cssi/SciSciGPT-SciSciNet huggingface dataset:
Data-driven, Interpretable Photometric Redshifts Trained on Heterogeneous and Unrepresentative Data

We present a new method for inferring photometric redshifts in deep galaxy and quasar surveys, based on a data-driven model of latent spectral energy distributions (SEDs) and a physical model of photometric fluxes as a function of redshift. This conceptually novel approach combines the advantages of both machine learning methods and template fitting methods by building template SEDs directly from the spectroscopic training data. This is made computationally tractable with Gaussian processes operating in flux–redshift space, encoding the physics of redshifts and the projection of galaxy SEDs onto photometric bandpasses. This method alleviates the need to acquire representative training data or to construct detailed galaxy SED models; it requires only that the photometric bandpasses and calibrations be known or have parameterized unknowns. The training data can consist of a combination of spectroscopic and deep many-band photometric data with reliable redshifts, which do not need to entirely spatially overlap with the target survey of interest or even involve the same photometric bands. We showcase the method on the i-magnitude-selected, spectroscopically confirmed galaxies in the COSMOS field. The model is trained on the deepest bands (from SUBARU and HST) and photometric redshifts are derived using the shallower SDSS optical bands only. We demonstrate that we obtain accurate redshift point estimates and probability distributions despite the training and target sets having very different redshift distributions, noise properties, and even photometric bands. Our model can also be used to predict missing photometric fluxes or to simulate populations of galaxies with realistic fluxes and redshifts, for example.
Generating Randomness from a Computable, Non-random Sequence of Qubits

Nies and Scholz introduced the notion of a state to describe an infinite sequence of qubits and defined quantum-Martin-Lof randomness for states, analogously to the well known concept of Martin-Lof randomness for elements of Cantor space (the space of infinite sequences of bits). We formalize how 'measurement' of a state in a basis induces a probability measure on Cantor space. A state is 'measurement random' (mR) if the measure induced by it, under any computable basis, assigns probability one to the set of Martin-Lof randoms. Equivalently, a state is mR if and only if measuring it in any computable basis yields a Martin-Lof random with probability one. While quantum-Martin-Lof random states are mR, the converse fails: there is a mR state, x which is not quantum-Martin-Lof random. In fact, something stronger is true. While x is computable and can be easily constructed, measuring it in any computable basis yields an arithmetically random sequence with probability one. I.e., classical arithmetic randomness can be generated from a computable, non-quantum random sequence of qubits.
Diffusion Processes Satisfying a Conservation Law Constraint

We investigate coupled stochastic differential equations governing N nonnegative continuous random variables that satisfy a conservation principle. In various fields a conservation law requires a set of fluctuating variables to be nonnegative and (if appropriately normalized) sum to one. As a result, any stochastic differential equation model to be realizable must not produce events outside of the allowed sample space. We develop a set of constraints on the drift and diffusion terms of such stochastic models to ensure that both the nonnegativity and the unit-sum conservation law constraints are satisfied as the variables evolve in time. We investigate the consequences of the developed constraints on the Fokker-Planck equation, the associated system of stochastic differential equations, and the evolution equations of the first four moments of the probability density function. We show that random variables, satisfying a conservation law constraint, represented by stochastic diffusion processes, must have diffusion terms that are coupled and nonlinear. The set of constraints developed enables the development of statistical representations of fluctuating variables satisfying a conservation law. We exemplify the results with the bivariate beta process and the multivariate Wright-Fisher, Dirichlet, and Lochner’s generalized Dirichlet processes.
Analysis of Magnetic Fields in Inertial Alfvén Wave Collisions

Turbulence in astrophysical and space plasmas is dominated by the nonlinear interaction of counter propagating Alfven waves. Most Alfven wave turbulence theories have been based on ideal plasma models for Alfven waves at large scales. However, in the inertial Alfven wave regime, relevant to magnetospheric plasmas, how the turbulent nonlinear interactions are modified by the dispersive nature of the waves remains to be explored. Here, we present the first laboratory evidence of the nonlinear interaction in the inertial regime. A comparison is made with the theory for MHD Alfven waves.
The Bounds on Lorentz and CPT violating parameters in the Higgs sector

In this talk, I discuss possible bounds on the Lorentz and CPT violating parameters in the Higgs sector of the so called minimal Standard-Model Extension (SME). The main motivation to this study is coming from the fact that unlike the parameters in the fermion and gauge sector, there are no published bounds on the parameters in the Higgs sector. From the one-loop contributions to the photon propagator the bounds on the CPT-even asymmetric coefficients are obtained and the cμν coefficients in the fermion sector determine the bound on the CPT-even symmetric coefficients. The CPT-odd coefficient is bounded from the non-zero vacuum expectation value of the Z-boson.
Pygmies, Giants, and Skins

