cssi/SciSciGPT-SciSciNet huggingface dataset:
Application of Synchronization to Formation Flying Spacecraft: Lagrangian Approach

This paper presents a unified synchronization framework with application to precision formation flying spacecraft. Central to the proposed innovation, in applying synchronization to both translational and rotational dynamics in the Lagrangian form, is the use of the distributed stability and performance analysis tool, called contraction analysis that yields exact nonlinear stability proofs. The proposed decentralized tracking control law synchronizes the attitude of an arbitrary number of spacecraft into a common time-varying trajectory with global exponential convergence. Moreover, a decentralized translational tracking control law based on oscillator phase synchronization is presented, thus enabling coupled translational and rotational maneuvers. Although the translational dynamics can be adequately controlled by linear control laws, the proposed method permits highly nonlinear systems with nonlinearly coupled inertia matrices such as the attitude dynamics of spacecraft whose large and rapid slew maneuvers justify the nonlinear control approach. The proposed method integrates both the trajectory tracking and synchronization problems in a single control framework.
Newly-Discovered Anomalies in Galactic Cosmic Rays: Time for Exotic Scenarios?

Abstract Recent observations of galactic cosmic rays (CR) in the 1-500 GeV energy range have revealed striking deviations from what deemed “standard.” The anomalies cut across hadronic and leptonic CRs. I discuss findings that challenge physical mechanisms long held responsible for the CR production in galactic supernova remnants (SNR). I also consider some new physics of particle acceleration in SNR shocks that is not part of conventional models but may explain the anomalies. However, a possible 20–30% excess remains unaccounted for in the e + / e + ratio over the range of a few 100 GeV. If not explained by future models, it suggests an additional source of positrons such as a dark matter decay/annihilation or pulsar contribution. Earlier efforts to explain both the e + / e − and p/He anomalies with the “standard” models by adjusting the SNR environmental parameters and multiple sources are critically assessed.
The cosmic equation of state

The cosmic spacetime is often described in terms of the Friedmann-Robertson-Walker (FRW) metric, though the adoption of this elegant and convenient solution to Einstein’s equations does not tell us much about the equation of state, p=wρ, in terms of the total energy density ρ and pressure p of the cosmic fluid. ΛCDM and the R h=ct Universe are both FRW cosmologies that partition ρ into (at least) three components, matter ρ m, radiation ρ r, and a poorly understood dark energy ρ de, though the latter goes one step further by also invoking the constraint w=−1/3. This condition is apparently required by the simultaneous application of the Cosmological principle and Weyl’s postulate. Model selection tools in one-on-one comparisons between these two cosmologies favor R h=ct, indicating that its likelihood of being correct is ∼90 % versus only ∼10 % for ΛCDM. Nonetheless, the predictions of ΛCDM often come quite close to those of R h=ct, suggesting that its parameters are optimized to mimic the w=−1/3 equation-of-state. In this paper, we explore this hypothesis quantitatively and demonstrate that the equation-of-state in R h=ct helps us to understand why the optimized fraction Ω m≡ρ m /ρ in ΛCDM today must be ∼0.27, an otherwise seemingly random variable. We show that when one forces ΛCDM to satisfy the equation-of-state w=(ρ r/3−ρ de)/ρ, the value of the Hubble radius today, c/H 0, can equal its measured value ct 0 only with Ω m∼0.27 when the equation-of-state for dark energy is w de=−1. (We also show, however, that the inferred values of Ω m and w de change in a correlated fashion if dark energy is not a cosmological constant, so that $w_{\mathrm {de}}\not= -1$ .) This peculiar value of Ω m therefore appears to be a direct consequence of trying to fit the data with the equation-of-state w=(ρ r/3−ρ de)/ρ in a Universe whose principal constraint is instead R h=ct or, equivalently, w=−1/3.
Scalable Hierarchical Clustering with Tree Grafting

We introduce Grinch, a new algorithm for large-scale, non-greedy hierarchical clustering with general linkage functions that compute arbitrary similarity between two point sets. The key components of Grinch are its rotate and graft subroutines that efficiently reconfigure the hierarchy as new points arrive, supporting discovery of clusters with complex structure. Grinch is motivated by a new notion of separability for clustering with linkage functions: we prove that when the linkage function is consistent with a ground-truth clustering, Grinch is guaranteed to produce a cluster tree containing the ground-truth, independent of data arrival order. Our empirical results on benchmark and author coreference datasets (with standard and learned linkage functions) show that Grinch is more accurate than other scalable methods, and orders of magnitude faster than hierarchical agglomerative clustering.
Time-Resolved Spectroscopy with SDSS

We present a brief technical outline of the newly-formed project, “Detection of Spectroscopic Differences over Time” (DS/DT). Our collaboration is using individual exposures from the SDSS spectroscopic archive to produce a uniformly-processed set of time-resolved spectra. Here we provide an overview of the properties and processing of the available data, and highlight the wide range of time base-lines present in the archive.
On quantum quasi-relative entropy

