cssi/SciSciGPT-SciSciNet huggingface dataset:
Geodesic Distance Histogram Feature for Video Segmentation

This paper proposes a geodesic-distance-based feature that encodes global information for improved video segmentation algorithms. The feature is a joint histogram of intensity and geodesic distances, where the geodesic distances are computed as the shortest paths between superpixels via their boundaries. We also incorporate adaptive voting weights and spatial pyramid configurations to include spatial information into the geodesic histogram feature and show that this further improves results. The feature is generic and can be used as part of various algorithms. In experiments, we test the geodesic histogram feature by incorporating it into two existing video segmentation frameworks. This leads to significantly better performance in 3D video segmentation benchmarks on two datasets.
Effects of lateral diffusion on the dynamics of desorption

The adsorbate dynamics during simultaneous action of desorption and lateral adsorbate diffusion is studied in a simple lattice-gas model by kinetic Monte Carlo simulations. It is found that the action of the coverage-conserving diffusion process during the course of the desorption has two distinct, competing effects: a general acceleration of the desorption process, and a coarsening of the adsorbate configuration through Ostwald ripening. The balance between these two effects is governed by the structure of the adsorbate layer at the beginning of the desorption process.
Stay Hungry, Stay Focused: Generating Informative and Specific Questions in Information-Seeking Conversations

We investigate the problem of generating informative questions in information-asymmetric conversations. Unlike previous work on question generation which largely assumes knowledge of what the answer might be, we are interested in the scenario where the questioner is not given the context from which answers are drawn, but must reason pragmatically about how to acquire new information, given the shared conversation history. We identify two core challenges: (1) formally defining the informativeness of potential questions, and (2) exploring the prohibitively large space of potential questions to find the good candidates. To generate pragmatic questions, we use reinforcement learning to optimize an informativeness metric we propose, combined with a reward function designed to promote more specific questions. We demonstrate that the resulting pragmatic questioner substantially improves the informativeness and specificity of questions generated over a baseline model, as evaluated by our metrics as well as humans.
RulePad: interactive authoring of checkable design rules

Good documentation offers the promise of enabling developers to easily understand design decisions. Unfortunately, in practice, design documents are often rarely updated, becoming inaccurate, incomplete, and untrustworthy. A better solution is to enable developers to write down design rules which are checked against code for consistency. But existing rule checkers require learning specialized query languages or program analysis frameworks, creating a barrier to writing project-specific rules. We introduce two new techniques for authoring design rules: snippet-based authoring and semi-natural-language authoring. In snippet-based authoring, developers specify characteristics of elements to match by writing partial code snippets. In semi-natural language authoring, a textual representation offers a representation for understanding design rules and resolving ambiguities. We implemented these approaches in RulePad. To evaluate RulePad, we conducted a between-subjects study with 14 participants comparing RulePad to the PMD Designer, a utility for writing rules in a popular rule checker. We found that those with RulePad were able to successfully author 13 times more query elements in significantly less time and reported being significantly more willing to use RulePad in their everyday work.
Privacy is What We Care About: Experimental Investigation of Federated Learning on Edge Devices

Federated Learning enables training of a general model through edge devices without sending raw data to the cloud. Hence, this approach is attractive for digital health applications, where data is sourced through edge devices and users care about privacy. Here, we report on the feasibility to train deep neural networks on the Raspberry Pi4s as edge devices. A CNN, a LSTM and a MLP was successfully trained on the MNIST data-set. Further, federated learning was demonstrated experimentally on IID and non-IID samples in a parametric study, to benchmark the model convergence. The weight updates from the workers are shared with the cloud to train the general model through federated learning. With the CNN and the non-IID samples a test-accuracy of up to 85% could be achieved within a training time of 2 minutes, while exchanging less than 10 MB data per device. In addition, we discuss federated learning from an use-case standpoint, elaborating on privacy risks and labeling requirements for the application of emotion detection from sound. Based on the experimental findings, we discuss possible research directions to improve model and system performance. Finally, we provide best practices for a practitioner, considering the implementation of federated learning.
Heterogeneity and patterning in the quasi-static behavior of granular materials

Heterogeneity is classified in five categories– topologic, geometric, kinematic, static, and constitutive–and the first four categories are investigated in a numerical DEM simulation of biaxial compression. The simulation experiments show that the topology and geometric fabric become more variable during loading. The measured fluctuations in inter-particle movements are large, they increase with loading, and they extend to distances of at least eight particle diameters. Deformation and rotation heterogeneity are large and are expressed in spatial patterning. Stress heterogeneity is moderate throughout loading.
DISTINGUISHING BETWEEN FORMATION CHANNELS FOR BINARY BLACK HOLES WITH LISA

