cssi/SciSciGPT-SciSciNet huggingface dataset:
Environmental Law and Three Economies: Navigating a Sprawling Field of Study, Practice, and Societal Governance in which Everything is Connected to Everything Else

From the Author's Introduction: This Essay addresses four of the challenges facing academics in this field, two of which are logistical and two conceptual. The two logistical challenges briefly addressed here are endemic in teaching environmental law - choosing what to teach and deciding how to teach it. Today some rough generalizations on strategies for curricular coping can be made, emerging from the evolution of environmental law courses at many American law schools over the past twenty-five years. The conceptual challenges may be even more difficult to tackle, and are addressed here at a greater length: first, how to frame and define this sprawling field, and then, how to understand environmental law's real life political dynamics - its practical realities and its unique role in societal governance. This Essay proposes a new way to address these conceptual challenges, the construct of three economies, splitting the realm in which environmental law and economics operate into three overlapping spheres. This model seeks to analyze the fictional civic role of governmental law, the inherent political tensions and contending forces that shape environmental law in daily practice, and, perhaps, how we are to conceptualize civic governance in the new millennium.
IT-Based Knowledge Capability and Commercialization of Innovations: Modeling the Impacts of Ambidexterity and Absorptive Capacity

The author provides a framework comprising of propositions for further research explicating the relations between IT-based knowledge capabilities IT-KC and Commercialization of Innovations CI. They posit that a firm's absorptive capacity and ambidexterity ability to explore and exploit affect CI. Further, absorptive capacity too can be an antecedent to ambidexterity. IT based knowledge capability which is an instantiation of IT capability is found to positively moderate the relationship between ambidexterity and commercialization of innovations, and also is an antecedent to potential and realized absorptive capacity. The author ties the seemingly isolated bits of literature together into an integrative theoretical model for testing.
Realigning Auditors' Accountability: Experimental Evidence

ABSTRACT We use experimental economic markets to examine the impact of changing institutional design features on audit quality. Specifically, we manipulate auditors' economic accountability to mana...
Sympathectomy: A "one hit wonder" for life?

Sympathectomy - A “one hit wonder” for life?Fabrizio R. Assis, M.D. Harikrishna Tandri, M.D.From Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
A New Dynamic Model of a Two-Wheeled Two-Flexible-Beam Inverted Pendulum Robot

Two-wheeled inverted pendulum robots are designed for self-balancing and they have remarkable advantages. In this paper, a new configuration and consequently dynamic model of one specific robot is presented and its dynamic behavior is analyzed. In this model, two cantilever beams are on the two-wheeled base and they are excited by voltages to the attached piezoelectric actuators. The mathematical model of this system is obtained using the extended Hamilton's Principle. The results show that the governing equations of motion are highly nonlinear and contain several coupled terms. These complex equations are solved numerically and the natural frequencies of the system are extracted. The system is then simulated in both lateral and horizontal plan movements. This paper proposes a new model of two-wheeled two-flexible-beam inverted pendulum Robot and investigates its complex dynamic; however, the derived equations will be validated experimentally and a suitable control strategy will be applied to the system to make it fully automated and more applicable in the future works.
The Generality of a Section of a Curve

Let f: C --> P^3 be a general curve of genus g, mapped to P^3 via a general linear series of degree d; and let Q be a general (and thus smooth) quadric. In this paper, we show that the points of intersection f(C) \cap Q give a general collection of 2d points on Q, except for exactly six exceptional cases. We also prove similar theorems for every other pair (r, n) for which, except for only finitely many pairs (d, g), the intersection of a general curve of genus g mapped to P^r via a general linear series of degree d, with a general hypersurface S of degree n, is a general collection of dn points on S. As explained in arXiv:1809.05980, these results play a key role in the author's proof of the Maximal Rank Conjecture
Analyzing and mitigating data stalls in DNN training

Training Deep Neural Networks (DNNs) is resource-intensive and time-consuming. While prior research has explored many different ways of reducing DNN training time, the impact of input data pipeline...
Two seemingly paradoxical results in linear models: the variance inflation factor and the analysis of covariance

A result from a standard linear model course is that the variance of the ordinary least squares (OLS) coefficient of a variable will never decrease when including additional covariates. The variance inflation factor (VIF) measures the increase of the variance. Another result from a standard linear model or experimental design course is that including additional covariates in a linear model of the outcome on the treatment indicator will never increase the variance of the OLS coefficient of the treatment at least asymptotically. This technique is called the analysis of covariance (ANCOVA), which is often used to improve the efficiency of treatment effect estimation. So we have two paradoxical results: adding covariates never decreases the variance in the first result but never increases the variance in the second result. In fact, these two results are derived under different assumptions. More precisely, the VIF result conditions on the treatment indicators but the ANCOVA result averages over them. Comparing the estimators with and without adjusting for additional covariates in a completely randomized experiment, I show that the former has smaller variance averaging over the treatment indicators, and the latter has smaller variance at the cost of a larger bias conditioning on the treatment indicators. Therefore, there is no real paradox.
Underclaiming and Overclaiming

Arguments that we have too much litigation (overclaiming) or too little (underclaiming) cannot be valid without estimating how many of the undecided claims that are brought (actual claims) or not brought (potential claims) have or lack legal merit. We identify the basic conceptual structure of such underclaiming and overclaiming arguments, which entails inferences about the distribution of actual or potential claims by their probability of success on the merits within a claims-processing institution. We then survey the available methods for estimating claim merit.
Concealer: SGX-based Secure, Volume Hiding, and Verifiable Processing of Spatial Time-Series Datasets.

