cssi/SciSciGPT-SciSciNet huggingface dataset:
Capital Punishment in Illinois in the Aftermath of the Ryan Commutations: Reforms, Economic Realities, and a New Saliency for Issues of Cost

In 2000 when Governor George Ryan unilaterally imposed a statewide moratorium on executions in Illinois, in response to accumulating evidence of more than a dozen wrongfully convicted persons on death row in Illinois. In 1999 the Illinois legislature created the Capital Litigation Trust Fund, to allow private, appointed defense counsel, state’s attorneys, and public defenders to be paid directly for the expenses of a capital trial from state appropriated funds, upon the approval of the trial court judge. Publishing new data on capital prosecutions in Illinois since 2000, this article documents evidence of state money spent at the county level on more than 500 capital prosecutions, the largest proportion from Cook County, which resulted in 17 death sentences imposed. More than 100 million dollars of state money has been spent out of the Capital Litigation Trust Fund alone by county state’s attorneys, appointed private counsel, and by public defenders. The availability of state funds changed the dynamics and economic and bureaucratic incentives for capital prosecution. In addition, over 64 million dollars has been spent by the state, the city of Chicago, and the counties in judgments involving wrongful convictions. This article presents data on capital prosecutions and murders across the state, and publishes for the first time the State’s Attorney’s own adopted guidelines for the selection of cases for capital prosecution. When patterns of capital prosecution are examined across the state as a whole, it becomes clear that the counties most likely to spend the state’s money on prosecuting first degree murder cases capitally are not those jurisdictions with the largest number of first degree murders. Nor is there a correspondence between the number of county capital prosecutions, the number of death sentences imposed, the number of murders or the murder rate in that county, and the amount of money spent by the county from the Capital Litigation Trust Fund. County by county disparities in capital case prosecutions and in expenditures of state money are startling. The absence of centralized review and the presence of many potential areas of conflicts of interest should submit the existing system to close scrutiny at this time of budgetary pressure.To view the comprehensive data base of original murder indictments filed by State’s Attorneys in individual counties in Illinois during the years 2000-2009, see Illinois Murder Indictments: 2000-2010 on the web. (To access the database, search for "Illinois Murder Indictments 2000-2010.") These indictments were critical to this analysis and are hereby made available as public records to other researchers and interested persons.
Going Public with Transnational Law: The 2002-2003 Supreme Court Term

Court watchers and scholars hail the Supreme Court's 2002 Tern as an international law watershed. With prominent international and foreign law citations in Lawrence v. Texas and Grutter v. Bollinger, two of the Term's highest profile decisions, international law purportedly arrived on "center stage" and, concomitantly, the appropriate role for international law in Supreme Court opinions quickly became a vogue topic of debate. However, this recent publicity obscures the role that foreign and international law has played in Supreme Court jurisprudence and perhaps unfairly depicts the Court as more isolationist than its record merits. The Court's international and foreign law citations were not, in and of themselves, revolutionary. It was the Court's decision to use such citations in the highest profile, potentially most controversial cases - with no dearth of domestic law or empirical evidence for guidance - that inevitably catapults the Court into a rather public, potentially polarizing, discussion of the proper role of transnational law in constitutional jurisprudence.
Nonoptimal Levels of Suburbanization

Suburbanization has many causes, among which is the attempt to relocate to acquire a more desirable vector of local public goods. The traditional economists' procedure for valuing public goods involves vertical aggregation of marginal willingness to pay, at a given income level. This approach is flawed by failing to recognize that individuals will not work for goods that cannot be acquired individually with higher incomes. There will be a parallel input market failure any time there is a public good output market failure, thus the 'given income' of the traditional valuation method is too low. Hence, traditional valuation methods result in underprovision of local public goods (for example, parks, safety, education, and environmental quality) at the urban centers. As a consequence, there will be nonoptimally large levels of suburban sprawl with substantial resulting welfare loss.
Existence of Equilibrium for Segmented Markets Models with Interest Rate Monetary Policies

Several studies have recently adopted the segmented markets model as a framework for monetary analysis. The characteristic assumption is that some households never participate in financial markets. This paper proves the existence of an equilibrium for segmented markets models where monetary policy is defined in terms of the short-term nominal interest rate. The model allows us to consider the important cases where monetary policy affects output, and responds to any sources of uncertainty, including output itself. The assumptions required for existence constrain the maximum value and the variability of the nominal interest rate. The period utility function is logarithmic. The proof is constructive, and shows how the model can be solved numerically. A similar proof can be used in the case that monetary policy is defined in terms of the bond supply.
"The Divine Institution of Marriage": An Overview of LDS Involvement in the Proposition 8 Campaign

