cssi/SciSciGPT-SciSciNet huggingface dataset:
Economic Consequences of Equity Compensation Disclosure

The primary role of equity compensation is to provide incentives to an effort-averse agent. Here, the author shows that the chosen level of equity incentives, when publicly disclosed, will also convey information about future earnings, causing two-way linkages between incentive compensation, and financial reporting. If (a) market prices respond more (less) to information, (b) the manager is more (less) risk averse, or (c) earnings are more (less) noisy, then the firm’s owners choose more pronounced (muted) incentives, in turn leading to greater (lower) future earnings. The model explains observed spurious correlations between firm performance and executive compensation, and it provides several new predictions linking managerial, earnings, and market determinants to optimal equity holdings.
The “process” of patenting: Why should we care about a potential U.S. Supreme Court decision in Bilski v. Doll?

Abstract In Bilski v. Doll, the U.S. Supreme Court is called to define one of the categories of patent-eligible subject matter, “process” patents. In 2008, the Court of Appeals for the Federal Circuit held that the category has a narrow meaning, and that to be eligible for a process patent under 35 U.S.C. § 101, the invention must involve a machine or apparatus or involve a transformation to a different state or thing, ultimately rejecting the patent application as unpatentable subject matter. The patent applicants have asked the U.S. Supreme Court to determine two issues: first, the meaning of “process” in 35 U.S.C. § 101 and whether the lower court properly relied on a “machine-or-transformation” test, and second, the test's potential conflict with 35 U.S.C. § 273, which provides protection for “method[s] of doing or conducting business.” The Court's decision could change the way that research and business are done, and patent protection for such investments. Parts 1 and 2 of this article address Bilski directly and what is and is not in dispute. Part 3 addresses the “machine-or-transformation” test, while Parts 4 and 5 address reasons not to adopt such a test.
Reassessing the Standard of Living in the Soviet Union: An Analysis Using Archival and Anthropometric Data

Both Western and Soviet estimates of GNP growth in the USSR indicate that GNP per capita grew in every decade, sometimes rapidly, from 1928 to 1985. While this measure suggests that the standard of living improved in the USSR throughout this period, it is unclear whether this economic growth translated into improved well-being for the population as a whole. This paper uses previously unpublished archival data on infant mortality and anthropometric studies of children conducted across the Soviet Union to reassess the standard of living in the USSR using these alternative measures of well-being. In the prewar period these data indicate a population extremely small in stature and sensitive to the political and economic upheavals visited upon the country by Soviet leaders and outside forces. Remarkably large and rapid improvements in child height, adult stature and infant mortality were recorded from approximately 1945 to 1970. While this period of physical growth was followed by stagnation in heights and an increase in adult male mortality, the physical growth record of the Soviet population compares favorably with that of other European countries at a similar level of development in this period.
Analysts, Industries, and Price Momentum

This paper examines the value of analysts as industry specialists. We show analysts create value in their recommendations mainly through their ability to rank stocks within indus tries. An industry-based recommendation strategy substantially improves the return to risk ratio and reduces price momentum tilt relative to portfolios that ignore industry informa tion. An examination of the links among analyst information, aggregated at the industry level, and industry returns and industry momentum shows that industry returns precede industry-aggregated analyst upgrades and downgrades, and the short-term industry price momentum phenomenon is partly explained by returns of firms with more analyst cover age leading those with less in that industry. Recommendation information is not valuable for predicting future relative industry returns, however.
Sympathy for the Diligent and the Demand for Workfare

Abstract We study the role of fairness concerns in the demand for redistribution through workfare. In the first part of the paper, we present new evidence from a survey. We show that individuals are more generous towards poor people whom they perceive to be diligent workers relative to poor people whom they perceive to be non-diligent, a social preference that we label sympathy for the diligent. This preference is much stronger than preferences regarding other characteristics of the poor, such as race, nationality, and disability. More important, we show that subjects with higher sympathy for the diligent have a stronger preference for workfare programs. In the second part of the paper, we incorporate our empirical findings into a model of income redistribution. We consider the case of a benevolent government with fairness concerns that prioritizes the well-being of individuals who exert the most effort. We characterize the optimal conditions under which the government introduces work requirements. Even if wasteful, work requirements can be optimal, because they allow for a better distinction between individuals who exert great effort and individuals who do not. However, if the government lacks commitment power, the availability of screening through work requirements leads to a lower equilibrium effort and, possibly, a Pareto-dominated allocation.
Self-Determination in Regional Human Rights Law: From Kosovo to Cameroon

