cssi/SciSciGPT-SciSciNet huggingface dataset:
Clear Notice for Conditions on Spending, Unclear Implications for States in Federal Healthcare Programs

This article explores an important case from the 2005-06 Supreme Court term, Arlington Central School District Board of Education v. Murphy. Murphy is a benchmark for Spending Clause jurisprudence, as the new Roberts Court adopted what was the dissenting view for years, but its significance has gone largely unnoticed. Additionally, Murphy may have critical implications for the federalism revolution and for the country's largest healthcare programs. These broad observations are focused in this article by the example of the Clawback Provision, a new Medicaid requirement that has been challenged by New Jersey, Texas, Maine, Missouri, and Kentucky. The Supreme Court denied original jurisdiction, but the states are likely to proceed in district court (they stand to lose millions of dollars under the Clawback Provision). This article finds that the Murphy analysis alters long-standing Spending Clause doctrine in a way that is likely to narrow Congress' ability to place conditions on federal funds. Also, if the Court was seeking to strengthen the states' position regarding conditions on federal spending, it missed the mark because it ignores the reality of long-standing spending programs and states' inability to reject federal conditions in such programs (as with the Clawback Provision). This piece also observes that Congress could not have anticipated the clarity required by Murphy when drafting the Medicare and Medicaid statutes and that this new standard could have far-reaching effects on these forty-year-old healthcare programs. Ultimately, the article calls for a fresh look at spending power jurisprudence in light of both Murphy and long-entrenched spending programs such as Medicare and Medicaid.
Money versus credit in the determination of output for small open economies

It is well known that in a small open economy where there is perfect substitutability between domestic and foreign assets and costless portfolio adjustment, the monetary authorities, pursuing an exchange-rate target, cannot control the money supply, but can influence the balance of payments through the use of domestic credit. It has been argued that domestic credit is therefore the relevant variable in output determination as well. This paper demonstrates, however, using a “new classical” structural model, that under the conditions that render the money supply uncontrollable, neither money nor domestic credit affects output.
The Derivatives Sourcebook

1 Introduction 2 Applications of Option-Pricing Theory: Twenty-Five Years Later* 3 Derivatives in a Dynamic Environment* 4 Classification Categories 5 Citations.
Renting to Owning: An Exploration of the Theory of Planned Behavior in the Homeownership Domain

This study extends the theory of planned behavior (TPB; Ajzen, 1991) to the domain of homeownership. We used a 4-year longitudinal data set of 919 low-and-moderate income renters to explore factors associated with greater homeownership intentions and actual home purchases. Our findings provide strong support for the TPB. Favorable attitudes and subjective norms and greater perceptions of control were all associated with greater homeownership intentions. Homeownership intentions, in turn, predicted home purchases during the following year. The analysis included relevant demographic and economic variables, and the significance of income and geographic location suggests a distinction between respondents' perceived versus actual control. Our use of a longitudinal panel data set represents an important advance over much of the prior TPB literature, which tends to use cross-sectional designs and focus on short-term goals. We discuss implications for behavioral prediction using the TPB as well as implications fo...
CEO Pay‐for‐Complexity and the Risk of Managerial Diversion from Multinational Diversification

Prior studies find that CEOs receive higher pay if the enterprise is more complex because more complex enterprises are, in theory, matched with the managerial skills of higher-ability CEOs. While multinational diversification is typically a characteristic of enterprise complexity, we argue that multinational diversification also introduces a risk that executives will divert an enterprise’s resources to obtain private benefits. We first establish that CEO pay is, on average, increasing in the extent of multinational diversification, consistent with intuition that more complex enterprises are matched to higher‐ability CEOs. We then demonstrate that the CEO pay‐for‐complexity premium is lower if the multinational diversification reflects a relatively high risk of managerial diversion. For sufficiently high levels of multinational diversification accompanied by a high risk of managerial diversion, we find that CEOs receive a relative reduction in pay rather than a pay premium for multinational diversification. We also find evidence that this pay effect occurs in part through adjustments to a CEO’s pay‐for‐performance sensitivity.
When Bad Speech Does Good

Defenses of offensive speech typically include a distancing disclaimer, highlighting the separation between the person offering the defense and the offensive content itself.1 Consider, for example, the well-worn quotation widely misattributed to Voltaire: “I disagree with what you say, but I will defend to the death your right to say it.”2 In this view, it is the principle, not the substance, of the speech that deserves respect. This view may be grounded in the belief, or some combination of beliefs, that one person’s “bad” speech is another person’s “good” speech;3 that even avowedly bad speech helps produce, or at least sharpen, good speech;4 that because the line between bad and good speech is a difficult one to draw, censorship is likely to stifle or chill good speech as well as bad speech;5 and/or that even if lines
The Corporate Lawyer and 'The Perjury Trilemma'