The Church of Jesus Christ of Latter-day Saints (LDS or Mormon church) was heavily involved in the passage of Proposition 8 in California in November 2008, which restricted marriages recognized under state law to those between a man and a woman (as construed by the California Supreme Court, it prospectively denied legal sanction for same-sex marriages while not interfering with such marriages previously recognized under state law). Church members participated in the Proposition 8 campaign a variety of ways, including extensive fundraising and various publicity efforts such as door-to-door get-out-the-word campaigns. Statements by the church and its leaders were a central part of the LDS Proposition 8 strategy. The church issued three official statements on Proposition 8, which combined theological and religious content with specific political, sociological, and legal claims. For instance, in their support for Proposition 8, LDS church leaders (most of them not legal professionals) made a series of detailed predictions about the legal consequences of same-sex marriage. These official declarations were supplemented and reinforced by a variety of unofficial statements from church leaders and members.This Article tells the story of LDS involvement with Proposition 8, in particular the legal claims made by the church and its leaders. It assembles the statements made by church leaders and church members during the Proposition 8 campaign, briefly examines the accuracy of some of the most broadly circulated claims, and discusses implications.
The Legal Services Restrictions: Lawyers in Florida, New York, Virginia, and Oregon Describe the Costs

Three years have passed since the 107th Congress imposed comprehensive restrictions on the Legal Services Corporation (LSC) and its lawyers who provide free representation to the nation's poor in civil legal matters. As legal services programs around the nation struggle to carry out their mission to provide high quality legal representation, the restrictions continue to cause real harm to the individuals and communities that rely on legal services lawyers. Injustices that cannot be addressed; clients who cannot be represented; precious funds raised through strenuous efforts that must be squandered on duplicative rent, furniture, and computers; and attorneys' fees that must be forfeited. In this Paper, I first describe my own experience as a legal services attorney in attempting to represent clients fully despite LSC's demand, based on the 1996 restrictions, that I either abandon them or forfeit my job. I then describe the circumstances of other legal services lawyers in Oregon, Virginia, and Florida who are endeavoring to provide high-quality legal representation to their clients while complying rigorously with the requirements embodied in the restrictions. In assessing these stories, it is useful first to consider the scope of the restrictions and the response of the LSC programs nationally following their enactment.
IS THE TRANSITION TO THE MARKET TOO IMPORTANT TO BE LEFT TO THE MARKET

Most models of Central and Eastern European transition fail to appreciate the de facto organising principles that governed life in the Soviet-type system. Concentration has instead been focused on the de jure pronouncements of what constituted these systems. It is this misidentification, we contend, that has caused the major problems for economists devising strategies for reform. Proper appreciation of the de facto realities underlying Soviet-type economies sheds light on the crucial role that market forces must be allowed to play in the process of transition.
THE SLOAN DIGITAL SKY SURVEY DATA RELEASE 7 SPECTROSCOPIC M DWARF CATALOG. II. STATISTICAL PARALLAX ANALYSIS

We present a statistical parallax analysis of low-mass dwarfs from the Sloan Digital Sky Survey. We calculate absolute r-band magnitudes (M{sub r} ) as a function of color and spectral type and investigate changes in M{sub r} with location in the Milky Way. We find that magnetically active M dwarfs are intrinsically brighter in M{sub r} than their inactive counterparts at the same color or spectral type. Metallicity, as traced by the proxy {zeta}, also affects M{sub r} , with metal-poor stars having fainter absolute magnitudes than higher metallicity M dwarfs at the same color or spectral type. Additionally, we measure the velocity ellipsoid and solar reflex motion for each subsample of M dwarfs. We find good agreement between our measured solar peculiar motion and previous results for similar populations, as well as some evidence for differing motions of early and late M-type populations in U and W velocities that cannot be attributed to asymmetric drift. The reflex solar motion and the velocity dispersions both show that younger populations, as traced by magnetic activity and location near the Galactic plane, have experienced less dynamical heating. We introduce a new parameter, the independent position altitude (IPA), to investigate populations as amore » function of vertical height from the Galactic plane. M dwarfs at all types exhibit an increase in velocity dispersion when analyzed in comparable IPA subgroups.« less
Towards Explainable AI: Significance Tests for Neural Networks.