The right of self-determination has long been celebrated for bringing independence and self-government to oppressed groups, yet it remains a highly controversial norm of international law. From the breakup of the Austro-Hungarian and Ottoman Empires after World WarI to the struggle of colonial territories for independence following World War II and the later dissolution of the former Yugoslavia, there has been an unavoidable conflict between the efforts of peoples to achieve independence and the demands of existing states to preserve their territorial integrity.
Overcoming element quality dependence of finite elements with adaptive extended stencil FEM (AES‐FEM)

The finite element methods (FEM) are important techniques in engineering for solving partial differential equations, but they depend heavily on element shape quality for stability and good performance. In this paper, we introduce the Adaptive Extended Stencil Finite Element Method (AES-FEM) as a means for overcoming this dependence on element shape quality. Our method replaces the traditional basis functions with a set of generalized Lagrange polynomial (GLP) basis functions, which we construct using local weighted least-squares approximations. The method preserves the theoretical framework of FEM, and allows imposing essential boundary conditions and integrating the stiffness matrix in the same way as the classical FEM. In addition, AES-FEM can use higher-degree polynomial basis functions than the classical FEM, while virtually preserving the sparsity pattern of the stiffness matrix. We describe the formulation and implementation of AES-FEM, and analyze its consistency and stability. We present numerical experiments in both 2D and 3D for the Poison equation and a time-independent convection-diffusion equation. The numerical results demonstrate that AES-FEM is more accurate than linear FEM, is also more efficient than linear FEM in terms of error versus runtime, and enables much better stability and faster convergence of iterative solvers than linear FEM over poor-quality meshes. Copyright © 2016 John Wiley & Sons, Ltd.
Embedding large graphs into a random graph

In this paper we consider the problem of embedding almost-spanning, bounded degree graphs in a random graph. In particular, let Δ⩾5, e>0, and let H be a graph on (1−e)n vertices and with maximum degree Δ. We show that a random graph Gn,p with high probability contains a copy of H, provided that p≫(n−1log1/Δn)2/(Δ+1). Our assumption on p is optimal up to the polylog factor. We note that this polylog term matches the conjectured threshold for the spanning case.
Vital Dimensions in Volume Perception: Can the Eye Fool the Stomach?:

Given the number of volume judgments made by consumers, for example, deciding which package is larger and by how much, it is surprising that little research pertaining to volume perceptions has bee...
Do bank lending relationships always enhance financial distress resolution? The case of Germany

This article examines the financial distress resolution benefit of bank lending relationships in a system where because of the lack of access to public debt and equity markets, banks do not face competition as providers of capital. For a sample of German firms, this article finds that companies in financial distress are more likely to be liquidated if they have high amounts of bank debt. Furthermore, companies that are saved show higher potential for financial distress a year prior to bankruptcy. This suggests that certain aspects of value of bank lending relationships are dependent on the financial system in which they occur.
A Theory of Chosen Preferences

We propose and develop a dynamic theory of endogenous preference formation in which people adopt worldviews that shape their judgments about their experiences. The framework highlights the role of mindset flexibility, a trait that determines the relative weights the decision-maker places on her current and anticipated worldviews when evaluating future outcomes. The theory generates rich behavioral dynamics, thereby illuminating a wide range of applications and providing potential explanations for a variety of observed phenomena.
Benchmark Design and Prior-independent Optimization

This paper compares two leading approaches for robust optimization in the models of online algorithms and mechanism design. Competitive analysis compares the performance of an online algorithm to an offline benchmark in worst-case over inputs, and prior-independent mechanism design compares the expected performance of a mechanism on an unknown distribution (of inputs, i.e., agent values) to the optimal mechanism for the distribution in worst case over distributions. For competitive analysis, a critical concern is the choice of benchmark. This paper gives a method for selecting a good benchmark. We show that optimal algorithm/mechanism for the optimal benchmark is equal to the prior-independent optimal algorithm/mechanism. We solve a central open question in prior-independent mechanism design, namely we identify the prior-independent revenue-optimal mechanism for selling a single item to two agents with i.i.d. and regularly distributed values. We use this solution to solve the corresponding benchmark design problem. Via this solution and the above equivalence of prior-independent mechanism design and competitive analysis (a.k.a. prior-free mechanism design) we show that the standard method for lower bounds of prior-free mechanisms is not generally tight for the benchmark design program.11For the full version of this work, see https://arxiv.org/abs/2001.10157.
Non-asymptotic behavior and the distribution of the spectrum of the finite Hankel transform operator

For fixed reals c>0, a>0 and α>−12, the circular prolate spheroidal wave functions (CPSWFs) or 2d-Slepian functions are the eigenfunctions of the finite Hankel transform operator, denoted by Hcα, w...
The initial mass function of low-mass stars and brown dwarfs in the W3 complex