This paper extends Monroe Freedman’s idea of the criminal lawyer’s “perjury trilemma” to current issues faced by corporate lawyers dealing with perceived pressures on the attorney-client privilege. The duties of criminal defense and corporate lawyers are more similar than they often seem. Corporate lawyers’ duties of honesty in dealing with third parties are closely analogous to criminal lawyers’ duties of honesty in dealing with a court. Both sets of lawyers also have an important interest in fostering open communications with their clients. Where their situations differ is not with respect to lawyer obligations but with respect to their clients’ rights. Corporations, for example, lack a privilege against self-incrimination possessed by individual criminal defendants. Further, many current issues of corporate fraud involve information not privileged at all because of the crime-fraud exception. Finally, corporate clients have a power to compel employees to disclose personally adverse information without the encouragement offered by protecting the information as privileged. These differences between rights of corporate clients and those of individual criminal defendants should lead corporate lawyers to rely less on arguments analogous to those of the “perjury trilemma” as they respond to today’s perceived pressures on the corporate attorney-client privilege.
Public Expenditure Research: What Have We Learned?

This article will examine progress made in the field of public expenditure theory in the quarter century since Burkhead and Miner (1971). First, normative expenditure theory will be examined along with its influence on analytic techniques, such as cost-benefit analysis. The second area of analysis will be empirical research on public expenditure decisions. Finally, areas for future research will be identified.
Google Glass While Driving

Is it legal to use Google Glass while driving? Most states ban texting while driving and a large number also forbid drivers from being able to see television and video screens. But do these statutes apply to Google Glass? Google advises users to check their states’ law and to “[r]ead up and follow the law!” Yet, laws designed for a tangible world are very difficult to apply to virtual screens projected by futuristic wearable technology. In short order, however, police and prosecutors across the country will be called upon to apply outdated distracted driving laws to Google Glass. This article describes how the plain language of most distracted driving statutes is not broad enough to reach Google Glass. Moreover, even statutes that arguably forbid drivers from “using” Glass are practically unenforceable because drivers could easily claim the devices were turned off or that they were being used for lawful functions—such as phone calls or GPS directions—that are allowed under texting while driving statutes. The lack of a clear prohibition on Google Glass while driving is troublesome. Social science evidence demonstrates that using hands-free devices while driving creates “cognitive tunnel vision” that drastically reduces drivers’ mental focus on the road. After analyzing the nation’s distracted driving laws and reviewing the social science evidence, this article proposes a statutory framework for effectively banning Google Glass while driving. * Associate Dean for Research and Faculty Development & Kelly Professor of Teaching Excellence, William & Mary Law School. I am grateful to Shane Rumbaugh—a first-wave Google Glass explorer—for loaning me his Glass for several weeks as I worked on this article. I also appreciate the research assistance of Alana Biltucci and Fred Dingledy. Jeff Bellin, Tara Grove, Fred Lederer, Alli Larsen, and Paul Marcus provided helpful comments. 1. FAQ: Can I use Glass While Driving or Bicycling?, GOOGLE GLASS, https://support.google.com/glass/answer/3064131?hl=en&ref_topic=3063354. (last visited Oct. 13, 2015). 756 ARIZONA STATE LAW JOURNAL [Ariz. St. L.J.
Elections, Ideology, or Opposition? Assessing Competing Explanations of Judicial Spending in the Mexican States

This study offers the first time series cross-section analysis of state courts in Mexico, explaining variation in judicial spending across Mexicos 31 states from 1993 to 2009. Cutting against mainstream accounts of judicial empowerment that highlight electoral competition, I conclude that increasing competition, while a necessary precondition for the emergence of new political actors, has mixed results after a minimum, threshold level. Ideological motivations, especially on the left, enhance court budgets, and these motivations surmount the dampening, Downsian effect of competition. Further, the reduced effect of the PAN (National Action Party, former opposition party) and the increased positive effect of the PRI (Institutional Revolutionary Party, former national hegemon) since 2000 suggest ways in which former opposition parties can become less ideological (or more pro-regime), whereas former dominant parties can become more ideological (or more anti-regime). The results not only identify clear relationships but also highlight the complex electoral and partisan sources of policy change in authoritarian and emerging democratic regimes. (JELC23, H76, K49)
Productivity Differences Between and Within Countries