The recent detections of GW150914 and GW151226 imply an abundance of stellar-mass binary black hole (BBH) mergers in the local universe. While ground-based gravitational wave detectors are limited to observing the final moments before a binary merges, space-based detectors, such as the Laser Interferometer Space Antenna (LISA), can observe binaries at lower orbital frequencies where such systems may still encode information about their formation histories. In particular, the orbital eccentricity and mass of BBHs in the LISA frequency band can be used together to discriminate between binaries formed in isolation in galactic fields and those formed in dense stellar environments such as globular clusters. In this letter, we explore the orbital eccentricity and mass of BBH populations as they evolve through the LISA frequency band. Overall we find that there are two distinct populations discernible by LISA. We show that up to of binaries formed either dynamically or in isolation have eccentricities that are measurable with LISA. Finally, we note how measured eccentricities of low-mass BBHs evolved in isolation could provide detailed constraints on the physics of black hole natal kicks and common-envelope evolution.
Planar sheets meet negative-curvature liquid interfaces

If an inextensible thin sheet adheres to a substrate with a negative Gaussian curvature, it will experience stress due to geometric frustration. We analyze the consequences of such geometric frustration using analytic arguments and numerical simulations. Both concentric wrinkles and eye-like folds are shown to be compatible with negative curvatures. Which pattern will be realized depends on the curvature of the substrate. We discuss both types of folding patterns and determine the phase diagram governing their appearance.
Monte Carlo Neutrino Transport Through Remnant Disks from Neutron Star Mergers

We present Sedonu, a new open source, steady-state, special relativistic Monte Carlo (MC) neutrino transport code, available at bitbucket.org/srichers/sedonu. The code calculates the energy- and angle-dependent neutrino distribution function on fluid backgrounds of any number of spatial dimensions, calculates the rates of change of fluid internal energy and electron fraction, and solves for the equilibrium fluid temperature and electron fraction. We apply this method to snapshots from two-dimensional simulations of accretion disks left behind by binary neutron star mergers, varying the input physics and comparing to the results obtained with a leakage scheme for the cases of a central black hole and a central hypermassive neutron star. Neutrinos are guided away from the densest regions of the disk and escape preferentially around 45° from the equatorial plane. Neutrino heating is strengthened by MC transport a few scale heights above the disk midplane near the innermost stable circular orbit, potentially leading to a stronger neutrino-driven wind. Neutrino cooling in the dense midplane of the disk is stronger when using MC transport, leading to a globally higher cooling rate by a factor of a few and a larger leptonization rate by an order of magnitude. We calculate neutrino pair annihilation rates and estimate that an energy of 2.8 × 10^(46) erg is deposited within 45° of the symmetry axis over 300 ms when a central BH is present. Similarly, 1.9 × 10^(48) erg is deposited over 3 s when an HMNS sits at the center, but neither estimate is likely to be sufficient to drive a gamma-ray burst jet.
FINET: Fast Inferring NETwork.

Numerous software has been developed to infer the gene regulatory network, a long-standing key topic in biology and computational biology. Yet the slowness and inaccuracy inherited in current software hamper their applications to the increasing massive data. Here, we develop a software, FINET (Fast Inferring NETwork), to infer a network with high accuracy and rapidity from big data. The high accuracy results from integrating algorithms with stability-selection, elastic-net, and parameter optimization. Tested by a known biological network, FINET infers interactions with over 94% precision. The high speed comes from partnering parallel computations implemented with Julia, a new compiled language that runs much faster than existing languages used in the current software, such as R, Python, and MATLAB. Regardless of FINET’s implementations with Julia, users with no background in the language or computer science can easily operate it, with only a user-friendly single command line. In addition, FINET can infer other networks such as chemical networks and social networks. Overall, FINET provides a confident way to efficiently and accurately infer any type of network for any scale of data.
The Combinatorics of MacMahon’s Partial Fractions

MacMahon showed that the generating function for partitions into at most k parts can be decomposed into a partial fraction-type sum indexed by the partitions of k. In the present work, a generalization of MacMahon’s result is given, which in turn provides a full combinatorial explanation.
D-brane dynamics and the quantum Hall effect

We study the recently proposed D-brane configuration [1] modeling the quantum Hall effect, focusing on the nature of the interactions between the charged particles. Our analysis indicates that the interaction is repulsive, which it should be for the ground state of the system to behave as a quantum Hall liquid. The strength of interactions varies inversely with the filling fraction, leading us to conclude that a Wigner crystal is the ground state at small ν. For larger rational ν (still less than unity), it is reasonable to expect a fractional quantum Hall ground state.
BÄCKLUND-TRANSFORMATION-RELATED RECURSION OPERATORS: APPLICATION TO THE SELF-DUAL YANG–MILLS EQUATION

