cssi/SciSciGPT-SciSciNet huggingface dataset:
Inferring Obstacles and Path Validity from Visibility-Constrained Demonstrations

Many methods in learning from demonstration assume that the demonstrator has knowledge of the full environment. However, in many scenarios, a demonstrator only sees part of the environment and they continuously replan as they gather information. To plan new paths or to reconstruct the environment, we must consider the visibility constraints and replanning process of the demonstrator, which, to our knowledge, has not been done in previous work. We consider the problem of inferring obstacle configurations in a 2D environment from demonstrated paths for a point robot that is capable of seeing in any direction but not through obstacles. Given a set of survey points, which describe where the demonstrator obtains new information, and a candidate path, we construct a Constraint Satisfaction Problem (CSP) on a cell decomposition of the environment. We parameterize a set of obstacles corresponding to an assignment from the CSP and sample from the set to find valid environments. We show that there is a probabilistically-complete, yet not entirely tractable, algorithm that can guarantee novel paths in the space are unsafe or possibly safe. We also present an incomplete, but empirically-successful, heuristic-guided algorithm that we apply in our experiments to 1) planning novel paths and 2) recovering a probabilistic representation of the environment.
Reduced-Rank Regression with Operator Norm Error

A common data analysis task is the reduced-rank regression problem: $$\min_{\textrm{rank-}k \ X} \|AX-B\|,$$ where $A \in \mathbb{R}^{n \times c}$ and $B \in \mathbb{R}^{n \times d}$ are given large matrices and $\|\cdot\|$ is some norm. Here the unknown matrix $X \in \mathbb{R}^{c \times d}$ is constrained to be of rank $k$ as it results in a significant parameter reduction of the solution when $c$ and $d$ are large. In the case of Frobenius norm error, there is a standard closed form solution to this problem and a fast algorithm to find a $(1+\varepsilon)$-approximate solution. However, for the important case of operator norm error, no closed form solution is known and the fastest known algorithms take singular value decomposition time. We give the first randomized algorithms for this problem running in time $$(nnz{(A)} + nnz{(B)} + c^2) \cdot k/\varepsilon^{1.5} + (n+d)k^2/\epsilon + c^{\omega},$$ up to a polylogarithmic factor involving condition numbers, matrix dimensions, and dependence on $1/\varepsilon$. Here $nnz{(M)}$ denotes the number of non-zero entries of a matrix $M$, and $\omega$ is the exponent of matrix multiplication. As both (1) spectral low rank approximation ($A = B$) and (2) linear system solving ($n = c$ and $d = 1$) are special cases, our time cannot be improved by more than a $1/\varepsilon$ factor (up to polylogarithmic factors) without a major breakthrough in linear algebra. Interestingly, known techniques for low rank approximation, such as alternating minimization or sketch-and-solve, provably fail for this problem. Instead, our algorithm uses an existential characterization of a solution, together with Krylov methods, low degree polynomial approximation, and sketching-based preconditioning.
Multi-messenger astrophysics with the cosmic neutrino background

The massive neutrinos of the Cosmic Neutrino Background (C$\nu$B) are fundamental ingredients of the radiation-dominated early universe and are important non-relativistic probes of the large-scale structure formation in the late universe. The dominant source of anisotropies in the neutrino flux distribution on the sky are highly amplified integrals of metric perturbations encountered during the non-relativistic phase of the C$\nu$B. This paper numerically compares the line-of-sight methods for computing C$\nu$B anisotropies with the Einstein-Boltzmann hierarchy solutions in linear theory for a range of neutrino masses. Angular power spectra are computed that are relevant to a future polarized tritium target run of the PTOLEMY experiment. Correlations between the C$\nu$B sky maps and galactic survey data are derived using line-of-sight techniques and discussed in the context of multi-messenger astrophysics.
Portfolio efficiency with high-dimensional data as conditioning information

Abstract In this paper, we build efficient portfolios using different frameworks proposed in the literature and drawing upon several datasets that contain an increasing number of predictors as conditioning information. We carry an extensive empirical study to investigate approaches that impose sparsity and dimensionality reduction, as well as possible latent factors driving the returns of the risky assets. In contrast to previous studies that made use of naive OLS and low-dimension information sets, we find that (i) accounting for large conditioning information sets, and (ii) the use of variable selection, shrinkage methods and factor models, such as the principal component regression and the partial least squares, provides better out-of-sample results as measured by Sharpe ratios, implied Sharpe ratios, and higher certainty equivalent returns (CER).
Sleep quality in the chronic stage of concussion is associated with poorer recovery: A systematic review:

BackgroundRecovery from a concussion varies based on a multitude of factors. One such factor is sleep disturbances. In our prior review, it was observed that in the acute phase, sleep disturbances ...
S*: A Heuristic Information-Based Approximation Framework for Multi-Goal Path Finding