We consider a quantum quasi-relative entropy SfK for an operator K and an operator convex function f. We show how to obtain the error bounds for the monotonicity and joint convexity inequalities fr...
TF-Ranking: Scalable TensorFlow Library for Learning-to-Rank

Learning-to-Rank deals with maximizing the utility of a list of examples presented to the user, with items of higher relevance being prioritized. It has several practical applications such as large-scale search, recommender systems, document summarization and question answering. While there is widespread support for classification and regression based learning, support for learning-to-rank in deep learning has been limited. We introduce TensorFlow Ranking, the first open source library for solving large-scale ranking problems in a deep learning framework. It is highly configurable and provides easy-to-use APIs to support different scoring mechanisms, loss functions and evaluation metrics in the learning-to-rank setting. Our library is developed on top of TensorFlow and can thus fully leverage the advantages of this platform. TensorFlow Ranking has been deployed in production systems within Google; it is highly scalable, both in training and in inference, and can be used to learn ranking models over massive amounts of user activity data, which can include heterogeneous dense and sparse features. We empirically demonstrate the effectiveness of our library in learning ranking functions for large-scale search and recommendation applications in Gmail and Google Drive. We also show that ranking models built using our model scale well for distributed training, without significant impact on metrics. The proposed library is available to the open source community, with the hope that it facilitates further academic research and industrial applications in the field of learning-to-rank.
A finiteness theorem for lagrangian fibrations

We consider (holomorphic) Lagrangian fibrations π : X → Pn that satisfy some natural hypotheses. We prove that there are only finitely many such Lagrangian fibrations up to deformation.
Statistical topology of perturbed two-dimensional lattices

The Voronoi cell of any atom in a lattice is identical. If atoms are perturbed from their lattice coordinates, then the topologies of the Voronoi cells of the atoms will change. We consider the distribution of Voronoi cell topologies in two-dimensional perturbed systems. These systems can be thought of as simple models of finite-temperature crystals. We give analytical results for the distribution of Voronoi topologies of points in two-dimensional Bravais lattices under infinitesimal perturbations and present a discussion with numerical results for finite perturbations.
Relative hyperbolicity and relative quasiconvexity for countable groups

We lay the foundations for the study of relatively quasiconvex subgroups of relatively hyperbolic groups. These foundations require that we first work out a coherent theory of countable relatively hyperbolic groups (not necessarily finitely generated). We prove the equivalence of Gromov, Osin and Bowditch’s definitions of relative hyperbolicity for countable groups. We then give several equivalent definitions of relatively quasiconvex subgroups in terms of various natural geometries on a relatively hyperbolic group. We show that each relatively quasiconvex subgroup is itself relatively hyperbolic, and that the intersection of two relatively quasiconvex subgroups is again relatively quasiconvex. In the finitely generated case, we prove that every undistorted subgroup is relatively quasiconvex, and we compute the distortion of a finitely generated relatively quasiconvex subgroup. 20F65, 20F67
Janus on the brane

We present a non-supersymmetric deformation of probe branes describing conformal defects of codimension two in AdS/CFT. The worldvolume of the probe branes is deformed from AdSp × S1 embedded in an AdSp+2 × ℳD − p − 2 background to an embedding of Janus form, which uses an AdSp−1 slicing of AdSp and in which the brane bends along the slicing coordinate. In field theory terms this realizes conformal interfaces on codimension- two defects. We discuss these “Janus on the brane” solutions for AdS3 × S1 D3-branes in the AdS5 × S5 solution of Type IIB, realizing interfaces on surface defects in $$ \mathcal{N} $$ = 4 SYM, and show that similar solutions exist for probe branes in AdSp+2 × S9−p vacua of M-theory and in the AdS6 × S4 solution of massive Type IIA.
Analytical Modeling of Participation Reduction in Superconducting Coplanar Resonator and Qubit Designs Through Substrate Trenching

A strategy aimed at decreasing dielectric loss in coplanar waveguides (CPWs) and qubits involves the creation of trenches in the underlying substrate within the gaps of the overlying metallization. Participation of contamination layers residing on surfaces and interfaces in these designs can be reduced due to the change in the effective dielectric properties between the groundplane and the conductor metallization. Although finite element method approaches have been previously applied to quantify this decrease, an analytical method is presented that can uniquely address geometries possessing small to intermediate substrate trench depths. Conformal mapping techniques produce transformed CPW and qubit geometries without substrate trenching but a nonuniform contamination layer thickness. By parametrizing this variation, one can calculate surface participation through use of a 2-D, analytical approximation that properly captures singularities in the electric field intensity near the metallization corners and edges. Examples demonstrate two regimes with respect to substrate trench depth that capture an initial increase in substrate-to-air surface participation due to the trench sidewalls and an overall decrease in surface participation due to the reduction in the effective dielectric constant and are compared with experimental measurements to extract loss tangents on this surface.
Curious Minds Wonder Alike: Studying Multimodal Behavioral Dynamics to Design Social Scaffolding of Curiosity