By using the self-dual Yang–Mills (SDYM) equation as an example, we study a method for relating symmetries and recursion operators of two partial differential equations connected to each other by a non-auto-Backlund transformation. We prove the Lie-algebra isomorphism between the symmetries of the SDYM equation and those of the potential SDYM (PSDYM) equation, and we describe the construction of the recursion operators for these two systems. Using certain known aspects of the PSDYM symmetry algebra, we draw conclusions regarding the Lie algebraic structure of the "potential symmetries" of the SDYM equation.
INTERGALACTIC GAS IN GROUPS OF GALAXIES: IMPLICATIONS FOR DWARF SPHEROIDAL FORMATION AND THE MISSING BARYONS PROBLEM

Radio galaxies with bent jets are predominantly located in groups and clusters of galaxies. We use bent-double radio sources, under the assumption that their jets are bent by ram pressure, to probe intragroup medium (IGM) gas densities in galaxy groups. This method provides a direct measurement of the intergalactic gas density and allows us to probe intergalactic gas at large radii and in systems whose IGM is too cool to be detected by the current generation of X-ray telescopes. We find gas with densities of 10{sup -3} to 10{sup -4} cm{sup -3} at group radii from 15 to 700 kpc. A rough estimate of the total baryonic mass in intergalactic gas is consistent with the missing baryons being located in the IGM of galaxy groups. The neutral gas will be easily stripped from dwarf galaxies with total masses of 10{sup 6}-10{sup 7} M{sub sun} in the groups studied here. Indications are that intragroup gas densities in less-massive systems like the Local Group should be high enough to strip gas from dwarfs like Leo T and, in combination with tides, produce dwarf spheroidals.
Elliptic Springer theory

We introduce an elliptic version of the Grothendieck–Springer sheaf and establish elliptic analogues of the basic results of Springer theory. From a geometric perspective, our constructions specialize geometric Eisenstein series to the resolution of degree-zero, semistable . The resulting objects are principal series examples of elliptic character sheaves, objects expected to play the role of character sheaves for loop groups.
Moments of the inverse participation ratio for the Laplacian on finite regular graphs

We investigate the rst and second moments of the inverse participation ratio (IPR) for all eigenvectors of the Laplacian on nite random regular graphs with n vertices and degree z. By exactly diagonalizing a large set of z-regular graphs, we nd that as n becomes large, the mean of the inverse participation ratio on each graph, when averaged over a large ensemble of graphs, approaches the numerical value 3. This universal number is understood as the large-n limit of the average of the quartic polynomial corresponding to the IPR over an appropriate (n 2)-dimensional hypersphere of R n . The graph ensemble mean variance of the inverse participation ratio for all graph Laplacian eigenvectors deviates from its continuous hypersphere average due to large graph- to-graph uctuations that arise from the existence of localized modes.
Measurement of Segmental Mobility during Constant Strain Rate Deformation of a Poly(methyl methacrylate) Glass

We describe an apparatus for performing constant strain rate deformations of polymer glasses while simultaneously measuring the segmental mobility with an optical probe reorientation method. Poly(methyl methacrylate) glasses were deformed at Tg – 19 K, for local strain rates between 3.7 × 10–5 and 1.2 × 10–4 s–1. In these experiments, the mobility initially increases in the preyield regime, by a factor of 40–160, as compared to the undeformed PMMA glass. The mobility then remains constant after yield, even as the stress is decreasing due to strain softening. This is consistent with the view that the sample is being pulled higher on the potential energy landscape in this regime. Higher strain rates lead to higher mobility in the postyield regime, and for the range of strain rates investigated, mobility and strain rate are linearly correlated. We observe that thermal history has no influence on mobility after yield and that deformation leads to a narrowing of the distribution of segmental relaxation times. ...
Two-loop beta function for complex scalar electroweak multiplets

We present the general form of the renormalizable four-point interactions of a complex scalar field furnishing an irreducible representation of SU(2), and derive a set of algebraic identities that facilitates the calculation of higher-order radiative corrections. As an application, we calculate the two-loop beta function for the SM extended by a scalar multiplet, and provide the result explicitly in terms of the group invariants. Our results include the evolution of the Higgs-portal couplings, as well as scalar “minimal dark matter”. We present numerical results for the two-loop evolution of the various couplings.
Object Detection with a Unified Label Space from Multiple Datasets

Given multiple datasets with different label spaces, the goal of this work is to train a single object detector predicting over the union of all the label spaces. The practical benefits of such an object detector are obvious and significant—application-relevant categories can be picked and merged form arbitrary existing datasets. However, naive merging of datasets is not possible in this case, due to inconsistent object annotations. Consider an object category like faces that is annotated in one dataset, but is not annotated in another dataset, although the object itself appears in the latter’s images. Some categories, like face here, would thus be considered foreground in one dataset, but background in another. To address this challenge, we design a framework which works with such partial annotations, and we exploit a pseudo labeling approach that we adapt for our specific case. We propose loss functions that carefully integrate partial but correct annotations with complementary but noisy pseudo labels. Evaluation in the proposed novel setting requires full annotation on the test set. We collect the required annotations (Project page: http://www.nec-labs.com/~mas/UniDet This work was part of Xiangyun Zhao’s internship at NEC Labs America.) and define a new challenging experimental setup for this task based on existing public datasets. We show improved performances compared to competitive baselines and appropriate adaptations of existing work.
The Effect of Visual Uncertainty on Implicit Motor Adaptation.

