cssi/SciSciGPT-SciSciNet huggingface dataset:
O My Sons and Daughters, How Do I Immiserate Thee: Let Me Count the Ways

This article argues that Neo-liberal policies of both action and inaction support the most virulent form of globalized Finance Capitalism. It outlines how such promotion is structured to reinforce fifteen patterns of economic consequences that reduce the standard of living of the great majority of American families, especially those headed by and including workers. These consequential patterns do not usually result from a single government action, but rather from reinforcements of specific decisions with one another. As a result political and economic elites from the United States become indifferent or worse to working family welfare, constituting an abandonment of Americans as individuals, sacrificed to the promotion and rehabilitation of the financial sector and wealth inequality. In turn, the fifteen consequential patterns cascade toward recurring depressions by stagnating real aggregate demand for production.
Prepayment Penalties: Efficiency and Predation

This paper presents evidence that reductions in mortgage interest rates associated with prepayment penalties are greater for riskier borrowers, as measured by mortgage type, credit scores, and loca...
Language between powers, power between languages. Further discussion of education and policy in Togoland under the French mandate, 1919-1945

In a follow-up to his article on linguistic colonialism during the German occupation of Togo, in a recent volume of the Cahiers d'Etudes africaines, the author examines the evolution of language and schooling under the French mandate administration (1919-1945). This era, marked by the absence of a concrete government education policy, presents a particularly complex and ambiguous problematic for the historian. Three themes guide the narrative: the development of schooling under the mandate; the anglophilia of the southern Togolese population; and the destabilization of the main indigenous language, Ewe. The first section offers also a critical review of a recent work on the subject by Marie-France Lange. Central to the argument is the desire to provoke a discussion of the role of language in colonial education policy. But above ail, in order to further advance the issue of African agency in African history, the author evokes the concept of “engagement” to explain the indigenous participation in their social, and economic and political development.
New Trends in Global Outsourcing of Commercial Surrogacy: A Call for Regulation

In a time where advancement of reproductive technologies evolve in a rapid pace beyond the ability of the government to anticipate public social costs associated with it; this Note seeks to address the legal, policy and ethical challenges surrounding commercial surrogacy - particularly from a global context. First, this Note explores India's role in commercial surrogacy, as a case study, to understand the growing global demand for surrogacy arrangements in countries that do not regulate commercial surrogacy. Second, this Note reviews comparatively the weaknesses in existing commercial surrogacy laws among some of the European Union states and the United States. Third, this Note identifies Israel as a successful model for regulating commercial surrogacy, through its commitment to providing sufficient resources, to enforce the laws, and to protect its citizens without compromising individual autonomy. Finally, this Note calls for more effective regulation of commercial surrogacy on a global scale - through leadership and coordination.
Cost of capital effects and changes in growth expectations around U.S. cross-listings ☆

Abstract This paper examines whether cross-listing in the U.S. reduces firms’ costs of capital. We estimate cost of capital effects implied by market prices and analyst forecasts, which accounts for changes in growth expectations around cross-listings. Firms with cross-listings on U.S. exchanges experience a decrease in their cost of capital between 70 and 120 basis points. These effects are sustained and exist after the Sarbanes-Oxley Act. We find smaller reductions for cross-listings in the over-the-counter market and for exchange-listings from countries with stronger legal institutions. For exchange-traded cross-listings, the cost of capital reduction accounts for over half of the increase in firm value, whereas for other types of cross-listings the valuation effects are primarily attributable to contemporaneous revisions in growth expectations.
When Does Tax Avoidance Result in Tax Uncertainty

ABSTRACT We investigate the relation between tax avoidance and tax uncertainty, where tax uncertainty is the amount of unrecognized tax benefits recorded over the same time period as the tax avoida...
On the Estimation of Treatment Effects with Endogenous Misreporting

Abstract Participation in social programs is often misreported in survey data, complicating the estimation of treatment effects. We propose a model to estimate treatment effects under endogenous participation and endogenous misreporting. We present an expression for the asymptotic bias of both OLS and IV estimators and discuss the conditions under which sign reversal may occur. We provide a method for eliminating this bias when researchers have access to information regarding participation and misreporting. We establish the consistency and asymptotic normality of our proposed estimator and assess its small sample performance through Monte Carlo simulations. An empirical example illustrates the proposed method.
Should consensus be 'the commission method' in the US? The perspective of the Federal Advisory Committee Act, regulations, and case law.