This paper proposes a system, entitled Concealer that allows sharing time-varying spatial data (e.g., as produced by sensors) in encrypted form to an untrusted third-party service provider to provide location-based applications (involving aggregation queries over selected regions over time windows) to users. Concealer exploits carefully selected encryption techniques to use indexes supported by database systems and combines ways to add fake tuples in order to realize an efficient system that protects against leakage based on output-size. Thus, the design of Concealer overcomes two limitations of existing symmetric searchable encryption (SSE) techniques: (i) it avoids the need of specialized data structures that limit usability/practicality of SSE in large scale deployments, and (ii) it avoids information leakages based on the output-size, which may leak data distributions. Experimental results validate the efficiency of the proposed algorithms over a spatial time-series dataset (collected from a smart space) and TPC-H datasets, each of 136 Million rows, the size of which prior approaches have not scaled to.
Eigenmode analysis of membrane stability in inviscid flow

We study the stability of a thin membrane (of zero bending rigidity) with a vortex sheet as a nonlinear eigenvalue problem in the parameter space of membrane mass (${R}_{1}$) and pretension (${T}_{0}$). With both ends fixed light membranes become unstable by a divergence instability and heavy membranes lose stability by flutter and divergence for a ${T}_{0}$ that increases with ${R}_{1}$. With the leading edge fixed and trailing edge free, or both edges free, membrane eigenmodes transition in shape across the stability boundary. We find good quantitative agreement with unsteady time-stepping simulations at small amplitude, but only qualitative similarities with the eventual steady-state large-amplitude motions.
Self-Supervised Learning with Cross-Modal Transformers for Emotion Recognition

Emotion recognition is a challenging task due to limited availability of in-the-wild labeled datasets. Self-supervised learning has shown improvements on tasks with limited labeled datasets in domains like speech and natural language. Models such as BERT learn to incorporate context in word embeddings, which translates to improved performance in downstream tasks like question answering. In this work, we extend self-supervised training to multi-modal applications. We learn multi-modal representations using a transformer trained on the masked language modeling task with audio, visual and text features. This model is fine-tuned on the downstream task of emotion recognition. Our results on the CMU-MOSEI dataset show that this pre-training technique can improve the emotion recognition performance by up to 3% compared to the baseline.
A formula for enumerating permutations with a fixed pinnacle set

Abstract In 2017 Davis, Nelson, Petersen, and Tenner pioneered the study of pinnacle sets of permutations and asked whether there exists a class of operations, which applied to a permutation in S n , can produce any other permutation with the same pinnacle set and no others. In this paper, we adapt a group action defined by Foata and Strehl to provide a way to generate all permutations with a given pinnacle set. From this we give an answer to a second question asked by Davis, Nelsen, Peterson, and Tenner, which asks for a closed non-recursive formula enumerating permutations with a given pinnacle set.
Two inequalities for convex equipotential surfaces

We establish two geometric inequalities, respectively, for harmonic functions in exterior Dirichlet problems, and for Green's functions in interior Dirichlet problems, where the boundary surfaces are smooth and convex. Both inequalities involve integrals over the mean curvature and the Gaussian curvature on an equipotential surface, and the normal derivative of the harmonic potential thereupon. These inequalities generalize a geometric conservation law for equipotential curves in dimension two, and offer solutions to two free boundary problems in three-dimensional electrostatics.
Stochastic Ricci Flow on Compact Surfaces

In this paper we introduce the stochastic Ricci flow (SRF) in two spatial dimensions. The flow is symmetric with respect to a measure induced by Liouville Conformal Field Theory. Using the theory of Dirichlet forms, we construct a weak solution to the associated equation of the area measure on a flat torus, in the full "$L^1$ regime" $\sigma< \sigma_{L^1}=2\sqrt\pi$ where $\sigma$ is the noise strength. We also describe the main necessary modifications needed for the SRF on general compact surfaces, and list some open questions.
Predictive Power of Nearest Neighbors Algorithm under Random Perturbation