We document substantial within-country {cross-municipality) differ ences in incomes for a large number of countries in the Americas. A significant fraction of the within-country differences cannot be explained by observed human capital. We conjecture that the sources of within-country and between-country differences are related. As a first step toward a unified framework, we propose a simple model incorporating differences in technological know-how across coun tries and differences in productive efficiency within countries. (JEL E23,131, J31, 015, 018, 047, R23) Spatial differences in income per capita motivate much of growth theory and development economics. While income differences across countries and across regions within some countries have been documented extensively, there is little systematic evidence on how interregional income differences (within countries) compare and relate to inequality across countries. The relative magnitudes of cross country, cross-municipality, and within-municipality differences are important for at least two related reasons. First, they shed light on the extent to which sources of major economic differences in income and productivity are national, local, and idiosyncratic in nature, documenting patterns that comprehensive explanations of growth and development should strive to match. Second, they signal the possible presence of important interlinkages between local and national determinants of pro ductivity, which would necessitate a unified theoretical framework for analysis. This paper documents the magnitudes of cross-country, cross-municipality, and within-municipality inequality in labor incomes and household expenditure for the Americas (Canada, Latin America, and the United States), a large geographic region containing almost 1 billion people and about 30 percent of global gross domestic product (GDP). Our contribution is two-fold. First, we document substantial within country (and cross-municipality) differences in output and standards of living for
Resilient Task Allocation in Heterogeneous Multi-Robot Systems

This letter presents a resilient mechanism to allocate heterogeneous robots to tasks under difficult environmental conditions such as weather events or adversarial attacks. Our primary objective is to ensure that each task is assigned the requisite level of resources, measured as the aggregated capabilities of the robots allocated to the task. By keeping track of task performance deviations under external perturbations, our framework quantifies the extent to which robot capabilities (e.g., visual sensing or aerial mobility) are affected by environmental conditions. This enables an optimization-based framework to flexibly reallocate robots to tasks based on the most degraded capabilities within each task. In the face of resource limitations and adverse environmental conditions, our algorithm relaxes the resource constraints corresponding to some tasks, thus exhibiting a graceful degradation of performance. Simulated experiments in a multi-robot coverage and target tracking scenario demonstrate the efficacy of the proposed approach.
A generalization of Bellman’s equation with application to path planning, obstacle avoidance and invariant set estimation

Abstract The standard Dynamic Programming (DP) formulation can be used to solve Multi-Stage Optimization Problems (MSOP’s) with additively separable objective functions. In this paper we consider a larger class of MSOP’s with monotonically backward separable objective functions; additively separable functions being a special case of monotonically backward separable functions. We propose a necessary and sufficient condition, utilizing a generalization of Bellman’s equation, for a solution of a MSOP, with a monotonically backward separable cost function, to be optimal. Moreover, we show that this proposed condition can be used to efficiently compute optimal solutions for two important MSOP’s; the optimal path for Dubin’s car with obstacle avoidance, and the maximal invariant set for discrete time systems.
SPLASH: Learnable activation functions for improving accuracy and adversarial robustness.

Abstract We introduce SPLASH units, a class of learnable activation functions shown to simultaneously improve the accuracy of deep neural networks while also improving their robustness to adversarial attacks. SPLASH units have both a simple parameterization and maintain the ability to approximate a wide range of non-linear functions. SPLASH units are: (1) continuous; (2) grounded ( f ( 0 ) = 0 ); (3) use symmetric hinges; and (4) their hinges are placed at fixed locations which are derived from the data (i.e. no learning required). Compared to nine other learned and fixed activation functions, including ReLU and its variants, SPLASH units show superior performance across three datasets (MNIST, CIFAR-10, and CIFAR-100) and four architectures (LeNet5, All-CNN, ResNet-20, and Network-in-Network). Furthermore, we show that SPLASH units significantly increase the robustness of deep neural networks to adversarial attacks. Our experiments on both black-box and white-box adversarial attacks show that commonly-used architectures, namely LeNet5, All-CNN, Network-in-Network, and ResNet-20, can be up to 31% more robust to adversarial attacks by simply using SPLASH units instead of ReLUs. Finally, we show the benefits of using SPLASH activation functions in bigger architectures designed for non-trivial datasets such as ImageNet.
DG Structure on the Length 4 Big From Small Construction

The big from small construction was introduced by Kustin and Miller in [A. Kustin and M. Miller, Constructing big Gorenstein ideals from small ones, J. Algebra 85 (1983) 303–322] and can be used to...
Designing building blocks for open-ended early literacy software