We develop a pivotal test to assess the statistical significance of the feature variables in a single-layer feedforward neural network regression model. We propose a gradient-based test statistic and study its asymptotics using nonparametric techniques. Under technical conditions, the limiting distribution is given by a mixture of chi-square distributions. The tests enable one to discern the impact of individual variables on the prediction of a neural network. The test statistic can be used to rank variables according to their influence. Simulation results illustrate the computational efficiency and the performance of the test. An empirical application to house price valuation highlights the behavior of the test using actual data.
The Origin of Behavior

We propose a single evolutionary explanation for the origin of several behaviors that have been observed in organisms ranging from ants to human subjects, including risk-sensitive foraging, risk aversion, loss aversion, probability matching, randomization, and diversification. Given an initial population of individuals, each assigned a purely arbitrary behavior with respect to a binary choice problem, and assuming that offspring behave identically to their parents, only those behaviors linked to reproductive success will survive, and less reproductively successful behaviors will disappear at exponential rates. When the uncertainty in reproductive success is systematic, natural selection yields behaviors that may be individually sub-optimal but are optimal from the population perspective; when reproductive uncertainty is idiosyncratic, the individual and population perspectives coincide. This framework generates a surprisingly rich set of behaviors, and the simplicity and generality of our model suggest that these derived behaviors are primitive and nearly universal within and across species.
On the Likelihood of a Transaction and the Amount of Time Provided the Broker to Sell Property

This work empirically investigates the effect of the amount of time provided the broker to market property (listing contract length) on the likelihood of a successful marketing attempt. Do shorter ...
Privatization in East Germany; A Survey of Current Issues

This paper deals with the privatization, restructuring, and liquidation of East German industrial firms. A partnership model is suggested for privatization where the Government’s property trust (Treuhandanstait (THA)) is made a silent partner of the private investors. The application of a general scheme of wage subsidies is rejected in the paper. Furthermore, the paper argues against restructuring policies of the Government’s property trust and proposes to set decreasing limits on the trust’s finances for the years following 1993. The decreasing financial inflow will force the Government’s trust to close firms, and will also signal the commitment of the Government to liquidate the trust itself by, say, the year 2000.
RACIAL ETIQUETTE AND SOCIAL CAPITAL: CHALLENGES FACING BLACK ENTREPRENEURS

Historically, successful free enterprise has been more difficult for minority entrepreneurs than it has been for whites. Certain bar­ riers limit access to capital and industry markets as well as access to skills and work experience that facilitate proper business develop­ ment and sustainability.1 Merely starting up a business does not mean it will fare well and lead to sustainable self-employment. Notwithstanding an unmistakable boost in minority business start­ ups and the fact that entrepreneurial activity is fifty percent greater among black Americans as compared to whites, black-owned firms have lower survival rates than nonminority firms.2 A contributing cause of this disparity has been, and continues to be, connected to the barriers and challenges rooted in our nation’s not-long-past ra­ cial-caste system and oppression of blacks. Inequity between the races has created a social condition that directly affects an entrepre­ neur’s access to social capital, which is vital to the successful emer­ gence and continued advancement of one’s business. This Article considers social etiquette practices in the United States and suggests that these practices have infected our freeenterprise system with racial bias. By considering socio-racial ineq­
Small Farms in the United States: Persistence Under Pressure

Ninety-one percent of U.S. farms are classified as small—gross cash farm income (GCFI) of less than $250,000. About 60 percent of these small farms are very small, generating GCFI of less than $10,000. These very small noncommercial farms, in some respects, exist independently of the farm economy because their operators rely heavily on off-farm income. The remaining small farms—small commercial farms—account for most small-farm production. Overall farm production, however, continues to shift to larger operations, while the number of small commercial farms and their share of sales maintain a long-term decline. The shift to larger farms will continue to be gradual, because some small commercial farms are profitable and others are willing to accept losses.
Integrable lattice models from gauge theory

These notes provide an introduction to recent work by Kevin Costello in which integrable lattice models of classical statistical mechanics in two dimensions are understood in terms of quantum gauge theory in four dimensions. This construction will be compared to the more familiar relationship between quantum knot invariants in three dimensions and Chern-Simons gauge theory. (Based on a Whittaker Colloquium at the University of Edinburgh and a lecture at Strings 2016 in Beijing.)
Deep NRSfM++: Towards Unsupervised 2D-3D Lifting in the Wild

The recovery of 3D shape and pose from 2D landmarks stemming from a large ensemble of images can be viewed as a non-rigid structure from motion (NRSfM) problem. Classical NRSfM approaches, however, are problematic as they rely on heuristic priors on the 3D structure (e.g. low rank) that do not scale well to large datasets. Learning-based methods are showing the potential to reconstruct a much broader set of 3D structures than classical methods – dramatically expanding the importance of NRSfM to atemporal unsupervised 2D to 3D lifting. Hitherto, these learning approaches have not been able to effectively model perspective cameras or handle missing/occluded points – limiting their applicability to in-the-wild datasets. In this paper, we present a generalized strategy for improving learning-based NRSfM methods [32] to tackle the above issues. Our approach, Deep NRSfM++, achieves state-of-the-art performance across numerous large-scale benchmarks, outperforming both classical and learning-based 2D-3D lifting methods.
Improving the dynamic range of single photon counting kinetic inductance detectors