Sensorimotor adaptation is influenced by both the size and variance of error information. In the present study, we varied visual uncertainty and error size in a factorial manner and evaluated their...
Non-trivial d-wise intersecting families

Abstract For an integer d ≥ 2 , a family F of sets is d-wise intersecting if for any distinct sets A 1 , A 2 , … , A d ∈ F , A 1 ∩ A 2 ∩ … ∩ A d ≠ ∅ , and non-trivial if ⋂ A ∈ F A = ∅ . Hilton and Milner conjectured that for k ≥ d ≥ 2 and large enough n, the extremal (i.e. largest) non-trivial d-wise intersecting family of k-element subsets of [ n ] is, up to isomorphism, one of the following two families: A ( k , d ) = { A ∈ ( [ n ] k ) : | A ∩ [ d + 1 ] | ≥ d } H ( k , d ) = { A ∈ ( [ n ] k ) : [ d − 1 ] ⊂ A , A ∩ [ d , k + 1 ] ≠ ∅ } ∪ { [ k + 1 ] ∖ { i } : i ∈ [ d − 1 ] } . The celebrated Hilton-Milner Theorem states that H ( k , 2 ) is the unique, up to isomorphism, extremal non-trivial intersecting family for k > 3 . We prove the conjecture and prove a stability theorem, stating that any large enough non-trivial d-wise intersecting family of k-element subsets of [ n ] is a subfamily of A ( k , d ) or H ( k , d ) .
Transverse momentum broadening of vector bosons in heavy ion collisions at the LHC

We calculate in perturbative QCD the transverse momentum broadening of vector bosons in heavy ion collisions at the Large Hadron Collider (LHC). We predict transverse momentum broadening of $W/Z$ bosons constructed from their leptonic decay channels, which should be a clean probe of initial-state medium effect. We also predict the upper limit of transverse momentum broadening of J/$\psi$ and $\Upsilon$ production as a function of N$_{\rm part}$ at the LHC energy.
Current and future neutrino oscillation constraints on leptonic unitarity

The unitarity of the lepton mixing matrix is a critical assumption underlying the standard neutrino-mixing paradigm. However, many models seeking to explain the as-yet-unknown origin of neutrino masses predict deviations from unitarity in the mixing of the active neutrino states. Motivated by the prospect that future experiments may provide a precise measurement of the lepton mixing matrix, we revisit current constraints on unitarity violation from oscillation measurements and project how next-generation experiments will improve our current knowledge. With the next-generation data, the normalizations of all rows and columns of the lepton mixing matrix will be constrained to ≲10% precision, with the e-row best measured at ≲1% and the τ-row worst measured at ∼10% precision. The measurements of the mixing matrix elements themselves will be improved on average by a factor of 3. We highlight the complementarity of DUNE, T2HK, JUNO, and IceCube Upgrade for these improvements, as well as the importance of ντ appearance measurements and sterile neutrino searches for tests of leptonic unitarity.
Deterministic APSP, Orthogonal Vectors, and More: Quickly Derandomizing Razborov-Smolensky

We show how to solve all-pairs shortest paths on n nodes in deterministicn3>/2>Ω ( √ log n) time, and how to count the pairs of orthogonal vectors among n 0−1 vectors in d = clog n dimensions in deterministicn2−1/O(log c) time. These running times essentially match the best known randomized algorithms of Williams [46] and Abboud, Williams, and Yu [8], respectively, and the ability to count was open even for randomized algorithms. By reductions, these two results yield faster deterministic algorithms for many other problems. Our techniques can also be used to deterministically count k-satisfiability (k-SAT) assignments on n variable formulas in 2n-n/O(k) time, roughly matching the best known running times for detecting satisfiability and resolving an open problem of Santhanam [24].A key to our constructions is an efficient way to deterministically simulate certain probabilistic polynomials critical to the algorithms of prior work, carefully applying small-biased sets and modulus-amplifying polynomials.
Robust Tensor Recovery with Fiber Outliers for Traffic Events