We combine ideas from uni-directional and bi-directional heuristic search, and approximation algorithms for the Traveling Salesman Problem, to develop a novel framework for a Multi-Goal Path Finding (MGPF) problem that provides a 2-approximation guarantee. MGPF aims to find a least-cost path from an origin to a destination such that each node in a given set of goals is visited at least once along the path. We present numerical results to illustrate the advantages of our framework over conventional alternates in terms of the number of expanded nodes and run time.
The Hilbert scheme of a pair of linear spaces

Let $$\mathcal {H}_{a,b}^n$$ denote the component of the Hilbert scheme whose general point parameterizes an a-plane union a b-plane meeting transversely in $${\mathbf {P}}^n$$ . We show that $$\mathcal {H}_{a,b}^n$$ is smooth and isomorphic to successive blow ups of $$\mathbf {Gr}(a,n) \times \mathbf {Gr}(b,n)$$ or $$\text {Sym}^2 \mathbf {Gr}(a,n)$$ along certain incidence correspondences. We classify the subschemes parameterized by $$\mathcal {H}_{a,b}^n$$ and show that this component has a unique Borel fixed point. We also study the birational geometry of this component. In particular, we describe the effective and nef cones of $$\mathcal {H}_{a,b}^n$$ and determine when the component is Fano. Moreover, we show that $$\mathcal {H}_{a,b}^n$$ is a Mori dream space for all values of a, b, n.
Interpretation of multiple solutions in fully iterative GF2 and GW schemes using local analysis of two-particle density matrices

Due to the presence of non-linear equations, iterative Green’s function methods can result in multiple different solutions even for simple molecular systems. In contrast to the wave-function methods, a detailed and careful analysis of such molecular solutions was not performed before. In this work, we use two-particle density matrices to investigate local spin and charge correlators that quantify the charge resonance and covalent characters of these solutions. When applied within the unrestricted orbital set, spin correlators elucidate the broken symmetry of the solutions, containing necessary information for building effective magnetic Hamiltonians. Based on GW and GF2 calculations of simple molecules and transition metal complexes, we construct Heisenberg Hamiltonians, four-spin-four-center corrections, and biquadratic spin–spin interactions. These Hamiltonian parameterizations are compared to previous wave-function calculations.
Managing global value chain triversity and innovation performance using social mechanisms

Scholarly interest in global value chain (GVC) has gained momentum in recent years, yet a comprehensive framework that elucidates how GVC affects an important firm-level outcome—innovation performance (IP)—is so far elusive. To address this gap, we develop a conceptual framework related to three diversities (activity, governance structure, and partner location) that lead firms in GVC utilize in order to enhance IP. We draw upon the GVC and social capital research to develop the framework. We propose that the three diversities (collectively termed as triversity) have the potential to affect lead firms’ IP positively as well as negatively. We further propose that mechanisms related to the three dimensions of social capital (relational, cognitive, and structural) help augment the positive effects and mitigate the negative effects of triversity on IP. This article contributes to the understanding of the relationship between GVC triversity, IP, and social mechanism.
Feature Learning in Infinite-Width Neural Networks

As its width tends to infinity, a deep neural network's behavior under gradient descent can become simplified and predictable (e.g. given by the Neural Tangent Kernel (NTK)), if it is parametrized appropriately (e.g. the NTK parametrization). However, we show that the standard and NTK parametrizations of a neural network do not admit infinite-width limits that can learn features, which is crucial for pretraining and transfer learning such as with BERT. We propose simple modifications to the standard parametrization to allow for feature learning in the limit. Using the *Tensor Programs* technique, we derive explicit formulas for such limits. On Word2Vec and few-shot learning on Omniglot via MAML, two canonical tasks that rely crucially on feature learning, we compute these limits exactly. We find that they outperform both NTK baselines and finite-width networks, with the latter approaching the infinite-width feature learning performance as width increases. More generally, we classify a natural space of neural network parametrizations that generalizes standard, NTK, and Mean Field parametrizations. We show 1) any parametrization in this space either admits feature learning or has an infinite-width training dynamics given by kernel gradient descent, but not both; 2) any such infinite-width limit can be computed using the Tensor Programs technique. Code for our experiments can be found at this http URL.
When Do Firms Trade Patents

Drawing on the Coase theorem, we consider a firm’s decision to transfer patent ownership to another firm in the markets for innovation. We deem that the proximity of a patent’s technology structure...
Contrasting effects of land cover on nesting habitat use and reproductive output for bumble bees