Understanding the equation of state (EOS) of neutron-rich matter is a central goal of nuclear physics that cuts across a variety of disciplines. Indeed, the limits of nuclear existence, the collision of energetic heavy ions, the structure of neutron stars, and the dynamics of core-collapse supernova all depend critically on the nuclear-matter EOS. In this contribution I focus on the EOS of cold baryonic matter with special emphasis on its impact on the structure, dynamics, and composition of neutron stars. In particular, I discuss how laboratory experiments on neutron skins as well as on Pygmy and Giant resonances can help us elucidate the structure of these fascinating objects.
Stabilizing moduli with a positive cosmological constant in heterotic M-theory

It is shown that strongly coupled heterotic M-theory with anti-five-branes in the S1/2 bulk space can have meta-stable vacua which break N = 1 supersymmetry and have a small, positive cosmological constant. This is demonstrated for the ``minimal'' heterotic standard model. This vacuum has the exact MSSM matter spectrum in the observable sector, a trivial hidden sector vector bundle and both five-branes and anti-five-branes in the bulk space. The K?hler moduli for which the cosmological constant has phenomenologically acceptable values are shown to also render the observable sector vector bundle slope-stable. A corollary of this result is that strongly coupled M-theory vacua with only five-branes in the S1/2 interval may have stabilized moduli, but at a supersymmetry preserving minimum with a large, negative cosmological constant.
Renormalization of the lattice heavy quark classical velocity

In the lattice formulation of the Heavy Quark Effective Theory (LHQET), the “classical velocity” v becomes renormalized. The origin of this renormalization is the reduction of Lorentz (or O(4)) invariance to (hyper)cubic invariance. The renormalization is finite and depends on the form of the decretization of the reduced heavy quark Dirac equation. For the Forward Time — Centered Space discretization, the renormalization is computed both perturbatively, to one loop, and non-perturbatively using two ensembles of lattices, one at β = 5.7 and the other at β = 6.1 The estimates agree, and indicate that for small classical velocities, ν → is reduced by about 25–30%.
Towards a deeper geometric, analytic and algorithmic understanding of margins

Given a matrix A, a linear feasibility problem of which linear classification is a special case aims to find a solution to a primal problem or a certificate for the dual problem which is a probability distribution . Inspired by the continued importance of ‘large-margin classifiers’ in machine learning, this paper studies a condition measure of A called its margin that determines the difficulty of both the above problems. To aid geometrical intuition, we first establish new characterizations of the margin in terms of relevant balls, cones and hulls. Our second contribution is analytical, where we present generalizations of Gordan's theorem, and variants of Hoffman's theorems, both using margins. We end by proving some new results on a classical iterative scheme, the Perceptron, whose convergence rates famously depends on the margin. Our results are relevant for a deeper understanding of margin-based learning and proving convergence rates of iterative schemes, apart from providing a unifying perspective on this vast topic.
A note contrasting two microscopic theories of the fractional quantum Hall effect

Two microscopic theories have been proposed for the explanation of the fractional quantum Hall effect, namely the Haldane–Halperin hierarchy theory and the composite fermion theory. Contradictory statements have been made regarding the relation between them, ranging from their being distinct to their being completely equivalent. This article attempts to provide a clarification of the issue. It is shown that the two theories postulate distinct microscopic mechanisms for the origin of the fractional quantum Hall effect, and make substantially different predictions that have allowed experiments to distinguish between them.
Einstein-Heisenberg Consistency Condition Interplay with Cosmological Constant Prediction in Resummed Quantum Gravity

We argue that our recent success in using our resummed quantum gravity (RQG) approach to Einstein’s general theory of relativity, in the context of the Planck scale cosmology formulation of Bonanno and Reuter, to estimate the value of the cosmological constant Λ supports the use of quantum mechanical consistency requirements to constrain the main uncertainty in that very promising result. This main uncertainty, which is due to the uncertainty in the value of the time ttr at which the transition from the Planck scale cosmology to the FRW model occurs, is shown to be reduced, by requiring consistency between the Heisenberg uncertainty principle and the known properties of the solutions of Einstein’s equations, from four orders of magnitude to the level of a factor of &#x1d4aa;(10). This lends more credibility to the overall RQG approach itself, in general, and to our estimate of Λ in particular.
Families of orthogonal Laurent polynomials, hyperelliptic lie algebras and elliptic integrals

ABSTRACTWe describe a family of polynomials discovered via a particular recursion relation, which have connections to Chebyshev polynomials of the first and the second kind, and the polynomial version of Pell's equation. Many of their properties are listed in Section 3. We show that these families of polynomials in the variable t satisfy certain second-order linear differential equations that may be of interest to mathematicians in conformal field theory and number theory. We also prove that these families of polynomials in the setting of Date–Jimbo–Kashiwara–Miwa algebras when multiplied by a suitable power of t are orthogonal with respect to explicitly described kernels. Particular cases lead to new identities of elliptic integrals (see Section 5).
Galilean Supersymmetry in 2+1 Dimensions

