cssi/SciSciGPT-SciSciNet huggingface dataset:
Global Existence and Analyticity for the 2D Kuramoto–Sivashinsky Equation

There is little analytical theory for the behavior of solutions of the Kuramoto–Sivashinsky equation in two spatial dimensions over long times. We study the case in which the spatial domain is a two-dimensional torus. In this case, the linearized behavior depends on the size of the torus—in particular, for different sizes of the domain, there are different numbers of linearly growing modes. We prove that small solutions exist for all time if there are no linearly growing modes, proving also in this case that the radius of analyticity of solutions grows linearly in time. In the general case (i.e., in the presence of a finite number of growing modes), we make estimates for how the radius of analyticity of solutions changes in time.
Finite sampling inequalities: An application to two-sample Kolmogorov–Smirnov statistics

Abstract We review a finite-sampling exponential bound due to Serfling and discuss related exponential bounds for the hypergeometric distribution. We then discuss how such bounds motivate some new results for two-sample empirical processes. Our development complements recent results by Wei and Dudley (2012) concerning exponential bounds for two-sided Kolmogorov–Smirnov statistics by giving corresponding results for one-sided statistics with emphasis on “adjusted” inequalities of the type proved originally by Dvoretzky et al. (1956) [3] and by Massart (1990) for one-sample versions of these statistics.
Partitioning Relational Matrices of Similarities or Dissimilarities Using the Value of Information

In this paper, we provide an approach to clustering relational matrices whose entries correspond to either similarities or dissimilarities between objects. Our approach is based on the value of information, a parameterized, information-theoretic criterion that measures the change in costs associated with changes in information. Optimizing the value of information yields a deterministic annealing style of clustering with many benefits. For instance, investigators avoid needing to a priori specify the number of clusters, as the partitions naturally undergo phase changes, during the annealing process, whereby the number of clusters changes in a data-driven fashion. The global-best partition can also often be identified.
High dimensional linear discriminant analysis: optimality, adaptive algorithm and missing data

The paper develops optimality theory for linear discriminant analysis in the high dimensional setting. A data‐driven and tuning‐free classification rule, which is based on an adaptive constrained l1‐minimization approach, is proposed and analysed. Minimax lower bounds are obtained and this classification rule is shown to be simultaneously rate optimal over a collection of parameter spaces. In addition, we consider classification with incomplete data under the missingness completely at random model. An adaptive classifier with theoretical guarantees is introduced and the optimal rate of convergence for high dimensional linear discriminant analysis under the missingness completely at random model is established. The technical analysis for the case of missing data is much more challenging than that for complete data. We establish a large deviation result for the generalized sample covariance matrix, which serves as a key technical tool and can be of independent interest. An application to lung cancer and leukaemia studies is also discussed.
Bayesian renewables scenario generation via deep generative networks

We present a method to generate renewable scenarios using Bayesian probabilities by implementing the Bayesian generative adversarial network (Bayesian GAN), which is a variant of generative adversarial networks based on two interconnected deep neural networks. By using a Bayesian formulation, generators can be constructed and trained to produce scenarios that capture different salient modes in the data, allowing for better diversity and more accurate representation of the underlying physical process. Compared to conventional statistical models that are often hard to scale or sample from, this method is model-free and can generate samples extremely efficiently. For validation, we use wind and solar times-series data from NREL integration data sets to train the Bayesian GAN. We demonstrate that proposed method is able to generate clusters of wind scenarios with different variance and mean value, and is able to distinguish and generate wind and solar scenarios simultaneously even if the historical data are intentionally mixed.
A Note on Discrete Fractional Integral Operators on the Heisenberg Group

We consider the discrete analogue of a fractional integral operator on the Heisenberg group, for which we are able to prove nearly sharp results by means of a simple argument of a combinatorial nature.
Tropical invariants from the secondary fan

In this paper, we consider weighted counts of tropical plane curves of particular combinatorial type through a certain number of generic points. We give a criterion, derived from tropical intersection theory on the secondary fan, for a weighted count to give a number invariant of the position of the points. By computing a certain intersection multiplicity, we show how Mikhalkin's approach to computing Gromov-Witten invariants fits into our approach. This begins to address a question raised by Dickenstein, Feichtner, and Sturmfels. We also give a geometric interpretation of the numbers we produce involving Chow quotients, and provide a counterexample showing that the tropical Severi variety is not always supported on the secondary fan. This paper is a revision of the preprint, "The Tropical Degree of Cones in the Secondary Fan."
Newton-stein method: an optimization method for GLMs via Stein's lemma