We have used archival infrared images obtained with the Wide Field Camera 3 on board the Hubble Space Telescope to constrain the initial mass function of low-mass stars and brown dwarfs in the W3 star-forming region. The images cover 438 arcmin$^2$, which encompasses the entire complex, and were taken in the filters F110W, F139M, and F160W. We have estimated extinctions for individual sources in these data from their colors and have dereddened their photometry accordingly. By comparing an area of the images that contains the richest concentration of previously identified W3 members to an area that has few members and is dominated by background stars, we have estimated the luminosity function for members of W3 with masses of 0.03-0.4 $M_\odot$. That luminosity function closely resembles data in typical nearby star-forming regions that have much smaller stellar populations than W3 ($\lesssim$500 vs. several thousand objects). Thus, we do not find evidence of significant variations in the initial mass function of low-mass stars and brown dwarfs with star forming conditions, which is consistent with recent studies of other distant massive star-forming regions.
Neighborhood Conditions and Recurrent Emergency Department Utilization by Children in the United States

Introduction: Recurrent emergency department (ED) utilization in the pediatric population is associated with multiple individual-level factors, including minority race and ethnicity and lower household income. How neighborhood conditions experienced by families, particularly social and physical environmental attributes that are potentially amenable to intervention, influence child ED utilization is less well understood. Objective: Evaluate the associations of neighborhood cohesion, safety, amenities (sidewalks, parks, recreation centers, libraries), and detractors (litter, rundown housing, vandalism) with child recurrent ED utilization and their contribution to utilization differences by race and ethnicity and income level. Methods: Cross-sectional study …
Genetic inactivation of SARM1 axon degeneration pathway improves outcome trajectory after experimental traumatic brain injury based on pathological, radiological, and functional measures

Traumatic brain injury (TBI) causes chronic symptoms and increased risk of neurodegeneration. Axons in white matter tracts, such as the corpus callosum (CC), are critical components of neural circuits and particularly vulnerable to TBI. Treatments are needed to protect axons from traumatic injury and mitigate post-traumatic neurodegeneration. SARM1 protein is a central driver of axon degeneration through a conserved molecular pathway. Sarm1−/− mice with knockout (KO) of the Sarm1 gene enable genetic proof-of-concept testing of the SARM1 pathway as a therapeutic target. We evaluated Sarm1 deletion effects after TBI using a concussive model that causes traumatic axonal injury and progresses to CC atrophy at 10 weeks, indicating post-traumatic neurodegeneration. Sarm1 wild-type (WT) mice developed significant CC atrophy that was reduced in Sarm1 KO mice. Ultrastructural classification of pathology of individual axons, using electron microscopy, demonstrated that Sarm1 KO preserved more intact axons and reduced damaged or demyelinated axons. Longitudinal MRI studies in live mice identified significantly reduced CC volume after TBI in Sarm1 WT mice that was attenuated in Sarm1 KO mice. MR diffusion tensor imaging detected reduced fractional anisotropy in both genotypes while axial diffusivity remained higher in Sarm1 KO mice. Immunohistochemistry revealed significant attenuation of CC atrophy, myelin loss, and neuroinflammation in Sarm1 KO mice after TBI. Functionally, Sarm1 KO mice exhibited beneficial effects in motor learning and sleep behavior. Based on these findings, Sarm1 inactivation can protect axons and white matter tracts to improve translational outcomes associated with CC atrophy and post-traumatic neurodegeneration.
Haptics-based volumetric modeling using dynamic spline-based implicit functions

This paper systematically presents a novel haptics-based volumetric modeling framework, which is founded upon volumetric implicit functions and powerful physics-based modeling. The volumetric implicit functions incorporate hierarchical B-splines, CSG-based functional composition, and knot insertion to facilitate multiresolution editing and level of details (LODs) control. Our dynamic volumes are semi-algebraic sets of implicit functions and are governed by the principle of dynamics, hence responding to sculpting forces in a natural and predictive manner. The versatility of our volumetric modeling affords users to easily modify both the geometry and the topology of modeled objects, while the inherent physical properties can offer an intuitive mechanism for direct manipulation. Moreover, we augment our modeling environment with a natural haptic interface, in order to take advantage of the additional realism associated with 3D haptic interaction. Coupling physics and haptics with implicit functions can realize all the potentials exhibited by volumetric modeling, physics-based modeling, and haptic interface. Furthermore, in order to directly manipulate existing volumetric datasets as well as point clouds, we develop a hierarchical fitting algorithm to reconstruct and represent discrete datasets using our continuous implicit functions, which permit users to further design and edit those 3D models in real-time using a large variety of haptic toolkits and visualize their interactive deformation at arbitrary resolution.
Competitive Debacle in Local Telephony: Is the 1996 Telecommunications Act to Blame?