Curiosity is the strong desire to learn or know more about something or someone. Since learning is often a social endeavor, social dynamics in collaborative learning may inevitably influence curiosity. There is a scarcity of research, however, focusing on how curiosity can be evoked in group learning contexts. Inspired by a recently proposed theoretical framework [30] that articulates an integrated socio-cognitive infrastructure of curiosity, in this work, we use data-driven approaches to identify fine-grained social scaffolding of curiosity in child-child interaction, and propose how they can be used to elicit and maintain curiosity in technology-enhanced learning environments. For example, we discovered sequential patterns of multimodal behaviors across group members and we describe those that maximize an individual’s utility, or likelihood, of demonstrating curiosity during open-ended problem-solving in group work. We also discovered, and describe here, behaviors that directly or in a mediated manner cause curiosity related conversational behaviors in the interaction, with twice as many interpersonal causal influences compared to intrapersonal ones. We explain how these findings form a solid foundation for developing curiosity-increasing learning technologies or even assisting a human coach to induce curiosity among learners.
C–essential surfaces in (3-manifold, graph) pairs

Let T be a graph in a compact, orientable 3–manifold M and let Γ be a subgraph. T can be placed in bridge position with respect to a Heegaard surface H. We show that if H is what we call (T,Γ)-c-weakly reducible in the complement of T then either a “degenerate” situation occurs or H can be untelescoped and consolidated into a collection of “thick surfaces” and “thin surfaces”. The thin surfaces are c-essential (c-incompressible and essential) in the graph exterior and each thick surface is a strongly irreducible bridge surface in the complement of the thin surfaces. This strengthens and extends previous results of Hayashi-Shimokawa and Tomova to graphs in 3-manifolds that may have non-empty boundary.
Localized exotic smoothness

Gompf's end-sum techniques are used to establish the existence of an infinity of non-diffeomorphic manifolds, all having the same trivial topology, but for which the exotic differentiable structure is confined to a region which is spatially limited. Thus, the smoothness is standard outside of a region which is topologically (but not smoothly) , where is the compact three ball. The exterior of this region is diffeomorphic to standard . In a spacetime diagram, the confined exoticness sweeps out a worldtube which, it is conjectured, might act as a source for certain non-standard solutions to the Einstein equations. It is shown that smooth Lorentz signature metrics can be globally continued from ones given on appropriately defined regions, including the exterior (standard) region. Similar constructs are provided for the topology of the Kruskal form of the Schwarzschild solution. This leads to conjectures about the existence of Einstein metrics which are externally identical to standard black hole ones, but none of which can be globally diffeomorphic to such standard objects. Certain aspects of the Cauchy problem are also discussed in terms of models which are `half-standard', say for all t<0, but for which t cannot be globally smooth.
Constraints on flavored 2d CFT partition functions

We study the implications of modular invariance on 2d CFT partition functions with abelian or non-abelian currents when chemical potentials for the charges are turned on, i.e. when the partition functions are “flavored”. We begin with a new proof of the transformation law for the modular transformation of such partition functions. Then we proceed to apply modular bootstrap techniques to constrain the spectrum of charged states in the theory. We improve previous upper bounds on the state with the greatest “mass-to-charge” ratio in such theories, as well as upper bounds on the weight of the lightest charged state and the charge of the weakest charged state in the theory. We apply the extremal functional method to theories that saturate such bounds, and in several cases we find the resulting prediction for the occupation numbers are precisely integers. Because such theories sometimes do not saturate a bound on the full space of states but do saturate a bound in the neutral sector of states, we find that adding flavor allows the extremal functional method to solve for some partition functions that would not be accessible to it otherwise.
Advanced anneal paths for improved quantum annealing

Advances in quantum annealing technology make it possible to obtain high quality approximate solutions of important NP-hard problems. With the newer generations of the D-Wave annealer, more advanced features are available which allow the user to have greater control of the anneal process. In this contribution, we study how such features can help in improving the quality of the solutions returned by the annealer. Specifically, we focus on two of these features: reverse annealing and h-gain. Reverse annealing (RA) was designed to allow refining a known solution by backward annealing from a classical state representing the solution to a mid-anneal point where a transverse field is present, followed by an ordinary forward anneal, which is hoped to improve on the previous solution. The h-gain (HG) feature stands for time-dependent gain in Hamiltonian linear (h) biases and was originally developed to help study freezeout times and phase transitions in spin glasses. Here we apply HG to bias the quantum state in the beginning of the annealing process towards the known solution as in the RA case, but using a different apparatus. We also investigate a hybrid reverse annealing/h-gain schedule, which has a backward phase resembling an RA step and whose forward phase uses the HG idea. To optimize the parameters of the schedules, we employ a Bayesian optimization framework. We test all techniques on a variety of input problems including the weighted Maximum Cut problem and the weighted Maximum Clique problem. Our results show that each technique may dominate the others depending on the input instance, and that the HG technique is a viable alternative to RA for some problems.
ParSeNet: A Parametric Surface Fitting Network for 3D Point Clouds