Event detection is gaining increasing attention in smart cities research. Large-scale mobility data serves as an important tool to uncover the dynamics of urban transportation systems, and more often than not the dataset is incomplete. In this article, we develop a method to detect extreme events in large traffic datasets, and to impute missing data during regular conditions. Specifically, we propose a robust tensor recovery problem to recover low-rank tensors under fiber-sparse corruptions with partial observations, and use it to identify events, and impute missing data under typical conditions. Our approach is scalable to large urban areas, taking full advantage of the spatio-temporal correlations in traffic patterns. We develop an efficient algorithm to solve the tensor recovery problem based on the alternating direction method of multipliers (ADMM) framework. Compared with existing l1 norm regularized tensor decomposition methods, our algorithm can exactly recover the values of uncorrupted fibers of a low-rank tensor and find the positions of corrupted fibers under mild conditions. Numerical experiments illustrate that our algorithm can achieve exact recovery and outlier detection even with missing data rates as high as 40% under 5% gross corruption, depending on the tensor size and the Tucker rank of the low rank tensor. Finally, we apply our method on a real traffic dataset corresponding to downtown Nashville, TN and successfully detect the events like severe car crashes, construction lane closures, and other large events that cause significant traffic disruptions.
Mapping and ablation of non-pulmonary vein drivers of persistent atrial fibrillation: Has a STAR been born?

While pulmonary vein isolation (PVI) remains the cornerstone for invasive treatment of atrial fibrillation (AF), patients with persistent AF still have a high rate of recurrence with this method. Stochastic Trajectory Analysis of Ranked signals (STAR) mapping uses data from multiple individual wavefronts during ongoing AF to identify local drivers of persistent AF. In this non-randomized study, STAR mapping and ablation showed significantly lower recurrence of atrial arrhythmias compared to a consecutive PVI-only cohort and a propensity-matched ‘conventional ablation’ cohort (consisting of PVI plus complex fractionated atrial electrogram ablation or linear ablation). This benefit was driven by a much lower rate of AF recurrence in the STAR (6.2%) cohort vs PVI-only (44%) or ‘conventional’ (40%) with no significant difference in atrial tachycardia recurrence. Additionally, AF termination rates during ablation were approximately three times higher in the STAR cohort. While the analysis is retrospective and not randomized, the STAR cohort was also the only cohort with complete cessation of anti-arrhythmic drugs at three months and Holter monitoring at 6 and 12 months post-ablation per protocol. While STAR mapping appears to be a very promising new tool for treating persistent AF, history predicts at least some regression to the mean when future randomized comparisons are made. The authors have planned a multicenter randomized trial of PVI plus STAR mapping vs PVI-only for persistent AF. The global community of electrophysiologists and patients with AF eagerly awaits the results.
We Have So Much in Common: Modeling Semantic Relational Set Abstractions in Videos

Identifying common patterns among events is a key capability for human and machine perception, as it underlies intelligent decision making. Here, we propose an approach for learning semantic relational set abstractions on videos, inspired by human learning. Our model combines visual features as input with natural language supervision to generate high-level representations of similarities across a set of videos. This allows our model to perform cognitive tasks such as set abstraction (which general concept is in common among a set of videos?), set completion (which new video goes well with the set?), and odd one out detection (which video does not belong to the set?). Experiments on two video benchmarks, Kinetics and Multi-Moments in Time, show that robust and versatile representations emerge when learning to recognize commonalities among sets. We compare our model to several baseline algorithms and show that significant improvements result from explicitly learning relational abstractions with semantic supervision. Code and models are available online (Project website: abstraction.csail.mit.edu).
Point Set Voting for Partial Point Cloud Analysis

The continual improvement of 3D sensors has driven the development of algorithms to perform point cloud analysis. In fact, techniques for point cloud classification and segmentation have in recent years achieved incredible performance driven in part by leveraging large synthetic datasets. Unfortunately these same state-of-the-art approaches perform poorly when applied to incomplete point clouds. This limitation of existing algorithms is particularly concerning since point clouds generated by 3D sensors in the real world are usually incomplete due to perspective view or occlusion by other objects. This paper proposes a general model for partial point clouds analysis wherein the latent feature encoding a complete point cloud is inferred by applying a point set voting strategy. In particular, each local point set constructs a vote that corresponds to a distribution in the latent space, and the optimal latent feature is the one with the highest probability. This approach ensures that any subsequent point cloud analysis is robust to partial observation while simultaneously guaranteeing that the proposed model is able to output multiple possible results. This paper illustrates that this proposed method achieves the state-of-the-art performance on shape classification, part segmentation and point cloud completion.
Control of Uniflagellar Soft Robots at Low Reynolds Number Using Buckling Instability