We consider a data corruption scenario in the classical $k$ Nearest Neighbors ($k$-NN) algorithm, that is, the testing data are randomly perturbed. Under such a scenario, the impact of corruption level on the asymptotic regret is carefully characterized. In particular, our theoretical analysis reveals a phase transition phenomenon that, when the corruption level $\omega$ is below a critical order (i.e., small-$\omega$ regime), the asymptotic regret remains the same; when it is beyond that order (i.e., large-$\omega$ regime), the asymptotic regret deteriorates polynomially. Surprisingly, we obtain a negative result that the classical noise-injection approach will not help improve the testing performance in the beginning stage of the large-$\omega$ regime, even in the level of the multiplicative constant of asymptotic regret. As a technical by-product, we prove that under different model assumptions, the pre-processed 1-NN proposed in \cite{xue2017achieving} will at most achieve a sub-optimal rate when the data dimension $d>4$ even if $k$ is chosen optimally in the pre-processing step.
Supertagging the Long Tail with Tree-Structured Decoding of Complex Categories

Although current CCG supertaggers achieve high accuracy on the standard WSJ test set, few systems make use of the categories’ internal structure that will drive the syntactic derivation during parsing. The tagset is traditionally truncated, discarding the many rare and complex category types in the long tail. However, supertags are themselves trees. Rather than give up on rare tags, we investigate constructive models that account for their internal structure, including novel methods for tree-structured prediction. Our best tagger is capable of recovering a sizeable fraction of the long-tail supertags and even generates CCG categories that have never been seen in training, while approximating the prior state of the art in overall tag accuracy with fewer parameters. We further investigate how well different approaches generalize to out-of-domain evaluation sets.
Giving a little to many or a lot to a few? The returns to variety in corporate philanthropy

We examine the returns to specialization versus variety in corporate philanthropy. Acknowledging the theoretical rationale for both a specialist and a generalist approach to philanthropy, we took a question-driven, abductive approach and found a robust positive association between philanthropic variety and firm profitability for donations by large US public corporations from 2003 to 2011. This association held for variety across causes but not within causes, for non-local giving and for giving by diversified firms, and was weaker for firms whose donations faced greater scrutiny. These findings are consistent with a moral hazard explanation whereby firms take advantage of the relatively inelastic support for philanthropy within a cause area by strategically spreading their donations across a wide range of supporter interests, thus maximizing profits.
A multi-sheath model for highly nonlinear plasma wakefields

An improved description for nonlinear plasma wakefields with phase velocities near the speed of light is presented and compared against fully kinetic particle-in-cell simulations. These wakefields are excited by intense particle beams or lasers pushing plasma electrons radially outward, creating an ion bubble surrounded by a sheath of electrons characterized by the source term S ≡ − 1 e n p ( ρ − J z / c ), where ρ and Jz are the charge and axial current densities, respectively. Previously, the sheath source term was described phenomenologically with a positive-definite function, resulting in a positive definite wake potential. In reality, the wake potential is negative at the rear of the ion column which is important for self-injection and accurate beam loading models. To account for this, we introduce a multi-sheath model in which the source term, S, of the plasma wake can be negative in regions outside the ion bubble. Using this model, we obtain a new expression for the wake potential and a modified differential equation for the bubble radius. Numerical results obtained from these equations are validated against particle-in-cell simulations for unloaded and loaded wakes. The new model provides accurate predictions of the shape and duration of trailing bunch current profiles that flatten plasma wakefields. It is also used to design a trailing bunch for a desired longitudinally varying loaded wakefield. We present beam loading results for laser wakefields and discuss how the model can be improved for laser drivers in future work. Finally, we discuss differences between the predictions of the multi- and single-sheath models for beam loading.
The Macroporosity of Rubble Pile Asteroid Ryugu and Implications for the Origin of Chondrules

We use the known surface boulder-size distribution of the C-type rubble pile asteroid Ryugu (NEA 162173) to determine its macroporosity, assuming it is a homogeneous granular aggregate. We show that the volume-frequency distribution of its boulders, cobbles and pebbles, is well represented by a lognormal function with $\sigma = 2.4 \pm 0.1$ and $\mu = 0.2 \pm 0.05$. Application of linear-mixture packing theory yields a value for the macroporosity of $\phi = 0.14 \pm 0.04$. Given its low bulk density of 1.19 gm cm$^{-3}$, this implies an average density for Ryugu's rocks of $1.38 \pm 0.07$ gm cm$^{-3}$ throughout its volume, consistent with a recent determination for surface boulders based on their thermal properties. This supports the spectrum-based argument that IDP's may be the best analog material available on Earth and suggests that high-density, well-lithified objects such as chondrules and chondrule-bearing chondrites may be rare on Ryugu. Implications of this result for the origin of chondrules, a long-standing problem in cosmochemistry, are discussed. We propose that chondrules and most chondrites formed together in rare lithification events, which occurred during the accretion of chondritic envelopes to large, differentiated planetesimals at a time when they were still hot from $^{26}$Al decay.
Temporal and spatial topography of cell proliferation in cancer.