This paper examines the drive for consensus from the perspective of the good government framework for federal advisory commissions in the United States. Specifically, the paper examines the Federal Advisory Committee Act (FACA) – the statute, its regulations, and case law. It shows that the FACA was intended to be an antidote to abuses in consensus-making processes, including the failure to fully include competing views on commissions. The index of suspicion in the FACA scheme rises when a group work product – including a consensus report – is to be the basis of recommendations to federal officials. Once FACA's requirements regarding committee composition are satisfied, the index of suspicion drops and FACA is indifferent to consensus-making; but the conditions for informed, meaningful participation apply to members who dissent from, as well as those who participate in, consensus. In negotiated rulemaking, the push for consensus and closure creates unacceptable tension with the good government goals of openness and accountability. Proponents of consensus-only bioethics commissions can learn from FACA-related legislative, agency, and judicial insights that consensus-seeking is not always desired by government officials; is rarely cost free; and that diversity and dissent enhance openness, accountability, and fairness. The burden of proof is therefore on proponents of a consensus-only standard for bioethics commissions to demonstrate that a drive for consensus furthers sound decision-making by government officials more than it sets back openness and accountability to a diverse public.
Knowledge-Based Intermediaries

A new generation of intermediaries is predicted to flourish in the emerging electronic markets. They rely on new information technologies such as the semantic Web, rule-based triggers, and knowledge-based constraint maintenance systems. These technologies do not automate or reduce intermediation, but inspire new types of intermediaries that rely on the technologies and complement them with human organizations. An interorganizational architecture based on multiple levels of intermediation is described, and arguments are presented for its usefulness in emerging electronic markets.
On the existence of Pareto Efficient and envy-free allocations

Abstract Envy-freeness and Pareto Efficiency are two major goals in welfare economics. The existence of an allocation that satisfies both conditions has been studied for a long time. Whether items are indivisible or divisible, it is impossible to achieve envy-freeness and Pareto Efficiency ex post even in the case of two people and two items. In contrast, in this work, we prove that, for any cardinal utility functions (including complementary utilities for example) and for any number of items and players, there always exists an ex ante mixed allocation which is envy-free and Pareto Efficient, assuming the allowable assignments satisfy an anonymity property. The problem remains open in the divisible case. We also investigate the communication complexity for finding a Pareto Efficient and envy-free allocation.
Three Generations of U.S. Lawyers: Generalists, Specialists, Project Managers

A simple framework for understanding the U.S. legal profession is a gradual progression through three generations of lawyers: the generalist, the specialist, and the project manager. The transition from one generation to the next is driven by the familiar story of supply and demand. The generalist era (colonial period to the end of World War II) gave way to the specialist era (post-War to early 2000s) because of a shortage of sophisticated business lawyers capable of serving the needs of large, growing, and increasingly regulated industrial and financial clients. Over a period of several decades, leading local practitioners with business expertise transformed their small local practices into regional and national powerhouses. The common feature of all these transformations was an associate-partner training model, which enabled firms to build sufficient human capital to keep pace with -- and thus profit from -- the legal needs of their clients. In contrast, the U.S. legal profession is now in transition from the specialist to the project manager era. This era is driven by the need for clients to obtain more and better legal work at a lower and more predictable cost. To keep pace with these new client needs (i.e., demands), lawyers working for large corporate clients will increasingly layer their specialized legal knowledge with the skills of the project manager. To the extent that outside lawyers and law firms resist this gravitational pull -- perhaps because they are too wedded to the success and prosperity of the specialist era -- they will lose their seat at the economic table. Thus, as the project manager era unfolds, old hierarchies in the U.S. legal profession will fall and new hierarchies will be created.
Intertemporal Price Discrimination in Storable Goods Markets