In this paper, we analyze the inverse dynamics and control of a bacteria-inspired uniflagellar robot in a fluid medium at low Reynolds number. Inspired by the mechanism behind the locomotion of flagellated bacteria, we consider a robot comprised of a flagellum -- a flexible helical filament -- attached to a spherical head. The flagellum rotates about the head at a controlled angular velocity and generates a propulsive force that moves the robot forward. When the angular velocity exceeds a threshold value, the hydrodynamic force exerted by the fluid can cause the soft flagellum to buckle, characterized by a dramatic change in shape. In this computational study, a fluid-structure interaction model that combines Discrete Elastic Rods (DER) algorithm with Lighthill's Slender Body Theory (LSBT) is employed to simulate the locomotion and deformation of the robot. We demonstrate that the robot can follow a prescribed path in three dimensional space by exploiting buckling of the flagellum. The control scheme involves only a single (binary) scalar input -- the angular velocity of the flagellum. By triggering the buckling instability at the right moment, the robot can follow an arbitrary path in three dimensional space. We also show that the complexity of the dynamics of the helical filament can be captured using a deep neural network, from which we identify the input-output functional relationship between the control inputs and the trajectory of the robot. Furthermore, our study underscores the potential role of buckling in the locomotion of natural bacteria.
Understanding the role of importance weighting for deep learning

The recent paper by Byrd & Lipton (2019), based on empirical observations, raises a major concern on the impact of importance weighting for the over-parameterized deep learning models. They observe that as long as the model can separate the training data, the impact of importance weighting diminishes as the training proceeds. Nevertheless, there lacks a rigorous characterization of this phenomenon. In this paper, we provide formal characterizations and theoretical justifications on the role of importance weighting with respect to the implicit bias of gradient descent and margin-based learning theory. We reveal both the optimization dynamics and generalization performance under deep learning models. Our work not only explains the various novel phenomenons observed for importance weighting in deep learning, but also extends to the studies where the weights are being optimized as part of the model, which applies to a number of topics under active research.
With group power comes great (individual) responsibility

When a group does harm, sometimes there’s no obvious individual who bears moral responsibility, and yet we still intuit that someone is to blame. This apparent ‘deficit’ of moral responsibility has...
Protected percolation: a new universality class pertaining to heavily-doped quantum critical systems

We present the results of computer simulations on a class of percolative systems that forms a new universality class. We show the results for the critical exponents for this new class, inferred from simulations of two- and three-dimensional lattices consisting of up to one billion lattice sites. These new percolative systems differ from standard percolative systems in that once a cluster breaks off the lattice spanning cluster, its sites become protected and cannot be removed. This situation closely mimics the situation in heavily-doped quantum critical systems where isolated magnetic clusters are protected from (further) Kondo screening. Our results indicate that protected percolation violates the Harris criterion, which yields a natural explanation as to why universal exponents for quantum phase transitions have been elusive.
Investment cycles and startup innovation

We find that venture capital-backed startups receiving their initial investment in hot markets are more likely to go bankrupt, but conditional on going public, are valued higher on the day of their initial public offering, have more patents, and have more citations to their patents. Our results suggest that VCs invest in riskier and more innovative startups in hot markets (rather than just worse firms). This is particularly true for the most experienced VCs. Furthermore, our results suggest that increased capital in hot times plays a causal role in shifting investments to more novel startups by lowering the cost of experimentation for early stage investors and allowing them to make riskier, more novel, investments.
Does Advertising Bias Product Reviews? An Analysis of Wine Ratings*

In markets for experience goods, publications exist to help consumers decide which products to purchase. However, in most cases these publications accept advertising from the very firms whose products they review, raising the possibility that they bias product reviews to favor advertisers. To test for biased product reviews, I exploit the fact that, of the two major U.S. wine publications, only Wine Spectator accepts advertising. Although the average Wine Spectator ratings earned by advertisers and non-advertisers are similar, I find that advertisers earn just less than one point higher Wine Spectator ratings than non-advertisers when I use Wine Advocate ratings to adjust for differences in quality. However, I find only weak evidence that the selective retasting of advertisers’ wines contributes to the higher ratings. Moreover, conditional on published ratings, Wine Spectator is no more likely to bestow awards upon advertisers. I conclude that while advertising may influence ratings on the margin, Wine Spectator appears largely to insulate reviewers from the influence of advertisers. (JEL Classification: L15, M37)
The optimal reservation utility in models of decision making in arranged marriages

The notion of a reservation quality or utility is a key feature of Batabyal's (2003a) recent analysis of decision making in arranged marriages. In this model, the magnitude of the reservation utility has a nontrivial impact on the decision to accept or decline a marriage proposal. Even so, Batabyal (2003a) treats the marrying agent's reservation utility as an exogenous variable. Consequently, the purpose of this article is to endogenize the reservation utility variable in the context of a dynamic and stochastic model of decision making in arranged marriages. Our analysis leads to two results. First, we demonstrate that the optimal reservation utility is the solution to a particular maximization problem. Second, we show that theoretical circumstances exist in which the optimal reservation utility is unique.
Open Adoption, Inheritance, and the Uncleing Principle

