover, we further reduce the domain gap by inducing the model to learn a mapping between two domains using the domain shared and private features. Additionally, we introduce a new dataset (SemanticUSL1) for domain adaptation for LiDAR point cloud semantic segmentation. The dataset has the same data format and ontology as SemanticKITTI. We conducted experiments on real-world datasets SemanticKITTI, SemanticPOSS, and SemanticUSL, which have differences in channel distributions, reflectivity distributions, diversity of scenes, and sensors setup. Using our approach, we can get a single projection-based Li-DAR full-scene semantic segmentation model working on both domains. Our model can keep almost the same performance on the source domain after adaptation and get an 8%-22% mIoU performance increase in the target domain.