We propose a novel, end-to-end trainable, deep network called ParSeNet that decomposes a 3D point cloud into parametric surface patches, including B-spline patches as well as basic geometric primitives. ParSeNet is trained on a large-scale dataset of man-made 3D shapes and captures high-level semantic priors for shape decomposition. It handles a much richer class of primitives than prior work, and allows us to represent surfaces with higher fidelity. It also produces repeatable and robust parametrizations of a surface compared to purely geometric approaches. We present extensive experiments to validate our approach against analytical and learning-based alternatives. Our source code is publicly available at: https://hippogriff.github.io/parsenet.
Multi-level Nonvolatile Photonic Memories Using Broadband Transparent Phase change materials

Here we demonstrate a low-loss multi-state photonic memory using broadband transparent phase change materials (GeSbSe), which can be efficiently reprogrammed on-chip.
Convexifying Sparse Interpolation With Infinitely Wide Neural Networks: An Atomic Norm Approach

This work examines the problem of exact data interpolation via sparse (neuron count), infinitely wide, single hidden layer neural networks with leaky rectified linear unit activations. Using the atomic norm framework of [Chandrasekaran et al. 2012], we derive simple characterizations of the convex hulls of the corresponding atomic sets for this problem under several different constraints on the weights and biases of the network, thus obtaining equivalent convex formulations for these problems. A modest extension of our proposed framework to a binary classification problem is also presented. We explore the efficacy of the resulting formulations experimentally, and compare with networks trained via gradient descent.
Financial crises and reform of the international financial system

Between December 1994 and March 1999, Mexico, Thailand, Indonesia, Korea, Malaysia, Russia and Brazil experienced major financial crises, which were associated with massive recessions and extreme movements of exchange rates. Similar crises have threatened Turkey and Argentina (2000 and 2001) and most recently Brazil (again). This article discusses the reform of the international financial system with a focus on the role of the IMF—reforms directed at crisis prevention, and those intended to improve the responses to crises. The article concludes with an appraisal of what has been achieved, and what remains to be done to make the international financial system safer. JEL no. E5, E6, F3, F4, G1
Automated Adversary Emulation for Cyber-Physical Systems via Reinforcement Learning

Adversary emulation is an offensive exercise that provides a comprehensive assessment of a system’s resilience against cyber attacks. However, adversary emulation is typically a manual process, making it costly and hard to deploy in cyber-physical systems (CPS) with complex dynamics, vulnerabilities, and operational uncertainties. In this paper, we develop an automated, domain-aware approach to adversary emulation for CPS. We formulate a Markov Decision Process (MDP) model to determine an optimal attack sequence over a hybrid attack graph with cyber (discrete) and physical (continuous) components and related physical dynamics. We apply model-based and model-free reinforcement learning (RL) methods to solve the discrete-continuous MDP in a tractable fashion. As a baseline, we also develop a greedy attack algorithm and compare it with the RL procedures. We summarize our findings through a numerical study on sensor deception attacks in buildings to compare the performance and solution quality of the proposed algorithms.
Patterns and Interactions in Network Security

Networks play a central role in cyber-security: networks deliver security attacks, suffer from them, defend against them, and sometimes even cause them. This article is a concise tutorial on the large subject of networks and security, written for all those interested in networking, whether their specialty is security or not. To achieve this goal, we derive our focus and organization from two perspectives. The first perspective is that, although mechanisms for network security are extremely diverse, they are all instances of a few patterns. Consequently, after a pragmatic classification of security attacks, the main sections of the tutorial cover the four patterns for providing network security, of which the familiar three are cryptographic protocols, packet filtering, and dynamic resource allocation. Although cryptographic protocols hide the data contents of packets, they cannot hide packet headers. When users need to hide packet headers from adversaries, which may include the network from which they are receiving service, they must resort to the pattern of compound sessions and overlays. The second perspective comes from the observation that security mechanisms interact in important ways, with each other and with other aspects of networking, so each pattern includes a discussion of its interactions.
Improved protein model quality assessment by integrating sequential and pairwise features using deep learning