We study intertemporal price discrimination when consumers can store for future consumption needs. We offer a simple model of demand dynamics, which we estimate using market-level data. Optimal pricing involves temporary price reductions that enable sellers to discriminate between price sensitive consumers, who stockpile for future consumption, and less price-sensitive consumers, who do not stockpile. We empirically quantify the impact of intertemporal price discrimination on profits and welfare. We find that sales (i ) capture 25–30 percent of the gap between non-discriminatory profits and (unattainable) third-degree price discrimination profits, (ii ) increase total welfare, and (iii) have a modest impact on consumer welfare. (JEL D11, D12, L11, L12, L81) Consumers are heterogeneous in many ways, including preferences, income, and transportation and storage costs. Faced with heterogeneous consumers, firms can benefit from price discrimination. When consumer types are unobservable, firms need to rely on screening mechanisms to separate them. Empirically, we know little about the potential benefits of price discrimination, or how well different screening mechanisms work. Furthermore, the impact of price discrimination on welfare, especially in an oligopoly setting, is ambiguous. In this paper, we empirically study the role of intertemporal price discrimination in storable goods markets. We estimate preferences and use the estimates to test whether price discrimination motives are driving sales. Finding that sales could be driven by price discrimination motives, we evaluate the effectiveness of sales as a price discrimination tool. We do so by comparing profits when there are sales to profits under third-degree price discrimination, where the seller can identify the different consumer types and prevent arbitrage. In order to compute optimal pricing and welfare under different counterfactual pricing regimes, we need to model how consumers respond to a temporary price
Entrenchment or incentive? CEO employment contracts and acquisition decisions

Abstract A long-standing controversy is whether CEO employment contracts insulate inferior managers from discipline leading to shareholder wealth destruction, or whether contracts alleviate managerial risk aversion and encourage value-enhancing decisions. Using a unique dataset on S&P 500 CEO employment contracts during 1993–2005, I find that acquirers with a CEO contract obtain better announcement returns, pay lower premiums for their targets, garner superior long-run post-acquisition operating performance, and undertake riskier deals than acquirers without a contract. Further investigation of individual contract provisions reveals substantial heterogeneity. Specifically, the fixed term rather than at will contract, longer contract duration, long-term equity incentives, accelerated stock and option vesting provisions in severance arrangement, and more refined definitions of just cause (good reason) for CEO termination (resignation) alleviate managerial risk aversion, reduce contracting ambiguity, and motivate value-creating decisions.
Large Scale Subject Category Classification of Scholarly Papers With Deep Attentive Neural Networks.

Subject categories of scholarly papers generally refer to the knowledge domain(s) to which the papers belong, examples being computer science or physics. Subject category classification is a prerequisite for bibliometric studies, organizing scientific publications for domain knowledge extraction, and facilitating faceted searches for digital library search engines. Unfortunately, many academic papers do not have such information as part of their metadata. Most existing methods for solving this task focus on unsupervised learning that often relies on citation networks. However, a complete list of papers citing the current paper may not be readily available. In particular, new papers that have few or no citations cannot be classified using such methods. Here, we propose a deep attentive neural network (DANN) that classifies scholarly papers using only their abstracts. The network is trained using nine million abstracts from Web of Science (WoS). We also use the WoS schema that covers 104 subject categories. The proposed network consists of two bi-directional recurrent neural networks followed by an attention layer. We compare our model against baselines by varying the architecture and text representation. Our best model achieves micro- F1 measure of 0.76 with F1 of individual subject categories ranging from 0.50 to 0.95. The results showed the importance of retraining word embedding models to maximize the vocabulary overlap and the effectiveness of the attention mechanism. The combination of word vectors with TFIDF outperforms character and sentence level embedding models. We discuss imbalanced samples and overlapping categories and suggest possible strategies for mitigation. We also determine the subject category distribution in CiteSeerX by classifying a random sample of one million academic papers.
Combination of deep neural network with attention mechanism enhances the explainability of protein contact prediction

Deep learning has emerged as a revolutionary technology for protein residue-residue contact prediction since the 2012 CASP10 competition. Considerable advancements in the predictive power of the deep learning-based contact predictions have been achieved since then. However, little effort has been put into interpreting the black-box deep learning methods. Algorithms that can interpret the relationship between predicted contact maps and the internal mechanism of the deep learning architectures are needed to explore the essential components of contact inference and improve their explainability. In this study, we present an attention-based convolutional neural network for protein contact prediction, which consists of two attention mechanism-based modules: sequence attention and regional attention. Our benchmark results on the CASP13 free-modeling targets demonstrate that the two attention modules added on top of existing typical deep learning models exhibit a complementary effect that contributes to prediction improvements. More importantly, the inclusion of the attention mechanism provides interpretable patterns that contain useful insights into the key fold-determining residues in proteins. We expect the attention-based model can provide a reliable and practically interpretable technique that helps break the current bottlenecks in explaining deep neural networks for contact prediction. The source code of our method is available at https://github.com/jianlin-cheng/InterpretContactMap.
Employer-Mandated Vaccination for COVID-19.