Abstract English has a convoluted relationship between its pronunciation and spelling, which obscures its phonological structure for early literacy learners. This convoluted relationship has implications for early literacy software, particularly for open-ended, child-driven designs. A tempting way to bypass this issue is to use manipulables (blocks) that are directly tied to phonemes. However, creating phoneme-based blocks leads to two design challenges: (a) how to represent phonemes visually in a child-accessible way and (b) how to account for context-dependent spelling. In the present work, we approached these challenges by developing a set of animated, onomatopoeia-based mnemonic characters, one per phoneme, that can take the shape of different graphemes. We applied the characters to a construction-based literacy app to simplify independent word-building for literacy beginners. We tested the app during a 13-week-long period with 4- to 5-year-olds in kindergarten classrooms. Children showed visible interest in the characters and properly grasped the principles of their functioning. However, the blocks were not sufficient to scaffold independent word building, leading children to rely on other scaffolding mechanisms. To test the characters’ efficiency as mnemonics, we evaluated their effect on the speed and accuracy of finding phonemes on a keyboard. The results suggest that there were both children who benefitted from the characters in this task and those who performed better without them. The factors that differentiated these two categories are currently unclear. To help further research on phonetic mnemonics in literacy learning software, we are making the characters available to the research community.
Effects of collision-enhanced charging on dust crystal

Numerical simulations of monolayer dust crystals in a radio frequency (RF) complex plasma were performed to examine the crystal structure and quantify the effects of including the collision-enhanced ion current in the charging model. A gaseous electronics conference (GEC) cell similar to a previous experimental work was modeled for a range of RF voltages, using a continuum description for the plasma and a particle description for dust grains. The time history of each dust grain was monitored. The dust charge was computed using both the orbital motion limited and the collision-enhanced charging (CEC) model applicable to the sheath region. The dust model accounted for the electric force, ion drag force, neutral drag force, gravity, and the ion wake. The CEC model produced a lower charge and lower electric force which agreed better with the experimental data. Then dust crystals composed of 40–100 grains were modeled and the levitation height and inter-particle spacing of the resulting crystals was examined. Including the collision-enhanced current reduced the inter-particle spacing but only had a minor effect on the levitation height.
Mechanical Responses of Primary-α Ti Grains in Polycrystalline Samples: Part II—Bayesian Estimation of Crystal-Level Elastic-Plastic Mechanical Properties from Spherical Indentation Measurements

In the second part of the work, a two-step Bayesian framework is utilized for the estimation of values of the single-crystal elastic constants as well as the initial slip resistances of the different slip families in the primary α-phase components in the Ti alloys of different compositions. These estimations are based on the spherical indentation measurements presented in Part I of this series. The first step of the two-step Bayesian framework established a reduced-order model which captures the dependence of the indentation property as a function of the relevant crystal-level (intrinsic) material properties and the crystallographic lattice orientation in the indentation deformation zone. This reduced-order model is calibrated to high-fidelity results obtained from suitable crystal-plasticity finite element simulations. The second step involved the calibration of the indentation measurements obtained within the primary α-phase (from Part I of this series) to the reduced-order model established in the first step. It is demonstrated that the protocols described above result in the successful generation of a comprehensive dataset of single-crystal elastic–plastic properties across a collection of Ti alloys while accounting for the implicit uncertainties in the spherical indentation measurements.
Theory of semidefinite programming for Sensor Network Localization

We analyze the semidefinite programming (SDP) based model and method for the position estimation problem in sensor network localization and other Euclidean distance geometry applications. We use SDP duality and interior-point algorithm theories to prove that the SDP localizes any network or graph that has unique sensor positions to fit given distance measures. Therefore, we show, for the first time, that these networks can be localized in polynomial time. We also give a simple and efficient criterion for checking whether a given instance of the localization problem has a unique realization in $$\mathcal{R}^2$$ using graph rigidity theory. Finally, we introduce a notion called strong localizability and show that the SDP model will identify all strongly localizable sub-networks in the input network.
Arrokoth’s necklace

Flyby images of (486958) Arrokoth (Ultima Thule, 2014 MU$_{69}$) show a comparatively bright "necklace" in the neck, or cleft between its two lobes, in contrast to its generally low albedo. We suggest that the necklace may be the result of thermally controlled ice deposition. The necklace is found in the most (orbitally averaged) shaded part of its surface. It may consist of clean, high albedo, ice condensed from vapor sublimed by dirty, low albedo, ice elsewhere; clean ice accumulates where the maximum temperatures are the lowest. Ammonia and propane have the necessary mesovolatile vapor pressure. Surrounding gas in the proto-Solar System would facilitate redeposition of molecules sublimed by warmer parts of the surface into the cleft, as well as smoothing the surface and explaining, by hydrodynamic drag, Arrokoth's slow (compared to its breakup rate) rotation. Alternatively, a layer of hoarfrost thick enough ($\gtrsim 0.1\,\mu$) to have a high albedo could have formed more recently.
Strangeness Vector and Axial-Vector Form Factors of the Nucleon