Motivation Accurately estimating protein model quality in the absence of experimental structure is not only important for model evaluation and selection, but also useful for model refinement. Progress has been steadily made by introducing new features and algorithms (especially deep neural networks), but the accuracy of quality assessment (QA) is still not very satisfactory, especially local QA on hard protein targets. Results We propose a new single-model-based QA method ResNetQA for both local and global quality assessment. Our method predicts model quality by integrating sequential and pairwise features using a deep neural network composed of both 1 D and 2 D convolutional residual neural networks (ResNet). The 2 D ResNet module extracts useful information from pairwise features such as model-derived distance maps, co-evolution information, and predicted distance potential from sequences. The 1 D ResNet is used to predict local (global) model quality from sequential features and pooled pairwise information generated by 2 D ResNet. Tested on the CASP12 and CASP13 datasets, our experimental results show that our method greatly outperforms existing state-of-the-art methods. Our ablation studies indicate that the 2 D ResNet module and pairwise features play an important role in improving model quality assessment. Availability https://github.com/AndersJing/ResNetQA. Supplementary information Supplementary data are available at Bioinformatics online.
JANUS: Benchmarking Commercial and Open-Source Cloud and Edge Platforms for Object and Anomaly Detection Workloads

With diverse IoT workloads, placing compute and analytics close to where data is collected is becoming increasingly important. We seek to understand what is the performance and the cost implication of running analytics on IoT data at the various available platforms. These workloads can be compute-light, such as outlier detection on sensor data, or compute-intensive, such as object detection from video feeds obtained from drones. In our paper, Janus, we profile the performance/$ and the compute versus communication cost for a compute-light IoT workload and a compute-intensive IoT workload. In addition, we also look at the pros and cons of some of the proprietary deep-learning object detection packages, such as Amazon Rekognition, Google Vision, and Azure Cognitive Services, to contrast with open-source and tunable solutions, such as Faster R-CNN (FRCNN). We find that AWS IoT Greengrass delivers at least 2X lower latency and 1.25X lower cost compared to all other cloud platforms for the compute-light outlier detection workload. For the compute-intensive streaming video analytics task, an open-source solution to object detection running on cloud VMs saves on dollar costs compared to proprietary solutions provided by Amazon, Microsoft, and Google, but loses out on latency (up to 6X). If it runs on a low-powered edge device, the latency is up to 49X lower.
Using quantum annealers to calculate ground state properties of molecules.

Quantum annealers are an alternative approach to quantum computing, which make use of the adiabatic theorem to efficiently find the ground state of a physically realizable Hamiltonian. Such devices are currently commercially available and have been successfully applied to several combinatorial and discrete optimization problems. However, the application of quantum annealers to problems in chemistry remains a relatively sparse area of research due to the difficulty in mapping molecular systems to the Ising model Hamiltonian. In this paper, we review two different methods for finding the ground state of molecular Hamiltonians using Ising model-based quantum annealers. In addition, we compare the relative effectiveness of each method by calculating the binding energies, bond lengths, and bond angles of the H3+ and H2O molecules and mapping their potential energy curves. We also assess the resource requirements of each method by determining the number of qubits and computation time required to simulate each molecule using various parameter values. While each of these methods is capable of accurately predicting the ground state properties of small molecules, we find that they are still outperformed by modern classical algorithms and that the scaling of the resource requirements remains a challenge.
Fermionic Topological Order on Generic Triangulations

Consider a finite triangulation of a surface M of genus g and assume that spin-less fermions populate the edges of the triangulation. The quantum dynamics of such particles takes place inside the algebra of canonical anti-commutation relations (CAR). Following Kitaev’s work on toric models, we identify a sub-algebra of CAR generated by elements associated to the triangles and vertices of the triangulation. We show that any Hamiltonian drawn from this sub-algebra displays topological spectral degeneracy. More precisely, if $${{\mathcal {P}}}$$ is any of its spectral projections, the Booleanization of the fundamental group $$\pi _1(M)$$ can be embedded inside the group of invertible elements of the corner algebra $${{\mathcal {P}}}\, \mathrm{CAR} \, {{\mathcal {P}}}$$ . As a consequence, $${{\mathcal {P}}}$$ decomposes in $$4^g$$ lower projections. Furthermore, a projective representation of $${{\mathbb {Z}}}_2^{4g}$$ is also explicitly constructed inside this corner algebra. Key to all these is a presentation of CAR as a crossed product with the Boolean group $$(2^X,\Delta )$$ , where X is the set of fermion sites and $$\Delta $$ is the symmetric difference of its sub-sets.
Racial Disparity in U.S. Imprisonment Across States and Over Time