When the US Food and Drug Administration (FDA) decided to grant emergency use authorization (EUA) for the first two vaccines for COVID-19, the United States' response to the pandemic entered a new phase. Initially, the greatest challenge is having enough doses of vaccine and administering them to all who want it. Yet even while many wait expectantly for their turn to be vaccinated, a significant minority of Americans are hesitant. Lack of information or misinformation about the vaccine, a long-standing and well-entrenched antivaccination movement, distrust of public health officials, and political polarization have left many people ambivalent or opposed to vaccination. According to a poll by the Kaiser Family Foundation taken in late November and early December 2020, 27% of respondents surveyed stated that they would "probably" or "definitely" not be willing to be vaccinated.1 Reflecting the sharp partisan divide that has characterized views about the pandemic, Democrats (86%) were far more likely than Republicans (56%) to be vaccinated. (Am J Public Health. Published online ahead of print February 4, 2021: e1-e4. https://doi.org/10.2105/AJPH.2020.306166).
A Critique of Self-Expressive Deep Subspace Clustering

Subspace clustering is an unsupervised clustering technique designed to cluster data that is supported on a union of linear subspaces, with each subspace defining a cluster with dimension lower than the ambient space. Many existing formulations for this problem are based on exploiting the self-expressive property of linear subspaces, where any point within a subspace can be represented as linear combination of other points within the subspace. To extend this approach to data supported on a union of non-linear manifolds, numerous studies have proposed learning an appropriate kernel embedding of the original data using a neural network, which is regularized by a self-expressive loss function on the data in the embedded space to encourage a union of linear subspaces prior on the data in the embedded space. Here we show that there are a number of potential flaws with this approach which have not been adequately addressed in prior work. In particular, we show the optimization problem is often ill-posed in multiple ways, which can lead to a degenerate embedding of the data, which need not correspond to a union of subspaces at all. We validate our theoretical results experimentally and additionally repeat prior experiments reported in the literature, where we conclude that a significant portion of the previously claimed performance benefits can be attributed to an ad-hoc post processing step rather than the clustering model.
A second cryoballoon system—New and improved?

Generations of cryoballoon transformed the atrial fibrillation ablation landscape. New advancements continue to make cryoballoon more successful and safer treatment. A new cryoballoon PolaRx from Boston Scientific has unique features compared to that of the Medtronic Arctic Front Advance system. Comparison of the two available cryoballoons will require ongoing larger trial and clinical experience. This article is protected by copyright. All rights reserved.
Gut microbiome diversity is an independent predictor of survival in cervical cancer patients receiving chemoradiation

Diversity of the gut microbiome is associated with higher response rates for cancer patients receiving immunotherapy but has not been investigated in patients receiving radiation therapy. Additionally, current studies investigating the gut microbiome and outcomes in cancer patients may not have adjusted for established risk factors. Here, we sought to determine if diversity and composition of the gut microbiome was independently associated with survival in cervical cancer patients receiving chemoradiation. Our study demonstrates that the diversity of gut microbiota is associated with a favorable response to chemoradiation. Additionally, compositional variation among patients correlated with short term and long-term survival. Short term survivor fecal samples were significantly enriched in Porphyromonas, Porphyromonadaceae, and Dialister, whereas long term survivor samples were significantly enriched in Escherichia Shigella, Enterobacteriaceae, and Enterobacteriales. Moreover, analysis of immune cells from cervical tumor brush samples by flow cytometry revealed that patients with a high microbiome diversity had increased tumor infiltration of CD4+ lymphocytes as well as activated subsets of CD4 cells expressing ki67+ and CD69+ over the course of radiation therapy. Modulation of the gut microbiota before chemoradiation might provide an alternative way to enhance treatment efficacy and improve treatment outcomes in cervical cancer patients. Travis Sims and Molly El Alam et al. show that diversity of gut microbiota is associated with a favorable response to chemoradiation for cervical cancer and use flow cytometry to show that patients with high microbiome diversity had increased tumor infiltration of CD4+ lymphocytes as well as activated subsets of CD4 cells expressing ki67+ and CD69+ throughout radiation therapy. These results reveal how modulation of the gut microbiota could potentially be used to improve treatment efficacy and outcome.
On the development of effective field theory