We consider the problem of efficiently computing the maximum likelihood estimator in Generalized Linear Models (GLMs) when the number of observations is much larger than the number of coefficients (n ≫ p ≫ 1). In this regime, optimization algorithms can immensely benefit from approximate second order information. We propose an alternative way of constructing the curvature information by formulating it as an estimation problem and applying a Stein-type lemma, which allows further improvements through subsampling and eigenvalue thresholding. Our algorithm enjoys fast convergence rates, resembling that of second order methods, with modest per-iteration cost. We provide its convergence analysis for the general case where the rows of the design matrix are samples from a sub-Gaussian distribution. We show that the convergence has two phases, a quadratic phase followed by a linear phase. Finally, we empirically demonstrate that our algorithm achieves the highest performance compared to various optimization algorithms on several data sets.
Stability of a natural sheaf over the cartesian square of the Hilbert scheme of points on a K3 surface

Let S be a K3 surface and \(S^{[n]}\) the Hilbert scheme of length n subschemes of S. Over the cartesian square \(S^{[n]}\times S^{[n]}\) there exists a natural reflexive rank \(2n-2\) coherent sheaf E, which is locally free away from the diagonal. The fiber of E over the point \((I_{Z_1},I_{Z_2})\), corresponding to ideal sheaves of distinct subschemes \(Z_1\ne Z_2\), is \(\mathrm{Ext}^1_S(I_{Z_1},I_{Z_2})\). We prove that E is slope stable if the rank of the Picard group of S is \(\le 19\). The Chern classes of \({\mathcal E}nd(E)\) are known to be monodromy invariant. Consequently, the sheaf \({\mathcal E}nd(E)\) is polystable-hyperholomorphic.
Cost-Effective HITs for Relative Similarity Comparisons

Similarity comparisons of the form "Is object a more similar to b than to c?" form a useful foundation in several computer vision and machine learning applications. Unfortunately, an embedding of n points is only uniquely specified by n 3 triplets, making collecting every triplet an expensive task. In noticing this difficulty, other researchers investigated more intelligent triplet sampling techniques, but they do not study their effectiveness or their potential drawbacks. Although it is important to reduce the number of collected triplets to generate a good embedding, it is also important to understand how best to display a triplet collection task to the user to better respect the worker's human constraints. In this work, we explore an alternative method for collecting triplets and analyze its financial cost, collection speed, and worker happiness as a function of the final embedding quality. We propose best practices for creating cost effective human intelligence tasks for collecting triplets. We show that rather than changing the sampling algorithm, simple changes to the crowdsourcing UI can drastically decrease the cost of collecting similarity comparisons. Finally, we provide a food similarity dataset as well as the labels collected from crowd workers.
007: Democratically Finding the Cause of Packet Drops

Network failures continue to plague datacenter operators as their symptoms may not have direct correlation with where or why they occur. We introduce 007, a lightweight, always-on diagnosis application that can find problematic links and also pinpoint problems for each TCP connection. 007 is completely contained within the end host. During its two month deployment in a tier-1 datacenter, it detected every problem found by previously deployed monitoring tools while also finding the sources of other problems previously undetected.
Linear invariance of intersections on unitary Rapoport-Zink spaces

We prove an invariance property of intersections of Kudla-Rapoport divisors on a unitary Rapoport-Zink space.
Chernoff-Hoeffding Bounds for Markov Chains: Generalized and Simplified

We prove the first Chernoff-Hoeffding bounds for general nonreversible finite-state Markov chains based on the standard L_1 (variation distance) mixing-time of the chain. Specifically, consider an ergodic Markov chain M and a weight function f: [n] -> [0,1] on the state space [n] of M with mean mu = E_{v = delta mu t ], is at most exp(-Omega( delta^2 mu t / T )) for 0 1. In fact, the bounds hold even if the weight functions f_i's for i in [t] are distinct, provided that all of them have the same mean mu. We also obtain a simplified proof for the Chernoff-Hoeffding bounds based on the spectral expansion lambda of M, which is the square root of the second largest eigenvalue (in absolute value) of M tilde{M}, where tilde{M} is the time-reversal Markov chain of M. We show that the probability Pr [ |X - mu t| >= delta mu t ] is at most exp(-Omega( delta^2 (1-lambda) mu t )) for 0 1. Both of our results extend to continuous-time Markov chains, and to the case where the walk starts from an arbitrary distribution x, at a price of a multiplicative factor depending on the distribution x in the concentration bounds.
Deep grabcut for object selection

Most previous bounding-box-based segmentation methods assume the bounding box tightly covers the object of interest. However it is common that a rectangle input could be too large or too small. In this paper, we propose a novel segmentation approach that uses a rectangle as a soft constraint by transforming it into an Euclidean distance map. A convolutional encoder-decoder network is trained end-to-end by concatenating images with these distance maps as inputs and predicting the object masks as outputs. Our approach gets accurate segmentation results given sloppy rectangles while being general for both interactive segmentation and instance segmentation. We show our network extends to curve-based input without retraining. We further apply our network to instance-level semantic segmentation and resolve any overlap using a conditional random field. Experiments on benchmark datasets demonstrate the effectiveness of the proposed approaches.
A local limit law for the empirical spectral distribution of the anticommutator of independent Wigner matrices