3122Background: Tumors are complex ecosystems where exogenous and endogenous cues are integrated to either stimulate or inhibit cancer cell proliferation. However, the nature of these complex cell ...
Safe CPS from unsafe controllers

Modern cyber-physical systems (CPS) interact with the physical world, hence their correctness is important. In this work, we build upon the Simplex Architecture, where control authority may switch from an unverified and potentially unsafe advanced controller to a verified-safe baseline controller in order to maintain system safety. We take the approach further by lifting the requirement that the baseline controller must be verified or even correct, instead also treating it as a black-box component. This change is important; there are many types of powerful control techniques---model predictive control and neural network controllers---that often work well in practice, but are unlikely to be formally proven correct due to complexity. We prove such an architecture maintains safety, and present case studies where model-predictive control provides safety for multi-robot coordination, and unverified neural networks provably prevent collisions for groups of F-16 aircraft.
Attention Flows are Shapley Value Explanations

Shapley Values, a solution to the credit assignment problem in cooperative game theory, are a popular type of explanation in machine learning, having been used to explain the importance of features, embeddings, and even neurons. In NLP, however, leave-one-out and attention-based explanations still predominate. Can we draw a connection between these different methods? We formally prove that — save for the degenerate case — attention weights and leave-one-out values cannot be Shapley Values. Attention flow is a post-processed variant of attention weights obtained by running the max-flow algorithm on the attention graph. Perhaps surprisingly, we prove that attention flows are indeed Shapley Values, at least at the layerwise level. Given the many desirable theoretical qualities of Shapley Values — which has driven their adoption among the ML community — we argue that NLP practitioners should, when possible, adopt attention flow explanations alongside more traditional ones.
Safe Occlusion-Aware Autonomous Driving via Game-Theoretic Active Perception

Autonomous vehicles interacting with other traffic participants heavily rely on the perception and prediction of other agents’ behaviors to plan safe trajectories. However, as occlusions limit the vehicle’s perception ability, reasoning about potential hazards beyond the field-of-view is one of the most challenging issues in developing autonomous driving systems. This paper introduces a novel analytical approach that poses the problem of safe trajectory planning under occlusions as a hybrid zero-sum dynamic game between the autonomous vehicle (evader), and an initially hidden traffic participant (pursuer). Due to occlusions, the pursuer’s state is initially unknown to the evader and may later be discovered by the vehicle’s sensors. The analysis yields optimal strategies for both players as well as the set of initial conditions from which the autonomous vehicle is guaranteed to avoid collisions. We leverage this theoretical result to develop a novel trajectory planning framework that provides worst-case safety guarantees while minimizing conservativeness by accounting for the autonomous vehicle’s ability to actively avoid other road users as soon as they are detected in future observations. Our framework is agnostic to the driving environment and suitable for various motion planners. We demonstrate our algorithm on challenging urban and highway driving scenarios using the open-source CARLA simulator.
MiniLMv2: Multi-Head Self-Attention Relation Distillation for Compressing Pretrained Transformers

We generalize deep self-attention distillation in MiniLM (Wang et al., 2020) by only using self-attention relation distillation for task-agnostic compression of pretrained Transformers. In particular, we define multi-head self-attention relations as scaled dot-product between the pairs of query, key, and value vectors within each self-attention module. Then we employ the above relational knowledge to train the student model. Besides its simplicity and unified principle, more favorably, there is no restriction in terms of the number of student's attention heads, while most previous work has to guarantee the same head number between teacher and student. Moreover, the fine-grained self-attention relations tend to fully exploit the interaction knowledge learned by Transformer. In addition, we thoroughly examine the layer selection strategy for teacher models, rather than just relying on the last layer as in MiniLM. We conduct extensive experiments on compressing both monolingual and multilingual pretrained models. Experimental results demonstrate that our models distilled from base-size and large-size teachers (BERT, RoBERTa and XLM-R) outperform the state-of-the-art.
A computational method for direct imputation of cell type-specific expression profiles and cellular compositions from bulk-tissue RNA-Seq in brain disorders.

The importance of cell type-specific gene expression in disease-relevant tissues is increasingly recognized in genetic studies of complex diseases. However, most gene expression studies are conducted on bulk tissues, without examining cell type-specific expression profiles. Several computational methods are available for cell type deconvolution (i.e. inference of cellular composition) from bulk RNA-Seq data, but few of them impute cell type-specific expression profiles. We hypothesize that with external prior information such as single cell RNA-seq and population-wide expression profiles, it can be computationally tractable to estimate both cellular composition and cell type-specific expression from bulk RNA-Seq data. Here we introduce CellR, which addresses cross-individual gene expression variations to adjust the weights of cell-specific gene markers. It then transforms the deconvolution problem into a linear programming model while taking into account inter/intra cellular correlations and uses a multi-variate stochastic search algorithm to estimate the cell type-specific expression profiles. Analyses on several complex diseases such as schizophrenia, Alzheimer's disease, Huntington's disease and type 2 diabetes validated the efficiency of CellR, while revealing how specific cell types contribute to different diseases. In summary, CellR compares favorably against competing approaches, enabling cell type-specific re-analysis of gene expression data on bulk tissues in complex diseases.
Principles of 3D compartmentalization of the human genome