Editor’s note: One of the most important developments in theoretical particle physics at the end of the 20th century and beginning of the twenty-first century has been the development of effective field theories (EFTs). Pursuing an effective field theory approach is a methodology for constructing theories, where a set of core principles is agreed upon, such as Lorentz symmetry and unitarity, and all possible interactions consistent with them are then compulsory in the theory. The utility of this approach to particle physics (and beyond) is wide ranging and undisputed, as evidenced by the recent formation of the international seminar series All Things EFT (Talks in the series can be viewed at https://www.youtube.com/channel/UC1_KF6kdJFoDEcLgpcegwCQ (accessed 21 December 2020).) which brings together each week the worldwide community of EFT practitioners. The text below is a lightly edited version of the talk given by Prof. Weinberg on September 30, 2020, which inaugurated the series. The talk reviews some of the early history of EFTs from the perspective of its pioneer and concludes with a discussion of EFT implications for future discovery.
A spectrographic method for determining trace amounts of lead in zircon and other minerals

The spectrographic determination of lead in zircon and in some other minerals has been made by the U 0 S. Geological Survey to provide data in a study of the age of the rocks that contained these particular minerals. Approximately 150 determinations have been made, and one reproducibility test (consisting of 25 observations) has been completed* The material used for the reproducibility test was a silica-base standard containing 30 ppm leado The average of the results was 30«4 ppa lead* On the whole, standard spectrographic procedures were applied, including the split-filter method for calibrating the photographic emulsions. The method is applicable to samples containing from 0*5 to 1,000 ppm lead with an estimated accuracy of from 6 to 10 percent* No chemical separations or concentrations are required 0 The other minerals tested by the method are apatite, sphene, microlite, allanite, and perthite*
Probabilistic conformance for cyber-physical systems

In system analysis, conformance indicates that two systems simultaneously satisfy the same set of specifications of interest; thus, the results from analyzing one system automatically transfer to the other, or one system can safely replace the other in practice. In this work, we study the probabilistic conformance of cyber-physical systems (CPS). We propose a notion of (approximate) probabilistic conformance for sets of complex specifications expressed by the Signal Temporal Logic (STL). Based on a novel statistical test, we develop the first statistical verification methods for the probabilistic conformance of a wide class of CPS. Using this method, we verify the conformance of the startup time of the widely-used full and simplified model of Toyota powertrain systems, the settling time of model-predictive-control-based and neural-network-based automotive lane-keeping controllers, as well as the maximal voltage deviation of full and simplified power grid systems.
Topological data analysis of collective and individual epithelial cells using persistent homology of loops

Interacting, self-propelled particles such as epithelial cells can dynamically self-organize into complex multicellular patterns, which are challenging to classify without a priori information. Classically, different phases and phase transitions have been described based on local ordering, which may not capture structural features at larger length scales. Instead, topological data analysis (TDA) determines the stability of spatial connectivity at varying length scales (i.e. persistent homology), and can compare different particle configurations based on the “cost” of reorganizing one configuration into another. Here, we demonstrate a topology-based machine learning approach for unsupervised profiling of individual and collective phases based on large-scale loops. We show that these topological loops (i.e. dimension 1 homology) are robust to variations in particle number and density, particularly in comparison to connected components (i.e. dimension 0 homology). We use TDA to map out phase diagrams for simulated particles with varying adhesion and propulsion, at constant population size as well as when proliferation is permitted. Next, we use this approach to profile our recent experiments on the clustering of epithelial cells in varying growth factor conditions, which are compared to our simulations. Finally, we characterize the robustness of this approach at varying length scales, with sparse sampling, and over time. Overall, we envision TDA will be broadly applicable as a model-agnostic approach to analyze active systems with varying population size, from cytoskeletal motors to motile cells to flocking or swarming animals.
Rapid response of nitrogen cycling gene transcription to labile carbon amendments in a soil microbial community