Notre principal resultat etablit la loi limite locale pour la distribution spectrale empirique de l’anti-commutateur de matrices de Wigner independantes dans l’esprit de la loi semi-circulaire locale. Notre approche adapte les techniques d’articles recents par Erdos–Yau–Yin. Nous utilisons aussi une description algebrique de la loi de l’anti-commutateur pour des variables libres due a Nica–Speicher, une variante de l’astuce de la linearisation de Haagerup–Schultz–Thorbjornsen et l’equation de Schwinger–Dyson. Une consequence de notre travail est une version deterministe assez simple de la loi semi-circulaire locale.
Multimodal Sparse Classifier for Adolescent Brain Age Prediction

The study of healthy brain development helps to better understand both brain transformation and connectivity patterns, which happen during childhood to adulthood. This study presents a sparse machine learning solution across whole-brain functional connectivity measures of three datasets, derived from resting state functional magnetic resonance imaging (rs-fMRI) and two task fMRI data including a working memory n-back task (nb-fMRI) and an emotion identification task (em-fMRI). The fMRI data are collected from the Philadelphia Neurodevelopmental Cohort (PNC) for the prediction of brain age in adolescents. Due to extremely large variable-to-instance ratio of PNC data, a high-dimensional matrix with several irrelevant and highly correlated features is generated, and hence a sparse learning approach is necessary to extract effective features from fMRI data. We propose a sparse learner based on the residual errors along the estimation of an inverse problem for extreme learning machine (ELM). Our proposed method is able to overcome the overlearning problem by pruning several redundant features and their corresponding output weights. The proposed multimodal sparse ELM classifier based on residual errors is highly competitive in terms of classification accuracy compared to its counterparts such as conventional ELM, and sparse Bayesian learning ELM.
Prediction and modularity in dynamical systems.

Identifying and understanding modular organizations is centrally important in the study of complex systems. Several approaches to this problem have been advanced, many framed in information-theoretic terms. Our treatment starts from the complementary point of view of statistical modeling and prediction of dynamical systems. It is known that for finite amounts of training data, simpler models can have greater predictive power than more complex ones. We use the trade-off between model simplicity and predictive accuracy to generate optimal multiscale decompositions of dynamical networks into weakly-coupled, simple modules. Statedependent and causal versions of our method are also proposed.
Connectedness of Brill–Noether Loci via Degenerations

Author(s): Osserman, Brian | Abstract: We show that limit linear series spaces for chains of curves are reduced. Using new advances in the foundations of limit linear series, we then use degenerations to study the question of connectedness for spaces of linear series with imposed ramification at up to two points. We find that in general, these spaces may not be connected even when they have positive dimension, but we prove a criterion for connectedness which generalizes the theorem previously proved by Fulton and Lazarsfeld in the case without imposed ramification.
Fast Budgeted Influence Maximization Over Multi-Action Event Logs

In a social network, influence maximization is the problem of identifying a set of users that own the maximum influence ability across the network. In this paper, a novel credit distribution (CD)-based model, termed as the multiaction CD (mCD) model, is introduced to quantify the influence ability of each user, which works with practical datasets where one type of action could be recorded for multiple times. Based on this model, influence maximization is formulated as a submodular maximization problem under a general knapsack constraint, which is NP hard. An efficient streaming algorithm with one-round scan over the user set is developed to find a suboptimal solution. Specifically, we first solve a special case of knapsack constraints, i.e., a cardinality constraint, and show that the developed streaming algorithm can achieve ( $({1}/{2})-\epsilon $ ) approximation of the optimality. Furthermore, for the general knapsack case, we show that the modified streaming algorithm can achieve ( $({1}/{3})-\epsilon $ ) approximation of the optimality. Finally, experiments are conducted over real Twitter dataset and demonstrate that the mCD model enjoys high accuracy compared to the conventional CD model in estimating the total number of people who get influenced in a social network. Moreover, through the comparison to the conventional CD, non-CD models, and the mCD model with the greedy algorithm on the performance of the influence maximization problem, we show the effectiveness and efficiency of the proposed mCD model with the streaming algorithm.
Approximate projections for decentralized optimization with SDP constraints

We consider distributed convex optimization problems that involve a separable objective function and nontrivial convex local constraints, such as Linear Matrix Inequalities (LMIs). We propose a decentralized, computationally inexpensive algorithm to solve such problems over time-varying directed networks of agents, that is based on the concept of approximate projections. Our algorithm is one of the consensus based methods in that, at every iteration, every agent performs a consensus update of its decision variables followed by an optimization step of its local objective function and local constraint. Unlike other methods, the last step of our method is not a projection to the feasible set, but instead a subgradient step in the direction that minimizes the local constraint violation. We show that the algorithm converges almost surely, i.e., every agent agrees on the same optimal solution, when the objective functions and constraint functions are nondifferentiable and their subgradients are bounded.
Using the Sequence-Space Jacobian to Solve and Estimate Heterogeneous-Agent Models