This article critiques current inheritance law relating to adopted children in light of the purposes of modern adoption law and the increasing prevalence of open adoptions. The article proposes an uncling principle to determine intestate inheritance rights in cases of open adoption in which a birth parent has maintained a qualifying functional relationship with the adopted-out child subsequent to the adoption. When applicable, the uncling principle would treat the adopted-out child and her birth parent as potential heirs of one another. Unlike the presently dominant all-or-nothing approach to inheritance rights arising from adoption, however, the proposal would not under any circumstances treat the birth parent as a legal parent of the adopted-out child for purposes of inheritance. Rather, the uncling principle would treat the birth parent as an uncle or aunt to the adopted-out child, and would similarly increase the distance on the family tree between the adopted-out child and members of her birth family by one line of inheritance and two degrees of kinship. When applicable, the uncling principle would better serve the interests of the adopted child, her adoptive family, and her birth family than does the all-or-nothing approach, under which the adopted-out child is either a child of her birth parents for purposes of inheritance or is a stranger to her birth parents for purposes of inheritance. The uncling principle affirms the parental role of the adoptive parents by refusing to treat a birth parent as a legal parent. Simultaneously, the uncling principle recognizes and validates the importance of the bond between the adopted child and her birth family when the birth parent has maintained a sufficient functional relationship with the adopted child subsequent to the adoption.
In-store referrals on the Internet

Abstract In the contemporary e-business, a retailer may display the links to the competing retailers directly (direct referral), or display the referral link provided by a third-party advertising agency (third-party referral), and these referrals may be either one-way or two-way. In this paper, we show that the referrals may align the retailers’ incentives and facilitate implicit collusion, and one-way referral may result in a mutually beneficial situation, thereby providing an economic rationale for these seemingly puzzling phenomena. Using third-party referrals may enhance the retailers’ collusion despite the potential disutility and revenue leakage, and referral services may be detrimental for the consumer welfare.
The welfare implications of electoral polarization

I consider the welfare implications of polarization in the preferences of political candidates, for different distributions of voter preferences, in a model of repeated elections. Candidates care about policy and also have an opportunity to engage in rent-seeking when in office. When candidates’ preferences are polarized they choose non-converging policies if elected. This creates policy costs from not securing re-election, decreasing equilibrium rent-seeking, and, for appropriate parameters, increasing voter welfare. I show that increasing polarization among voters increases the range of parameters for which polarization in candidate preferences is socially optimal if only if utility functions are not too concave. Moreover, with multiple dimensions of policy disagreement, it is optimal to have greater polarization of candidate preferences in dimensions in which voters are more polarized if and only if utility functions are not too concave. I discuss the implications of these results for electoral competition in the United States.
The holdout problem, urban sprawl, and eminent domain

Abstract Developers attempting land assembly often face a potential holdout problem that raises the cost of development. To minimize this extra cost, developers will prefer land whose ownership is less dispersed. This creates a bias toward development at the urban fringe where average lot sizes are larger, resulting in urban sprawl. This paper examines the link between the holdout problem and urban sprawl and discusses possible remedies, including the use of eminent domain for urban redevelopment.
FASTQuick: rapid and comprehensive quality assessment of raw sequence reads.

BACKGROUND Rapid and thorough quality assessment of sequenced genomes on an ultra-high-throughput scale is crucial for successful large-scale genomic studies. Comprehensive quality assessment typically requires full genome alignment, which costs a substantial amount of computational resources and turnaround time. Existing tools are either computationally expensive owing to full alignment or lacking essential quality metrics by skipping read alignment. FINDINGS We developed a set of rapid and accurate methods to produce comprehensive quality metrics directly from a subset of raw sequence reads (from whole-genome or whole-exome sequencing) without full alignment. Our methods offer orders of magnitude faster turnaround time than existing full alignment-based methods while providing comprehensive and sophisticated quality metrics, including estimates of genetic ancestry and cross-sample contamination. CONCLUSIONS By rapidly and comprehensively performing the quality assessment, our tool will help investigators detect potential issues in ultra-high-throughput sequence reads in real time within a low computational cost at the early stages of the analyses, ensuring high-quality downstream results and preventing unexpected loss in time, money, and invaluable specimens.
A Model of R&D Valuation and the Design of Research Incentives

Abstract We develop a real options model of R&D valuation that takes into account the uncertainty in the quality (or efficacy) of the research output, the time and cost to completion, and the market demand for the R&D output. The model is then applied to study the problem of pharmaceutical under-investment in R&D for vaccines to treat diseases affecting the developing regions of the world. To address this issue, world organizations and private foundations are willing to sponsor vaccine R&D, but there is no consensus on how to administer the sponsorship effectively. Different research incentive contracts are examined using our valuation model. Their effectiveness is measured in the following five dimensions: expected cost to the sponsor, probability of development success, consumer surplus generated, expected number of successful vaccinations and expected cost per person successfully vaccinated. We find that, in general, purchase commitment plans (pull subsidies) are more effective than cost subsidy plans (push subsidies). Moreover, we find that a hybrid subsidy plan constructed from a purchase commitment combined with a sponsor research cost-sharing subsidy is the most effective.
Determinants of market reactions to restatement announcements

