cssi/SciSciGPT-SciSciNet huggingface dataset:
Measuring the Cosmological 21 cm Monopole with an Interferometer

A measurement of the cosmological 2 1c m signal remains a promising but as-of-yet unattained ambition of radio astronomy. A positive detection would provide direct observations of key unexplored epochs of our cosmic history, including the cosmic dark ages and reionization. In this paper, we concentrate on measurements of the spatial monopole of the 2 1c m brightness temperature as a function of redshift (the “global signal”). Most global experiments to date have been single-element experiments. In this paper, we show how an interferometer can be designed to be sensitive to the monopole mode of the sky, thus providing an alternate approach to accessing the global signature. We provide simple rules of thumb for designing a global signal interferometer and use numerical simulations to show that a modest array of tightly packed antenna elements with moderately sized primary beams (FWHM of 40 ~ ) can compete with typical single-element experiments in their ability to constrain phenomenological parameters pertaining to reionization and the pre-reionization era. We also provide a general data analysis framework for extracting the global signal from interferometric measurements (with analysis of single-element experiments arising as a special case) and discuss trade-offs with various data analysis choices. Given that interferometric measurements are able to avoid a number of systematics inherent in single-element experiments, our results suggest that interferometry ought to be explored as a complementary way to probe the global signal.
Emergent Supersymmetry in Local Equilibrium Systems

Many physical processes we observe in nature involve variations of macroscopic quantities over spatial and temporal scales much larger than microscopic molecular collision scales and can be considered as in local thermal equilibrium. In this paper we show that any classical statistical system in local thermal equilibrium has an emergent supersymmetry at low energies. We use the framework of non-equilibrium effective field theory for quantum many-body systems defined on a closed time path contour and consider its classical limit. Unitarity of time evolution requires introducing anti-commuting degrees of freedom and BRST symmetry which survive in the classical limit. The local equilibrium is realized through a Z2 dynamical KMS symmetry. We show that supersymmetry is equivalent to the combination of BRST and a specific consequence of the dynamical KMS symmetry, to which we refer as the special dynamical KMS condition. In particular, we prove a theorem stating that a system satisfying the special dynamical KMS condition is always supersymmetrizable. We discuss a number of examples explicitly, including model A for dynamical critical phenomena, a hydrodynamic theory of nonlinear diffusion, and fluctuating hydrodynamics for relativistic charged fluids.
The Time evolution of bias

We study the evolution of the bias factor b and the mass-galaxy correlation coefficient r in a simple analytic model for galaxy formation and the gravitational growth of clustering. The model shows that b and r can be strongly time dependent but tend to approach unity even if galaxy formation never ends as the gravitational growth of clustering debiases the older galaxies. The presence of random fluctuations in the sites of galaxy formation relative to the mass distribution can cause large and rapidly falling bias values at high redshift.
Rare events in networks with internal and external noise

We study rare events in networks with both internal and external noise, and develop a general formalism for analyzing rare events that combines pair-quenched techniques and large-deviation theory. The probability distribution, shape, and time scale of rare events are considered in detail for extinction in the Susceptible-Infected-Susceptible model as an illustration. We find that when both types of noise are present, there is a crossover region as the network size is increased, where the probability exponent for large deviations no longer increases linearly with the network size. We demonstrate that the form of the crossover depends on whether the endemic state is localized near the epidemic threshold or not.
Null Geodesics in Perturbed Spacetimes

We present a generalization and refinement of the Sachs-Wolfe technique, which unifies many of the approaches taken to date and clarifies both the physical and the mathematical character of the method. We illustrate the formalism with a calculation of the behavior of light passing a moving lens on a Minkowski background
Spatiospectral concentration in the Cartesian plane

We pose and solve the analogue of Slepian’s time–frequency concentration problem in the two-dimensional plane, for applications in the natural sciences. We determine an orthogonal family of strictly bandlimited functions that are optimally concentrated within a closed region of the plane, or, alternatively, of strictly spacelimited functions that are optimally concentrated in the Fourier domain. The Cartesian Slepian functions can be found by solving a Fredholm integral equation whose associated eigenvalues are a measure of the spatiospectral concentration. Both the spatial and spectral regions of concentration can, in principle, have arbitrary geometry. However, for practical applications of signal representation or spectral analysis such as exist in geophysics or astronomy, in physical space irregular shapes, and in spectral space symmetric domains will usually be preferred. When the concentration domains are circularly symmetric in both spaces, the Slepian functions are also eigenfunctions of a Sturm–Liouville operator, leading to special algorithms for this case, as is well known. Much like their one-dimensional and spherical counterparts with which we discuss them in a common framework, a basis of functions that are simultaneously spatially and spectrally localized on arbitrary Cartesian domains will be of great utility in many scientific disciplines, but especially in the geosciences.
Gamma Lines without a Continuum: Thermal Models for the Fermi-LAT 130 GeV Gamma Line