We develop a simple coordinate transformation that can be employed to compensate for the nonlinearity introduced by a microwave kinetic inductance detector’s (MKID) homodyne readout scheme. This coordinate system is compared to the canonically used polar coordinates and is shown to improve the performance of the filtering method often used to estimate a photon’s energy. For a detector where the coordinate nonlinearity is primarily responsible for limiting its resolving power, this technique leads to increased dynamic range, which we show by applying the transformation to data from a hafnium MKID designed to be sensitive to photons with wavelengths in the 800- to 1300-nm range. The new coordinates allow the detector to resolve photons with wavelengths down to 400 nm, raising the resolving power at that wavelength from 6.8 to 17.
Regressive Mortgage Credit Redistribution in the Post-Crisis Era

We analyze the effects of a recent piece of consumer protection regulation -- Dodd-Frank -- on mortgage originations. Dodd-Frank aimed at reducing mortgage fees and abuses against vulnerable borrowers, but increased the costs of originating mortgages. We find it triggered a substantial redistribution of credit from middle-class households to wealthy households. Lenders reduced credit to middle-class households by 15%, and increased it to wealthy households by 21%, after controlling for drivers of the demand for housing, local house prices, and foreclosures. Large lenders found it less costly to react to Dodd-Frank. We thus instrument households' exposure to Dodd-Frank with the pre-crisis share of mortgages originated by large lenders in each county. The redistribution of credit from the middle-class to the wealthy was higher in counties more exposed to large lenders, which are similar to other counties. Results hold at the individual-loan level and zip-code level, at the intensive margin (amount lent) and extensive margin (number of loans originated), and for accepted and rejected loans. Changes in the distribution of refinancing loans do not explain the results.
Exploring the Impacts of Conformer Selection Methods on Ion Mobility Collision Cross Section Predictions.

The prediction of structure dependent molecular properties, such as collision cross sections as measured using ion mobility spectrometry, are crucially dependent on the selection of the correct pop...
The Impact of Presentation Style on Human-In-The-Loop Detection of Algorithmic Bias.

While decision makers have begun to employ machine learning, machine learning models may make predictions that bias against certain demographic groups. Semi-automated bias detection tools often present reports of automatically-detected biases using a recommendation list or visual cues. However, there is a lack of guidance concerning which presentation style to use in what scenarios. We conducted a small lab study with 16 participants to investigate how presentation style might affect user behaviors in reviewing bias reports. Participants used both a prototype with a recommendation list and a prototype with visual cues for bias detection. We found that participants often wanted to investigate the performance measures that were not automatically detected as biases. Yet, when using the prototype with a recommendation list, they tended to give less consideration to such measures. Grounded in the findings, we propose information load and comprehensiveness as two axes for characterizing bias detection tasks and illustrate how the two axes could be adopted to reason about when to use a recommendation list or visual cues.
Get on the BAND Wagon: A Bayesian Framework for Quantifying Model Uncertainties in Nuclear Dynamics

We describe the Bayesian Analysis of Nuclear Dynamics (BAND) framework, a cyberinfrastructure that we are developing which will unify the treatment of nuclear models, experimental data, and associated uncertainties. We overview the statistical principles and nuclear-physics contexts underlying the BAND toolset, with an emphasis on Bayesian methodology's ability to leverage insight from multiple models. In order to facilitate understanding of these tools we provide a simple and accessible example of the BAND framework's application. Four case studies are presented to highlight how elements of the framework will enable progress on complex, far-ranging problems in nuclear physics. By collecting notation and terminology, providing illustrative examples, and giving an overview of the associated techniques, this paper aims to open paths through which the nuclear physics and statistics communities can contribute to and build upon the BAND framework.
Impact of hospital volume on mortality for brain metastases treated with radiation.