A revised global fit of electroweak ep and p elastic scattering data has been performed, with the goal of determining the strange quark contribution to the vector and axial-vector form factors of the nucleon in the momentum-transfer range 0 < Q 2 < 1 GeV 2 . The two vector (electric and magnetic) form factors G s (Q 2 ) and G s (Q 2 ) are strongly constrained by ep elastic scattering data, while the major source of information on the axial-vector form factor G s (Q 2 ) is p scattering data. Combining the two kinds of data into a single global fit makes possible additional precision in the deter- mination of these form factors, and provides a unique way to determine the strange quark contribution to the nucleon spin, S , independently of leptonic deep-inelastic scattering. The fit makes use of data from the BNL-E734, SAMPLE, HAPPEx, G0, and PVA4 ex- periments; we will also compare the result of the fit with recent data from MiniBooNE, and anticipate how this fit can be improved when new data from MicroBooNE become available.
Cell-type specific analysis of physiological action of estrogen in mouse oviducts

One of the endogenous estrogens, 17β-estradiol (E2 ) is a female steroid hormone secreted from the ovary. It is well established that E2 causes biochemical and histological changes in the uterus. However, it is not completely understood how E2 regulates the oviductal environment in vivo. In this study, we assessed the effect of E2 on each oviductal cell type, using an ovariectomized-hormone-replacement mouse model, single-cell RNA-sequencing (scRNA-seq), in situ hybridization, and cell-type-specific deletion in mice. We found that each cell type in the oviduct responded to E2 distinctively, especially ciliated and secretory epithelial cells. The treatment of exogenous E2 did not drastically alter the transcriptomic profile from that of endogenous E2 produced during estrus. Moreover, we have identified and validated genes of interest in our datasets that may be used as cell- and region-specific markers in the oviduct. Insulin-like growth factor 1 (Igf1) was characterized as an E2 -target gene in the mouse oviduct and was also expressed in human fallopian tubes. Deletion of Igf1 in progesterone receptor (Pgr)-expressing cells resulted in female subfertility, partially due to an embryo developmental defect and embryo retention within the oviduct. In summary, we have shown that oviductal cell types, including epithelial, stromal, and muscle cells, are differentially regulated by E2 and support gene expression changes, such as growth factors that are required for normal embryo development and transport in mouse models. Furthermore, we have identified cell-specific and region-specific gene markers for targeted studies and functional analysis in vivo.
Streaming ResLSTM with Causal Mean Aggregation for Device-Directed Utterance Detection

In this paper, we propose a streaming model to distinguish voice queries intended for a smart-home device from background speech. The proposed model consists of multiple CNN layers with residual connections, followed by a stacked LSTM architecture. The streaming capability is achieved by using unidirectional LSTM layers and a causal mean aggregation layer to form the final utterance-level prediction up to the current frame. In order to avoid redundant computation during online streaming inference, we use a caching mechanism for every convolution operation. Experimental results on a device-directed vs. non device-directed task show that the proposed model yields an equal error rate reduction of 41% compared to our previous best model on this task. Furthermore, we show that the proposed model is able to accurately predict earlier in time compared to the attention-based models.
Jack Weinstein: Reimagining the Role of the District Court Judge

This essay, for a symposium issue of the Federal Sentencing Reporter dedicated to the impact of Judge Jack Weinstein on the occasion of his retirement from the federal bench, highlights how Judge Weinstein has re-imagined the role of the district court judge. Through his judicial opinions, extrajudicial writings and speeches, and his innovative use of the court’s supervisory authority, Judge Weinstein has challenged, and in some cases altered, the status quo in the realm of criminal sentencing. In doing so, he has established a forceful example of how district court judges can use their position to advocate for and effect reform more broadly in the system they are called upon to administer – an example that some other judges already have embraced. In his scholarship, Judge Weinstein also has turned his critical lens inward and examined whether this work is consistent with the judicial role. He concludes that it is, but offers valuable guidance for other judges considering following in his footsteps for how to do so in a way that minimizes concerns about partiality. In the end, Judge Weinstein concludes that such work is not only permissible but required when judges perceive injustice. Few will be as creative, prolific, or persuasive as Judge Weinstein has been. But he leaves behind a fully articulated vision of an active district court judge and invites other judges to consider the kind of judge they want to be given the limits and possibilities that accompany their position. https://doi.org/10.1525/fsr.2021.33.3.163
Hierarchical planning for resource allocation in emergency response systems