Chromatin is organized in the nucleus via CTCF loops and compartmental domains. Here, we compare different cell types to identify distinct paradigms of compartmental domain formation in human tissues. We identify and quantify compartmental forces correlated with histone modifications characteristic of transcriptional activity and previously underappreciated roles for distinct compartmental domains correlated with the presence of H3K27me3 and H3K9me3, respectively. We present a computer simulation model capable of predicting compartmental organization based on the biochemical characteristics of independent chromatin features. Using this model, we show that the underlying forces responsible for compartmental domain formation in human cells are conserved and that the diverse compartmentalization patterns seen across cell types are due to differences in chromatin features. We extend these findings to Drosophila to suggest that the same principles are at work beyond humans. These results offer mechanistic insights into the fundamental forces driving the 3D organization of the genome.
Automated segmentation of choroidal layers from 3-dimensional macular optical coherence tomography scans.

Abstract Background Changes in choroidal thickness are associated with various ocular diseases, and the choroid can be imaged using spectral-domain optical coherence tomography (SD-OCT) and enhanced depth imaging OCT (EDI-OCT). New method Eighty macular SD-OCT volumes from 80 patients were obtained using the Zeiss Cirrus machine. Eleven additional control subjects had two Cirrus scans done in one visit along with enhanced depth imaging (EDI-OCT) using the Heidelberg Spectralis machine. To automatically segment choroidal layers from the OCT volumes, our graph-theoretic approach was utilized. The segmentation results were compared with reference standards from two independent graders, and the accuracy of automated segmentation was calculated using unsigned/signed border positioning/thickness errors and Dice similarity coefficient (DSC). The repeatability and reproducibility of our choroidal thicknesses were determined by intraclass correlation coefficient (ICC), coefficient of variation (CV), and repeatability coefficient (RC). Results The mean unsigned/signed border positioning errors for the choroidal inner and outer surfaces are 3.39 ± 1.26 µm (mean ± standard deviation)/− 1.52 ± 1.63 µm and 16.09 ± 6.21 µm/4.73 ± 9.53 µm, respectively. The mean unsigned/signed choroidal thickness errors are 16.54 ± 6.47 µm/6.25 ± 9.91 µm, and the mean DSC is 0.949 ± 0.025. The ICC (95% confidence interval), CV, RC values are 0.991 (0.977–0.997), 2.48%, 14.25 µm for the repeatability and 0.991 (0.977–0.997), 2.49%, 14.30 µm for the reproducibility studies, respectively. Comparison with existing method(s) The proposed method outperformed our previous method using choroidal vessel segmentation and inter-grader variability. Conclusions This automated segmentation method can reliably measure choroidal thickness using different OCT platforms.
A verified decision procedure for univariate real arithmetic with the BKR algorithm.

We formalize the univariate fragment of Ben-Or, Kozen, and Reif’s (BKR) decision procedure for first-order real arithmetic in Isabelle/HOL. BKR’s algorithm has good potential for parallelism and was designed to be used in practice. Its key insight is a clever recursive procedure that computes the set of all consistent sign assignments for an input set of univariate polynomials while carefully managing intermediate steps to avoid exponential blowup from naively enumerating all possible sign assignments (this insight is fundamental for both the univariate case and the general case). Our proof combines ideas from BKR and a follow-up work by Renegar that are well-suited for formalization. The resulting proof outline allows us to build substantially on Isabelle/HOL’s libraries for algebra, analysis, and matrices. Our main extensions to existing libraries are also detailed.
Cybersecurity of Electric Vehicle Smart Charging Management Systems

In concept, a smart charging management system (SCMS) optimizes the charging of plug-in vehicles (PEVs) and provides various grid services including voltage control, frequency regulation, peak shaving, renewable energy integration support, spinning reserve, and emergency demand response. These functionalities largely depend upon data collected from various entities such as PEVs, electric vehicle supply equipment (EVSE), service providers, and utilities. SCMS can be susceptible to both cyber and physical threats (e.g. man-in-the-middle attack, data intrigued attack, denial of charging, physical-attack) due to interactions of and interdependencies between cyber and physical components. Cyber-physical threats through highly connected malware vectors raise various concerns including public safety hazards to vehicle operators and those in the immediate vicinity as well as disruptions to electric grid operations. This paper describes the concept of SCMS and provides a comprehensive review of cybersecurity aspects of EVSEs and SCMSs with their possible impacts on the power grid and society. It also contributes to the development of cybersecurity measures to the SCMSs. Various functions of SCMS are reviewed in detail including peak shaving, demand charge reduction, frequency regulation, spinning reserve, renewable integration support, distribution congestion management, reactive power compensation, and emergency demand response with unidirectional PEVs charging. Also, a critical literature survey on current practices of SCMS cybersecurity is provided to explore major impacts and challenges of cyber-physical attacks and to identify research gaps and vulneraibilities in currently available SCMSs technologies.
Consistent Risk Estimation in Moderately High-Dimensional Linear Regression