Recent claims of a line in the Fermi-LAT photon spectrum at 130GeV are sug- gestive of dark matter annihilation in the galactic center and other dark matter-dominated regions. If the Fermi feature is indeed due to dark matter annihilation, the best-fit line cross-section, together with the lack of any corresponding excess in continuum photons, poses an interesting puzzle for models of thermal dark matter: the line cross-section is too large to be generated radiatively from open Standard Model annihilation modes, and too small to provide efficient dark matter annihilation in the early universe. We discuss two mechanisms to solve this puzzle and illustrate each with a simple reference model in which the dominant dark matter annihilation channel is photonic final states. The first mech- anism we employ is resonant annihilation, which enhances the annihilation cross-section during freezeout and allows for a sufficiently large present-day annihilation cross section. Second, we consider cascade annihilation, with a hierarchy between p-wave and s-wave pro- cesses. Both mechanisms require mass near-degeneracies and predict states with masses closely related to the dark matter mass; resonant freezeout in addition requires new charged particles at the TeV scale.
Transmission statistics and focusing in single disordered samples

We show in microwave experiments and random matrix calculations that in samples with a large number of channels the statistics of transmission for different incident channels relative to the average transmission is determined by a single parameter, the participation number of the eigenvalues of the transmission matrix, M. Its inverse, M-1, is equal to the variance of relative total transmission of the sample, while the contrast in maximal focusing is equal to M. The distribution of relative total transmission changes from Gaussian to negative exponential over the range in which M-1 changes from 0 to 1. This provides a framework for transmission and imaging in single samples.
Lattice chiral gauge theories

Abstract I review the substantial progress which has been made recently with the non-perturbative construction of chiral gauge theories on the lattice. In particular, I discuss three different approaches: a gauge invariant method using fermions which satisfy the Ginsparg-Wilson relation, and two gauge non-invariant methods, one using different cutoffs for the fermions and the gauge fields, and one using gauge fixing. Open problems within all three approaches are addressed.
Quantifying residual finiteness of arithmetic groups

The normal Farb growth of a group quantifies how well-approximated the group is by its finite quotients. We show that any S-arithmetic subgroup of a higher rank Chevalley group G has normal Farb growth n dim(G) .
Holographic two dimensional QCD and Chern-Simons term

We present a holographic realization of large N c massless QCD in two dimensions using a D2/D8 brane construction. The flavor axial anomaly is dual to a three dimensional Chern-Simons term which turns out to be of leading order, and it affects the meson spectrum and holographic renormalization in crucial ways. The massless flavor bosons that exist in the spectrum are found to decouple from the heavier mesons, in agreement with the general lore of non-Abelian bosonization. We also show that an external dynamical photon acquires a mass through the three dimensional Chern-Simons term as expected from the Schwinger mechanism. Massless two dimensional QCD at large N c exhibits anti-vector-meson dominance due to the axial anomaly.
Sample autocovariances of long-memory time series

We find the asymptotic distribution of the sample autocovariances of long-memory processes in cases of finite and infinite fourth moment. Depending on the interplay of assumptions on moments and the intensity of dependence, there are three types of convergence rates and limit distributions. In particular, a normal approximation with the standard rate does not always hold in practically relevant cases.
Driving Intention Recognition and Lane Change Prediction on the Highway

This paper proposes a framework to recognize driving intentions and to predict driving behaviors of lane changing on the highway by using externally sensable traffic data from the host-vehicle. The framework consists of a driving characteristic estimator and a driving behavior predictor. A driver's implicit driving characteristic information is uniquely determined and detected by proposed the online-estimator. Neural-network based behavior predictor is developed and validated by testing with the real naturalistic traffic data from Next Generation Simulation (NGSIM), which demonstrates the effectiveness in identifying the driving characteristics and transforming into accurate behavior prediction in real-world traffic situations.
Computable bounds for Rasmussen’s concordance invariant

Given a diagram D of a knot K, we give easily computable bounds for Rasmussen’s concordance invariant s(K). The bounds are not independent of the diagram D chosen, but we show that for diagrams satisfying a given condition the bounds are tight. As a corollary we improve on previously known Bennequin-type bounds on the slice genus.
Transit Detection of Radial Velocity Planets