Episodic inputs of labile carbon (C) to soil can rapidly stimulate nitrogen (N) immobilization by soil microorganisms. However, the transcriptional patterns that underlie this process remain unclear. In order to better understand the regulation of N cycling in soil microbial communities, we conducted a 48-h laboratory incubation with agricultural soil where we stimulated the uptake of inorganic N by amending the soil with glucose. We analyzed the metagenome and metatranscriptome of the microbial communities at four time points that corresponded with changes in N availability. The relative abundances of genes remained largely unchanged throughout the incubation. In contrast, glucose addition rapidly increased the transcription of genes encoding ammonium and nitrate transporters, enzymes responsible for N assimilation into biomass, and genes associated with the N regulatory network. This upregulation coincided with an increase in transcripts associated with glucose breakdown and oxoglutarate production, demonstrating a connection between C and N metabolism. When concentrations of ammonium were low, we observed a transient upregulation of genes associated with the nitrogen-fixing enzyme nitrogenase. Transcripts for nitrification and denitrification were downregulated throughout the incubation, suggesting that dissimilatory transformations of N may be suppressed in response to labile C inputs in these soils. These results demonstrate that soil microbial communities can respond rapidly to changes in C availability by drastically altering the transcription of N cycling genes.IMPORTANCE A large portion of activity in soil microbial communities occurs in short time frames in response to an increase in C availability, affecting the biogeochemical cycling of nitrogen. These changes are of particular importance as nitrogen represents both a limiting nutrient for terrestrial plants as well as a potential pollutant. However, we lack a full understanding of the short-term effects of labile carbon inputs on the metabolism of microbes living in soil. Here, we found that soil microbial communities responded to labile carbon addition by rapidly transcribing genes encoding proteins and enzymes responsible for inorganic nitrogen acquisition, including nitrogen fixation. This work demonstrates that soil microbial communities respond within hours to carbon inputs through altered gene expression. These insights are essential for an improved understanding of the microbial processes governing soil organic matter production, decomposition, and nutrient cycling in natural and agricultural ecosystems.
Men's body mass index and infertility

Background In men, excess weight may be linked with altered testosterone, estradiol levels, poor semen quality and infertility. We investigated whether higher BMI among men is associated with infertility and if so, to what extent that effect might be mediated by altered sexual function. Methods A retrospective cohort study of pregnancies from 1999 through 2005 based within the Norwegian Mother and Child Cohort Study (MoBa). Questionnaires assessed mother's and father's height and weight and time to pregnancy. Our sample comprises 26 303 planned pregnancies. Couples were considered infertile if they took >or=12 months to achieve pregnancy or received infertility treatment. Results After adjusting for the woman's BMI, coital frequency and the ages and smoking habits of both partners, the odds ratio for infertility was 1.20 for overweight men [BMI 25-29.9; 95% confidence interval (CI) = 1.04-1.38] and 1.36 for obese men (BMI 30-34.9; 95% CI = 1.13-1.63) relative to men with low-normal BMI (20.0-22.4). When BMI was divided into eight categories, there was a trend of increased infertility with increased male BMI. The effect of men's BMI was nearly identical when coital frequency was not included indicating that the effect is not mediated by sexual dysfunction in heavier men. Conclusions This study adds further support that men with excess body weight are at increased risk of infertility. Values may be underestimated because the most severe cases, couples who do not conceive, are not included in this birth cohort. Research is needed to see if weight loss improves fertility for these men.
Polymeric Surfactants Based on Oleic Acid— Lamellar Liquid Crystal Polymerization of Sodium Oleate/Water/Hexadecane/4-Allyl-1, 6-Heptadiene-4-ol System And Sodium Oleate/Water/Hexadecane/Pentaerythritol Triarylate System

The lamellar liquid crystalline (LLC) regions of sodium oleate (NaOL)/ water/hexadecane/4-allyl-1,6-heptadiene-4-ol (AHD-ol) system and sodium oleate (NaOL) /water/hexadecane/pentaerythritol triacrylate (PETA) system were determined when the weight ratio of NaOL to hexadecane was kept at 90:10. Copolymerization was accomplished in the LLC phases of the above systems. AHD-ol and PETA acted as the cross-linking agents between the double bonds on NaOL backbones in their systems respectively. Interlayer spacing determinations showed that the cross-linking agents, AHD-ol and PETA, were solubilized in the middle of the NaOL hydrocarbon chains in each LLC system. As a result, the copolymerization reactions were confined within each monolayer of the amphiphilic bilayer. The systems after the copolymerization were a mixture of water and LLC phase for the former system and a mixture of LLC phase and solid particles for the latter one. The aqueous solutions of copolymers exhibited surface activity, but with both higher CMC and surface tension values than NaOL aqueous solutions.
Non-Excludable Dynamic Mechanism Design