Understanding habitat quality is central to understanding the distributions of species on the landscape, as well as to conserving and restoring at-risk species. Although it is well-known that many species require different resources throughout their life cycles, pollinator conservation efforts focus almost exclusively on forage resources. Here, we evaluate nesting habitat for bumble bees by locating nests directly on the landscape. We compared colony density and colony reproductive output for Bombus impatiens, the common eastern bumble bee, across three different land cover types (hay fields, meadows, and forests). We also recorded nest site characteristics, e.g., the position of each nest site, for all Bombus nests located during surveys to tease apart species-specific patterns of habitat use. We found that B. impatiens nests exclusively underground in two natural land cover types, forests and meadows, but not in hay fields. B. impatiens nested at similar densities in both in meadows and forests, but colonies in forests had much higher reproductive output. In contrast to B. impatiens, B. griseocollis frequently nested on the surface of the ground and was almost always found in meadows. B. bimaculatis nests were primarily below ground in meadows. B. perplexis nested below ground in all three habitat types, including hay fields. For some bumble bee species in this system, e.g., B. griseocollis and B. bimaculatis, meadows, the habitat type with abundant forage resources, may be sufficient to maintain them throughout their life cycles. However, B. impatiens might benefit from heterogeneous landscapes with forests and meadows. Further research would be needed to evaluate whether hay fields are high-quality nesting sites for the one species that used them, B. perplexis. Synthesis and applications. In the past, Bombus nesting studies have been perceived as prohibitively labor-intensive. This example shows that it is possible to directly measure nesting habitat use and quality for bumble bee species. Applying these methods to more areas, especially areas where at-risk Bombus spp. are abundant, is an important next step for identifying bumble bee habitat needs throughout their life cycles.
Financial distress and the cost of labor: Evidence from a natural experiment

Employees bear significant costs in bankruptcy. Theoretical models predict they will accept lower wages in the face of financial distress to avoid such costs. Using a natural experiment, I test this theory and find an exogenous increase in default risk causes a decrease in employee wages. The effect is economically meaningful: the reduction in aggregate annual wages equals 10% of the firm’s earnings and 33% of its interest expense. As expected, it is concentrated in financially vulnerable firms and those with fewer agency conflicts. Employees thus represent an important financial resource for firms in the midst of financial distress.
SOS lower bound for exact planted clique

We prove a SOS degree lower bound for the planted clique problem on the Erdos-Renyi random graph G(n, 1/2). The bound we get is degree d = Ω(ϵ2 log n/ log log n) for clique size ω = n1/2−ϵ, which is almost tight. This improves the result of [5] for the "soft" version of the problem, where the family of the equality-axioms generated by x1 + ... + xn = ω is relaxed to one inequality x1 + ... + xn ≥ ω. As a technical by-product, we also "naturalize" certain techniques that were developed and used for the relaxed problem. This includes a new way to define the pseudo-expectation, and a more robust method to solve out the coarse diagonalization of the moment matrix.
Probing DNA-protein interactions using single-molecule diffusivity contrast

Abstract Single-molecule fluorescence investigations of protein-nucleic acid interactions require robust means to identify the binding state of individual substrate molecules in real time. Here, we show that diffusivity contrast, widely used in fluorescence correlation spectroscopy at the ensemble level and in single-particle tracking on individual (but slowly diffusing) species, can be used as a general readout to determine the binding state of single DNA molecules with unlabeled proteins in solution. We first describe the technical basis of drift-free single-molecule diffusivity measurements in an anti-Brownian electrokinetic trap. We then cross-validate our method with protein-induced fluorescence enhancement, a popular technique to detect protein binding on nucleic acid substrates with single-molecule sensitivity. We extend an existing hydrodynamic modeling framework to link measured diffusivity to particular DNA-protein structures and obtain good agreement between the measured and predicted diffusivity values. Finally, we show that combining diffusivity contrast with protein-induced fluorescence enhancement allows simultaneous mapping of binding stoichiometry and location on individual DNA-protein complexes, potentially enhancing single-molecule views of relevant biophysical processes.
Information-Theoretic Abstractions for Planning in Agents With Computational Constraints

In this paper, we develop a framework for path-planning on abstractions that are not provided to the agent a priori but instead emerge as a function of the available computational resources. We show how a path-planning problem in an environment can be systematically approximated by solving a sequence of easier-to-solve problems on abstractions of the original space. The properties of the problem are analyzed, and a number of theoretical results are presented and discussed. A numerical example is presented to show the utility of the approach and to corroborate the theoretical findings. We conclude by providing a discussion detailing the connections of the proposed approach to anytime algorithms and bounded rationality.
Structure of the capsid size-determining scaffold of "satellite" bacteriophage P4

P4 is a mobile genetic element (MGE) that can exist as a plasmid or integrated into its Escherichia coli host genome, but becomes packaged into phage particles by a helper bacteriophage, such as P2. P4 is the original example of what we have termed "molecular piracy", the process by which one MGE usurps the life cycle of another for its own propagation. The P2 helper provides most of the structural gene products for assembly of the P4 virion. However, when P4 is mobilized by P2, the resulting capsids are smaller than those normally formed by P2 alone. The P4-encoded protein responsible for this size change is called Sid, which forms an external scaffolding cage around the P4 procapsids. We have determined the high-resolution structure of P4 procapsids, allowing us to build an atomic model for Sid as well as the gpN capsid protein. Sixty copies of Sid form an intertwined dodecahedral cage around the T = 4 procapsid, making contact with only one out of the four symmetrically non-equivalent copies of gpN. Our structure provides a basis for understanding the sir mutants in gpN that prevent small capsid formation, as well as the nms "super-sid" mutations that counteract the effect of the sir mutations, and suggests a model for capsid size redirection by Sid.
System Identification for Propellers at High Incidence Angles