The orbital parameters of extra-solar planets have a significant impact on the probability that the planet will transit the host star. This was recently demonstrated by the transit detection of HD 17156b whose favourable eccentricity and argument of periastron dramatically increased its transit likelihood. We present a study which provides a quantitative analysis of how these two orbital parameters effect the geometric transit probability as a function of period. Further, we apply these results to known radial velocity planets and show that there are unexpectedly high transit probabilities for planets at relatively long periods. For a photometric monitoring campaign which aims to determine if the planet indeed transits, we calculate the significance of a null result and the subsequent constraints that may be applied to orbital parameters.
Universal noise and Efimov physics

Probability distributions for correlation functions of particles interacting via random-valued fields are discussed as a novel tool for determining the spectrum of a theory. In particular, this method is used to determine the energies of universal N -body clusters tied to Efimov trimers, for even N , by investigating the distribution of a correlation function of two particles at unitarity. Using numerical evidence that this distribution is log-normal, an analytical prediction for the N -dependence of the N -body binding energies is made.
Automated and Robust Quantification of Colocalization in Dual-Color Fluorescence Microscopy: A Nonparametric Statistical Approach

Colocalization is a powerful tool to study the interactions between fluorescently labeled molecules in biological fluorescence microscopy. However, existing techniques for colocalization analysis have not undergone continued development especially in regards to robust statistical support. In this paper, we examine two of the most popular quantification techniques for colocalization and argue that they could be improved upon using ideas from nonparametric statistics and scan statistics. In particular, we propose a new colocalization metric that is robust, easily implementable, and optimal in a rigorous statistical testing framework. Application to several benchmark data sets, as well as biological examples, further demonstrates the usefulness of the proposed technique.
Chatter Classification in Turning Using Machine Learning and Topological Data Analysis

Abstract Chatter identification and detection in machining processes has been an active area of research in the past two decades. Part of the challenge in studying chatter is that machining equations that describe its occurrence are often nonlinear delay differential equations. The majority of the available tools for chatter identification rely on defining a metric that captures the characteristics of chatter, and a threshold that signals its occurrence. The difficulty in choosing these parameters can be somewhat alleviated by utilizing machine learning techniques. However, even with a successful classification algorithm, the transferability of typical machine learning methods from one data set to another remains very limited. In this paper we combine supervised machine learning with Topological Data Analysis (TDA) to obtain a descriptor of the process which can detect chatter. The features we use are derived from the persistence diagram of an attractor reconstructed from the time series via Takens embedding. We test the approach using deterministic and stochastic turning models, where the stochasticity is introduced via the cutting coefficient term. Our results show a 97% successful classification rate on the deterministic model labeled by the stability diagram obtained using the spectral element method. The features gleaned from the deterministic model are then utilized for characterization of chatter in a stochastic turning model where there are very limited analysis methods.
Observational viability and stability of nonlocal cosmology

We show that the nonlocal gravity models, proposed to explain current cosmic acceleration without dark energy, pass two essential tests: first, they can be defined so as not to alter the, observationally correct, general relativity predictions for gravitationally bound systems. Second, they are stable, ghost-free, with no additional excitations beyond those of general relativity. In this they differ from their, ghostful, localized versions. The systems' initial value constraints are the same as in general relativity, and our nonlocal modifications never convert the original gravitons into ghosts.
Functional partitioning of a liquid-like organelle during assembly of axonemal dyneins

Ciliary motility is driven by axonemal dyneins that are assembled in the cytoplasm before deployment to cilia. Motile ciliopathy can result from defects in the dyneins themselves or from defects in factors required for their cytoplasmic pre-assembly. Recent work demonstrates that axonemal dyneins, their specific assembly factors, and broadly-acting chaperones are concentrated in liquid-like organelles in the cytoplasm called DynAPs (Dynein Axonemal Particles). Here, we use in vivo imaging in Xenopus to show that inner dynein arm (IDA) and outer dynein arm (ODA) subunits are partitioned into non-overlapping sub-regions within DynAPs. Using affinity- purification mass-spectrometry of in vivo interaction partners, we also identify novel partners for inner and outer dynein arms. Among these, we identify C16orf71/Daap1 as a novel axonemal dynein regulator. Daap1 interacts with ODA subunits, localizes specifically to the cytoplasm, is enriched in DynAPs, and is required for the deployment of ODAs to axonemes. Our work reveals a new complexity in the structure and function of a cell-type specific liquid-like organelle that is directly relevant to human genetic disease.
JWST Transit Spectra. I. Exploring Potential Biases and Opportunities in Retrievals of Tidally Locked Hot Jupiters with Clouds and Hazes