Dynamic mechanism design expands the scope of allocations that can be implemented and the performance that can be attained compared to static mechanisms. Even under stringent participation constraints and restrictions on transfers, recent work demonstrated that it is possible for a designer to extract the surplus of all players as revenue when players have quasilinear utilities and the number of interactions is large. Much of the analysis has focused on excludable environments (i.e., any player can be excluded from trade without affecting the utilities of others). The mechanisms presented in the literature, however, do not extend to non-excludable environments. Two prototypical examples of such environments are: (i) public projects, where all players must have the same allocation; and (ii) non-disposable goods, where each item must be allocated to some player. We show a general mechanism that can asymptotically extract full surplus as revenue in such environments. Moreover, we provide a tight characterization for general environments, and identify necessary and sufficient conditions on the possibility of asymptotic full surplus extraction. Our characterization is based on the geometry of achievable utility sets -- convex sets that delineate the expected utilities that can be implemented by static mechanisms. Our results provide a reduction from dynamic to static mechanism design: the geometry of the achievable utility set of static mechanisms completely determines whether it is possible to fully extract surplus in the limit.
Law, the Laws of Nature and Ecosystem Energy Services: A Case of wilful Blindness

Ecosystems services include the collection, concentration, and storage of solar energy as fossil fuels (e.g., coal, petroleum, and natural gas). These concentrated forms of energy were produced by ancient ecosystem services. However, our legal and economic systems fail to recognise the value of the ecosystem service subsidies embedded in fossil fuels. This ecosystem services price subsidy causes overuse and waste of fossil fuels in the free market: fossil fuels are consumed more quickly than they can be replaced by ecosystem services and in far larger quantities than they would be if the price of fossil fuels included the cost of solar energy collection, concentration and manufacturing of raw fossil fuels. Moreover, burning fossil fuels produces enormous environmental, human health and welfare costs and damage. Virtually no legal literature on ecosystem services, sustainable development, or sustainable energy, considers fossil fuels in this context. Without understanding stored energy as an ecosystem service, we cannot reasonably expect to manage our fossil fuel energy resources sustainably. International and domestic energy law and policy systems generally ignore this feature of fossil fuel energy, a blind spot that explains why reducing greenhouse gas emissions from fossil fuels is fundamentally a political challenge. This paper will use new understandings emerging from the field of complex systems to critique existing legal decision-making models that do not adequately account for energy ecosystem services in policy design, resource allocation and project approvals. The paper proposes a new "least-social-cost" decision-making legal structure that includes ecosystem energy services.
Another Mystery Snail in the Adirondacks: DNA Barcoding Reveals the First Record of Sinotaia cf. Quadrata (Caenogastropoda: Viviparidae) from North America

Invasive mollusks pose a serious threat to global freshwater diversity and have been implicated in many ecosystem-altering invasion events over the past few decades. Biomonitoring surveys are therefore a key tool for ensuring biosecurity in diversity hotspots and vulnerable habitats. In this study, we use DNA barcoding to provide the first record of the viviparid, Sinotaia cf. quadrata (Benson, 1842) from North America. Reciprocal monophyly and low genetic divergence (uncorrected p-distance: 0.004) with a Bellamya quadrata (Benson, 1842) individual from the type region (China) provides strong support for this identification. The species was recovered as part of a routine biomonitoring survey of the Adirondack region of northern New York. Only three adults were recovered (no populations or juveniles) suggesting that the discovery represents a very recent arrival. Considering the proximity of the sampling site from the massive St. Lawrence River, it is likely that S. cf. quadrata was introduced into the St. Lawrence, probably via the aquarium plant trade, and was able to spread into smaller river system in northern New York and possibly other border states. This record represents the fourth alien viviparid, the third of which is of Asian origin that have made its way to New York waters. Future biomonitoring efforts for the upcoming summer period will involve targeted searches for S. cf. quadrata to determine the extent of its spread in the region.
Nonlocal Effective Electromagnetic Wave Characteristics of Composite Media: Beyond the Quasistatic Regime

Predicting effective characteristics of electromagnetic waves in composite media is an old problem with limited solutions. A new theoretical framework overcomes those limitations.
Contact model for elastically anisotropic bodies and efficient implementation into the discrete element method