This article challenges the conventional wisdom that the Telecommunications Act of 1996 is to blame for the lack of competition in local telephony, and that somehow the federal courts are complicit in this failure. It begins by highlighting the continued importance of local wireline infrastructure and how little competition exists. Moving to the 1996 Act's network disaggregation provisions, the article argues that while the statute itself is sound, the regulations promulgated under it evince basic economic confusion that has harmed not only the incumbent telephone companies, but ironically, the new competitors whom the regulations were designed to help. A new regulatory approach is proposed to overcome the current impasse. The piece next considers provisions to open long distance to local telephone monopolies, and finds again that implementing regulations, not the Act itself, is to blame. A new regulatory paradigm is offered. Finally, the argument shifts to the thorny issue of federalism to find that Congress in fact devised a rather elegant and innovative new scheme that has been misunderstood. The article parts company with most critical commentary to suggest that tossing out the 1996 Act is altogether too draconic - rather the focus of regulatory reform should be to develop regulations that coincide with economic reality.
Reconstruction of the crossing type of a point set from the compatible exchange graph of noncrossing spanning trees

Abstract Let P be a set of n points in the plane, no three in a line. The order type of P specifies, for every ordered triple, a positive or negative orientation; and the crossing type (for short, x-type) of P specifies, for every unordered pair of line segments spanned by P, whether they cross each other. Keller and Perles (2016) proved that the x-type of P can be reconstructed from the exchange graph G 0 ( P ) of noncrossing spanning trees. In this paper, we show that the x-type of P can already be reconstructed from the compatible exchange graph G 1 ( P ) , which is a subgraph of G 0 ( P ) . The proof crucially relies on the analysis of maximal sets of pairwise noncrossing edges ( m snes) between two disjoint planar straight-line graphs. m snes are a bipartite analogue of triangulations of planar straight-line graphs; they correspond to maximal cliques in G 1 ( P ) .
Simulating Nonstationary Spatio-Temporal Poisson Processes using the Inversion Method

We study the problem of simulating a class of nonstationary spatio-temporal Poisson processes. The Poisson intensity function is non-stationary and piecewise linear in both the time dimension and the spatial location dimensions. We propose an exact simulation algorithm based on the inversion method. This simulation algorithm adopts three advantages. First, the entire procedure involves only closed-form computation with no need for numerical integration or numerical inversion of any function. Each step in the algorithm only requires exact arithmetic operations. Second, the proposed algorithm is sample efficient, especially compared to the thinning method when the maximum intensity value is much larger than the minimum intensity value. Third, the algorithm generates arrivals sequentially, one at a time in ascending order, so that they can be conveniently fed into real-time or online decision-making tools.
TMTpro Complementary Ion Quantification Increases Plexing and Sensitivity for Accurate Multiplexed Proteomics at the MS2 Level

Multiplexed proteomics is a powerful tool to assay cell states in health and disease, but accurate quantification of relative protein changes is impaired by interference from co-isolated peptides. Interference can be reduced by using MS3-based quantification, but this reduces sensitivity and requires specialized instrumentation. An alternative approach is quantification by complementary ions, the balancer group-peptide conjugates, which allows accurate and precise multiplexed quantification at the MS2 level and is compatible with most proteomics instruments. However, complementary ions of the popular TMT-tag form inefficiently and multiplexing is limited to five channels. Here, we evaluate and optimize complementary ion quantification for the recently released TMTpro-tag, which increases complementary ion plexing capacity to eight channels (TMTproC). Furthermore, the beneficial fragmentation properties of TMTpro increase sensitivity for TMTproC, resulting in ∼65% more proteins quantified compared to TMTpro-MS3 and ∼18% more when compared to real-time-search TMTpro-MS3 (RTS-SPS-MS3). TMTproC quantification is more accurate than TMTpro-MS2 and even superior to RTS-SPS-MS3. We provide the software for quantifying TMTproC data as an executable that is compatible with the MaxQuant analysis pipeline. Thus, TMTproC advances multiplexed proteomics data quality and widens access to accurate multiplexed proteomics beyond laboratories with MS3-capable instrumentation.
Gps-Denied Navigation Using Sar Images And Neural Networks

Unmanned aerial vehicles (UAV) often rely on GPS for navigation. GPS signals, however, are very low in power and easily jammed or otherwise disrupted. This paper presents a method for determining the navigation errors present at the beginning of a GPS-denied period utilizing data from a synthetic aperture radar (SAR) system. This is accomplished by comparing an online-generated SAR image with a reference image obtained a priori. The distortions relative to the reference image are learned and exploited with a convolutional neural network to recover the initial navigational errors, which can be used to recover the true flight trajectory throughout the synthetic aperture. The proposed neural network approach is able to learn to predict the initial errors on both simulated and real SAR image data.
A Novel Bayesian Approach for the Two-Dimensional Harmonic Retrieval Problem