Many of the exoplanets for which we can obtain the highest SNR transit spectra are tidally locked. The atmospheres on tidally-locked planets likely exhibit large differences between the day and night side of the planet, the poles, and the morning versus evening terminators. In this paper, we illustrate how the combined effects of aerosols and day-night temperature gradients shape transit spectra of tidally-locked exoplanets when full 3D structures are taken into account and evaluate the implications for retrievals of atmospheric properties. To do this, we have developed a new code, METIS, which can compute transit spectra for an arbitrary longitude-latitude-altitude grid of temperatures and pressures. Using METIS, we pair flexible treatments of clouds and hazes with simple parameterized day-night temperature gradients to compute transit spectra and perform retrieval experiments across a wide array of possible exoplanet atmospheric properties. Our key findings are that: (1) the presence of aerosols typically increases the effects of day-night temperature gradients on transit spectra; (2) ignoring day-night temperature gradients when attempting to perform Bayesian parameter estimation will still return biased results when aerosols are present, as has already been shown for clear atmospheres in the literature; (3) when a day-night temperature gradient is present and accounted for in the retrieval model, some transit spectra can provide sufficient information to constrain temperatures and the width of the transition from day to night. The presence of clouds and hazes can actually tighten such constraints, but also weaken constraints on metallicity. This paper represents a step towards the larger goal of developing models and theory of adequate complexity to match the superior quality data that will soon be available.
On Disentangling Spoof Trace for Generic Face Anti-spoofing.

Prior studies show that the key to face anti-spoofing lies in the subtle image pattern, termed “spoof trace", e.g., color distortion, 3D mask edge, Moire pattern, and many others. Designing a generic anti-spoofing model to estimate those spoof traces can improve both generalization and interpretability. Yet, this is a challenging task due to the diversity of spoof types and the lack of ground truth. This work designs a novel adversarial learning framework to disentangle the spoof traces from input faces as a hierarchical combination of patterns. With the disentangled spoof traces, we unveil the live counterpart from spoof face, and synthesize realistic new spoof faces after a proper geometric correction. Our method demonstrates superior spoof detection performance on both seen and unseen spoof scenarios while providing visually-convincing estimation of spoof traces. Code is available at https://github.com/yaojieliu/ECCV20-STDN.
Damping of Inter-Area Oscillations via Modulation of Aggregated Loads

Low frequency electromechanical oscillations can pose a threat to the stability of power systems if not properly addressed. This paper proposes a novel methodology to damp these inter-area oscillations using loads, the demand side of the system. In the proposed methodology, loads are assigned to an aggregated cluster whose demand is modulated for oscillation damping. The load cluster control action is obtained from an optimal output feedback control (OOFC) strategy. The paper presents an extension to the regular OOFC formulation by imposing a constraint on the sum of the rows in the optimal gain matrix. This constraint is useful when the feedback signals are generator speeds. In this case, the sum of the rows of the optimal gain matrix is the droop gain of each load actuator. Time-domain simulations of a large-scale power system are used to demonstrate the efficacy of the proposed control algorithm. Two different cases are considered: a power imbalance and a line fault. The simulation results show that the proposed controllers successfully damp inter-area oscillations under different operating conditions and with different clustering for the events considered. In addition, the simulations illustrate the benefit of the proposed extension to the OOFC that enable load to provide a combination of droop control and small signal stability augmentation.
Decentralized Electric Vehicle Charging Control via a Novel Shrunken Primal-Multi-Dual Subgradient (SPMDS) Algorithm

The charging processes of a large number of electric vehicles (EVs) require coordination and control for the alleviation of their impacts on the distribution network and for the provision of various grid services. However, the scalability of existing EV charging control paradigms are limited by either the EV population size or the distribution network dimension, largely impairing EVs’ aggregate service capability and applicability. To overcome the scalability barrier, this paper, motivated by the optimal EV charging scheduling problem for the valley-filling service, (1) proposes a novel dimension reduction methodology by grouping EVs (primal decision variables) and establishing voltage (global coupled constraints) updating subsets for each EV group in the distribution network and (2) develops a novel decentralized shrunken primal-multidual subgradient (SPMDS) optimization algorithm to solve this reduced-dimension problem. The efficiency and efficacy of the proposed algorithm are demonstrated through simulations over a modified IEEE 123-bus test feeder.
AutoCone: An OmniDirectional Robot for Lane-Level Cone Placement

This paper summarizes the progress in developing a rugged, low-cost, automated ground cone robot network capable of traffic delineation at lane-level precision. A holonomic omnidirectional base with a traffic delineator was developed to allow flexibility in initialization. RTK GPS was utilized to reduce minimum position error to 2 centimeters. Due to recent developments, the cost of the platform is now less than $1,600. To minimize the effects of GPS-denied environments, wheel encoders and an Extended Kalman Filter were implemented to maintain lane-level accuracy during operation and a maximum error of 1.97 meters through 50 meters with little to no GPS signal. Future work includes increasing the operational speed of the platforms, incorporating lanelet information for path planning, and cross-platform estimation.
Material selection method for a perovskite solar cell design based on the genetic algorithm