We analyze the contribution of changes in the black–white racial disparity in imprisonment to changes in the black incarceration rate. We also describe the behavior of the racial disparity across states and across time. We use state level incarceration data for non-Hispanic black and white males to perform a decomposition of the black incarceration rate. This allows us to attribute changes in black incarceration to changes in the racial disparity and changes in the overall incarceration rate. We use a Fourier approximation to identify structural change points for the racial disparity at both the state and national level. The large increase in black imprisonment between 1978 and 1999 was driven by increases in the overall rate of imprisonment, while the smaller decrease which occurred between 1999 and 2014 was driven by reductions in the black–white racial disparity. For many states, the racial disparity increased starting in the mid-1980s, where this increase may have been linked to the crack epidemic. Many states experienced a downturn in the racial disparity starting in the 1990s. Whatever its other effects, this suggests that the 1994 crime bill did not aggravate the preexisting racial disparity in imprisonment. California’s experience has been strongly counter to national trends with a large increase in the racial disparity beginning in the early 1990s and continuing until near the end of our sample. While the racial disparity in imprisonment has been falling since 1996, it remains quite high as of 2014. Future work is required to better understand the policy determinants of this disparity.
Re-exploring the Trade and Environment Nexus Through the Diffusion of Pollution

Some scholars argue that trade liberalization results in the ‘outsourcing’ of pollution to pollution havens, and this may explain the existence of the environmental Kuznets curve (EKC) in industrialized countries. Others claim that trade liberalization effectively lessens pollution by allowing a more efficient use of resources and the diffusion of clean technologies. In part because these theories generate similar predictions for industrialized countries, empirical studies have not provided conclusive evidence for either argument. To identify the role of trade, I draw on the insights of the diffusion literature and estimate a spatial regression model where trade operates as the mechanism through which pollution shifts from country to country. Using the case of per-capita carbon dioxide $$(\hbox {CO}_2)$$ emissions, I find that once spatial correlation is accounted for, the EKC disappears. The effect is even stronger when looking at trade from developing countries only, providing support for the pollution haven hypothesis. I also show that the $$\hbox {CO}_2$$ trajectory of developing countries goes above the one previously followed by industrialized countries. This suggests that developing countries carry on an additional carbon burden above the one that would be obtained in the absence of trade. These findings contribute to the literature on the non-economic effects of trade and the constraints of domestic policies when the costs of trade are low.
Fostering Cumulative Innovation in the Biopharmaceutical Industry: The Role of Patents and Antitrust

This Article addresses the question of how the tools of patent and antitrust law can best be used to foster the cumulative process that is biopharmaceutical innovation. This issue is of particular moment because we have begun in recent years to see a substantial amount of vertical and horizontal integration in the biopharmaceutical industry. The Article argues that although horizontal concentration may be useful for appropriating the value of a lengthy and expensive research and development process, a role for competition needs to be preserved. In the context of the biopharmaceutical industry, broad patents, particularly on upstream invention, represent the main threat to competition. Thus patent law needs to take the lead in preserving competition, primarily by limiting the scope of patents on upstream invention. Antitrust law, and especially the theory of innovation markets, should play a secondary role. In this secondary role, innovation markets theory will actually support certain market transactions that aggregate patent rights, in particular procompetitive patent pools. Innovation markets theory will, however, have an important impact in restricting market transactions if the transactions will give a single entity control over what appears to be a fundamental platform technology.
Price and income elasticity of Indian exports—The role of supply-side bottlenecks ☆

Abstract This paper estimates the short-term and long-run price and income elasticity of Indian exports, and investigates the role of supply-side bottlenecks in shaping India’s export demand relationship. We use disaggregated export volume data for 45 Indian industries over the period 1990–2013, as well as industry-specific international relative prices, for estimation. Our results indicate that Indian exports are sensitive to international relative-price competitiveness, world demand, and energy shortages. In addition, binding supply-side constraints (notably energy shortages) dampen price responsiveness in the short-term.
On InstaHide, Phase Retrieval, and Sparse Matrix Factorization

In this work, we examine the security of InstaHide, a scheme recently proposed by \cite{hsla20} for preserving the security of private datasets in the context of distributed learning. To generate a synthetic training example to be shared among the distributed learners, InstaHide takes a convex combination of private feature vectors and randomly flips the sign of each entry of the resulting vector with probability 1/2. A salient question is whether this scheme is secure in any provable sense, perhaps under a plausible complexity-theoretic assumption. The answer to this turns out to be quite subtle and closely related to the average-case complexity of a multi-task, missing-data version of the classic problem of phase retrieval that is interesting in its own right. Motivated by this connection, under the standard distributional assumption that the public/private feature vectors are isotropic Gaussian, we design an algorithm that can actually recover a private vector using only the public vectors and a sequence of synthetic vectors generated by InstaHide.
Federal Arbitration Act Preemption