Risk estimation is at the core of many learning systems. The importance of this problem has motivated researchers to propose different schemes, such as cross validation, generalized cross validation, and Bootstrap. The theoretical properties of such estimators have been extensively studied in the low-dimensional settings, where the number of predictors $p$ is much smaller than the number of observations $n$ . However, a unifying methodology accompanied with a rigorous theory is lacking in high-dimensional settings. This paper studies the problem of risk estimation under the moderately high-dimensional asymptotic setting $n,p \rightarrow \infty $ and $n/p \rightarrow \delta >1$ ( $\delta $ is a fixed number), and proves the consistency of three risk estimators that have been successful in numerical studies, i.e., leave-one-out cross validation (LOOCV), approximate leave-one-out (ALO), and approximate message passing (AMP)-based techniques. A corner stone of our analysis is a bound that we obtain on the discrepancy of the ‘residuals’ obtained from AMP and LOOCV. This connection not only enables us to obtain a more refined information on the estimates of AMP, ALO, and LOOCV, but also offers an upper bound on the convergence rate of each estimator.
Splittings of right-angled Artin groups

We show that if a right-angled Artin group A(Γ) has a non-trivial, minimal action on a tree T with more than two ends, then Γ contains a separating subgraph Λ such that A(Λ) stabilizes an edge in T.
Climate-Society Feedback Effects: Be Wary of Unidentified Connections

Feedbacks within the climateâ€“economy system are complex. The research analyzing the relationship between human activities and the climate is considerable, with particular focus on intra-system feedback effects: environmental tipping points, and climate-triggered social tipping points, like migration, to a lesser extent (Kopp et al., 2016; van Ginkel et al., 2018). Due to their cross-disciplinary nature (Guerrero et al., 2018), two-way interactions between the environment and society, whereby movement in either system can trigger inter-system feedbacks (Lade et al., 2013; Yletyinen et al., 2019) as humans respond to a changing environment thereby further changing the environment, have only recently received attention by a growing inter-disciplinary research community. With the aim of improving climate policy and its tools, such as the social cost of carbon, we describe these socialâ€“ecological system (SES) feedbacks and place them in the existing taxonomy for tipping points applied by mainstream climateâ€“economy models. Drawing lessons from SES research and related interdisciplinary literature, we discuss the value of and method by which to modify social-cost integrated assessment models (SC-IAMs), like Nordhaus' Dynamic Integrated Climate Economy. As it is critical that climate policy include these risks to the stability of the climateâ€“economy system, we conclude with a research agenda for the identification, quantification, and integration of climateâ€“society feedbacks into SC-IAMs.
Criticality, the list color function, and list coloring the cartesian product of graphs

We introduce a notion of color-criticality in the context of chromatic-choosability. We define a graph $G$ to be strong $k$-chromatic-choosable if $\chi(G) = k$ and every $(k-1)$-assignment for which $G$ is not list-colorable has the property that the lists are the same for all vertices. That is the usual coloring is, in some sense, the obstacle to list-coloring. We prove basic properties of strongly chromatic-choosable graphs such as chromatic-choosability and vertex-criticality, and we construct infinite families of strongly chromatic-choosable graphs. We derive a sufficient condition for the existence of at least two list colorings of strongly chromatic-choosable graphs and use it to show that: if $M$ is a strong $k$-chromatic-choosable graph with $|E(M)| \leq |V(M)|(k-2)$ and $H$ is a graph that contains a Hamilton path, $w_1, w_2, \ldots, w_m$, such that $w_i$ has at most $\rho \geq 1$ neighbors among $w_1, \ldots, w_{i-1}$, then $\chi_{\ell}(M \square H) \le k+ \rho - 1$. We show that this bound is sharp for all $\rho \ge 1$ by generalizing the theorem to apply to $H$ that are $(M,\rho)$-Cartesian accommodating which is a notion we define with the help of the list color function, $ P_{\ell}(G,k)$, the list analogue of the chromatic polynomial. We also use the list color function to determine the list chromatic number of certain star-like graphs: $\chi_{\ell}(M \square K_{1,s}) =$ $k \; \text{if } s < P_{\ell}(M,k)$, or $k+1 \; \text{if } s \geq P_{\ell}(M,k)$, where $M$ is a strong $k$-chromatic-choosable graph. We show that $ P_{\ell}(M,k)$ equals $P(M,k)$, the chromatic polynomial, when $M$ is an odd cycle, complete graph, or the join of an odd cycle with a complete graph.
Energy-Optimal Motion Planning for Agents: Barycentric Motion and Collision Avoidance Constraints