In this work, we propose a method of selecting the most desirable combinations of material for a perovskite solar cell design utilizing the genetic algorithm. Solar cells based on the methylammonium lead halide, CH 3 NH 3 PbX 3 , attract researchers due to the benefits of their high absorption coefficient and sharp Urbach tail, long diffusion length and carrier lifetime, and high carrier mobility. However, their poor stability under exposure to moisture still poses a challenge. In our work, we assigned stability index, power conversion efficiency index, and cost-effectiveness index for each material based on the available experimental data in the literature, and our algorithm determined the TiO 2 /CH 3 NH 3 PbI 2.1 Br 0.9 /Spiro-OMeTAD as the most well-balanced solution in terms of cost, efficiency, and stability. The proposed method can be extended further to aid the material selection in all-perovskite multijunction solar cell design as more data on perovskite materials become available in the future.
CopulaGNN: Towards Integrating Representational and Correlational Roles of Graphs in Graph Neural Networks

Graph-structured data are ubiquitous. However, graphs encode diverse types of information and thus play different roles in data representation. In this paper, we distinguish the \textit{representational} and the \textit{correlational} roles played by the graphs in node-level prediction tasks, and we investigate how Graph Neural Network (GNN) models can effectively leverage both types of information. Conceptually, the representational information provides guidance for the model to construct better node features; while the correlational information indicates the correlation between node outcomes conditional on node features. Through a simulation study, we find that many popular GNN models are incapable of effectively utilizing the correlational information. By leveraging the idea of the copula, a principled way to describe the dependence among multivariate random variables, we offer a general solution. The proposed Copula Graph Neural Network (CopulaGNN) can take a wide range of GNN models as base models and utilize both representational and correlational information stored in the graphs. Experimental results on two types of regression tasks verify the effectiveness of the proposed method.
Spatial receptive field structure of double-opponent cells in macaque V1.

Double-opponent cells in macaque area V1 respond to spatial chromatic contrast in visual scenes. What information they carry is debated because their receptive field organization has not been chara...
Representation Learning with Statistical Independence to Mitigate Bias

Presence of bias (in datasets or tasks) is inarguably one of the most critical challenges in machine learning applications that has alluded to pivotal debates in recent years. Such challenges range from spurious associations between variables in medical studies to the bias of race in gender or face recognition systems. Controlling for all types of biases in the dataset curation stage is cumbersome and sometimes impossible. The alternative is to use the available data and build models incorporating fair representation learning. In this paper, we propose such a model based on adversarial training with two competing objectives to learn features that have (1) maximum discriminative power with respect to the task and (2) minimal statistical mean dependence with the protected (bias) variable(s). Our approach does so by incorporating a new adversarial loss function that encourages a vanished correlation between the bias and the learned features. We apply our method to synthetic data, medical images (containing task bias), and a dataset for gender classification (containing dataset bias). Our results show that the learned features by our method not only result in superior prediction performance but also are unbiased. The code is available at https://github.com/QingyuZhao/BR-Net/.
The Business(es) of the Chinese State

The state industrial sector is the Achilles heel of China's otherwise remarkable economic performance over the past two decades. Most other countries in transition from socialism have transformed SOEs into commercial entities through systematic, market-driven restructuring and privatisation to become more efficient and competitive. In China, a series of innovative, if often administrative, insitutional reforms since 1978 have begun to achieve the Chinese authorities' goal of ‘separating governemtn from business.’ But the Chinese State still maintains ownership of key enterprises, and government agencies carry out shareholder functions typically performed by private owners in a market economy. Although privatisation and restructuring of SOEs is occurring, it mostly pertains to small and medium sized firms. For the principal businesses, by contrast, the creation of large state enterprise groups and holding companies (and experiments in other forms of ‘state asset management’) have become the main form of restructuring. Today, China's SOEs still account for more than one-quarter of national production, two-thirds of total assets, more than half of urban employment and almost three-quarters of investment. While direct budgetary subsidies have declined, explicit and implicit subsidies are still making their way to prop up loss-making SOEs through the financial system and other routes. At the same time, SOEs are still producing non-marketable products, resulting in a sizeable inventory overhang. These inefficiencies and distortions represent a drain on the country's resources and thus present a challenge to the Chinese leadership for reform. This paper sheds light on these challenges by analysing the incentives and constraints on China's SOE reform programme. Four critical aspects of the reforms are highlighted and evaluated against the backdrop of international experience: clarification of property rights; establishment of large group/holding companies and other new organisational structures; improved corporate governance incentives; and implementation of international financial accounting and auditing practices. The paper concludes with policy recommendations.
Old Rule, New Theory: Revising the Personal Benefit Requirement for Tipper/Tippee Liability Under the Misappropriation Theory of Insider Trading