BACKGROUND: The impact of hospital volume on cancer patient survival has been demonstrated in the surgical literature, but sparsely for patients receiving radiation therapy (RT). This analysis addresses the impact of hospital volume on patients receiving RT for the most common central nervous system tumor: brain metastases. MATERIALS AND METHODS: Analysis was conducted using the National Cancer Database (NCDB) from 2010–2015 for patients with metastatic brain disease from lung cancer, breast cancer, and colorectal cancer requiring RT. Hospital volume was stratified as high-volume (≥ 12 brain RT/year), moderate (5–11 RT/year), and low (< 5 RT/year). The effect of hospital volume on overall survival was assessed using a multivariable Cox regression model. RESULTS: A total of 18,841 patients [9479 (50.3%) men, 9362 (49.7%) women; median age 64 years] met the inclusion criteria. 16.7% were treated at high-volume hospitals, 36.5% at moderate-volume, and the remaining 46.8% at low-volume centers. Multivariable analysis revealed that mortality was significantly improved in high-volume centers (HR: 0.95, p = 0.039) compared with low-volume centers after accounting for multiple demographics including age, sex, race, insurance status, income, facility type, Charlson-Deyo score and receipt of palliative care. CONCLUSION: Hospitals performing 12 or more brain RT procedures per year have significantly improved survival in brain metastases patients receiving radiation as compared to lower volume hospitals. This finding, independent of additional demographics, indicates that the increased experience associated with increased volume may improve survival in this patient population.
Transcriptome-wide investigation of stop codon readthrough in Saccharomyces cerevisiae.

Translation of mRNA into a polypeptide is terminated when the release factor eRF1 recognizes a UAA, UAG, or UGA stop codon in the ribosomal A site and stimulates nascent peptide release. However, stop codon readthrough can occur when a near-cognate tRNA outcompetes eRF1 in decoding the stop codon, resulting in the continuation of the elongation phase of protein synthesis. At the end of a conventional mRNA coding region, readthrough allows translation into the mRNA 3’-UTR. Previous studies with reporter systems have shown that the efficiency of termination or readthrough is modulated by cis-acting elements other than stop codon identity, including two nucleotides 5’ of the stop codon, six nucleotides 3’ of the stop codon in the ribosomal mRNA channel, and stem-loop structures in the mRNA 3’-UTR. It is unknown whether these elements are important at a genome-wide level and whether other mRNA features proximal to the stop codon significantly affect termination and readthrough efficiencies in vivo. Accordingly, we carried out ribosome profiling analyses of yeast cells expressing wild-type or temperature-sensitive eRF1 and developed bioinformatics strategies to calculate readthrough efficiency, and to identify mRNA and peptide features which influence that efficiency. We found that the stop codon (nt +1 to +3), the nucleotide after it (nt +4), the codon in the P site (nt -3 to -1), and 3’-UTR length are the most influential features in the control of readthrough efficiency, while nts +5 to +9 had milder effects. Additionally, we found low readthrough genes to have shorter 3’-UTRs compared to high readthrough genes in cells with thermally inactivated eRF1, while this trend was reversed in wild-type cells. Together, our results demonstrated the general roles of known regulatory elements in genome-wide regulation and identified several new mRNA or peptide features affecting the efficiency of translation termination and readthrough.
Meta-GWAS for quantitative trait loci identification in soybean

We report a meta-Genome Wide Association Study involving 73 published studies in soybean (Glycine max L. [Merr.]) covering 17,556 unique accessions, with improved statistical power for robust detection of loci associated with a broad range of traits. De novo GWAS and meta-analysis were conducted for composition traits including fatty acid and amino acid composition traits, disease resistance traits, and agronomic traits including seed yield, plant height, stem lodging, seed weight, seed mottling, seed quality, flowering timing, and pod shattering. To examine differences in detectability and test statistical power between single- and multi-environment GWAS, comparison of meta-GWAS results to those from the constituent experiments were performed. Using meta-GWAS analysis and the analysis of individual studies, we report 483 peaks at 393 unique loci. Using stringent criteria to detect significant marker trait associations, 59 candidate genes were identified, including 17 agronomic traits loci, 19 for seed related traits, and 33 for disease reaction traits. This study identified potentially valuable candidate genes that affect multiple traits. The success in narrowing down the genomic region for some loci through overlapping mapping results of multiple studies is a promising avenue for community-based studies and plant breeding applications.
Motif-driven Dense Subgraph Discovery in Directed and Labeled Networks

Dense regions in networks are an indicator of interesting and unusual information. However, most existing methods only consider simple, undirected, unweighted networks. Complex networks in the real-world often have rich information though: edges are asymmetrical and nodes/edges have categorical and numerical attributes. Finding dense subgraphs in such networks in accordance with this rich information is an important problem with many applications. Furthermore, most existing algorithms ignore the higher-order relationships (i.e., motifs) among the nodes. Motifs are shown to be helpful for dense subgraph discovery but their wide spectrum in heterogeneous networks makes it challenging to utilize them effectively. In this work, we propose quark decomposition framework to locate dense subgraphs that are rich with a given motif. We focus on networks with directed edges and categorical attributes on nodes/edges. For a given motif, our framework builds subgraphs, called quarks, in varying quality and with hierarchical relations. Our framework is versatile, efficient, and extendible. We discuss the limitations and practical instantiations of our framework as well as the role confusion problem that needs to be considered in directed networks. We give an extensive evaluation of our framework in directed, signed-directed, and node-labeled networks. We consider various motifs and evaluate the quark decomposition using several real-world networks. Results show that quark decomposition performs better than the state-of-the-art techniques. Our framework is also practical and scalable to networks with up to 101M edges.
Non-Interactive Anonymous Router