As robotic swarm systems emerge, it is increasingly important to provide strong guarantees on energy consumption and safety to maximize system performance. One approach to achieve these guarantees is through constraint-driven control, where agents seek to minimize energy consumption subject to a set of safety and task constraints. In this paper, we provide an equivalent sufficient and necessary optimality condition for an energy-minimizing agent with integrator dynamics that only depends on the state and control actions of the agent. In particular, we show that the agent must have a continuous control input at the transition between unconstrained and constrained trajectories. In addition, we present and analyze barycentric motion and collision avoidance constraints to be used in constraint-driven control of swarms.
Subdimensional Expansion for Multi-Objective Multi-Agent Path Finding

Conventional multi-agent path planners typically determine a path that optimizes a single objective, such as path length. Many applications, however, may require multiple objectives, say time-to-completion and fuel use, to be simultaneously optimized in the planning process. Often, these criteria may not be directly compared and sometimes lie in competition with each other. Simply applying standard multi-objective search algorithms to multi-agent path finding may prove to be inefficient because the size of the space of possible solutions, i.e., the Pareto-optimal set, can grow exponentially with the number of agents. This letter presents an approach that bypasses this so-called curse of dimensionality by leveraging our prior multi-agent work with a framework called subdimensional expansion. One example of subdimensional expansion, when applied to A $^*$ , is called M $^*$ and M $^*$ was limited to a single objective function. We combine principles of dominance and subdimensional expansion to create a new algorithm, named multi-objective M $^*$ (MOM $^*$ ), which dynamically couples agents for planning only when those agents have to “interact” with each other. MOM $^*$ computes the complete Pareto-optimal set for multiple agents efficiently and naturally trades off sub-optimal approximations of the Pareto-optimal set and computational efficiency. Our approach is able to find the complete Pareto-optimal set for problem instances with hundreds of solutions which the standard multi-objective A $^*$ algorithms could not find within a bounded time.
Informing Real-Time Corrections in Corrective Shared Autonomy Through Expert Demonstrations

C orrective Shared Autonomy is a method where human corrections are layered on top of an otherwise autonomous robot behavior. Specifically, a Corrective Shared Autonomy system leverages an external controller to allow corrections across a range of task variables (e.g., spinning speed of a tool, applied force, path) to address the specific needs of a task. However, this inherent flexibility makes the choice of what corrections to allow at any given instant difficult to determine. This choice of corrections includes determining appropriate robot state variables, scaling for these variables, and a way to allow a user to specify the corrections in an intuitive manner. This letter enables efficient Corrective Shared Autonomy by providing an automated solution based on Learning from Demonstration to both extract the nominal behavior and address these core problems. Our evaluation shows that this solution enables users to successfully complete a surface cleaning task, identifies different strategies users employed in applying corrections, and points to future improvements for our solution.
Imparting Barely Visible Impact Damage to a Stitched Composite Large-Scale Pressure Box

The Pultruded Rod Stitched Efficient Unitized Structure (PRSEUS) is a concept that was developed by The Boeing Company to address the complex structural design aspects associated with a pressurized hybrid wing body (HWB) aircraft configuration, which has been a focus of the NASA Environmentally Responsible Aviation Project. The NASA-Boeing structural development for the HWB aircraft culminated in testing of the multi-bay box, which is an 80%-scale representation of the pressurized center-body section. This structure was tested in the NASA Langley Research Center Combined Loads Test System facility. As part of this testing, barely visible impact damage was imparted to the interior and exterior of the test article to demonstrate compliance with a condition representative of the requirements for Category 1 damaged composite structure as defined by the Federal Aviation Regulations. Interior impacts were imparted using an existing spring-loaded impactor, while the exterior impacts were imparted using a newly designed, gravity-driven impactor. This paper describes the impacts to the test article, and the design of the gravitydriven guided-weight impactor. The guided-weight impactor proved to be a very reliable method to impart barely visible impact damage in locations which are not easily accessible for a traditional drop-weight impactor, while at the same time having the capability to be highly configurable for use on other aircraft structures.
Planning low-carbon distributed power systems: Evaluating the role of energy storage

Abstract This paper introduces a mathematical formulation of energy storage systems into a generation capacity expansion framework to evaluate the role of energy storage in the decarbonization of distributed power systems. The modeling framework accounts for dynamic charging/discharging efficiencies and maximum cycling powers as well as cycle and calendar degradation of a Li-ion battery system. Results from a small-scale distributed power system indicate that incorporating the dynamic efficiencies and cycling powers of batteries in the generation planning problem does not significantly change the optimal generation portfolio, while adding substantial computational burden. In contrast, accounting for battery degradation leads to substantially different generation expansion outcomes, especially in deep decarbonization scenarios with larger energy storage capacities. Under the assumptions used in this study, it is found that battery energy storage is economically viable for 2020 only under strict carbon emission constraints. In contrast, given the projected technology advances and corresponding cost reductions, battery energy storage exhibits an attractive option to enable deep decarbonization in 2050.
Sequestration of the exocytic SNARE Psy1 into multiprotein nodes reinforces polarized morphogenesis in fission yeast.