Under the classical theory of insider trading, tipper/tippee liability may arise only when the tipper makes the relevant disclosure to obtain a personal benefit. Courts are divided, however, as to whether this personal benefit requirement applies to the misappropriation theory of insider trading. This Note argues that because the personal benefit requirement is severely flawed, courts should not impose it in misappropriation cases. Instead, courts adjudicating misappropriation cases should require that (1) the tipper was at least reckless as to whether he or she would either benefit personally or harm the information source by tipping, and (2) the tipper was at least reckless as to whether someone in the line of tippees would use the information to trade. This standard should be subject only to the tipper's defense that the disclosure was made in a good faith attempt to prevent criminal activity reasonably certain to cause substantial physical or financial harm to others.
Innovative Clusters and the Industry Life Cycle

The purpose of this paper is to link the propensity for innovative activity to spatially cluster to the stage of the industry life cycle. The theory of knowledge spillovers, based on the knowledge production function for innovative activity, suggests that geographic proximity matters the most where tacit knowledge plays an important role in the generation of innovative activity. According to the emerging literature of the industry life cycle, tacit knowledge plays the most important role during the early stages of the industry life cycle. Based on a data base that identifies innovative activity for individual states and specific industries for the United States, the empirical evidence suggests that the propensity for innovative activity is shaped by the stage of the industry life cycle. While the generation of new economic knowledge tends to result in a greater propensity for innovative activity to cluster during the early stages of the industry life cycle, innovative activity tends to be more highly dispersed during the mature and declining stages of the life cycle, particularly after controlling for the extent to which the location of production is geographically concentrated. This may suggest that the positive agglomeration effects during the early stages of the industry life cycle become replaced by congestion effects during the latter stages of the industry life cycle.
Dynamic Patterns of Human Rights Practices

The science of human rights requires valid comparisons of repression levels across time and space. Though extensive data collection efforts have made such comparisons possible in principle, statistical measures based on simple additive scales made them rare in practice. This article uses a dynamic measurement model that contrasts with current approaches by (1) accounting for the fact that human rights indicators vary in the level of information they provide about the latent level of repression, (2) allowing realistic descriptions of measurement uncertainty in the form of credible intervals and (3) providing a theoretical motivation for modeling temporal dependence in human rights levels. It presents several techniques, which demonstrate that the dynamic ordinal item-response theory model outperforms its static counterpart. D id repression decrease in Guatemala after the end of the Cold War? Is Uzbekistan more repressive today than when it first emerged as an internationally recognized state? Was Latin America a more repressive region than the rest of the world in the 1980s? The promise of a science of international human rights relies on the ability to provide transparent and realistic answers to descriptive questions like these. Unfortunately, as we argue in this article, the measures currently used in the quantitative human rights literature are ill suited to the task of comparing levels of repression between countries or over time. In this article, we provide a theoretically motivated measurement model that enhances researchers’ ability to compare repression levels across time and space. This project provides several distinct contributions to political science research. We emphasize the special importance of measurement in human rights research relative to many other areas of political science. Precise scoring of repression levels using available information in different countries is directly useful to international and non-governmental organizations (NGOs). Moreover, considerable scholarly attention focuses on the efficacy of ‘‘naming and shaming’’ campaigns, which are claimed to improve human rights practices. 1 If these claims are true, the availability of messages that can more precisely
Agent-Based Computational Economics: Growing Economies From the Bottom Up

Agent-based computational economics (ACE) is the computational study of economies modeled as evolving systems of autonomous interacting agents. Thus, ACE is a specialization of economics of the basic complex adaptive systems paradigm. This study outlines the main objectives and defining characteristics of the ACE methodology and discusses similarities and distinctions between ACE and artificial life research. Eight ACE research areas are identified, and a number of publications in each area are highlighted for concrete illustration. Open questions and directions for future ACE research are also considered. The study concludes with a discussion of the potential benefits associated with ACE modeling, as well as some potential difficulties.
From Representations to Compositions: Governance Beyond the Three-Sector Society