A classical problem in city-scale cyber-physical systems (CPS) is resource allocation under uncertainty. Typically, such problems are modeled as Markov (or semi-Markov) decision processes. While online, offline, and decentralized approaches have been applied to such problems, they have difficulty scaling to large decision problems. We present a general approach to hierarchical planning that leverages structure in city-level CPS problems for resource allocation under uncertainty. We use emergency response as a case study and show how a large resource allocation problem can be split into smaller problems. We then create a principled framework for solving the smaller problems and tackling the interaction between them. Finally, we use real-world data from Nashville, Tennessee, a major metropolitan area in the United States, to validate our approach. Our experiments show that the proposed approach out-performs state-of-the-art approaches used in the field of emergency response.
As-applied nondelegation

Two doctrines dominate discussions of administrative law: nondelegation and Chevron. The nondelegation doctrine assesses whether a statute or provision facially delegates legislative power from Congress to the agency. For a number of reasons, including perhaps the drastic consequences that would flow from striking down an entire regulatory scheme on such grounds, the doctrine is unenforced. Thus, often the only applicable doctrine is Chevron, which accepts that Congress will enact broad statutes with numerous ambiguities and simply requires courts to ask whether an agency’s regulation reasonably interprets those ambiguities. These doctrines do not account for another possibility: an as-applied nondelegation challenge. Although the doctrine and scholarly literature do not appear to have considered such an approach, not only is its possibility clear, it would be more theoretically supportable than a facial doctrine under various theories of judicial review currently espoused by the Court and scholars. For example, if Chevron’s core assumption is that statutory ambiguities in broad statutes are implicit delegations of authority to agencies to resolve those ambiguities, then a broad organic statute can be understood as a series of narrower, implicit delegations, each of which must be individually assessed for a nondelegation violation. This approach is further consistent with the Court’s existing preference for as-applied challenges generally, and is invited by one of the best scholarly explications of that preference. As Richard Fallon has argued, even if “rights” are rights against “rules,” which must be challenged facially, as-applied challenges are merely challenges to subrules; a statute is but a series of subrules, some of which might be valid and others invalid; and the invalid ones can usually be separated from the valid ones. If that is right, then a broad organic statute can be considered a series of subdelegations, any one of which might be valid or invalid and where the invalid subdelegations can usually be separated from the valid ones.The implications of such a doctrine are numerous — and attractive. It would better serve nondelegation interests by dramatically reducing any adverse consequences from finding a violation of the nondelegation doctrine. The doctrine would replace the “major questions” exception to Chevron, which the literature has rightly rejected, with a rigorous and coherent theory. Finally, an as-applied nondelegation doctrine could be determinative in a handful of upcoming and important Supreme Court cases. Indeed, it has the potential to reshape how administrative law is litigated.
First-principles model of optimal translation factors stoichiometry

Enzymatic pathways have evolved uniquely preferred protein expression stoichiometry in living cells, but our ability to predict the optimal abundances from basic properties remains underdeveloped. Here we report a biophysical, first-principles model of growth optimization for core mRNA translation, a multi-enzyme system that involves proteins with a broadly conserved stoichiometry spanning two orders of magnitude. We show that predictions from maximization of ribosome usage in a parsimonious flux model constrained by proteome allocation agree with the conserved ratios of translation factors. The analytical solutions, without free parameters, provide an interpretable framework for the observed hierarchy of expression levels based on simple biophysical properties, such as diffusion constants and protein sizes. Our results provide an intuitive and quantitative understanding for the construction of a central process of life, as well as a path toward rational design of pathway-specific enzyme expression stoichiometry.
The Truth is Out There: Revamping Federal Antidiscrimination Enforcement for the Twenty-First Century

Employment discrimination laws in the United States have not created full equality in the workplace. The federal legislative and executive branches need to take a more active role to vindicate the public interest and help promote equality in the private sector. This article details how the current system of enforcement falls short, makes the case for more nonjudicial federal involvement in enforcing employment discrimination, and proposes a new agency model for private sector discrimination cases to accomplish that. The proposed model is inspired by the Truth Commission, a fact-finding body designed to promote transitional justice, an appropriate goal here where our employment discrimination laws are a piece of our transition from a society that used the law to enslave some segments of the population and even after slavery was abolished to keep some dependent on others for their support. Having a federal agency that engages in factfinding, recommending action, and publicizing its decisions would help solidify the norm against discrimination, which, in turn, would help employers and employees both internalize that norm, prevent discrimination in the workplace, and minimize litigation in the long run.
SteaLTE: Private 5G Cellular Connectivity as a Service with Full-stack Wireless Steganography