We review work done in collaboration with C. B. Thorn1 on Super-Galilei invariant field theory and its application to the reformulation of superstring theory in terms of constituent superstring-bits.
Wave-Function Engineering for Spectrally Uncorrelated Biphotons in the Telecommunication Band Based on a Machine-Learning Framework

Generating indistinguishable single photons is often a key step in photonic quantum information processing. To this end, the design of a nonlinear crystal's poling profile involves many parameters, while performance is also restricted by the pump spectrum, and traditional optimization algorithms cannot efficiently keep up. However, with the benefit of an elegant machine-learning framework, the authors can simultaneously optimize poling profile and pump spectrum to produce high-purity single photons over the whole telecommunication band. Periodic peaks in the machine-learning poling profile cleverly compensate for group-velocity mismatch and other nonidealities.
A priori estimates for the free-boundary Euler equations with surface tension in three dimensions

We derive a priori estimates for the incompressible free-boundary Euler equations with surface tension in three spatial dimensions. Working in Lagrangian coordinates, we provide a priori estimates for the local existence when the initial velocity, which is rotational, belongs to H 3, with some additional regularity on the normal component of the initial velocity. This lowers the requirement on the regularity of initial data in the Lagrangian setting. Our methods are direct and involve three key elements: estimates for the pressure, the boundary regularity provided by the mean curvature, and the Cauchy invariance.
Mutational Pressure Drives Differential Genome Conservation in Two Bacterial Endosymbionts of Sap-Feeding Insects.

Compared with free-living bacteria, endosymbionts of sap-feeding insects have tiny and rapidly evolving genomes. Increased genetic drift, high mutation rates, and relaxed selection associated with host control of key cellular functions all likely contribute to genome decay. Phylogenetic comparisons have revealed massive variation in endosymbiont evolutionary rate, but such methods make it difficult to partition the effects of mutation versus selection. For example, the ancestor of Auchenorrhynchan insects contained two obligate endosymbionts, Sulcia and a betaproteobacterium (BetaSymb; called Nasuia in leafhoppers) that exhibit divergent rates of sequence evolution and different propensities for loss and replacement in the ensuing ∼300 Ma. Here, we use the auchenorrhynchan leafhopper Macrosteles sp. nr. severini, which retains both of the ancestral endosymbionts, to test the hypothesis that differences in evolutionary rate are driven by differential mutagenesis. We used a high-fidelity technique known as duplex sequencing to measure and compare low-frequency variants in each endosymbiont. Our direct detection of de novode novo mutations reveals that the rapidly evolving endosymbiont (Nasuia) has a much higher frequency of single-nucleotide variants than the more stable endosymbiont (Sulcia) and a mutation spectrum that is potentially even more AT-biased than implied by the 83.1% AT content of its genome. We show that indels are common in both endosymbionts but differ substantially in length and distribution around repetitive regions. Our results suggest that differences in long-term rates of sequence evolution in Sulcia versus BetaSymb, and perhaps the contrasting degrees of stability of their relationships with the host, are driven by differences in mutagenesis.
Second-Order NLP Adversarial Examples.

Adversarial example generation methods in NLP rely on models like language models or sentence encoders to determine if potential adversarial examples are valid. In these methods, a valid adversarial example fools the model being attacked, and is determined to be semantically or syntactically valid by a second model. Research to date has counted all such examples as errors by the attacked model. We contend that these adversarial examples may not be flaws in the attacked model, but flaws in the model that determines validity. We term such invalid inputs second-order adversarial examples. We propose the constraint robustness curve and associated metric ACCS as tools for evaluating the robustness of a constraint to second-order adversarial examples. To generate this curve, we design an adversarial attack to run directly on the semantic similarity models. We test on two constraints, the Universal Sentence Encoder (USE) and BERTScore. Our findings indicate that such second-order examples exist, but are typically less common than first-order adversarial examples in state-of-the-art models. They also indicate that USE is effective as constraint on NLP adversarial examples, while BERTScore is nearly ineffectual. Code for running the experiments in this paper is available at this https URL.
Broadband Field Enhancement and Giant Nonlinear Effects in Terminated Unidirectional Plasmonic Waveguides

Unidirectional wave propagation in nonreciprocal structures enables exciting opportunities to control and enhance wave-matter interactions in extreme ways. Within this context, here we investigate the possibility of using terminated unidirectional plasmonic waveguides to enhance typically weak nonlinear effects by orders of magnitude. We theoretically demonstrate remarkable levels of electric field enhancement and confinement (field hot spots) when the unidirectional waveguiding structure is terminated with a suitable boundary that fully stops the one-way mode. Such a large field enhancement, originating from a nonresonant effect, is fundamentally different from the narrow-band field concentration effects in resonant plasmonic structures. Instead, it is analogous to the broadband response of plasmonic tapers, but without the need for any adiabatic impedance matching. We show that this effect can indeed lead to a substantial boosting of nonlinear light-matter interactions, exemplified by an improvement of several orders of magnitude in the third-harmonic-generation efficiency, which is of large significance for several applications. More broadly, our findings show the potential of extreme nonreciprocal configurations for enhanced wave-matter interactions.
Next waves in veridical network embedding