Sparse signal recovery algorithms like sparse Bayesian learning work well but the complexity quickly grows when tackling higher dimensional parametric dictionaries. In this work we propose a novel Bayesian strategy to address the two dimensional harmonic retrieval problem, through remodeling and reparameterization of the standard data model. This new model allows us to introduce a block sparsity structure in a manner that enables a natural pairing of the parameters in the two dimensions. The numerical simulations demonstrate that the inference algorithm developed (H-MSBL) does not suffer from source identifiability issues and is capable of estimating the harmonic components in challenging scenarios, while maintaining a low computational complexity.
Robust k-Center with Two Types of Radii

In the non-uniform k-center problem, the objective is to cover points in a metric space with specified number of balls of different radii. Chakrabarty, Goyal, and Krishnaswamy [ICALP 2016, Trans. on Algs. 2020] (CGK, henceforth) give a constant factor approximation when there are two types of radii. In this paper, we give a constant factor approximation for the two radii case in the presence of outliers. To achieve this, we need to bypass the technical barrier of bad integrality gaps in the CGK approach. We do so using “the ellipsoid method inside the ellipsoid method”: use an outer layer of the ellipsoid method to reduce to stylized instances and use an inner layer of the ellipsoid method to solve these specialized instances. This idea is of independent interest and could be applicable to other problems.
Taking Responsibility Personally: On John Gardner’s From Personal Life to Private Law

This essay, written for a panel honoring the late John Gardner, explores a tension in his book’s highly engaging and illuminating account of the relationship between “personal life” and “private law.” For the most part, the book sets out to explain how private law’s doctrines help us to act as we ought to act by reproducing, with greater specificity, the rules and norms of morality. At crucial moments, however, it suggests that private law serves its function by departing dramatically from morality. In particular, it argues that private law’s conferral of broad discretion on victims of legal wrongs to decide whether and how to pursue claims against wrongdoers has no moral counterpart. I suggest, to the contrary, that personal life does contain analogues to private law’s powers and liabilities. I further maintain that Gardner’s reluctance to recognize them reflects a problematic understanding of interpersonal responsibility as monadic answerability to reason rather than dyadic answerability to another.
Graph Contrastive Learning Automated

Self-supervised learning on graph-structured data has drawn recent interest for learning generalizable, transferable and robust representations from unlabeled graphs. Among many, graph contrastive learning (GraphCL) has emerged with promising representation learning performance. Unfortunately, unlike its counterpart on image data, the effectiveness of GraphCL hinges on ad-hoc data augmentations, which have to be manually picked per dataset, by either rules of thumb or trial-and-errors, owing to the diverse nature of graph data. That significantly limits the more general applicability of GraphCL. Aiming to fill in this crucial gap, this paper proposes a unified bi-level optimization framework to automatically, adaptively and dynamically select data augmentations when performing GraphCL on specific graph data. The general framework, dubbed JOint Augmentation Optimization (JOAO), is instantiated as min-max optimization. The selections of augmentations made by JOAO are shown to be in general aligned with previous "best practices" observed from handcrafted tuning: yet now being automated, more flexible and versatile. Moreover, we propose a new augmentation-aware projection head mechanism, which will route output features through different projection heads corresponding to different augmentations chosen at each training step. Extensive experiments demonstrate that JOAO performs on par with or sometimes better than the state-of-the-art competitors including GraphCL, on multiple graph datasets of various scales and types, yet without resorting to any laborious dataset-specific tuning on augmentation selection. We release the code at this https URL.
Wing flutter boundary prediction using unsteady Euler aerodynamic method

Modifications to an existing three-dimensional, implicit, upwind Euler/Navier-Stokes code (CFL3D Version 2.1) for the aeroelastic analysis of wings are described. These modifications, which were previously added to CFL3D Version 1.0, include the incorporation of a deforming mesh algorithm and the addition of the structural equations of motion for their simultaneous time-integration with the government flow equations. The paper gives a brief description of these modifications and presents unsteady calculations which check the modifications to the code. Euler flutter results for an isolated 45 degree swept-back wing are compared with experimental data for seven freestream Mach numbers which define the flutter boundary over a range of Mach number from 0.499 to 1.14. These comparisons show good agreement in flutter characteristics for freestream Mach numbers below unity. For freestream Mach numbers above unity, the computed aeroelastic results predict a premature rise in the flutter boundary as compared with the experimental boundary. Steady and unsteady contours of surface Mach number and pressure are included to illustrate the basic flow characteristics of the time-marching flutter calculations and to aid in identifying possible causes for the premature rise in the computational flutter boundary.
Logarithmic Regret for Reinforcement Learning with Linear Function Approximation