Propellers used for electric vertical takeoff and landing (eVTOL) aircraft propulsion systems experience a wide range of aerodynamic conditions, including large incidence angles relative to oncomin...
Reconciling Rewards with Predictive State Representations

Predictive state representations (PSRs) are models of controlled non-Markov observation sequences which exhibit the same generative process governing POMDP observations without relying on an underlying latent state. In that respect, a PSR is indistinguishable from the corresponding POMDP. However, PSRs notoriously ignore the notion of rewards, which undermines the general utility of PSR models for control, planning, or reinforcement learning. Therefore, we describe a sufficient and necessary accuracy condition which determines whether a PSR is able to accurately model POMDP rewards, we show that rewards can be approximated even when the accuracy condition is not satisfied, and we find that a non-trivial number of POMDPs taken from a well-known third-party repository do not satisfy the accuracy condition. We propose reward-predictive state representations (R-PSRs), a generalization of PSRs which accurately models both observations and rewards, and develop value iteration for R-PSRs. We show that there is a mismatch between optimal POMDP policies and the optimal PSR policies derived from approximate rewards. On the other hand, optimal R-PSR policies perfectly match optimal POMDP policies, reconfirming R-PSRs as accurate state-less generative models of observations and rewards.
Art as Collateral

High market values and the recognition of its investment potential have made art a source of collateral for loans. Firms specializing in art lending have emerged to serve this market. This study explores the effect of regional variations in economic and financial conditions on art-backed lending activities. We show that demand for art loans increases when the economy experiences a downturn and liquidity needs are high. We compare the demand for art-secured loans with home equity loans and test a pecking order theory for the borrowing of high-net-worth individuals. The findings suggest that art as substitute collateral is increasingly used when home equity loans are difficult to obtain. TOPICS:Real assets/alternative investments/private equity, real estate, wealth management Key Findings ▪ With the expansion of the high-end market for art and increased attention to its economic value in recent years, art has become an asset that can serve as loan collateral. The Uniform Commercial Code, under which debtors retain possession while creditors can register their security interest in the collateral, may also facilitate some of the art-backed lending in the United States. ▪ Using various regional economic performance measures, this study shows that some collectors borrow when the economy does not perform well. This supports the hypothesis that art may serve, in certain circumstances, as emergency collateral to smooth adverse economic shocks. ▪ Art is used as collateral when home equity loans are difficult to obtain and when housing prices decline. This is also consistent with the hypothesis that collectors borrow from nonbank creditors in times of financial distress to meet liquidity needs.
Description of longitudinal space charge effects in beams and plasma through dielectric permittivity

We develop a universal framework that allows for quickly solving a wide class of problems for longitudinal space charge effects in beams and plasmas in cylindrical geometry. We introduce the longitudinal dielectric permittivity for the beam of charged particles, which describes its collective space charge response. The analysis yields an effective plasma frequency, which depends on the transverse geometry of the system. This dielectric permittivity mirrors the dielectric permittivity of plasma and matches the one dimensional expression once the transverse size of the beam is large. Several particle species can be included as additive terms describing susceptibility of each specie. The developed approach allows to study stability criteria for collective beam–beam and beam–plasma instabilities for arbitrary transverse distributions in particle densities.
The threshold conjecture for the energy critical hyperbolic Yang--Mills equation

Author(s): Oh, Sung-Jin; Tataru, Daniel | Abstract: This article represents the fourth and final part of a four-paper sequence whose aim is to prove the Threshold Conjecture as well as the more general Dichotomy Theorem for the energy critical $4+1$ dimensional hyperbolic Yang--Mills equation. The Threshold Theorem asserts that topologically trivial solutions with energy below twice the ground state energy are global and scatter. The Dichotomy Theorem applies to solutions in arbitrary topological class with large energy, and provides two exclusive alternatives: Either the solution is global and scatters, or it bubbles off a soliton in either finite time or infinite time. Using the caloric gauge developed in the first paper, the continuation/scattering criteria established in the second paper, and the large data analysis in an arbitrary topological class at optimal regularity in the third paper, here we perform a blow-up analysis which shows that the failure of global well-posedness and scattering implies either the existence of a soliton with at most the same energy bubbling off, or the existence existence of a nontrivial self-similar solution. The proof is completed by showing that the latter solutions do not exist.
Modeling complex network patterns in international trade