Anonymous routing is one of the most fundamental online privacy problems and has been studied extensively for decades. Almost all known approaches for anonymous routing (e.g., mix-nets, DC-nets, and others) rely on multiple servers or routers to engage in some interactive protocol; and anonymity is guaranteed in the threshold model, i.e., if one or more of the servers/routers behave honestly.
Efficient ancilla-free reversible and quantum circuits for the Hidden Weighted Bit function

The Hidden Weighted Bit function plays an important role in the study of classical models of computation. A common belief is that this function is exponentially hard to implement using reversible ancilla-free circuits, even though introducing a small number of ancillae allows a very efficient implementation. In this paper, we refute the exponential hardness conjecture by developing a polynomial-size reversible ancilla-free circuit computing the Hidden Weighted Bit function. Our circuit has size O(n^6.42), where n is the number of input bits. We also show that the Hidden Weighted Bit function can be computed by a quantum ancilla-free circuit of size O(n^2). The technical tools employed come from a combination of Theoretical Computer Science (Barringtons theorem) and Physics (simulation of fermionic Hamiltonians) techniques.
Alternating Projections Gridless Covariance-Based Estimation For DOA

We present a gridless sparse iterative covariance-based estimation method based on alternating projections for direction-of-arrival (DOA) estimation. The gridless DOA estimation is formulated in the reconstruction of Toeplitz-structured low rank matrix, and is solved efficiently with alternating projections. The method improves resolution by achieving sparsity, deals with single-snapshot data and coherent arrivals, and, with co-prime arrays, estimates more DOAs than the number of sensors. We evaluate the proposed method using simulation results focusing on co-prime arrays.
Existence and exactness of exponential Riesz sequences and frames for fractal measures

We study the construction of exponential frames and Riesz sequences for a class of fractal measures on ℝd generated by infinite convolution of discrete measures using the idea of frame towers and Riesz-sequence towers. The exactness and overcompleteness of the constructed exponential frame or Riesz sequence is completely classified in terms of the cardinality at each level of the tower. Using a version of the solution of the Kadison-Singer problem, known as the Rϵ-conjecture, we show that all these measures contain exponential Riesz sequences of infinite cardinality. Furthermore, when the measure is the middle-third Cantor measure, or more generally for self-similar measures with no-overlap condition, there are always exponential Riesz sequences of maximal possible Beurling dimension.
Streaming approximation resistance of every ordering CSP

An ordering constraint satisfaction problem (OCSP) is given by a positive integer k and a constraint predicate Π mapping permutations on {1,…,k} to {0,1}. Given an instance of OCSP(Π) on n variables and m constraints, the goal is to find an ordering of the n variables that maximizes the number of constraints that are satisfied, where a constraint specifies a sequence of k distinct variables and the constraint is satisfied by an ordering on the n variables if the ordering induced on the k variables in the constraint satisfies Π. Ordering constraint satisfaction problems capture natural problems including "Maximum acyclic subgraph (MAS)" and "Betweenness". In this work we consider the task of approximating the maximum number of satisfiable constraints in the (single-pass) streaming setting, where an instance is presented as a stream of constraints. We show that for every Π, OCSP(Π) is approximation-resistant to o(n)-space streaming algorithms, i.e., algorithms using o(n) space cannot distinguish streams where almost every constraint is satisfiable from streams where no ordering beats the random ordering by a noticeable amount. This space bound is tight up to polylogarithmic factors. In the case of MAS our result shows that for every e > 0, MAS is not 1/2+e-approximable in o(n) space. The previous best inapproximability result only ruled out a 3/4-approximation in o(√ n) space. Our results build on recent works of Chou, Golovnev, Sudan, Velingker, and Velusamy who show tight, linear-space inapproximability results for a broad class of (non-ordering) constraint satisfaction problems (CSPs) over arbitrary (finite) alphabets. Our results are obtained by building a family of appropriate CSPs (one for every q) from any given OCSP, and applying their work to this family of CSPs. To convert the resulting hardness results for CSPs back to our OCSP, we show that the hard instances from this earlier work have the following "small-set expansion" property: If the CSP instance is viewed as a hypergraph in the natural way, then for every partition of the hypergraph into small blocks most of the hyperedges are incident on vertices from distinct blocks. By exploiting this combinatorial property, in combination with the hardness results of the resulting families of CSPs, we give optimal inapproximability results for all OCSPs.
Polee: RNA-Seq analysis using approximate likelihood.