Embedding nodes of a large network into a metric (e.g., Euclidean) space has become an area of active research in statistical machine learning, which has found applications in natural and social sciences. Generally, a representation of a network object is learned in a Euclidean geometry and is then used for subsequent tasks regarding the nodes and/or edges of the network, such as community detection, node classification and link prediction. Network embedding algorithms have been proposed in multiple disciplines, often with domain-specific notations and details. In addition, different measures and tools have been adopted to evaluate and compare the methods proposed under different settings, often dependent of the downstream tasks. As a result, it is challenging to study these algorithms in the literature systematically. Motivated by the recently proposed Veridical Data Science (VDS) framework, we propose a framework for network embedding algorithms and discuss how the principles of predictability, computability and stability apply in this context. The utilization of this framework in network embedding holds the potential to motivate and point to new directions for future research.
Studying pulsar emission regions using interstellar scattering

Radio-wave scattering in the interstellar plasma provides the means to circumvent the diffraction limit for earth-based instruments, and to image the emission regions of pulsars. For the past 25 years, observers have sought to exploit this fact to learn how pulsars shine. I review the techniques developed, and summarize measurements of size of emission regions of pulsars to date.
Dark Matter, Dark Energy and Fundamental Acceleration

We discuss the existence of an acceleration scale in galaxies and galaxy clusters and its relevance for the nature of dark matter. The presence of the same acceleration scale found at very differen...
Causal inference in high dimensions: A marriage between Bayesian modeling and good frequentist properties.

We introduce a framework for estimating causal effects of binary and continuous treatments in high dimensions. We show how posterior distributions of treatment and outcome models can be used together with doubly robust estimators. We propose an approach to uncertainty quantification for the doubly robust estimator which utilizes posterior distributions of model parameters and (1) results in good frequentist properties in small samples, (2) is based on a single MCMC, and (3) improves over frequentist measures of uncertainty which rely on asymptotic properties. We consider a flexible framework for modeling the treatment and outcome processes within the Bayesian paradigm that reduces model dependence, accommodates nonlinearity, and achieves dimension reduction of the covariate space. We illustrate the ability of the proposed approach to flexibly estimate causal effects in high dimensions and appropriately quantify uncertainty. We show that our proposed variance estimation strategy is consistent when both models are correctly specified, and we see empirically that it performs well in finite samples and under model misspecification. Finally, we estimate the effect of continuous environmental exposures on cholesterol and triglyceride levels. This article is protected by copyright. All rights reserved.
A DSO Framework for Comprehensive Market Participation of DER Aggregators

In this paper, a distribution system operator (DSO)framework is proposed to optimally coordinate distributed energy resources (DER) aggregators’ comprehensive participation in retail energy market as well as wholesale energy and regulation markets. Various types of DER aggregators, including energy storage aggregators (ESAGs), dispatchable distributed generation aggregators (DDGAGs), electric vehicles charging stations (EVCSs), and demand response aggregators (DRAGs), are modeled in the proposed DSO framework. Distribution network constraints are considered by using a linearized power flow. The problem is modeled using mixed-integer linear programming (MILP) which can be solved by commercial solvers. Case studies are performed to analyze the interactions between DER aggregators and wholesale/retail electricity markets.
A Syllabi Analysis of Social Media for Teaching and Learning Courses

The purpose of this syllabi analysis was to explore the structure of courses focused on social media and geared towards education professionals. Fourteen course syllabi from institutions located within the United States (U.S) were analyzed as part of this investigation. The results of the analysis revealed a total of 46 unique topic themes across the different courses. The findings show that the most common course objectives aimed to encourage application and practice of social media as part of a learning experience and the learners’ professional practice. In total, the syllabi listed 67 unique required readings including: non-peer reviewed publications, peer-reviewed journal articles, and textbooks. Last, the analysis of the assignments listed in the syllabi show that in these social media courses, there were a mix of traditional and non-traditional assessment methods. These non-traditional assessment methods focused on integrating social media as part of the assessment in which learners were required to create a social media account, become familiar with it, and create learning experiences incorporating a specific social media platform.
YouTube Videos Demonstrating the Nasopharyngeal Swab Technique for SARS-CoV-2 Specimen Collection: Content Analysis.