This paper examines the role of complex network patterns in determining international trade. The author proposes two empirical approaches to better identify the influences that the full structure of the trade network has on individual bilateral flows. The first uses gravity models that incorporate novel network covariates. The second uses exponential random graph models (ERGMs) that analyze trade from a network perspective. Estimates from both types of models provide strong evidence that network dependencies are influential determinants at both the intensive and extensive margin. Direct comparisons of the two approaches indicate that each can outperform the other at capturing and replicating certain types of network features. These results indicate that complex network patterns are an important determinant of trade, that gravity models can capture much of this dependency even if not explicitly controlled for, and that empirical network models such as ERGMs can be valuable tools for better capturing certain network patterns.
The network structure of overnight index swap rates

Abstract Graph theoretical techniques are utilized to examine the centrality structure of overnight index swap (OIS) networks. Correlation based graphs are constructed to encode pairwise relationships between distinct OIS rates. Multiple notions of graph centrality are considered, and the time evolution of these measures is studied. A principal component analysis based centrality measure is constructed to examine comovements between full OIS curves. Numerical examples demonstrating these ideas are provided.
Human-in-the-Loop Model Explanation via Verbatim Boundary Identification in Generated Neighborhoods.

The black-box nature of machine learning models limits their use in case-critical applications, raising faithful and ethical concerns that lead to trust crises. One possible way to mitigate this issue is to understand how a (mispredicted) decision is carved out from the decision boundary. This paper presents a human-in-the-loop approach to explain machine learning models using verbatim neighborhood manifestation. Contrary to most of the current eXplainable Artificial Intelligence (XAI) systems, which provide hit-or-miss approximate explanations, our approach generates the local decision boundary of the given instance and enables human intelligence to conclude the model behavior. Our method can be divided into three stages: 1) a neighborhood generation stage, which generates instances based on the given sample; 2) a classification stage, which yields classifications on the generated instances to carve out the local decision boundary and delineate the model behavior; and 3) a human-in-the-loop stage, which involves human to refine and explore the neighborhood of interest. In the generation stage, a generative model is used to generate the plausible synthetic neighbors around the given instance. After the classification stage, the classified neighbor instances provide a multifaceted understanding of the model behavior. Three intervention points are provided in the human-in-the-loop stage, enabling humans to leverage their own intelligence to interpret the model behavior. Several experiments on two datasets are conducted, and the experimental results demonstrate the potential of our proposed approach for boosting human understanding of the complex machine learning model.
Negotiation and executive gender pay gaps in nonprofit organizations

This study examines gender pay gaps among nonprofit executives and how compensation negotiability influences these disparities. Using tax return data from IRS Form 990 filings, we find that females earn 8.9% lower total compensation than men in our sample. Further, we observe that settings more conducive to negotiation manifest in larger pay disparities, whereas settings that limit executives’ opportunities to negotiate or that encourage females in particular to negotiate produce smaller gender pay gaps. Our nonprofit setting constrains mechanisms, such as labor force participation rates and risk preferences, that are thought to explain the pay gap, and our results are robust to using a Heckman correction model and matched samples. These findings provide evidence from a large-scale archival dataset of a plausible mechanism for the gender pay gap and point to a potential cost of work environments where negotiations play a larger role in setting compensation.
Uniform Sobolev estimates on compact manifolds involving singular potentials

We obtain generalizations of the uniform Sobolev inequalities of Kenig, Ruiz and the fourth author \cite{KRS} for Euclidean spaces and Dos Santos Ferreira, Kenig and Salo \cite{DKS} for compact Riemannian manifolds involving critically singular potentials $V\in L^{n/2}$. We also obtain the analogous improved quasimode estimates of the the first, third and fourth authors \cite{BSS} , Hassell and Tacy \cite{HassellTacy}, the first and fourth author \cite{SBLog}, and Hickman \cite{Hickman} as well as analogues of the improved uniform Sobolev estimates of \cite{BSSY} and \cite{Hickman} involving such potentials. Additionally, on $S^n$, we obtain sharp uniform Sobolev inequalities involving such potentials for the optimal range of exponents, which extend the results of S. Huang and the fourth author \cite{SHSo}. For general Riemannian manifolds we improve the earlier results in \cite{BSS} by obtaining quasimode estimates for a larger (and optimal) range of exponents under the weaker assumption that $V\in L^{n/2}$.
Hollow cathode characteristics in high current discharges

Current and potential distributions have been measured in various large hollow cathodes for currents from 0.9 to 7 kA and mass flows from 0.001 to 16 g/sec. For a variety of cavity diameters and shapes, all operated at 7 kA and 4 g/sec, the cathode current attaches over 0.6 cm of the cavity near the open end, where the electric field is less than 10 V/cm. However, for a fixed cathode configuration, the current conduction pattern is sensitive to both the current and the mass flows. For a given current, a range of mass flows is determined for maximum current penetration into the cavity, with the extent of penetration increasing as current decreases. Spectroscopic and photographic studies of AII radiance and electron densities within the cavity indicate a corresponding distribution of highly conducting plasma.
All States Close but Red Districts Reopen: The Politics of In-Person Schooling During the COVID-19 Pandemic