Taking as given the existing Supreme Court case law, this Article seeks to develop an overall framework for analyzing when the Federal Arbitration Act preempts state law. The framework is not conclusive but instead highlights areas of uncertainty for future legal development. Some fundamental principles of FAA preemption are resolved: State laws that single out arbitration are preempted when they invalidate arbitration agreements; general contract law defenses, even when applied to invalidate arbitration agreements, ordinarily are not preempted; and parties can incorporate by reference state arbitration laws into their contracts and avoid FAA preemption. As the analytical framework developed here illustrates, however, a number of issues remain unsettled. The lower courts are divided on whether the FAA preempts state laws that apply to arbitration agreements and to some other contract clauses, but not to contracts generally. Questions remain to be answered as to when general contract law defenses, while ordinarily saved from preemption, may nonetheless single out arbitration agreements and be preempted after all. Finally, the Supreme Court still has to decide how to deal with “second generation” FAA preemption cases - cases involving state laws that regulate the arbitration process rather than invalidating the parties’ arbitration agreement. The Article identifies and categorizes five alternative theories of FAA preemption and examines how selected second generation preemption cases likely would be decided under each of those theories.
Does Smart Routing Matter

The authors compare the performance of broker smart routers for non-marketable and marketable orders. For non-marketable orders, smart routers that “naively” route to maximize liquidity rebates perform significantly worse than those routing to maximize fill rates. The difference between the best and worst broker smart router is about 10% of the bid–ask spread (0.4 basis points), on average, for large-cap stocks and 6% of the spread (0.9 basis points), on average, for small-cap stocks,which is a significant difference for cost-sensitive traders. The authors also find that the best brokers perform well across all liquidity groups. For marketable orders, however, smart router performance generally does not vary significantly across brokers.
ENHANCED CITIZENSHIP VERIFICATION AND CHILDREN'S MEDICAID COVERAGE

This paper examines the impact of Deficit Reduction Act of 2005 mandated citizenship verification requirements on the Medicaid coverage of children using state administrative data from Georgia. Our analysis focuses on children enrolled in Medicaid prior to the reform in the eligibility category for which the reform is most likely to be binding. We find that these children were slightly more likely to exit during the first "high impact" recertification in which the enhanced citizenship verification was binding than children whose first recertification occurred just prior to the reform. In addition, we observe a slightly lower re-entry probability among children exiting during a "high impact" first recertification. Assuming at least some of the exiting children are non-citizens, the fact that the exit and re-entry rates associated with a “high impact” first recertification are only modestly different from other first recertification months suggests that the reform is probably not having a dramatic impact on citizens.
Prizes, Publicity and Patents: Non-Monetary Awards as a Mechanism to Encourage Innovation†

This paper exploits the selection of prize-winning technologies among exhibitors at the Crystal Palace Exhibition in 1851 to examine whether—and how—ex post prizes that are awarded to high-quality innovations may encourage future innovation. U.S. patent data indicate a 40 per cent increase after 1851 in patenting for prize-winners compared with other exhibits. Results are robust to controlling for technology-specific pre-trends and for the quality of patents. A comparison of changes in patenting for prize-winners with changes for technologies that were described on the front page of the Scientific American suggests that publicity for promising research fields may be an important mechanism by which ex post prizes encourage future innovation.
Synergies and Internal Agency Conflicts: The Double‐Edged Sword of Mergers

This paper investigates the interaction between synergies and internal agency conflicts that emerges endogenously in multi-division firms. A divisional manager’s entrenchment choice depends directly on the specificity of her division’s assets, because the specificity governs whether entrenchment activities reduce the likelihood of her division being divested. The presence of synergies, by modifying the difference between the value of assets in their current use and in alternative uses, may alter the divisional manager’s entrenchment incentive. In “the double-edged sword of mergers,” synergy and internal agency effects are of opposite sign and merger gains may not be increasing in expected synergies. We characterize when divisions should optimally stand alone and when they should be part of a merged firm. We predict an absence of diversifying mergers in industries plagued by misdeployed assets, offer a novel explanation for the cross-sectional variation in postmerger valuation, and explain why mergers may be valuable ex ante while leading to successful divestitures ex post.
"John Brown Went Off to War": Considering Veterans Courts as Problem-Solving Courts