This article argues that even in its most critical moments public ad ministrative thought has failed to move beyond the basic socio-politi cal coordinates and divisions of modernity, elements of what the author calls the "political ontology of representation." Contempo rary theories of governance, however, raise serious questions about the efficacy of this ontology and so open avenues for rethinking the nature of the field's inquiry and practices. These theories gesture to ward a conception of order that breaks from the tripartite state-mar ket-civil society model. But governance theory is not without its own lingering fidelity to representation that generates some important ten sions and contradictions between its empirico-historical account of the contemporary world and its conceptual apparatus. The article concludes with an outline of how to advance our understanding of governance beyond representation and modernity through a funda mentally relational rather than sectoral conceptualization of gov erning called the "analysis of compositions."
Digital Market Perfection

Google’s, Apple’s, and other companies’ automated assistants are increasingly serving as personal shoppers. These digital intermediaries will save us time by purchasing grocery items, transferring bank accounts, and subscribing to cable. The literature has only begun to hint at the paradigm shift needed to navigate the legal risks and rewards of this coming era of automated commerce. This Article begins to fill that gap first by surveying legal battles related to contract exit, data access, and deception that will determine the extent to which automated assistants are able to help consumers to search and switch, potentially bringing tremendous societal benefits. Whereas observers have largely focused on protecting consumers and sellers from digital intermediaries’ market power, sellers like Amazon, Comcast, and Wells Fargo can also harm consumers by obstructing automated assistants. Advancing consumer welfare in the automated era requires not just consumer protection, but digital intermediary protection. The Article also shows the unpredictable side of the end of switching costs. If digital assistants become pervasive, they could gain the ability to rapidly direct millions of consumers to new purchases whenever a lower price or new innovation becomes available. Significantly accelerated consumer switching — what I call hyperswitching — does not inevitably harm society. But in the extreme it could make some large markets more volatile, raising unemployment costs or financial stability concerns as more firms fail. This new kind of disruption could pose challenges for commercial and banking regulators akin to those familiar to securities regulators, who deploy idiosyncratic tools such as a pause button for the stock market. Even if sellers prevent extreme hyperswitching, managers may strategically prepare for hyperswitching with economically costly behavior such as hoarding liquid assets or forming conglomerates to provide insurance against a sudden exodus of customers. The transaction-cost focused literature has missed more macro-level drawbacks. The regulatory architecture reflects these scholarly gaps. One set of agencies regulates automated assistants for consumer protection and antitrust violations, but does not go beyond those microeconomic inquiries. Nor do they prioritize strengthening digital intermediaries. Regulators with more macroeconomic missions lack jurisdiction over automated assistants. The intellectual framework and regulatory architecture should expand to encompass both the upsides and downsides of digital consumer sovereignty.
Basic goods, the effects of commodity transfers and the international economic order

Abstract A two good, two region and three income group macro model is constructed to explore possible effects of aid on distribution of welfare. One region, the North, has two income groups characterized by different endowments and proportions of consumption of the basic and the luxury goods. We study policies that result in transfers of goods from the high income group of the North to the South. In one case, the transfer is of luxury or investment goods; under the conditions, it is shown to produce a change in relative prices that induces an increase in the welfare of the North and decreases the welfare of the South, even under conditions of (Walrasian) stability of the markets. In a second case, the high income group in the North transfers, instead, basic goods to the South. It is shown that under the conditions an increase in welfare of the South can only occur at the expense of a decrease in welfare of the low income group in the North. Therefore, in general, aid in the form of commodity transfers cannot be relied upon to equalize overall welfare: under the conditions there is necessarily a trade-off between more North-South equality and greater equality within the North. When aid is endorsed to pursue NIEO objectives, a close examination of international and domestic markets seems in order, so as to avoid the conditions studied here. The formation of (international) coalitions among the different groups is also discussed.
Fiscal Policies, Capital Formation and Capitalism

Abstract This lecture examines the effect of tax policy and social security retirement benefits on capital accumulation and economic welfare. The paper begins by examining how capital income taxes reduce the real return to savers and then discusses the welfare loss of capital income taxation relative to the alternatives of taxing consumption and labor income. The analysis shows that capital income taxes impose a very substantial deadweight loss even if they do not alter private saving. The first section of the paper also discusses the theory and empirical literature on the effect of capital income taxes on national saving. The second part of the lecture deals with social security retirement benefits. In 1994, the aged members of the U.S. population will receive cash and medical benefits that cost the government $530 billion or $16,000 per person over age 65. The likely impact of these benefits on private saving and the empirical evidence on this subject are then reviewed. The second part of the paper concludes by discussing the welfare loss of unfunded social security benefits and the possibilities for alternative arrangements. A final section discusses the implications of international capital flows for this analysis. As capital flows become more important, particularly in Europe, the response of government policy may be to compete for foreign capital inflows and to tax domestic savers more heavily. This would lead to a smaller total volume of capital. The sharp decline in the net national saving rate — from more than 8 percent of GDP in the United States in the 1970s to only 4.5 percent in the 1980s and from more than 14 percent of GDP in Europe in the 1970s to 9.9 percent in the 1980s — may not only create lower real incomes and slower growth but may weaken capitalism itself. In the United States, a decade of slow growth has increased protectionist tendencies in international trade and led to a new interest in industrial policies that expand the role of government in guiding the direction of technology and of private investment. In these ways, the government policies that discourage saving might make the Schumpeterian vision of a shift from private capitalism to a government-dominated economy more likely.
Taxation of Interest Income