Reinforcement learning (RL) with linear function approximation has received increasing attention recently. However, existing work has focused on obtaining $\sqrt{T}$-type regret bound, where $T$ is the number of interactions with the MDP. In this paper, we show that logarithmic regret is attainable under two recently proposed linear MDP assumptions provided that there exists a positive sub-optimality gap for the optimal action-value function. More specifically, under the linear MDP assumption (Jin et al. 2019), the LSVI-UCB algorithm can achieve $\tilde{O}(d^{3}H^5/\text{gap}_{\text{min}}\cdot \log(T))$ regret; and under the linear mixture MDP assumption (Ayoub et al. 2020), the UCRL-VTR algorithm can achieve $\tilde{O}(d^{2}H^5/\text{gap}_{\text{min}}\cdot \log^3(T))$ regret, where $d$ is the dimension of feature mapping, $H$ is the length of episode, $\text{gap}_{\text{min}}$ is the minimal sub-optimality gap, and $\tilde O$ hides all logarithmic terms except $\log(T)$. To the best of our knowledge, these are the first logarithmic regret bounds for RL with linear function approximation. We also establish gap-dependent lower bounds for the two linear MDP models.
Dynamic Planning and Learning under Recovering Rewards

Motivated by emerging applications such as live-streaming e-commerce, promotions and recommendations, we introduce a general class of multi-armed bandit problems that have the following two features: (i) the decision maker can pull and collect rewards from at most $K$ out of $N$ different arms in each time period; (ii) the expected reward of an arm immediately drops after it is pulled, and then non parametrically recovers as the idle time increases. With the objective of maximizing expected cumulative rewards over $T$ time periods, we propose, construct and prove performance guarantees for a class of "Purely Periodic Policies". For the offline problem when all model parameters are known, our proposed policy obtains an approximation ratio that is at the order of $1-\mathcal O(1/\sqrt{K})$, which is asymptotically optimal when $K$ grows to infinity. For the online problem when the model parameters are unknown and need to be learned, we design an Upper Confidence Bound (UCB) based policy that approximately has $\widetilde{\mathcal O}(N\sqrt{T})$ regret against the offline benchmark. Our framework and policy design may have the potential to be adapted into other offline planning and online learning applications with non-stationary and recovering rewards.
Self-Damaging Contrastive Learning

The recent breakthrough achieved by contrastive learning accelerates the pace for deploying unsupervised training on real-world data applications. However, unlabeled data in reality is commonly imbalanced and shows a long-tail distribution, and it is unclear how robustly the latest contrastive learning methods could perform in the practical scenario. This paper proposes to explicitly tackle this challenge, via a principled framework called Self-Damaging Contrastive Learning (SDCLR), to automatically balance the representation learning without knowing the classes. Our main inspiration is drawn from the recent finding that deep models have difficult-to-memorize samples, and those may be exposed through network pruning. It is further natural to hypothesize that long-tail samples are also tougher for the model to learn well due to insufficient examples. Hence, the key innovation in SDCLR is to create a dynamic self-competitor model to contrast with the target model, which is a pruned version of the latter. During training, contrasting the two models will lead to adaptive online mining of the most easily forgotten samples for the current target model, and implicitly emphasize them more in the contrastive loss. Extensive experiments across multiple datasets and imbalance settings show that SDCLR significantly improves not only overall accuracies but also balancedness, in terms of linear evaluation on the full-shot and few-shot settings. Our code is available at: this https URL.
Mapper Interactive: A Scalable, Extendable, and Interactive Toolbox for the Visual Exploration of High-Dimensional Data

The mapper algorithm is a popular tool from topological data analysis for extracting topological summaries of high-dimensional datasets. In this paper, we present Mapper Interactive, a web-based framework for the interactive analysis and visualization of high-dimensional point cloud data. It implements the mapper algorithm in an interactive, scalable, and easily extendable way, thus supporting practical data analysis. In particular, its command-line API can compute mapper graphs for 1 million points of 256 dimensions in about 3 minutes (4 times faster than the vanilla implementation). Its visual interface allows on-the-fly computation and manipulation of the mapper graph based on user-specified parameters and supports the addition of new analysis modules with a few lines of code. Mapper Interactive makes the mapper algorithm accessible to nonspecialists and accelerates topological analytics workflows.
Stereopagnosia: Fooling Stereo Networks with Adversarial Perturbations

Author(s): Mundhra, Mukund | Advisor(s): Soatto, Stefano | Abstract: This work studies the effect of adversarial perturbations of images on the estimates of disparity by deep learning models trained for stereo. We show that imperceptible additive perturbations can significantly alter the disparity map, and correspondingly the perceived geometry of the scene. These perturbations not only affect the specific model they are crafted for, but transfer to models with different architecture, trained with different loss functions. We show that, when used for adversarial data augmentation, our perturbations result in trained models that are more robust, without sacrificing overall accuracy of the model. This is unlike what has been observed in image classification, where adding the perturbed images to the training set makes the model less vulnerable to adversarial perturbations, but to the detriment of overall accuracy. We test our method using the most recent stereo networks and evaluate their performance on public benchmark datasets.
Physics-Informed Neural Network Method for Forward and Backward Advection-Dispersion Equations