Background: Real-time polymerase chain reaction using nasopharyngeal swabs is currently the most widely used diagnostic test for SARS-CoV-2 detection. However, false negatives and the sensitivity of this mode of testing have posed challenges in the accurate estimation of the prevalence of SARS-CoV-2 infection rates. Objective: The purpose of this study was to evaluate whether technical and, therefore, correctable errors were being made with regard to nasopharyngeal swab procedures. Methods: We searched a web-based video database (YouTube) for videos demonstrating SARS-CoV-2 nasopharyngeal swab tests, posted from January 1 to May 15, 2020. Videos were rated by 3 blinded rhinologists for accuracy of swab angle and depth. The overall score for swab angle and swab depth for each nasopharyngeal swab demonstration video was determined based on the majority score with agreement between at least 2 of the 3 reviewers. We then comparatively evaluated video data collected from YouTube videos demonstrating the correct nasopharyngeal swab technique with data from videos demonstrating an incorrect nasopharyngeal swab technique. Multiple linear regression analysis with statistical significance set at P=.05 was performed to determine video data variables associated with the correct nasopharyngeal swab technique. Results: In all, 126 videos met the study inclusion and exclusion criteria. Of these, 52.3% (66/126) of all videos demonstrated the correct swab angle, and 46% (58/126) of the videos demonstrated an appropriate swab depth. Moreover, 45.2% (57/126) of the videos demonstrated both correct nasopharyngeal swab angle and appropriate depth, whereas 46.8% (59/126) of the videos demonstrated both incorrect nasopharyngeal swab angle and inappropriate depth. Videos with correct nasopharyngeal swab technique were associated with the swab operators identifying themselves as a medical professional or as an Ear, Nose, Throat–related medical professional. We also found an association between correct nasopharyngeal swab techniques and recency of video publication date (relative to May 15, 2020). Conclusions: Our findings show that over half of the videos documenting the nasopharyngeal swab test showed an incorrect technique, which could elevate false-negative test rates. Therefore, greater attention needs to be provided toward educating frontline health care workers who routinely perform nasopharyngeal swab procedures.
Quantitative stability in the geometry of semi-discrete optimal transport

We show quantitative stability results for the geometric "cells" arising in semi-discrete optimal transport problems. Our results show two types of stability, the first is stability of the associated Laguerre cells in measure, without any connectedness or regularity assumptions on the source measure. The second is stability in Hausdorff measure, under a Poincar{e}-Wirtinger inequality and a regularity assumption equivalent to the Ma-Trudinger-Wang conditions of regularity in Monge-Amp{e}re. This last result also yields stability in the uniform norm of the dual potential functions, all three stability results come with explicit quantitative bounds. Our methods utilize a combination of graph theory, convex geometry, and Monge-Amp{e}re regularity theory.
Event-Driven Receding Horizon Control For Distributed Persistent Monitoring on Graphs

We consider the optimal multi-agent persistent monitoring problem defined on a set of nodes (targets) inter-connected through a fixed graph topology. The objective is to minimize a measure of mean overall node state uncertainty evaluated over a finite time interval by controlling the motion of a team of agents. Prior work has addressed this problem through on-line parametric controllers and gradient-based methods, often leading to low-performing local optima or through off-line computationally intensive centralized approaches. This paper proposes a computationally efficient event-driven receding horizon control approach providing a distributed on-line gradient-free solution to the persistent monitoring problem. A novel element in the controller, which also makes it parameter-free, is that it self-optimizes the planning horizon over which control actions are sequentially taken in event-driven fashion. Numerical results show significant improvements compared to state of the art distributed on-line parametric control solutions.
Intermediate energy proton irradiation: Rapid, high-fidelity materials testing for fusion and fission energy systems

Abstract Fusion and advanced fission power plants require advanced nuclear materials to function under new, extreme environments. Understanding the evolution of mechanical and functional properties during radiation damage is essential to the design and commercial deployment of these systems. The shortcomings of existing methods could be addressed by a new technique - intermediate energy proton irradiation (IEPI) - using beams of 10–30 MeV protons to rapidly and uniformly damage bulk material specimens before direct testing of engineering properties. IEPI is shown to achieve high fidelity to fusion and fission environments in both primary damage production and transmutation, often superior to nuclear reactor or typical (low-range) ion irradiation. Modeling demonstrates that high dose rates (0.1–1 DPA/per day) can be achieved in bulk material specimens (100–300 μm) with low temperature gradients and induced radioactivity. The capabilities of IEPI are demonstrated through a 12 MeV proton irradiation and tensile test of 250 μm thick tensile specimens of a nickel alloy (Alloy 718), reproducing neutron-induced data. These results demonstrate that IEPI enables high throughput assessment of materials under reactor-relevant conditions, positioning IEPI to accelerate the pace of engineering-scale radiation damage testing and allow for quicker and more effective design of nuclear energy systems.
Open Source Iris Recognition Hardware and Software with Presentation Attack Detection