We propose a general and highly efficient method for solving and estimating general equilibrium heterogeneous-agent models with aggregate shocks in discrete time. Our approach relies on the rapid computation of sequence-space Jacobians—the derivatives of perfect-foresight equilibrium mappings between aggregate sequences around the steady state. Our main contribution is a fast algorithm for calculating Jacobians for a large class of heterogeneous-agent problems. We combine this algorithm with a systematic approach to composing and inverting Jacobians to solve for general equilibrium impulse responses. We obtain a rapid procedure for likelihood-based estimation and computation of nonlinear perfect-foresight transitions. We apply our methods to three canonical heterogeneous-agent models: a neoclassical model, a New Keynesian model with one asset, and a New Keynesian model with two assets.
Secure and Transparent Audit Logs with BlockAudit

Abstract Audit logs serve as a critical component in enterprise business systems and are used for auditing, storing, and tracking changes made to the data. However, audit logs are vulnerable to a series of attacks enabling adversaries to tamper data and corresponding audit logs without getting detected. Among them, two well-known attacks are “the physical access attack,” which exploits root privileges, and “the remote vulnerability attack,” which compromises known vulnerabilities in database systems. In this paper, we present BlockAudit: a scalable and tamper-proof system that leverages the design properties of audit logs and security guarantees of blockchain to enable secure and trustworthy audit logs. Towards that, we construct the design schema of BlockAudit and outline its functional and operational procedures. We implement our design on a custom-built Practical Byzantine Fault Tolerance (PBFT) blockchain system and evaluate the performance in terms of latency, network size, payload size, and transaction rate. Our results show that conventional audit logs can seamlessly transition into BlockAudit to achieve higher security and defend against the known attacks on audit logs.
Irrelevance of linear controllability to nonlinear dynamical networks

There has been tremendous development in linear controllability of complex networks. Real-world systems are fundamentally nonlinear. Is linear controllability relevant to nonlinear dynamical networks? We identify a common trait underlying both types of control: the nodal “importance”. For nonlinear and linear control, the importance is determined, respectively, by physical/biological considerations and the probability for a node to be in the minimum driver set. We study empirical mutualistic networks and a gene regulatory network, for which the nonlinear nodal importance can be quantified by the ability of individual nodes to restore the system from the aftermath of a tipping-point transition. We find that the nodal importance ranking for nonlinear and linear control exhibits opposite trends: for the former large-degree nodes are more important but for the latter, the importance scale is tilted towards the small-degree nodes, suggesting strongly the irrelevance of linear controllability to these systems. The recent claim of successful application of linear controllability to Caenorhabditis elegans connectome is examined and discussed. Linear controllability theories have stimulated research on control of complex networks. Here the authors investigate the concordance between linear and nonlinear approaches in ranking the importance of nodes in nonlinear networks, and conclude that linear controllability may not be applicable.
Social Security reform in the presence of informality

Abstract As populations age, countries across the globe are dealing with the issue of how to fund retirement consumption for their workers. The design of Social Security programs is more difficult when the country also exhibits an informal economy where workers avoid the taxation of the government and are not entitled to its benefits. In this paper, I study the example of Chile–a country that transitioned from a pay-as-you-go Social Security system to a system of private, individual retirement accounts in 1981 and also exhibits a significant informal sector–in order to quantify the transitional welfare impact of Social Security privatization when workers have the option to evade the public system through informality. I construct an OLG model which allows households to split working time between a taxed formal sector, an untaxed informal sector, and home production. I find large long-run welfare gains of roughly 10 and 15 percent for low and high-productivity workers, respectively. However, these gains come at the expense of losses for two groups: low-productivity workers who are retired at the time of the reform and high-productivity workers within 5 years of retirement at the time of the reform. The presence of informality leads to conflicting mechanisms: (1) the elasticity channel decreases welfare gains from reform: including informality as imperfectly substitutable with formality in utility decreases labor supply elasticity and renders the pay-as-you-go payroll tax less distortionary, and (2) the market wage channel increases welfare gains from reform: the privatization of the Social Security system causes wage growth which informal workers can receive without facing the distortions of any remaining taxation in the formal sector. Quantitative results indicate the elasticity channel is stronger in the case of Chile, and the inclusion of informality decreases the long-run welfare gains from privatizing Social Security.
Deep Convolutional Neural Network for Automated Detection of Mind Wandering using EEG Signals