How did political factors and public health affect state and local education decisions during the COVID-19 pandemic, especially the continuation of in-person schooling? Using an original data set o...
Workflow to Investigate Subtle Differences in Wine Volatile Metabolome Induced by Different Root Systems and Irrigation Regimes

To allow for a broad survey of subtle metabolic shifts in wine caused by rootstock and irrigation, an integrated metabolomics-based workflow followed by quantitation was developed. This workflow was particularly useful when applied to a poorly studied red grape variety cv. Chambourcin. Allowing volatile metabolites that otherwise may have been missed with a targeted analysis to be included, this approach allowed deeper modeling of treatment differences which then could be used to identify important compounds. Wines produced on a per vine basis, over two years, were analyzed using SPME-GC-MS/MS. From the 382 and 221 features that differed significantly among rootstocks in 2017 and 2018, respectively, we tentatively identified 94 compounds by library search and retention index, with 22 confirmed and quantified using authentic standards. Own-rooted Chambourcin differed from other root systems for multiple volatile compounds with fewer differences among grafted vines. For example, the average concentration of β-Damascenone present in own-rooted vines (9.49 µg/L) was significantly lower in other rootstocks (8.59 µg/L), whereas mean Linalool was significantly higher in 1103P rootstock compared to own-rooted. β-Damascenone was higher in regulated deficit irrigation (RDI) than other treatments. The approach outlined not only was shown to be useful for scientific investigation, but also in creating a protocol for analysis that would ensure differences of interest to the industry are not missed.
Why do People Participate in Small Online Communities

Many benefits of online communities---such as obtaining new information, opportunities, and social connections---increase with size. Thus, a "successful'' online community often evokes an image of hundreds of thousands of users, and practitioners and researchers alike have sought to devise methods to achieve growth and thereby, success. On the other hand, small online communities exist in droves and many persist in their smallness over time. Turning to the highly popular discussion website Reddit, which is made up of hundreds of thousands of communities, we conducted a qualitative interview study examining how and why people participate in these persistently small communities, in order to understand why these communities exist when popular approaches would assume them to be failures. Drawing from twenty interviews, this paper makes several contributions: we describe how small communities provide unique informational and interactional spaces for participants, who are drawn by the hyperspecific aspects of the community; we find that small communities do not promote strong dyadic interpersonal relationships but rather promote group-based identity; and we highlight how participation in small communities is part of a broader, ongoing strategy to curate participants' online experience. We argue that online communities can be seen as nested niches: parts of an embedded, complex, symbiotic socio-informational ecosystem. We suggest ways that social computing research could benefit from more deliberate considerations of interdependence between diverse scales of online community sizes.
Electrohydrodynamic instabilities in freely suspended viscous films under normal electric fields

We study electrohydrodynamic instabilities in a freely suspended liquid film subject to a normal electric field. The behavior of the system is characterized as a function of the relevant dimensionless groups during the linear and nonlinear regimes of growth. We demonstrate how the coupling of flow and surface charge transport in different modes of instability can give rise to manifold nonlinear phenomena such as tip streaming or pinching of the film into droplets.
Online State Estimation for Time-Varying Systems

The paper investigates the problem of estimating the state of a time-varying system with a linear measurement model; in particular, the paper considers the case where the number of measurements available can be smaller than the number of states. In lieu of a batch linear least-squares (LS) approach well-suited for static networks, where a sufficient number of measurements could be collected to obtain a full-rank design matrix the paper proposes an online algorithm to estimate the possibly time-varying state by processing measurements as and when available. The design of the algorithm hinges on a generalized LS cost augmented with a proximal-point-type regularization. With the solution of the regularized LS problem available in closed-form, the online algorithm is written as a linear dynamical system where the state is updated based on the previous estimate and based on the new available measurements. Conditions under which the algorithmic steps are in fact a contractive mapping are shown, and bounds on the estimation error are derived for different noise models. Numerical simulations are provided to corroborate the analytical findings.
F-SIOL-310: A Robotic Dataset and Benchmark for Few-Shot Incremental Object Learning

Deep learning has achieved remarkable success in object recognition tasks through the availability of large scale datasets like ImageNet. However, deep learning systems suffer from catastrophic forgetting when learning incrementally without replaying old data. For real-world applications, robots also need to incrementally learn new objects. Further, since robots have limited human assistance available, they must learn from only a few examples. However, very few object recognition datasets and benchmarks exist to test incremental learning capability for robotic vision. Further, there is no dataset or benchmark specifically designed for incremental object learning from a few examples. To fill this gap, we present a new dataset termed F-SIOL-310 (Few-Shot Incremental Object Learning) which is specifically captured for testing few-shot incremental object learning capability for robotic vision. We also provide benchmarks and evaluations of 8 incremental learning algorithms on F-SIOL-310 for future comparisons. Our results demonstrate that the few-shot incremental object learning problem for robotic vision is far from being solved.
Formative Justice: The Regulative Principle of Education