We propose a discretization-free approach based on the physics-informed neural network (PINN) method for solving coupled advection-dispersion and Darcy flow equations with space-dependent hydraulic conductivity. In this approach, the hydraulic conductivity, hydraulic head, and concentration fields are approximated with deep neural networks (DNNs). We assume that the conductivity field is given by its values on a grid, and we use these values to train the conductivity DNN. The head and concentration DNNs are trained by minimizing the residuals of the flow equation and ADE and using the initial and boundary conditions as additional constraints. The PINN method is applied to one- and two-dimensional forward advection-dispersion equations (ADEs), where its performance for various Peclet numbers ($Pe$) is compared with the analytical and numerical solutions. We find that the PINN method is accurate with errors of less than 1% and outperforms some conventional discretization-based methods for $Pe$ larger than 100. Next, we demonstrate that the PINN method remains accurate for the backward ADEs, with the relative errors in most cases staying under 5% compared to the reference concentration field. Finally, we show that when available, the concentration measurements can be easily incorporated in the PINN method and significantly improve (by more than 50% in the considered cases) the accuracy of the PINN solution of the backward ADE.
Political Bias and Factualness in News Sharing across more than 100,000 Online Communities

As civil discourse increasingly takes place online, misinformation and the polarization of news shared in online communities have become ever more relevant concerns with real world harms across our society. Studying online news sharing at scale is challenging due to the massive volume of content which is shared by millions of users across thousands of communities. Therefore, existing research has largely focused on specific communities or specific interventions, such as bans. However, understanding the prevalence and spread of misinformation and polarization more broadly, across thousands of online communities, is critical for the development of governance strategies, interventions, and community design. Here, we conduct the largest study of news sharing on reddit to date, analyzing more than 550 million links spanning 4 years. We use non-partisan news source ratings from Media Bias/Fact Check to annotate links to news sources with their political bias and factualness. We find that, compared to left-leaning communities, right-leaning communities have 105% more variance in the political bias of their news sources, and more links to relatively-more biased sources, on average. We observe that reddit users’ voting and re-sharing behaviors generally decrease the visibility of extremely biased and low factual content, which receives 20% fewer upvotes and 30% fewer exposures from crossposts than more neutral or more factual content. This suggests that reddit is more resilient to low factual content than Twitter. We show that extremely biased and low factual content is very concentrated, with 99% of such content being shared in only 0.5% of communities, giving credence to the recent strategy of community-wide bans and quarantines.
Factors Affecting Handling Qualities of a Lift-Fan Aircraft During Steep Terminal Area Approaches

The XV-5B lift-fan aircraft was used to explore the factors affecting handling qualities in the terminal area. A 10 deg ILS approach task was selected to explore these problems. Interception of the glide slope at 457.2 m, glide slope tracking, deceleration along the glide slope to a spot hover were considered. Variations in airplane deck angle, deceleration schedule, and powered-lift management were studied. The overall descent performance envelope was identified on the basis of fan stall, maximum comfortable descent rate, and controllability restrictions. The collective-lift stick provided precise glide slope tracking capability. The pilot preferred a deck-parallel attitude for which he used powered lift to control glide slope and pitch attitude to keep the angle of attack near zero. Workload was reduced when the deceleration schedule was delayed until the aircraft was well established on the glide slope, since thrust vector changes induced flight path disturbances.
Business Entity Matching with Siamese Graph Convolutional Networks

Data integration has been studied extensively for decades and approached from different angles. However, this domain still remains largely rule-driven and lacks universal automation. Recent developments in machine learning and in particular deep learning have opened the way to more general and efficient solutions to data-integration tasks. In this paper, we demonstrate an approach that allows modeling and integrating entities by leveraging their relations and contextual information. This is achieved by combining siamese and graph neural networks to effectively propagate information between connected entities and support high scalability. We evaluated our approach on the task of integrating data about business entities, demonstrating that it outperforms both traditional rule-based systems and other deep learning approaches.
Spar I Liquid Mixing Experiment

The Liquid Mixing Experiment had as its objective the observation of effects of residual acceleration on a confined fluid system. The fluid system was chosen to be similar geometrically and in fluid properties to a range of materials science experiments. Samples were constructed as cylinders 2.2 cm long by 0.6 cm diameter. Half of each cylinder was composed of pure indium, and the other half of each was an alloy of 80 wt% indium and 25 wt% lead. The samples were enclosed in aluminum cartridges and placed in heaters mounted in three mutually perpendicular directions. The experimental procedure consisted of melting the samples when the low-g phase of the payload trajectory was attained, holding the samples in a molten state for approximately 200 seconds, and then quenching to resolidification before leaving low-g. Two of the samples exhibited very little fluid motion. The third sample, which was oriented nearly parallel to a payload radius, showed a great deal of flow.
Discovering Generalizable Skills via Automated Generation of Diverse Tasks