Mind wandering (MW) is a ubiquitous phenomenon which reflects a shift in attention from task-related to task-unrelated thoughts. There is a need for intelligent interfaces that can reorient attention when MW is detected due to its detrimental effects on performance and productivity. In this paper, we propose a deep learning model for MW detection using Electroencephalogram (EEG) signals. Specifically, we develop a channel-wise deep convolutional neural network (CNN) model to classify the features of focusing state and MW extracted from EEG signals. This is the first study that employs CNN to automatically detect MW using only EEG data. The experimental results on the collected dataset demonstrate promising performance with 91.78% accuracy, 92.84% sensitivity, and 90.73% specificity. Finally, a data augmentation scheme is applied to increase the size of training dataset and improve the classification results.
Environmental, social, and governance practices and perceived tail risk

Using the implied volatility smirk on individual equity securities to measure perceived tail risk, we find that better environmental, social and governance (ESG) practices significantly reduce ex‐ante expectations of a left‐tail event. Our findings are robust to using multiple model specifications and to adjusting for potential endogeneity concerns. We also show that, while practices in each ESG pillar are important in reducing perceived tail risk, the environmental pillar plays the most important role. Our results indicate that investors consider strong ESG practices to be insurance against left‐tail events rather than wasteful investment borne out of managers’ own values or self‐interest.
THE SHRINKING SPACE FOR ANOMALIES

The existing literature documents a number of cross-sectional stock return anomalies. This article examines how pervasive these anomalies are and whether factor models provide valid inferences on anomalous returns. First, by shrinking the stock space along the dimension of a predictive variable, we show that the book-to-market ratio (BM) and net stock issues effects are pervasive, whereas the size, momentum, and illiquidity effects are driven mainly by stocks on the long side, and the idiosyncratic volatility, accrual, capital expenditure, and sales growth effects mainly by stocks on the short side. Second, we provide evidence that commonly used factor models have limited explanatory power of stock returns. Restricting to stock samples where the four-factor model adequately explains the size, BM, and momentum effects, we show that only the idiosyncratic volatility, accrual, and net stock issues effects remain significant.
Bridging the Domain Gap for Ground-to-Aerial Image Matching

The visual entities in cross-view (e.g. ground and aerial) images exhibit drastic domain changes due to the differences in viewpoints each set of images is captured from. Existing state-of-the-art methods address the problem by learning view-invariant images descriptors. We propose a novel method for solving this task by exploiting the gener- ative powers of conditional GANs to synthesize an aerial representation of a ground-level panorama query and use it to minimize the domain gap between the two views. The synthesized image being from the same view as the ref- erence (target) image, helps the network to preserve im- portant cues in aerial images following our Joint Feature Learning approach. We fuse the complementary features from a synthesized aerial image with the original ground- level panorama features to obtain a robust query represen- tation. In addition, we employ multi-scale feature aggre- gation in order to preserve image representations at dif- ferent scales useful for solving this complex task. Experi- mental results show that our proposed approach performs significantly better than the state-of-the-art methods on the challenging CVUSA dataset in terms of top-1 and top-1% retrieval accuracies. Furthermore, we evaluate the gen- eralization of the proposed method for urban landscapes on our newly collected cross-view localization dataset with geo-reference information.
Uniform hyperbolicity of the graphs of nonseparating curves via bicorn curves

We show that the graphs of nonseparating curves for oriented finite type surfaces are uniformly hyperbolic. Our proof follows the proof of uniform hyperbolicity of the graphs of curves for closed surfaces due to Przytycki-Sisto, while introducing new arguments using homology to certify that certain curves are nonseparating. As demonstrated by Aramayona-Valdez, this proves also that the graph of nonseparating curves for any oriented infinite type surface with finite positive genus is hyperbolic.
Joint Pricing and Inventory Management with Strategic Customers

We consider a model wherein the seller sells a product to customers over an infinite horizon. At each time, the seller decides a set of purchase options offered to customers and the inventory reple...
Stationary wave profiles for nonlocal particle models of traffic flow on rough roads

We study a nonlocal particle model describing traffic flow on rough roads. In the model, each driver adjusts the speed of the car according to the condition over an interval in the front, leading to a system of nonlocal ODEs which we refer to as the Follow-the-Leaders model. Assuming that the road condition is discontinuous, we seek stationary wave profiles (see Definition 1.1) for the system of ODEs across this discontinuity. We derive a nonlocal delay differential equation with discontinuous coefficient, satisfied by the profiles, together with conditions on the asymptotic values as $$x\rightarrow \pm \infty $$. Results on existence, uniqueness, and local stability are established for all cases. We show that, depending on the case, there might exist a unique profile, infinitely many profiles, or no profiles at all. The stability result also depends on cases. Various numerical simulations are presented. Finally, we establish convergence of these profiles to those of a local particle model, as well as those of a nonlocal PDE model.
Parametric analysis of semidefinite optimization

ABSTRACTIn this paper, we study parametric analysis of semidefinite optimization problems w.r.t. the perturbation of the objective function. We study the behaviour of the optimal partition and opti...
A cycle structure theorem for hamiltonian graphs