Cell shape requires mechanisms that restrict polarized growth to defined sites. Here, we show that the fission yeast exocytic t-SNARE is sequestered into megadalton-sized nodes along cell sides, wh...
Circumventing Circumvention: An Economic Analysis of the Role of Education and Enforcement

The role of education and enforcement in ensuring compliance with a law or policy has been debated for more than a century now. We reopen this debate in the context of security circumvention by emp...
Approximate inversion of discrete Fourier integral operators

Abstract This paper introduces a factorization for the inverse of discrete Fourier integral operators of size N × N that can be applied in quasi-linear time. The factorization starts by approximating the operator with the butterfly factorization. Next, a hierarchical matrix representation is constructed for the hermitian matrix arising from composing the Fourier integral operator with its adjoint. This representation is inverted efficiently with a new algorithm based on the hierarchical interpolative factorization. By combining these two factorizations, an approximate inverse factorization for the Fourier integral operator is obtained as a product of O ( log ⁡ N ) sparse matrices with O ( N ) entries. The resulting approximate inverse factorization can be used as a direct solver or as a preconditioner. Numerical examples on 1D and 2D Fourier integral operators, including a generalized Radon transform, demonstrate the performance of this new approach.
Elastocaloric signature of nematic fluctuations

The elastocaloric effect (ECE) relates changes in entropy to changes in strain experienced by a material. As such, ECE measurements can provide valuable information about the entropy landscape proximate to strain-tuned phase transitions. For ordered states that break only point symmetries, bilinear coupling of the order parameter with strain implies that the ECE can also provide a window on fluctuations above the critical temperature and hence, in principle, can also provide a thermodynamic measure of the associated susceptibility. To demonstrate this, we use the ECE to sensitively reveal the presence of nematic fluctuations in the archetypal Fe-based superconductor Ba([Formula: see text])2[Formula: see text] By performing these measurements simultaneously with elastoresistivity in a multimodal fashion, we are able to make a direct and unambiguous comparison of these closely related thermodynamic and transport properties, both of which are sensitive to nematic fluctuations. As a result, we have uncovered an unanticipated doping dependence of the nemato-elastic coupling and of the magnitude of the scattering of low-energy quasi-particles by nematic fluctuations-while the former weakens, the latter increases dramatically with increasing doping.
Graph-based Reinforcement Learning for Active Learning in Real Time: An Application in Modeling River Networks.

Effective training of advanced ML models requires large amounts of labeled data, which is often scarce in scientific problems given the substantial human labor and material cost to collect labeled data. This poses a challenge on determining when and where we should deploy measuring instruments (e.g., in-situ sensors) to collect labeled data efficiently. This problem differs from traditional pool-based active learning settings in that the labeling decisions have to be made immediately after we observe the input data that come in a time series. In this paper, we develop a real-time active learning method that uses the spatial and temporal contextual information to select representative query samples in a reinforcement learning framework. To reduce the need for large training data, we further propose to transfer the policy learned from simulation data which is generated by existing physics-based models. We demonstrate the effectiveness of the proposed method by predicting streamflow and water temperature in the Delaware River Basin given a limited budget for collecting labeled data. We further study the spatial and temporal distribution of selected samples to verify the ability of this method in selecting informative samples over space and time.
Charge sensitivity of a cavity-embedded Cooper pair transistor limited by single-photon shot noise

Using an operator scattering approach, we analyze the quantum dynamics of an ultrasensitive electrometer—a Cooper pair transistor embedded in a quarter-wave microwave cavity (cCPT). While the cCPT is inherently a tunable, strongly nonlinear system affording a diverse range of functionalities, we restrict our present analysis to a necessary first investigation of its linear charge sensing capabilities, limiting to low pump powers corresponding to an average cavity photon number of ≲1. Assuming realizable cCPT parameters [B. L. Brock et al., Phys. Rev. Applied 15, 044009 (2021)], and not including noise from the subsequent amplifier chain, we predict the fundamental, photon shot noise-limited charge sensitivity to be 0.12μe/Hz when the pumped cavity has an average of one photon and the cCPT is operated close to charge degeneracy. This is to be compared with a first reported charge sensitivity value of 14μe/Hz in the single-photon regime [B. L. Brock et al., arXiv:2102.05362 (2021)].
Network Effects and Switching Costs in the US Wireless Industry

I develop an empirical framework to disentangle dierent sources of consumer inertia in the US wireless industry. The use of a detailed data set allows me to identify preference heterogeneity from consumer type-specific market shares and switching costs from churn rates. Identification of a localized network eect comes from comparing the dynamics of distinct local markets. The central condition for identification is that neither the characteristics defining consumer heterogeneity nor the characteristics defining reference groups are a (weak) subset of the other. Being able to separate switching costs and network eects is important as both can lead to