Fifth-generation (5G) systems will extensively employ radio access network (RAN) softwarization. This key innovation enables the instantiation of "virtual cellular networks" running on different slices of the shared physical infrastructure. In this paper, we propose the concept of Private Cellular Connectivity as a Service (PCCaaS), where infrastructure providers deploy covert network slices known only to a subset of users. We then present SteaLTE as the first realization of a PCCaaS-enabling system for cellular networks. At its core, SteaLTE utilizes wireless steganography to disguise data as noise to adversarial receivers. Differently from previous work, however, it takes a full-stack approach to steganography, contributing an LTE-compliant stegano-graphic protocol stack for PCCaaS-based communications, and packet schedulers and operations to embed covert data streams on top of traditional cellular traffic (primary traffic). SteaLTE balances undetectability and performance by mimicking channel impairments so that covert data waveforms are almost indistinguishable from noise. We evaluate the performance of SteaLTE on an indoor LTE-compliant testbed under different traffic profiles, distance and mobility patterns. We further test it on the outdoor PAWR POWDER platform over long-range cellular links. Results show that in most experiments SteaLTE imposes little loss of primary traffic throughput in presence of covert data transmissions (< 6%), making it suitable for undetectable PCCaaS networking.
Remaking the World to Save It: Applying U.S. Environmental Laws to Climate Engineering Projects

Given the high levels of greenhouse gases already in the atmosphere and the likelihood of growing emissions in the future, even aggressive limits on greenhouse gas emissions might ultimately fail to prevent dangerous climate disruptions. To prepare for this risk, some scientists have started to explore techniques that directly influence or control global and regional climatic systems to offset climate change effects. As climate engineering research expands, U.S. environmental law could become an important first forum for efforts to control nascent climate engineering technologies. Federal and state agencies should start now to map out regulatory strategies and guidance for potential requests to authorize climate engineering experiments or to control objectionable projects. Climate engineering will also offer an unprecedented test of the scope of federal judicial power and the institutional competence of U.S. courts to review environmental projects designed to have a literally global impact. Prior climate change tort actions have tested the ability of courts to ascribe responsibility or assign liabilities to individual parties for damages caused by widely dispersed global activities. Climate engineering presents the mirror image of climate change public nuisance actions: rather than affixing responsibility for a share of a global phenomenon, lawsuits against climate engineering projects can pursue a clearly identifiable small number of parties who expressly and intentionally attempt to create global climate effects. Federal courts in particular may need to review key doctrines (including standing, political question, redressability and proximate causation) to account for a potential role as the domestic court system of first resort for legal challenges to global environmental remediation projects.
Spiking Neural Networks with Single-Spike Temporal-Coded Neurons for Network Intrusion Detection

Spiking neural network (SNN) is interesting due to its strong bio-plausibility and high energy efficiency. However, its performance is falling far behind conventional deep neural networks (DNNs). In this paper, considering a general class of single-spike temporal-coded integrate-and-fire neurons, we analyze the input-output expressions of both leaky and nonleaky neurons. We show that SNNs built with leaky neurons suffer from the overly-nonlinear and overly-complex input-output response, which is the major reason for their difficult training and low performance. This reason is more fundamental than the commonly believed problem of nondifferentiable spikes. To support this claim, we show that SNNs built with nonleaky neurons can have a less-complex and less-nonlinear input-output response. They can be easily trained and can have superior performance, which is demonstrated by experimenting with the SNNs over two popular network intrusion detection datasets, i.e., the NSL-KDD and the AWID datasets. Our experiment results show that the proposed SNNs outperform a comprehensive list of DNN models and classic machine learning models. This paper demonstrates that SNNs can be promising and competitive in contrast to common beliefs.
On the Generalization Properties of Adversarial Training.

Modern machine learning and deep learning models are shown to be vulnerable when testing data are slightly perturbed. Existing theoretical studies of adversarial training algorithms mostly focus on either adversarial training losses or local convergence properties. In contrast, this paper studies the generalization performance of a generic adversarial training algorithm. Specifically, we consider linear regression models and two-layer neural networks (with lazy training) using squared loss under low-dimensional and high-dimensional regimes. In the former regime, after overcoming the non-smoothness of adversarial training, the adversarial risk of the trained models can converge to the minimal adversarial risk. In the latter regime, we discover that data interpolation prevents the adversarially robust estimator from being consistent. Therefore, inspired by successes of the least absolute shrinkage and selection operator (LASSO), we incorporate the L1 penalty in the high dimensional adversarial learning and show that it leads to consistent adversarially robust estimation. A series of numerical studies are conducted to demonstrate how the smoothness and L1 penalization help improve the adversarial robustness of DNN models.
Low-Dimensional Pinned Distance Sets Via Spherical Averages