Abstract An n -vertex graph is called pancyclic if it contains a cycle of length l for every l such that 3 ≤ l ≤ n . We establish the following result: Let C be a hamiltonian cycle in an n -vertex graph G . Suppose C contains consecutive vertices x , y such that d ( x ) + d ( y ) ≥ n . Then G is either (i) pancyclic, (ii) bipartite, or (iii) missing only an ( n − 1)-cycle. Moreover, (iii) can occur only if G has a very explicit structure “near” x and y . This result can be used to show that three well-known hamiltonian degree conditions (due to Chvatal, Fan, and Bondy) actually imply that a graph is essentially pancyclic.
Ashamed to be Selfish

We study a decision maker (DM) who has preferences over choice problems, which are sets of payoff allocations between herself and a passive recipient. An example of such a set is the collection of possible allocations in the classic dictator game. The choice of an allocation from the set is observed by the recipient, whereas the choice of the set itself is not. Behaving selfishly under observation, in the sense of not choosing the normatively best allocation, inflicts shame on the DM. We derive a representation that identifies the DM’s private ranking of allocations, her subjective norm, and her shame. The normatively best allocation can be further characterized as the Nash solution of a bargaining game induced by the second-stage choice problem.
Wnt family member 4 (WNT4) and WNT3A activate cell-autonomous Wnt signaling independent of porcupine O-acyltransferase or Wnt secretion

Porcupine O-acyltransferase (PORCN) is considered essential for Wnt secretion and signaling. However, we observed that PORCN inhibition does not phenocopy the effects of WNT4 knockdown in WNT4-dependent breast cancer cells. This suggests a unique relationship between PORCN and WNT4 signaling. To examine the role of PORCN in WNT4 signaling, here we overexpressed WNT4 or WNT3A in breast cancer, ovarian cancer, and fibrosarcoma cell lines. Conditioned media from these lines and co-culture systems were used to assess the dependence of Wnt secretion and activity on the critical Wnt secretion proteins PORCN and Wnt ligand secretion (WLS) mediator. We observed that WLS is universally required for Wnt secretion and paracrine signaling. In contrast, the dependence of WNT3A secretion and activity on PORCN varied across the cell lines, and WNT4 secretion was PORCN-independent in all models. Surprisingly, WNT4 did not exhibit paracrine activity in any tested context. Absent the expected paracrine activity of secreted WNT4, we identified cell-autonomous Wnt signaling activation by WNT4 and WNT3A, independent of PORCN or Wnt secretion. The PORCN-independent, cell-autonomous Wnt signaling demonstrated here may be critical in WNT4-driven cellular contexts or in those that are considered to have dysfunctional Wnt signaling.
Back to the Future: Fiscal Rules for Regaining Sustainability

This paper assesses the cyclicality and sustainability of fiscal policy in Belize and applies a stochastic simulation model to determine the optimal set of fiscal rules. The empirical analysis shows that fiscal policy in Belize has been significantly procyclical and unsustainable much of the period since 1976. While the government’s recent commitment to maintain a primary surplus of at least 2 percent of GDP until 2021 is supporting debt reduction, stochastic simulations indicate that further improvement in the primary balance is necessary to reliably bring the debt-to-GDP ratio to a sustainable path. Given Belize’s history of large economic shocks, this paper proposes explicit fiscal rules designed for countercyclical policy and debt sustainability. It recommends integrating such rules into a well-designed fiscal responsibility law and establishing an independent fiscal council to improve accountability and transparency.
Sequentially embeddable graphs

We call a (not necessarily planar) embedding of a graph $G$ in the plane \emph{sequential} if its vertices lie in $\mathbb Z^2$ and the line segments between adjacent vertices contain no interior integer points. In this note, we prove (i) a graph $G$ has a sequential embedding if and only if $G$ is 4-colorable, and (ii) if $G$ is planar, then $G$ has a sequential planar embedding.
Secundum Civilis: The Constitution as an Enlightenment Code

The American Constitution has been the subject of heated debate since its formation. This article simply introduces a new argument. It suggests that there exists a “form”1 of an Enlightenment era code, which is met by the Constitution, and that the requirements of this form can be derived from inspection of the three great codes of the time: the Prussian, the Austrian, and the French. It further notes that these requirements are (1) Roman law influence; (2) natural law influence; and (3) that they perform the same functions—they abrogate the prior laws on their respective subjects and they are “complete” in themselves, covering the whole aspect of a legal field. In the process of doing so, the essay shows that the common law was not the only source of inspiration for the framers of the Constitution; it also shows heavy civil law influences. The article opens with a “preliminary title”, which introduces the subject, its sources, and instructs the readers as to how to read the article itself. The whole closes by laying forth a few broad possible consequences of accepting this view, while leaving a full discussion on the consequences of this understanding to a later article.
Antenna Arrangement Verification for Low Sidelobe Levels