Background/ContextConcepts of justice relevant to making personal and public decisions about education.PurposeTo clarify a concept of formative justice that persons and the public often ignore in m...
Self-Bayesian Aberration Removal via Constraints for Ultracold Atom Microscopy

High-resolution imaging of ultracold atoms typically requires custom high numerical aperture (NA) optics, as is the case for quantum gas microscopy. These high NA objectives involve many optical elements, each of which contributes to loss and light scattering, making them unsuitable for quantum backaction limited ``weak'' measurements. We employ a low-cost high NA aspheric lens as an objective for a practical and economical---although aberrated---high-resolution microscope to image $^{87}\mathrm{Rb}$ Bose-Einstein condensates. Here, we present a methodology for digitally eliminating the resulting aberrations that is applicable to a wide range of imaging strategies and requires no additional hardware. We recover nearly the full NA of our objective, thereby demonstrating a simple and powerful digital aberration correction method for achieving optimal microscopy of quantum objects. This reconstruction relies on a high-quality measure of our imaging system's even-order aberrations from density-density correlations measured with differing degrees of defocus. We demonstrate our aberration compensation technique using phase-contrast imaging, a dispersive imaging technique directly applicable to quantum backaction limited measurements. Furthermore, we show that our digital correction technique reduces the contribution of photon shot noise to density-density correlation measurements which would otherwise contaminate the desired quantum projection noise signal in weak measurements.
Scrybe: A Secure Audit Trail for Clinical Trial Data Fusion

Clinical trials are a multi-billion dollar industry. One of the biggest challenges facing the clinical trial research community is satisfying Part 11 of Title 21 of the Code of Federal Regulations and ISO 27789. These controls provide audit requirements that guarantee the reliability of the data contained in the electronic records. Context-aware smart devices and wearable IoT devices have become increasingly common in clinical trials. Electronic Data Capture (EDC) and Clinical Data Management Systems (CDMS) do not currently address the new challenges introduced using these devices. The healthcare digital threat landscape is continually evolving, and the prevalence of sensor fusion and wearable devices compounds the growing attack surface. We propose Scrybe, a permissioned blockchain, as a method of storing proof of clinical trial data provenance. We illustrate how Scrybe addresses each control and the limitations of the Ethereum-based blockchains. Finally, we provide a proof-of-concept integration with REDCap to show tamper resistance.
A control-theoretic perspective on optimal high-order optimization

We provide a control-theoretic perspective on optimal tensor algorithms for minimizing a convex function in a finite-dimensional Euclidean space. Given a function $$\varPhi : {\mathbb {R}}^d \rightarrow {\mathbb {R}}$$ that is convex and twice continuously differentiable, we study a closed-loop control system that is governed by the operators $$\nabla \varPhi $$ and $$\nabla ^2 \varPhi $$ together with a feedback control law $$\lambda (\cdot )$$ satisfying the algebraic equation $$(\lambda (t))^p\Vert \nabla \varPhi (x(t))\Vert ^{p-1} = \theta $$ for some $$\theta \in (0, 1)$$ . Our first contribution is to prove the existence and uniqueness of a local solution to this system via the Banach fixed-point theorem. We present a simple yet nontrivial Lyapunov function that allows us to establish the existence and uniqueness of a global solution under certain regularity conditions and analyze the convergence properties of trajectories. The rate of convergence is $$O(1/t^{(3p+1)/2})$$ in terms of objective function gap and $$O(1/t^{3p})$$ in terms of squared gradient norm. Our second contribution is to provide two algorithmic frameworks obtained from discretization of our continuous-time system, one of which generalizes the large-step A-HPE framework of Monteiro and Svaiter (SIAM J Optim 23(2):1092–1125, 2013) and the other of which leads to a new optimal p-th order tensor algorithm. While our discrete-time analysis can be seen as a simplification and generalization of Monteiro and Svaiter (2013), it is largely motivated by the aforementioned continuous-time analysis, demonstrating the fundamental role that the feedback control plays in optimal acceleration and the clear advantage that the continuous-time perspective brings to algorithmic design. A highlight of our analysis is that we show that all of the p-th order optimal tensor algorithms that we discuss minimize the squared gradient norm at a rate of $$O(k^{-3p})$$ , which complements the recent analysis in Gasnikov et al. (in: COLT, PMLR, pp 1374–1391, 2019), Jiang et al. (in: COLT, PMLR, pp 1799–1801, 2019) and Bubeck et al. (in: COLT, PMLR, pp 492–507, 2019).
Extreme event probability estimation using PDE-constrained optimization and large deviation theory, with application to tsunamis