This paper proposes the first known to us open source hardware and software iris recognition system with presentation attack detection (PAD), which can be easily assembled for about 75 USD using Raspberry Pi board and a few peripherals. The primary goal of this work is to offer a low-cost baseline for spoof-resistant iris recognition, which may (a) stimulate research in iris PAD and allow for easy prototyping of secure iris recognition systems, (b) offer a low-cost secure iris recognition alternative to more sophisticated systems, and (c) serve as an educational platform. We propose a lightweight image complexity-guided convolutional network for fast and accurate iris segmentation, domain-specific human-inspired Binarized Statistical Image Features (BSIF) to build an iris template, and to combine 2D (iris texture) and 3D (photometric stereo-based) features for PAD. The proposed iris recognition runs in about 3.2 seconds and the proposed PAD runs in about 4.5 seconds on Raspberry Pi 3B+. The hardware specifications and all source codes of the entire pipeline are made available along with this paper.
The "Unsettled Paradox": The Internet, The State, and the Consent of the Governed

Settlement of the New World called for a fundamental re-examination of the nature of sovereignty and statehood. Reconciling the paradoxical notion that the state is somehow both an agent of the people, and a sovereign possessing final and unappealable power, was a central pre-occupation of political thought of the 17th and 18th centuries. This paradox was resolved by the radical English Whig theorists (and their equally radical republican counterparts in the Americas) by the development of a new theory of sovereignty, one that relocated the locus of truly sovereign power away from the legislative assembly C away, indeed, from all governmental institutions C to the people. The state's power, in this view, is entirely derivative -- the very opposite of 'sovereign' -- flowing ultimately from the 'consent of the governed' who, possessing sovereign but delegable power, constitute the state as their agent for the purpose of engaging in collective action conducive to their pursuit of happiness. This normative view of states as constituted agents whose power derives from the people's collective will and their ultimately indivisible sovereignty -- a view that has come to be labeled the 'Liberal' theory of statehood --is an a-territorial one insofar as it discards notions of physical territoriality and physical power as the bases for the exercise of sovereign power. And, as such, it appears well suited to an increasingly a-territorial, networked world. The settlement of the new domain of cyberspace may enable us to take more seriously than ever before the possibility that individuals in the ordinary course of their affairs can create governmental entities that lack territorial status, a-territorial consensual associations with no geographical referents whatsoever onto which a portion of their 'sovereignty' devolves. This paper explores some of the implications this may have for evolving normative theories of statehood and for related questions of the extraterritorial assertion of jurisdiction by existing territorial states.
Legal Reform: China's Law-Stability Paradox

In the 1980s and 1990s, China devoted extensive resources to constructing a legal system, in part in the belief that legal institutions would enhance both stability and regime legitimacy. Why, then, did China's leadership retreat from using law when faced with perceived increases in protests, citizen complaints, and social discontent in the 2000s? This law-stability paradox suggests that party-state leaders do not trust legal institutions to play primary roles in addressing many of the most complex issues resulting from China's rapid social transformation. This signifies a retreat not only from legal reform, but also from the rule-based model of authoritarian governance that has contributed much to the resilience of the Chinese system. The law-stability paradox also highlights the difficulties facing efforts by China's new leadership to reinvigorate legal reform.
Wages, Violence and Health in the Household

Three quarters of all violence against women is perpetrated by domestic partners. I study both the economic causes and consequences of domestic violence. I find that decreases in the male-female wage gap reduce violence against women, consistent with a household bargaining model. The relationship between the wage gap and violence suggests that reductions in violence may provide an alternative explanation for the well-established finding that child health improves when mothers control a greater share of the household resources. Using instrumental variable and propsensity score techniques to control for selection into violent relationships, I find that violence against pregnant women negatively affects the health of their children at birth. This work sheds new light on the health production process as well as observed income gradients in health and suggests that in addition to addressing concerns of equity, pay parity can also improve the health of American women and children via reductions in violence.
Treaties as Law of the Land: The Supremacy Clause and the Judicial Enforcement of Treaties