Space-to-earth Wireless Power Transfer (WPT) in large scale will not be allowed unless the side lobe levels (SLL) can be reduced many orders of magnitude lower than the current technology allows. In particular, high SLL could potentially interfere with aircraft communications around the beam, while the area inside the beam would necessarily be a no-fly zone, similar as over nuclear power plants. To overcome this, the transmitting antenna must be cleverly designed and controlled. In this work, independent validation of the layout, spacing, and envelope arrangement of a design first proposed in 2016 is performed and presented. This design involves a hexagonal design with a triangular antenna element arrangement and a spacing of 0.8 wavelengths using the Dolph-Chebychev beam profile. While this has been shown to produce −240 dB SLL in the AWR Design Environment already, it will now be analyzed using the MATLAB Phased Array System Toolbox. The design will also be investigated on a smaller scale, with the potential for use in other applications, including the powering of low orbit weather balloons or unmanned aerial vehicles (UAVs). The possibility of very low SLL would be transformational in these and other WPT applications, including space solar power, and could greatly benefit humanity and the environment.
Relocating Acoustic Emission in Rocks with Unknown Velocity Structure with Machine Learning

Inversion of hypocenters is the first and most fundamental step in the study of seismic activities. It requires solving the nonlinear relation between the travel time and hypocenter locations, which is heavily dependent on the knowledge of the medium properties, most importantly the velocity structure. In this study, we prove that machine learning (ML) methods including artificial neural networks (ANNs) and support vector machines (SVMs) can relocate hypocenters without a priori knowledge of the velocity structure. We train ML models with acoustic emissions (AEs) created by breaking pencil leads at known locations on a laboratory fault, using the relative P-wave arrival time as the input and AE source locations as the output. The resultant ML models can accurately relocate AEs on the fault surface. With carefully chosen training strategies, the ANN model achieved better accuracy than the SVM model. This study suggests that ML methods can provide effective and accurate approaches for relocating seismic events in a medium with unknown velocity structures.
An engineered serum albumin-binding AAV9 capsid achieves improved liver transduction after intravenous delivery in mice

Recombinant adeno-associated viral (AAV) vectors are frequently used to deliver DNA into cells and are currently the leading platform for therapeutic gene delivery in humans. Presently, there is a need for optimized AAV vectors with improved transduction efficiencies in target tissues. In these studies, an engineered albumin-binding consensus domain (ABDCon) peptide was incorporated into the AAV9 capsid via fusion to the N-terminus of the AAV9 VP2 capsid protein to generate a variant AAV9 capsid with albumin-binding properties. The variant capsid, called AAV9-ABDCon, formed viable genome-containing vector particles and exhibited binding to human serum albumin. The AAV9 capsid, on the other hand, was not found to bind to human serum albumin by the methods used in this study. In C57BL/6J mice, AAV9-ABDCon achieved significantly higher levels of liver transduction compared with AAV9 following intravenous administration. These findings show that incorporation of the ABDCon peptide into the capsid VP2 N-terminus may be a potential method to augment AAV-mediated liver-directed gene delivery.
Zero forcing and maximum nullity for hypergraphs

Abstract The concept of zero forcing is extended from graphs to uniform hypergraphs in analogy with the way zero forcing was defined as an upper bound for the maximum nullity of the family of symmetric matrices whose nonzero pattern of entries is described by a given graph: A family of symmetric hypermatrices is associated with a uniform hypergraph and zeros are forced in a null vector. The value of the hypergraph zero forcing number and maximum nullity are determined for various families of uniform hypergraphs and the effects of several graph operations on the hypergraph zero forcing number are determined. The hypergraph zero forcing number is compared to the infection number of a hypergraph and the iteration process in hypergraph power domination.
Non-linear functionals, deficient topological measures, and representation theorems on locally compact spaces

We study non-linear functionals, including quasi-linear functionals, p-conic quasi-linear functionals, d-functionals, r-functionals, and their relationships to deficient topological measures and topological measures on locally compact spaces. We prove representation theorems and show, in particular, that there is an order-preserving, conic-linear bijection between the class of finite deficient topological measures and the class of bounded p-conic quasi-linear functionals. Our results imply known representation theorems for finite topological measures and deficient topological measures. When the space is compact we obtain four equivalent definitions of a quasi-linear functional and four equivalent definitions of functionals corresponding to deficient topological measures.
Using machine learning to detect misstatements

Machine learning offers empirical methods to sift through accounting datasets with a large number of variables and limited a priori knowledge about functional forms. In this study, we show that these methods help detect and interpret patterns present in ongoing accounting misstatements. We use a wide set of variables from accounting, capital markets, governance, and auditing datasets to detect material misstatements. A primary insight of our analysis is that accounting variables, while they do not detect misstatements well on their own, become important with suitable interactions with audit and market variables. We also analyze differences between misstatements and irregularities, compare algorithms, examine one-year- and two-year-ahead predictions and interpret groups at greater risk of misstatements.
Morphology-Agnostic Visual Robotic Control