Abstract We examine the market reaction to a sample of 403 restatements announced from 1995 to 1999. We document an average abnormal return of about −9 percent over a 2-day announcement window. We find that more negative returns are associated with restatements involving fraud, affecting more accounts, decreasing reported income and attributed to auditors or management (but not the Securities and Exchange Commission). There appears to be an additional penalty for announcements that do not quantify the restatement. Finally, we provide evidence on the relation between restatement announcements and analyst earnings forecast dispersion, bid–ask spreads and subsequent revisions in analyst earnings forecasts.
Ideological media bias

Abstract I develop a model of the market for news in which consumers and reporters both ideologically misinterpret information and have biased beliefs about the extent to which others misinterpret information. I show that for some parameter values, in equilibrium: (i) a monopolist media outlet hires a politically moderate reporter but duopolist outlets use relatively extreme, differentiated reporters; (ii) in duopoly, consumers think of their preferred outlet's news reporter as relatively unbiased and the other outlet's reporter as relatively biased; (iii) consumers, in the aggregate, may be less informed in duopoly than monopoly, despite more consumers receiving news in duopoly.
Identifying reference spans: topic modeling and word embeddings help IR

The CL-SciSumm 2016 shared task introduced an interesting problem: given a document D and a piece of text that cites D, how do we identify the text spans of D being referenced by the piece of text? The shared task provided the first annotated dataset for studying this problem. We present an analysis of our continued work in improving our system’s performance on this task. We demonstrate how topic models and word embeddings can be used to surpass the previously best performing system.
Endogenous Information Flows and the Clustering of Announcements

We consider the strategic timing of information releases in a dynamic disclosure model. Because investors don't know whether or when the firm is informed, the firm will not necessarily disclose immediately. We show that bad market news can trigger the immediate release of information by firms. Conversely, good market news slows the release of information by firms. Thus, our model generates clustering of negative announcements. Surprisingly, this result holds only when firms can preemptively disclose their own information prior to the arrival of external information. These results have implications for conditional variance and skewness of stock returns. (JEL D21, D83, G12, G14, L11)
Equilibrium with investors using a diversity of deviation measures.

Abstract It has been argued that investors who optimize their portfolios with attention paid only to mean and standard deviation will all end up choosing some multiple of a certain master fund portfolio. Justification for the capital asset pricing model of classical portfolio theory, which relates individual assets to such a master fund, has come from this direction in particular. Attempts have been made to provide solid mathematical support by showing that the imputed behavior of investors is a consequence of price equilibrium in a market in which assets are traded subject to budget constraints, and optimization is carried out with respect to utility functions that depend only on mean and standard deviation. In recent years, reliance on standard deviation has come under increasing criticism because of inconsistencies in its effect on portfolio preferences. One response has been to introduce generalized measures of deviation which lead to alternative master funds. The market implications of such extensions of theory have hitherto been unclear, but in this paper the existence of equilibrium is established in circumstances where nonstandard deviations are admitted. Equilibrium is guaranteed even when different investors use different measures of deviation and thereby end up with portfolios scaled from different master funds. Whether they employ the same measure or not, they may impose caps on deviation, which likewise may be different.
Financial Intermediation in the Early Roman Empire

I evaluate the effectiveness of financial markets in the early Roman Empire in this article. I review the theory of financial intermediation to describe a hierarchy of financial sources and survey briefly the history of financial intermediation in eighteenth-century Western Europe to provide a standard against which to evaluate the Roman evidence. I then describe the nature of financial arrangements in the early Roman Empire in terms of this hierarchy. This exercise reveals the extent to which the Roman economy resembled more recent societies and sheds light on the prospects for economic growth in the Roman Empire. n this article I use a theoretical hierarchy of financial sources to evaluate the effectiveness of the financial markets in the early Roman Empire. The goal of this exercise is two-fold. First, it reveals the extent to which the Roman economy resembled more recent societies. No ancient historian claims that the Romans operated in a twentieth-century mode, but most of the financial institutions that we take for granted today are less than two centuries old. More relevant is how the Roman financial system compares with the advanced agrarian economies of the eighteenth century. Second, this exploration sheds light on the prospects for economic growth in the Roman Empire. Good financial markets and institutions help people who have ideas for production get resources to implement those ideas. Empirical investigations of recent economic growth have exposed a clear connection between financial institutions and economic growth; without these markets and institutions, the prospects for economic progress appear far more limited.' I argue that the Romans had a sophisticated financial structure, which had the potential to promote growth. Recent archaeological research on various parts of the Roman economy has suggested a capacity for growth.2 This article