Courts in recent years have perceived threshold obstacles to the enforcement of treaties deriving from their nature as contracts between nations that generally depend for their efficacy on the interest and honor of the parties, rather than on domestic adjudication. This approach to treaty enforcement is in tension with the Constitution's declaration that treaties are part of the law of the land and its instruction to judges to give them effect. The Founders understood that treaties depended on interest and honor on the international plane, but they made treaties enforceable in our courts anyway in order to avoid the international friction that could be expected to result from treaty violations and to capture the benefits of a reputation for treaty compliance. The Supremacy Clause gives treaties a domestic judicial sanction that they would otherwise lack. It makes treaties enforceable in the courts in the same circumstances as the other two categories of norms specified in the clause - federal statutes and the Constitution itself.The sole exception to this rule is for treaties that are non-self-executing in the sense contemplated by the Court in Foster v. Neilson. The concept of a non-self-executing treaty fits uneasily with the Supremacy Clause, as reflected in the common but untenable view that non-self-executing treaties lack the force of domestic law. According to Foster, a non-self-executing treaty is not enforceable in the courts because it is addressed to the political branches. But determining which treaties are so addressed has been challenging. Treaties generally leave the question of domestic implementation to the domestic laws of the states-parties, and our domestic law (the Supremacy Clause) directs judges to give them effect. The author argues that the Supremacy Clause establishes a default rule that treaties are directly enforceable in the courts like other laws, rebuttable only by a clear statement that the obligations imposed by the treaty are subject to legislative implementation.If the stipulation had to appear in the text of the treaty, the clear statement rule would present problems for U.S. treaty makers seeking to control the domestic consequences of multilateral treaties. To address this problem, the treaty makers have developed a new form of clear statement, the "declaration" of non-self-execution. However, scholars have questioned the compatibility of such declarations with the Supremacy Clause. The author concludes that the treaty makers have the power to limit the domestic effects of treaties through declarations of non-self-execution. On the other hand, if the Constitution were understood to establish a default rule of non-self-execution, declarations of self-execution would stand on more tenuous ground. Thus, a default rule of self-execution is not only more consistent with the constitutional text and structure and with Supreme Court precedent, it is also normatively attractive because it leaves the treaty makers with the power to control the domestic consequences of the treaties they conclude.
Probability, Individualization, and Uniqueness in Forensic Science Evidence: Listening to the Academies

These are dark days for the forensic sciences.Newspaper and magazine articles, op-ed headlines, televisionnews, and radio talk shows refer to the field as "seriouslydeficient,"' a "dismal science," "in disarray,"' even "clueless."'The stimulus for this negative publicity blitz is a report of acongressionally-mandated "independent forensic sciencecommittee at the National Academy of Sciences."' This belatedreport' calls for structural and cultural changes in the forensicscience community ranging from the separation of laboratoriesand police departments, to a uniform, enforceable code ofethics' and standardized testimony." The report found that:
Neuromemrisitive Architecture of HTM with On-Device Learning and Neurogenesis

Hierarchical temporal memory (HTM) is a biomimetic sequence memory algorithm that holds promise for invariant representations of spatial and spatio-temporal inputs. This article presents a comprehensive neuromemristive crossbar architecture for the spatial pooler (SP) and the sparse distributed representation classifier, which are fundamental to the algorithm. There are several unique features in the proposed architecture that tightly link with the HTM algorithm. A memristor that is suitable for emulating the HTM synapses is identified and a new Z-window function is proposed. The architecture exploits the concept of synthetic synapses to enable potential synapses in the HTM. The crossbar for the SP avoids dark spots caused by unutilized crossbar regions and supports rapid on-chip training within two clock cycles. This research also leverages plasticity mechanisms such as neurogenesis and homeostatic intrinsic plasticity to strengthen the robustness and performance of the SP. The proposed design is benchmarked for image recognition tasks using Modified National Institute of Standards and Technology (MNIST) and Yale faces datasets, and is evaluated using different metrics including entropy, sparseness, and noise robustness. Detailed power analysis at different stages of the SP operations is performed to demonstrate the suitability for mobile platforms.
Why Is Long-Horizon Equity Less Risky? A Duration-Based Explanation of the Value Premium

We propose a dynamic risk-based model that captures the value premium. Firms are modeled as long-lived assets distinguished by the timing of cash flows. The stochastic discount factor is specified so that shocks to aggregate dividends are priced, but shocks to the discount rate are not. The model implies that growth firms covary more with the discount rate than do value firms, which covary more with cash flows. When calibrated to explain aggregate stock market behavior, the model accounts for the observed value premium, the high Sharpe ratios on value firms, and the poor performance of the CAPM. THIS PAPER PROPOSES A DYNAMIC RISK-BASED MODEL that captures both the high expected returns on value stocks relative to growth stocks, and the failure of the capital asset pricing model to explain these expected returns. The value premium, first noted by Graham and Dodd (1934), is the finding that assets with a high ratio of price to fundamentals (growth stocks) have low expected returns relative to assets with a low ratio of price to fundamentals (value stocks). This finding by itself is not necessarily surprising, as it is possible that the premium on value stocks represents compensation for bearing systematic risk. However, Fama and French (1992) and others show that the capital asset pricing model (CAPM) of Sharpe (1964) and Lintner (1965) cannot account for the value premium: While the CAPM predicts that expected returns should rise with the beta on the market portfolio, value stocks have higher expected returns yet do not have higher betas than growth stocks. To model the difference between value and growth stocks, we introduce a cross-section of long-lived firms distinguished by the timing of their cash flows. Firms with cash flows weighted more to the future endogenously have high price ratios, while firms with cash flows weighted more to the present have low price ratios. Analogous to long-term bonds, growth firms are high-duration ∗ Lettau is at the Stern School of Business at New York University. Wachter is at the Wharton
The Defense Attorney as Mediator in Plea Bargains