We propose and compare methods for the estimation of extreme event probabilities in complex systems governed by PDEs. Our approach is guided by ideas from large deviation theory (LDT) and borrows tools from PDE-constrained optimization. The systems under consideration involve random parameters and we are interested in quantifying the probability that a scalar function of the system state solution is at or above a threshold. If this threshold is large, these probabilities are small and their accurate estimation is challenging. Our methods first compute parameters that minimize the LDT-rate function over the set of parameters leading to extreme events. These solutions provide asymptotic information about small probability events. We propose a series of methods to refine these estimates, namely methods based on importance sampling and on geometric approximation of the extreme event sets. Results are formulated for general parameter distributions and detailed expressions are provided when the underlying distribution is Gaussian. We give theoretical and numerical arguments showing that the performance of our methods is insensitive to the extremeness of the events we are interested in. We illustrate the application of our approach to quantify the probability of extreme tsunami events on shore. Tsunamis are typically caused by a sudden, unpredictable change of the ocean floor elevation during an earthquake. We model this change as a random process, which takes into account the underlying physics. We use the one-dimensional shallow water equation to model tsunamis numerically. Adjoint methods are used to compute the derivatives needed to solve the LDT-optimization problem. We present a comparison of the methods for extreme event probability estimation, and find which type of ocean floor elevation change leads to the largest tsunamis on shore.
Model Selection for Cross-lingual Transfer

Transformers that are pre-trained on multilingual corpora, such as, mBERT and XLM-RoBERTa, have achieved impressive cross-lingual transfer capabilities. In the zero-shot transfer setting, only English training data is used, and the fine-tuned model is evaluated on another target language. While this works surprisingly well, substantial variance has been observed in target language performance between different fine-tuning runs, and in the zero-shot setup, no target-language development data is available to select among multiple fine-tuned models. Prior work has relied on English dev data to select among models that are fine-tuned with different learning rates, number of steps and other hyperparameters, often resulting in suboptimal choices. In this paper, we show that it is possible to select consistently better models when small amounts of annotated data are available in auxiliary pivot languages. We propose a machine learning approach to model selection that uses the fine-tuned model’s own internal representations to predict its cross-lingual capabilities. In extensive experiments we find that this method consistently selects better models than English validation data across twenty five languages (including eight low-resource languages), and often achieves results that are comparable to model selection using target language development data.
A fast and accurate physics-informed neural network reduced order model with shallow masked autoencoder

Abstract Traditional linear subspace reduced order models (LS-ROMs) are able to accelerate physical simulations in which the intrinsic solution space falls into a subspace with a small dimension, i.e., the solution space has a small Kolmogorov n-width. However, for physical phenomena not of this type, e.g., any advection-dominated flow phenomena such as in traffic flow, atmospheric flows, and air flow over vehicles, a low-dimensional linear subspace poorly approximates the solution. To address cases such as these, we have developed a fast and accurate physics-informed neural network ROM, namely nonlinear manifold ROM (NM-ROM), which can better approximate high-fidelity model solutions with a smaller latent space dimension than the LS-ROMs. Our method takes advantage of the existing numerical methods that are used to solve the corresponding full order models. The efficiency is achieved by developing a hyper-reduction technique in the context of the NM-ROM. Numerical results show that neural networks can learn a more efficient latent space representation on advection-dominated data from 1D and 2D Burgers' equations. A speedup of up to 2.6 for 1D Burgers' and a speedup of 11.7 for 2D Burgers' equations are achieved with an appropriate treatment of the nonlinear terms through a hyper-reduction technique. Finally, a posteriori error bounds for the NM-ROMs are derived that take account of the hyper-reduced operators.
Weakly-Supervised Visual-Retriever-Reader for Knowledge-based Question Answering.

Knowledge-based visual question answering (VQA) requires answering questions with external knowledge in addition to the content of images. One dataset that is mostly used in evaluating knowledge-based VQA is OK-VQA, but it lacks a gold standard knowledge corpus for retrieval. Existing work leverage different knowledge bases (e.g., ConceptNet and Wikipedia) to obtain external knowledge. Because of varying knowledge bases, it is hard to fairly compare models’ performance. To address this issue, we collect a natural language knowledge base that can be used for any VQA system. Moreover, we propose a Visual Retriever-Reader pipeline to approach knowledge-based VQA. The visual retriever aims to retrieve relevant knowledge, and the visual reader seeks to predict answers based on given knowledge. We introduce various ways to retrieve knowledge using text and images and two reader styles: classification and extraction. Both the retriever and reader are trained with weak supervision. Our experimental results show that a good retriever can significantly improve the reader’s performance on the OK-VQA challenge. The code and corpus are provided in https://github.com/luomancs/retriever_reader_for_okvqa.git.