An inequality is derived for the average energies of pinned distance measures. This refines Mattila’s theorem on distance sets (Mattila in Mathematika 34:207–228, 1987) to pinned distance sets, and gives an analogue of Liu’s theorem (Liu in Geom Funct Anal 29:283–294, 2019) for pinned distance sets of dimension smaller than 1.
A Regularized Vector Autoregressive Hidden Semi-Markov model, with Application to Multivariate Financial Data

Hidden Markov model (HMM) has been a popular choice for financial time series modeling due to its advantage in capturing dynamic regimes. However, HMM's implicit assumption that the state duration follows a geometric distribution is too strong to hold in practice. In this work, we propose a regularized vector autoregressive hidden semi-Markov model to analyze multivariate financial time series. One challenge in such a model setting is that the number of parameters is too large to be reliably estimated unless the time series is extremely long. To address this issue, an augmented EM algorithm is developed for parameter estimation by using regularized estimators for the state-dependent covariance matrices and autoregression matrices in the M-step. The performance of the proposed model is evaluated in a simulation experiment, and demonstrated with the New York Stock Exchange financial portfolio data.
Screen2Vec: Semantic Embedding of GUI Screens and GUI Components

Representing the semantics of GUI screens and components is crucial to data-driven computational methods for modeling user-GUI interactions and mining GUI designs. Existing GUI semantic representations are limited to encoding either the textual content, the visual design and layout patterns, or the app contexts. Many representation techniques also require significant manual data annotation efforts. This paper presents Screen2Vec, a new self-supervised technique for generating representations in embedding vectors of GUI screens and components that encode all of the above GUI features without requiring manual annotation using the context of user interaction traces. Screen2Vec is inspired by the word embedding method Word2Vec, but uses a new two-layer pipeline informed by the structure of GUIs and interaction traces and incorporates screen- and app-specific metadata. Through several sample downstream tasks, we demonstrate Screen2Vec’s key useful properties: representing between-screen similarity through nearest neighbors, composability, and capability to represent user tasks.
Numerical semigroups of small and large type

A numerical semigroup is a sub-semigroup of the natural numbers that has a finite complement. Some of the key properties of a numerical semigroup are its Frobenius number F, genus g and type t. It ...
High-quality automated program repair

Automatic program repair (APR) has recently gained attention because it proposes to fix software defects with no human intervention. To automatically fix defects, most APR tools use the developer-written tests to (a) localize the defect, and (b) generate and validate the automatically produced candidate patches based on the constraints imposed by the tests. While APR tools can produce patches that appear to fix the defect for 11--19% of the defects in real-world software, most of the patches produced are not correct or acceptable to developers because they overfit to the tests used during the repair process. This problem is known as the patch overfitting problem. To address this problem, I propose to equip APR tools with additional constraints derived from natural-language software artifacts such as bug reports and requirements specifications that describe the bug and intended software behavior but are not typically used by the APR tools. I hypothesize that patches produced by APR tools while using such additional constraints would be of higher quality. To test this hypothesis, I propose an automated and objective approach to evaluate the quality of patches, and propose two novel methods to improve the fault localization and developer-written test suites using natural-language software artifacts. Finally, I propose to use my patch evaluation methodology to analyze the effect of the improved fault localization and test suites on the quality of patches produced by APR tools for real-world defects.
Ultra-Lightweight Speech Separation Via Group Communication

Model size and complexity remain the biggest challenges in the deployment of speech enhancement and separation systems on low-resource devices such as earphones and hearing aids. Although methods such as compression, distillation and quantization can be applied to large models, they often come with a cost on the model performance. In this paper, we provide a simple model design paradigm that explicitly designs ultra-lightweight models without sacrificing the performance. Motivated by the sub-band frequency-LSTM (F-LSTM) architectures, we introduce the group communication (GroupComm), where a feature vector is split into smaller groups and a small processing block is used to perform inter-group communication. Unlike standard F-LSTM models where the sub-band outputs are concatenated, an ultra-small module is applied on all the groups in parallel, which allows a significant decrease on the model size. Experiment results show that comparing with a strong baseline model which is already lightweight, GroupComm can achieve on par performance with 35.6 times fewer parameters and 2.3 times fewer operations.
Graphon Games

We propose a way to approximate games played over networks of increasing size by using the graph limiting concept of graphon. To this end, we introduce the new class of graphon games for populations of infinite size. We investigate existence and uniqueness properties of the Nash equilibrium of graphon games and we derive upper bounds for the distance between the Nash equilibria of the infinite population graphon game and of finite population sampled network games. We then show that it is possible to design almost optimal interventions for sampled network games by relying on the graphon model.