The analysis of mRNA transcript abundance with RNA-Seq is a central tool in molecular biology research, but often analyses fail to account for the uncertainty in these estimates, which can be significant, especially when trying to disentangle isoforms or duplicated genes. Preserving uncertainty necessitates a full probabilistic model of the all the sequencing reads, which quickly becomes intractable, as experiments can consist of billions of reads. To overcome these limitations, we propose a new method of approximating the likelihood function of a sparse mixture model, using a technique we call the Polya tree transformation. We demonstrate that substituting this approximation for the real thing achieves most of the benefits with a fraction of the computational costs, leading to more accurate detection of differential transcript expression and transcript coexpression.
Reciprocal repulsions instruct the precise assembly of parallel hippocampal networks

Mammalian medial and lateral hippocampal networks preferentially process spatial- and object-related information, respectively. However, the mechanisms underlying the assembly of such parallel netw...
Social-distancing fatigue: Evidence from real-time crowd-sourced traffic data.

Abstract Introduction To mitigate the COVID-19 pandemic and prevent overwhelming the healthcare system, social-distancing policies such as school closure, stay-at-home orders, and indoor dining closure have been utilized worldwide. These policies function by reducing the rate of close contact within populations and result in decreased human mobility. Adherence to social distancing can substantially reduce disease spread. Thus, quantifying human mobility and social-distancing compliance, especially at high temporal resolution, can provide great insight into the impact of social distancing policies. Methods We used the movement of individuals around New York City (NYC), measured via traffic levels, as a proxy for human mobility and the impact of social-distancing policies (i.e., work from home policies, school closure, indoor dining closure etc.). By data mining Google traffic in real-time, and applying image processing, we derived high resolution time series of traffic in NYC. We used time series decomposition and generalized additive models to quantify changes in rush hour/non-rush hour, and weekday/weekend traffic, pre-pandemic and following the roll-out of multiple social distancing interventions. Results Mobility decreased sharply on March 14, 2020 following declaration of the pandemic. However, levels began rebounding by approximately April 13, almost 2 months before stay-at-home orders were lifted, indicating premature increase in mobility, which we term social-distancing fatigue. We also observed large impacts on diurnal traffic congestion, such that the pre-pandemic bi-modal weekday congestion representing morning and evening rush hour was dramatically altered. By September, traffic congestion rebounded to approximately 75% of pre-pandemic levels. Conclusion Using crowd-sourced traffic congestion data, we described changes in mobility in Manhattan, NYC, during the COVID-19 pandemic. These data can be used to inform human mobility changes during the current pandemic, in planning for responses to future pandemics, and in understanding the potential impact of large-scale traffic interventions such as congestion pricing policies.
Passing the Test: A Model-Based Analysis of Safe School-Reopening Strategies.

BACKGROUND: The COVID-19 pandemic has induced historic educational disruptions. In April 2021, about 40% of U.S. public school students were not offered full-time in-person education. OBJECTIVE: To assess the risk for SARS-CoV-2 transmission in schools. DESIGN: An agent-based network model was developed to simulate transmission in elementary and high school communities, including home, school, and interhousehold interactions. SETTING: School structure was parametrized to reflect average U.S. classrooms, with elementary schools of 638 students and high schools of 1451 students. Daily local incidence was varied from 1 to 100 cases per 100 000 persons. PARTICIPANTS: Students, faculty, staff, and adult household members. INTERVENTION: Isolation of symptomatic individuals, quarantine of an infected individual's contacts, reduced class sizes, alternative schedules, staff vaccination, and weekly asymptomatic screening. MEASUREMENTS: Transmission was projected among students, staff, and families after a single infection in school and over an 8-week quarter, contingent on local incidence. RESULTS: School transmission varies according to student age and local incidence and is substantially reduced with mitigation measures. Nevertheless, when transmission occurs, it may be difficult to detect without regular testing because of the subclinical nature of most children's infections. Teacher vaccination can reduce transmission to staff, and asymptomatic screening improves understanding of local circumstances and reduces transmission. LIMITATION: Uncertainty exists about the susceptibility and infectiousness of children, and precision is low regarding the effectiveness of specific countermeasures, particularly with new variants. CONCLUSION: With controlled community transmission and moderate mitigation, elementary schools can open safety, but high schools require more intensive mitigation. Asymptomatic screening can facilitate reopening at higher local incidence while minimizing transmission risk. PRIMARY FUNDING SOURCE: Centers for Disease Control and Prevention through the Council of State and Territorial Epidemiologists, National Institute of Allergy and Infectious Diseases, National Institute on Drug Abuse, and Facebook.
APo-VAE: Text Generation in Hyperbolic Space