Why is interest income taxed so much more heavily than other forms of capital income? This differential tax treatment has generated substantial tax arbitrage, resulting in lower tax revenue, efficiency costs, and apparently net gains to rich borrowers and net losses to poor lenders, together suggesting that this tax treatment makes no sense on welfare grounds. In examining this argument more formally, this paper reveals two omitted considerations that can help explain the existing tax treatment. First, the forecasted increase in the market interest rate results in a redistribution from rich borrowers to poor lenders. Yet this redistribution comes at no marginal efficiency cost, starting from a situation with no distortions to portfolio choice, so at the margin dominates further redistribution through the income tax. In addition, information about an individual's portfolio choice reveals information about her earnings ability, even controlling for observed labor income, if those who are more able tend to be less risk averse. By making use of this extra information about earnings ability, the tax system can be better tailored to redistribute from able to less able, for any given efficiency cost.
Audit committee financial expertise and earnings management: The role of status $

Regulatory pressure to increase both audit committee financial expertise and board independence has resulted in lower status for audit committees relative to management. This status differential is relevant because expertise and relative status are important determinants of each party's ability to influence outcomes, particularly when parties face conflicting goals. We find that audit committees with both financial expertise and high relative status are associated with lower levels of earnings management, as measured by accounting irregularities and abnormal accruals. These results speak to benefits and limitations of financial expertise, which have been the focus of considerable debate.
The networking function of investment banks: Evidence from private investments in public equity☆

Abstract We examine investment banks' networking function in capital markets, using a sample of Private Investments in Public Equity (PIPEs). We argue that investment banks develop relationships with investors through repeat dealings, and that investment banks' networks of relationship investors form the basis of their networking function. We find that investment banks, especially those with larger investor networks, help issuers attract investors. Correspondingly, an issuer that desires more investors is more likely to hire an investment bank than place the shares directly. We also find that issuers pay higher fees to hire investment banks with larger investor networks. Our empirical findings suggest that the networking function of investment banks is important in securities offerings.
Repatriation Tax Costs and U.S. Multinational Companies' Shareholder Payouts

ABSTRACT: This paper examines whether and to what extent repatriation tax costs constrain U.S. multinational companies' (MNCs) distributions to shareholders. During the 1987–2004 sample period, I find that repatriation tax costs decrease U.S. MNCs' dividend payments, and the economic magnitude of the effect is substantial. I do not find evidence that repatriation tax costs decrease U.S. MNCs' share repurchases, on average. I find cross-sectional variation in the effect of repatriation tax costs on share repurchases based on U.S. MNCs' opportunities to fund repurchases through external borrowing and to minimize the incremental U.S. cash tax cost of repatriations. I do not observe an association between repatriation tax costs and U.S. MNCs' dividend payments or share repurchases during a more recent time period (2009–2014). This study contributes to our understanding of the impact of the current U.S. worldwide tax system on U.S. MNCs' real decisions and of the determinants of firms' payout policies.
Pluralism and Professionalism: The Question of Authority

Professionalism initiatives tend to presume that the only sources of authority material to a lawyer's cultivation of professional values are grounded in the law and speak to all lawyers. In reality, many lawyers find meaning and purpose in sources of authority that are particular and extralegal. Expanding our conception of authority will equip lawyers to engage questions of professional identity in new, more powerful ways.
Short-termism of executive compensation

Abstract This paper presents an optimal contracting theory of short-term firm behavior. Contracts inducing short-sighted managerial behavior arise as shareholders’ response to conflicting intergenerational managerial incentives. High-return projects may last longer than the tenure of managers who implement them. Consequently, inducing managers to act in the long-term interests of firms requires the alignment of incentives across multiple managers. Such action comes at greater costs than providing incentives for a single manager and leads to contracts that favor short-term behavior. Long-term firm value maximization is further impeded when only the quality of accepted projects–but not those of declined projects–is public. In that case, shareholders find it costly to induce long-term project selection among managers who can earn all information rents from short-term projects but must sacrifice information rents from long-term projects to future managers.