The learning efficiency of an intelligent agent can be greatly improved by utilizing a useful set of skills. However, the design of robot skills can often be intractable in real-world applications due to the prohibitive amount of effort and expertise that it requires. In this work, we introduce Skill Learning In Diversified Environments (SLIDE), a method to discover generalizable skills via automated generation of a diverse set of tasks. As opposed to prior work on unsupervised discovery of skills which incentivizes the skills to produce different outcomes in the same environment, our method pairs each skill with a unique task produced by a trainable task generator. To encourage generalizable skills to emerge, our method trains each skill to specialize in the paired task and maximizes the diversity of the generated tasks. A task discriminator defined on the robot behaviors in the generated tasks is jointly trained to estimate the evidence lower bound of the diversity objective. The learned skills can then be composed in a hierarchical reinforcement learning algorithm to solve unseen target tasks. We demonstrate that the proposed method can effectively learn a variety of robot skills in two tabletop manipulation domains. Our results suggest that the learned skills can effectively improve the robot’s performance in various unseen target tasks compared to existing reinforcement learning and skill learning methods.
Marginal energy intensity of water supply

Reducing global carbon emissions will require diverse industrial sectors to use energy more efficiently, electrify, and operate intermittently. The water sector is a transformation target, but we lack high-resolution energy quantification tools to guide operational, infrastructure, and policy interventions in complex water sourcing, treatment, and distribution networks. We propose that the marginal energy intensity (MEI) of water supply be used to quantify the location-specific, instantaneous embedded energy in water delivered to consumers. We describe a rigorous computational framework for computing MEI and elucidate the sensitivity of MEI to generalizable water system features using two representative water supply systems. When incorporated in multi-objective operational and planning models, MEI will serve as a robust and informative metric for guiding optimal implementation of water efficiency, conservation, and retrofit programs.
Conformal wave expansions for flat space amplitudes

The extended BMS algebra contains a conformal subgroup that acts on the celestial sphere as SO(1, 3). It is of interest to perform mode expansions of free fields in Minkowski spacetime that realize this symmetry in a simple way. In the present work we perform such a mode expansion for massive scalar fields using the unitary principal series representations of SO(1, 3) with a view to developing a holographic approach to gravity in asymptotically flat spacetime. These mode expansions are also of use in studying holography in three-dimensional de Sitter spacetime.
Scaling of urban income inequality in the USA

Urban scaling analysis, the study of how aggregated urban features vary with the population of an urban area, provides a promising framework for discovering commonalities across cities and uncoveri...
Capturing Membrane Trafficking Events During 3D Angiogenic Development in Vitro

OBJECTIVES Vesicular trafficking dictates protein localization, functional activity, and half-life, providing a critically important regulatory step in tissue development; however, there is little information detailing endothelial-specific trafficking signatures. This is due, in part, to limitations in visualizing trafficking events in endothelial tissues. Our aim in this investigation was to explore the use of a 3-dimensional (3D) in vitro sprouting model to image endothelial membrane trafficking events. METHODS Endothelial cells were challenged to grow sprouts in a fibrin bead assay. Thereafter, spouts were transfected with fluorescent proteins and stained for various cell markers. Sprouts were then imaged for trafficking events using live and fixed-cell microscopy. RESULTS Our results demonstrate that fibrin bead sprouts have a strong apicobasal polarity marked by apical localization of proteins moesin and podocalyxin. Comparison of trafficking mediators Rab27a and Rab35 between 3D sprouts and 2D culture showed that vesicular carriers can be imaged at high resolution, exhibiting proper membrane polarity solely in 3D sprouts. Lastly, we imaged exocytic events of von Willebrand Factor and demonstrated a distinct imaging advantage for monitoring secretion events in 3D sprouts as compared with 2D culture. CONCLUSIONS Our results establish that the fibrin bead sprouting assay is well-suited for imaging of trafficking events during angiogenic growth.
Doubly Geometric Quantum Control

A general approach to quantum control is presented, designing control waveforms that suppress both environmental noise and control errors at the same time.
Optimal Design of Mechanical Engineering Systems

The capability of mathematical optimization to support the mechanical design process is finally reaching wide recognition, as evidenced by significant applications in industry. The article reviews key issues and challenges for design optimization theorists and practitioners. Large scale system design and topology or configuration design are identified as the most important areas of future design optimization research. Some new approaches for partitioning large design problems and for topology optimization of multi-component structural systems are introduced.