In this paper, I seek to contextualize veterans courts in light of the therapeutic jurisprudence (TJ) movement, the turn to problem-solving courts of all sorts (especially focusing on mental health courts), and the societal ambivalence that we have shown to veterans in the four decades since the Vietnam war. I argue that TJ’s focuses on how law actually impacts people’s lives, on the law’s influence on emotional life and psychological well-being and on the need for law to value psychological health and avoid the imposition of anti-therapeutic consequences whenever possible can serve as a template for a veterans courts model (if we are to expand these courts robustly). TJ is the explicit inspiration for many of the most important problem-solving courts (including Judge Ginger Lerner-Wren’s mental health court in Broward County), but it is also clear that many such courts – specifically, some drug courts – do not follow TJ principles, existing, instead in a “due process-free zone” (implicitly rejecting the basic TJ premise that therapeutic outcomes cannot trump due process). Just as mental health courts should ensure that defendants receive dignity and respect, and are given a sense of voice and validation, so should veterans courts. And we must also recognize that our treatment of injured war veterans provides a vivid example of society’s general ambivalence toward guaranteeing robust social rights. Thirty years ago, in Falter v. Veterans’ Administration, a case seeking to force the VA into implementing a “patients’ bill of rights” at VA hospitals, the trial judge observed that the case was basically about “how [plaintiffs] are treated as human beings.” This observation must be at the forefront of any assessment of veterans courts. Despite general low recidivism rates, Veterans Courts have received criticism as some have argued that they provide veterans with a “hall pass“ to certain criminal-defense rights that others do not have, and that, from an entirely different perspective, they are stigmatizing because they perpetuate the stereotype that veterans are returning “war-crazy.” I address these and other criticisms in my paper.One critical issue that has received almost no attention we are just beginning to take seriously in the mental health courts context: how can we assure that there is experienced, dedicated, knowledgeable counsel assigned to represent defendants in such tribunals? We know that if there has been any constant in modern mental disability law in its thirty-five-year history, it is the near-universal reality that counsel assigned to represent individuals at involuntary civil commitment cases are likely to be ineffective. How can we be sure that counsel in these cases be more effective?I offer some conclusions and suggestions for those jurisdictions that are implementing veterans courts, so as to optimally assure adherence to TJ values in a court setting that continues to provide litigants with the full range of constitutional rights to which they are entitled.
Section 1367: Another Party Heard From

It was appropriate, if not essential, for Congress to overturn the the Supreme Court's decision in Finley v. United States, to negate the untoward implications for pendent and ancillary jurisdiction that both commentators and lower courts found in the reasoning and language of the opinion. Congress did respond, through the enactment of 28 U.S.C. § 1367. Professors Freer and Arthur have been critical of the process that led to its enactment, as well as of the end product. Perhaps a larger number of interested and knowledgeable persons should have been consulted, and perhaps a better statute would have resulted. Perhaps members of Congress and their staffs should have more carefully scrutinized the bill that became § 1367; and if they had, perhaps they would have better appreciated all the subtleties of the bill, and altered the bill to change some of its effects. But all of that is water under the bridge, except for the lessons that might be learned. For the present, students of § 1367 can help attorneys, courts, and legislators by illuminating the dark corners, revealing how § 1367 resolves certain issues, arguing for or against those resolutions, identifying what questions § 1367 poses or leaves unanswered, and taking a position on how those questions ought to be resolved. Professors Freer, Arthur, Burbank, Mengler, and Rowe have contributed valuably to that cause. In this volume of the Journal, a few more of us get to contribute our thoughts. We are other parties heard from, like the additional voices that § 1367 sometimes allows into lawsuits.
Constitutional Structure, Judicial Discretion, and the Eighth Amendment

The Supreme Court recently resolved a longstanding split in its Eighth Amendment jurisprudence when it declared that the cruel and unusual punishments clause delegates to federal courts broad discretion to exercise “independent judgment” to evaluate the propriety of punishments authorized by state law. The Court claimed authority to displace a punishment—however widely employed—based on the Court’s own assessment of the penological effectiveness of the punishment and the moral culpability of the particular class of offenders. Notably, the Court did not, and has not in the modern era, attempted to justify its approach in terms of either the text or the history of the Eighth Amendment. Drawing on the broader lessons of the constitutional structure, this paper argues that the Court’s assumption of broad discretion to displace state law contradicts the implications of constitutionally prescribed lawmaking procedures, the political safeguards of federalism, and the Supremacy Clause. In this regard, the Court’s current approach to the Eighth Amendment ignores the lessons of prominent historical episodes in which federal courts first embraced, and then abandoned as unconstitutional, similar assumptions of broad judicial discretion—most notably, the early controversy surrounding federal common law crimes and the later embrace of so-called general common law under the Swift doctrine. Absent clear evidence that a particular constitutional text, such as the Eighth Amendment, authorizes this degree of judicial discretion, the constitutional structure—as illuminated by these past instances of discretion and retreat—counsels against it.
Group Polarization in Board Decisions About CEO Compensation

This study examines how chief executive officer CEO compensation decisions may be influenced by a major group decision-making tendency referred to as group polarization among outside directors. I start by explaining why outside directors on average tend to support relatively high low CEO compensation when they previously witnessed relatively high low CEO compensation across different boards. Group polarization theory then suggests that when outside directors on average tend to support relatively high low CEO compensation prior to board discussions, they will support even higher lower focal CEO compensation after the discussions. In addition, this study proposes three important moderators of the group polarization effect. Specifically, 1 demographic homogeneity among outside directors and 2 the similarity of the minority's prior decision context are proposed to weaken the group polarization effect, whereas 3 outside directors' power relative to inside directors is predicted to strengthen it. Longitudinal analyses 1995-2006 of Fortune 500 CEOs' compensation provide support for these theoretical predictions. This study contributes to corporate governance research on CEO compensation by advancing a novel group decision-making approach to examining this important decision.