In this article, it will be argued that defense counsel’s function in plea bargains is identical to that of a mediator, seeking to reconcile the positions of the defendant and the prosecution. Within this framework, the plea bargain should be seen as part of the broad conception of Alternative Dispute Resolution (ADR) which first made its appearance at the end of the 1970s. An analysis of plea bargains in the Western world as part of the broader concept of ADR actually shows that it is the defense attorney, rather than the court or the other parties to the issue, who functions as mediator, assessing the interests of the parties caught up in the dispute. This also gives expression to the philosophy of privatization upon which mediation is based. The tactics of influence and use of an impression of force employed by the defense attorney in plea bargains are identical in every way to those used by mediators. The methods of persuasion, use of pressure, delineation of the debate, manipulations, and numerous other parameters are identical to those employed by the mediator. As a result, if indeed the mediation function of the defense attorney in plea bargaining may be recognized, it will be argued that this has implications rooted in applying the accumulated experience of the mediator in private litigation to plea bargains in criminal cases. The full article is published in 9 Pepp. Disp. Resol. L. J. 495 (2009)
UNWRAPPING RACIAL HARASSMENT LAW

This article is based on a pioneering empirical study of racial harassment in the workplace in which we statistically analyze federal court opinions from 1976 to 2002. Part I offers an overview of racial harassment law and research, noting its common origin with and its close dependence upon sexual harassment legal jurisprudence. In order to put the study’s analysis in context, Part I describes the dispute resolution process from which racial harassment cases arise. Parts II and III present a clear picture of how racial harassment law has played out in the courts—who are the plaintiffs and defendants, the nature of the claims, who wins and loses, and what factors affect those outcomes. We consider dozens of characteristics of the parties, the nature of the harassment, and litigation characteristics (such as the forum, type of proceedings, and legal issues). While it reveals that individuals in all kinds of occupations, in all parts of the country, of all races, and of both genders complain about racial harassment—it also shows that African Americans are disproportionately likely to be plaintiffs. While Whites are the most likely harassers, minority individuals also are defendants. The data also discloses that the most typical legal proceeding is the court’s consideration of the defendants’ motion for summary judgment where the judges end up terminating most plaintiffs’ cases. In fact, the judicial opinions in this study find in the plaintiffs’ favor only 21.5% of the time. (In contrast, an earlier study revealed that judges in sexual harassment cases find in the plaintiffs’ favor 48% of the time – more than twice as often as in racial harassment cases.) As it turns out in racial harassment cases, the race of the plaintiff and of the alleged harasser makes a difference in the parties’ success rates, but the gender of the plaintiff does not. Judges are a bit more likely to find racial harassment when plaintiffs allege blatant racist behavior rather than more subtle and contextual racism. Results vary depending on the location of the case. Part IV provides an integrated analysis of the data, including a look at how racial harassment litigation has evolved over time. It also offers explanations and implications of the study’s results. This article contributes detailed baseline data for litigants, judges, and legislators. Each group can draw upon the totality of racial harassment cases to guide their decisionmaking. The article also offers a sound basis for creating a new racial harassment jurisprudence that should be distinct from both sexual harassment and racial discrimination jurisprudence. UNWRAPPING RACIAL HARASSMENT LAW Pat K. Chew* & Robert E. Kelley*
Sentencing policy, implied demographic welfare weights, and the theory of sentencing reform

Abstract Different groups of people experience varying degrees of different externalities. By a government's choice of expenditures to control externalities, it affects different members of the population differently. These choices implicitly “value” the welfare of members of different groups. This paper adapts the framework developed by Ahmad and Stern (Journal of Public Economics, 1984, 25, 259–298) to infer welfare weights underlying a system of commodity taxation, to infer the welfare weights implicitly attached to different population groups by US sentencing policy. Welfare-improving directions of sentencing reform are also calculated.
Consumer Credit-Risk Models Via Machine-Learning Algorithms

Abstract We apply machine-learning techniques to construct nonlinear nonparametric forecasting models of consumer credit risk. By combining customer transactions and credit bureau data from January 2005 to April 2009 for a sample of a major commercial bank’s customers, we are able to construct out-of-sample forecasts that significantly improve the classification rates of credit-card-holder delinquencies and defaults, with linear regression R2’s of forecasted/realized delinquencies of 85%. Using conservative assumptions for the costs and benefits of cutting credit lines based on machine-learning forecasts, we estimate the cost savings to range from 6% to 25% of total losses. Moreover, the time-series patterns of estimated delinquency rates from this model over the course of the recent financial crisis suggest that aggregated consumer credit-risk analytics may have important applications in forecasting systemic risk.