Existing approaches for visuomotor robotic control typically require characterizing the robot in advance by calibrating the camera or performing system identification. We propose mavric , an approach that works with minimal prior knowledge of the robot's morphology, and requires only a camera view containing the robot and its environment and an unknown control interface. mavric revolves around a mutual information-based method for self-recognition, which discovers visual “control points” on the robot body within a few seconds of exploratory interaction, and these control points in turn are then used for visual servoing. mavric can control robots with imprecise actuation, no proprioceptive feedback, unknown morphologies including novel tools, unknown camera poses, and even unsteady handheld cameras. We demonstrate our method on visually-guided 3D point reaching, trajectory following, and robot-to-robot imitation.
Justification-Based Reliability in Machine Learning

With the advent of Deep Learning, the field of machine learning (ML) has surpassed human-level performance on diverse classification tasks. At the same time, there is a stark need to characterize and quantify reliability of a model's prediction on individual samples. This is especially true in applications of such models in safety-critical domains of industrial control and healthcare. To address this need, we link the question of reliability of a model's individual prediction to the epistemic uncertainty of the model's prediction. More specifically, we extend the theory of Justified True Belief (JTB) in epistemology, created to study the validity and limits of human-acquired knowledge, towards characterizing the validity and limits of knowledge in supervised classifiers. We present an analysis of neural network classifiers linking the reliability of its prediction on a test input to characteristics of the support gathered from the input and hidden layers of the network. We hypothesize that the JTB analysis exposes the epistemic uncertainty (or ignorance) of a model with respect to its inference, thereby allowing for the inference to be only as strong as the justification permits. We explore various forms of support (for e.g., k-nearest neighbors (k-NN) and lp-norm based) generated for an input, using the training data to construct a justification for the prediction with that input. Through experiments conducted on simulated and real datasets, we demonstrate that our approach can provide reliability for individual predictions and characterize regions where such reliability cannot be ascertained.
Signatures of Ion Pairing and Aggregation in the Vibrational Spectroscopy of Super-Concentrated Aqueous Lithium Bistriflimide Solutions

Aqueous electrolytes in the super-concentrated regime, where water and ions are present in a similar concentration, have recently drawn considerable interest for their potential application in elec...
Aggregate Fluctuations in Networks With Drift-Diffusion Models Driven by Stable Non-Gaussian Disturbances

The focus of this article is to quantify measures of aggregate fluctuations for consensus-seeking networks subject to exogenous noise with $\alpha$ -stable distributions. This type of noise is generated by a class of random measures with heavy-tailed probability distributions. We define a cumulative-scale parameter over the output variables, as a measure of aggregate fluctuation. We show that this class of measures can be characterized implicitly, because finding its explicit form in terms of network parameters is, in general, almost impossible. To this end, we offer several tractable upper bounds in terms of Laplacian spectrum and statistics of the input noise. Our results suggest that relying on Gaussian-based optimal design algorithms will result in nonoptimal solutions for dynamical networks that operate in the face of non-Gaussian noise.
Fast and Controllable Prelithiation of Hard Carbon Anodes for Lithium-Ion Batteries

Hard carbon has been extensively investigated as anode materials for high-energy lithium-ion batteries owing to its high capacity, long cycle life, good rate capability, and low cost of production. However, it suffers from a large irreversible capacity and thus low initial coulombic efficiency (ICE), which hinders its commercial use. Here, we developed a fast and controllable prelithiation method based on a chemical reaction using a lithium-containing reagent (1 M lithium biphenylide dissolved in tetrahydrofuran). The prelithiation extent can be easily controlled by tuning the reaction time. An SEI layer is formed during chemical prelithiation, and the ICE of prelithiated hard carbon in half-cell format can be increased to ∼106% in 30 s. When matched with a LiNi1/3Co1/3Mn1/3O2 cathode, the full cell with the prelithiated hard carbon anode exhibits a much improved ICE (90.2 vs 75%) and cycling performance than those of the pristine full cell. This facile prelithiation method is proved to be a practical sol...
Fast Underwater Image Enhancement for Improved Visual Perception

In this letter, we present a conditional generative adversarial network-based model for real-time underwater image enhancement. To supervise the adversarial training, we formulate an objective function that evaluates the perceptual image quality based on its global content, color, local texture, and style information. We also present EUVP, a large-scale dataset of a paired and an unpaired collection of underwater images (of ‘poor’ and ‘good’ quality) that are captured using seven different cameras over various visibility conditions during oceanic explorations and human-robot collaborative experiments. In addition, we perform several qualitative and quantitative evaluations which suggest that the proposed model can learn to enhance underwater image quality from both paired and unpaired training. More importantly, the enhanced images provide improved performances of standard models for underwater object detection, human pose estimation, and saliency prediction. These results validate that it is suitable for real-time preprocessing in the autonomy pipeline by visually-guided underwater robots. The model and associated training pipelines are available at https://github.com/xahidbuffon/funie-gan .