Natural language often exhibits inherent hierarchical structure ingrained with complex syntax and semantics. However, most state-of-the-art deep generative models learn embeddings only in Euclidean vector space, without accounting for this structural property of language. In this paper, we investigate text generation in a hyperbolic latent space to learn continuous hierarchical representations. An Adversarial Poincare Variational Autoencoder (APo-VAE) is presented, where both the prior and variational posterior of latent variables are defined over a Poincare ball via wrapped normal distributions. By adopting the primal-dual formulation of Kullback-Leibler divergence, an adversarial learning procedure is introduced to empower robust model training. Extensive experiments in language modeling, unaligned style transfer, and dialog-response generation demonstrate the effectiveness of the proposed APo-VAE model over VAEs in Euclidean latent space, thanks to its superb capabilities in capturing latent language hierarchies in hyperbolic space.
Interlaminar fracture toughness of a quasi 3D braided composite

Fiber reinforced composite materials are a heavily sought after material for next generation vehicles for light-weighting components due to their high specific strength and stiffness. However, thes...
Wavelet field decomposition and UV ‘opaqueness’

A large body of work over several decades indicates that, in the presence of gravitational interactions, there is loss of localization resolution within a fundamental (∼ Planck) length scale l. We develop a general formalism based on wavelet decomposition of fields that takes this UV ‘opaqueness’ into account in a natural and mathematically well-defined manner. This is done by requiring fields in a local Lagrangian to be expandable in only the scaling parts of a (complete or, in a more general version, partial) wavelet Multi-Resolution Analysis. This delocalizes the interactions, now mediated through the opaque regions, inside which they are rapidly decaying. The opaque regions themselves are capable of discrete excitations of ∼ 1/l spacing. The resulting effective Feynman rules, which give UV regulated and (perturbatively) unitary physical amplitudes, resemble those of string field theory.
Distributed Data Storage and Fusion for Collective Perception in Resource-Limited Mobile Robot Swarms

In this letter, we propose an approach to the distributed storage and fusion of data for collective perception in resource-limited robot swarms. We demonstrate our approach in a distributed semantic classification scenario. We consider a team of mobile robots, in which each robot runs a pre-trained classifier of known accuracy to annotate objects in the environment. We provide two main contributions: (i) a decentralized, shared data structure for efficient storage and retrieval of the semantic annotations, specifically designed for low-resource mobile robots; and (ii) a voting-based, decentralized algorithm to reduce the variance of the calculated annotations in presence of imperfect classification. We discuss theory and implementation of both contributions, and perform an extensive set of realistic simulated experiments to evaluate the performance of our approach.
Analyzing Societal Impact of COVID-19: A Study During the Early Days of the Pandemic

In this paper, we collect and study Twitter communications to understand the societal impact of COVID-19 in the United States during the early days of the pandemic. With infections soaring rapidly, users took to Twitter asking people to self isolate and quarantine themselves. Users also demanded closure of schools, bars, and restaurants as well as lockdown of cities and states. We methodically collect tweets by identifying and tracking trending COVID-related hashtags. We first manually group the hashtags into six main categories, namely, 1) General COVID, 2) Quarantine, 3) Panic Buying, 4) School Closures, 5) Lockdowns, and 6) Frustration and Hope, and study the temporal evolution of tweets in these hashtags. We conduct a linguistic analysis of words common to all hashtag groups and specific to each hashtag group and identify the chief concerns of people as the pandemic gripped the nation (e.g., exploring bidets as an alternative to toilet paper). We conduct sentiment analysis and our investigation reveals that people reacted positively to school closures and negatively to the lack of availability of essential goods due to panic buying. We adopt a state-of-the-art semantic role labeling approach to identify the action words (e.g., fear, test), which capture the actions people are referring to in the tweets. We then leverage an LSTM-based dependency parsing model to analyze the context of the above-mentioned action words (e.g., verb deal is accompanied by nouns such as anxiety, stress, and crisis). Finally, we develop a scalable seeded topic modeling approach to automatically categorize and isolate tweets into hashtag groups and experimentally validate that our topic model provides a grouping similar to our manual grouping. Our study presents a systematic way to construct an aggregated picture of peoples' response to the pandemic and lays the groundwork for future fine-grained linguistic and behavioral analysis.