We introduce a contact law for the normal force generated between two contacting, elastically anisotropic bodies of arbitrary geometry. The only requirement is that their surfaces be smooth and frictionless. This anisotropic contact law is obtained from a simplification of the exact solution to the continuum elasticity problem and takes the familiar form of Hertz’ contact law, with the only difference being the orientation-dependence of the material-specific contact modulus. The contact law is remarkably accurate when compared with the exact solution, for a wide range of materials and surface geometries. We describe a computationally efficient implementation of the contact law into a discrete element method code, taking advantage of the precomputation of the contact modulus over all possible orientations. Finally, we showcase two application examples based on real materials where elastic anisotropy of the particles induces noticeable effects on macroscopic behavior. Notably, the second example demonstrates the ability to engineer tunable vibrational band gaps in a one-dimensional granular crystal by mere rotation of the constituent spherical particles.
Reversible Models for Wireless Multi-Channel Multiple Access

This paper presents a network layer model for a wireless multiple access system with both persistent and nonpersistent users. There is a single access point with multiple identical channels. Each user who wants to send a file first scans a subset of the channels to find one that is idle. If at least one idle channel is found, the user transmits a file over that channel. If no idle channel is found, a persistent user will repeat the access attempt at a later time, while a nonpersistent user will leave. This is a useful mathematical model for situations where a group of persistent users stay near an access point for an extended period of time while nonpersistent users come and go. Users have heterogeneous activity behavior, file upload rates, and service durations. The system is a complex multi-dimensional Markov chain. The steady state probabilities are found by exploiting a latent reversibility property and leveraging a discrete Fourier transform. This enables simple expressions for throughput and blocking probability.
Comments on the quantum field theory of the Coulomb gas formalism

The holomorphic Coulomb gas formalism, as developed by Feigin-Fuchs, Dotsenko-Fateev and Felder, is a set of rules for computing minimal model observables using free field techniques. We attempt to derive and clarify these rules using standard techniques of quantum field theory. We begin with a careful examination of the timelike linear dilaton. Although the background charge of the model breaks the scalar field’s continuous shift symmetry, the exponential of the action remains invariant under a discrete shift because the background charge is imaginary. Gauging this symmetry makes the dilaton compact and introduces winding modes into the spectrum. One of these winding operators corresponds to the anti-holomorphic completion of the BRST current first introduced by Felder, and the full left/right cohomology of this BRST charge isolates the irreducible representations of the Virasoro algebra within the degenerate Fock space of the linear dilaton. The “supertrace” in the BRST complex reproduces the minimal model partition function and exhibits delicate cancellations between states with both momentum and winding. The model at the radius $$ R=\sqrt{pp^{\prime }} $$ has two marginal operators corresponding to the Dotsenko-Fateev “screening charges”. Deforming by them, we obtain a model that might be called a “BRST quotiented compact timelike Liouville theory”. The Hamiltonian of the zero-mode quantum mechanics of this model is not Hermitian, but it is PT-symmetric and exactly solvable. Its eigenfunctions have support on an infinite number of plane waves, suggesting an infinite reduction in the number of independent states in the full quantum field theory. Applying conformal perturbation theory to the exponential interactions reproduces the Coulomb gas calculations of minimal model correlation functions. In contrast to spacelike Liouville, these “resonance correlators” are finite because the zero mode is compact. We comment on subtleties regarding the reflection operator identification, as well as naive violations of truncation in correlators with multiple reflection operators inserted. This work is part of an attempt to understand the relationship between the JT model of two dimen- sional gravity and the worldsheet description of the (2, p) minimal string as suggested by Seiberg and Stanford.
Irradiance modulates thermal niche in a previously undescribed low-light and cold-adapted nano-diatom

Diatoms have well-recognized roles in fixing and exporting carbon and supplying energy to marine ecosystems, but only recently have we begun to explore the diversity and importance of nano- and pico-diatoms. Here we describe a small (~5 {micro}m) diatom from the genus Chaetoceros isolated from a wintertime temperate estuary (2 {degrees}C, Narragansett Bay, RI), with a unique obligate specialization for low-light environments (< 120 {micro}mol photons m-2 sec-1). This diatom exhibits a striking interaction between irradiance and thermal responses whereby as temperatures increase, so does its susceptibility to light stress. Historical 18S rRNA amplicon data from our study site show this isolate was abundant throughout a six-year period, and its presence strongly correlates with winter and early spring months when light and temperature are low. Two ASVs matching this isolate had a circumpolar distribution in Tara Polar Ocean Circle samples, indicating its unusual light and temperature requirements are adaptations to life in a cold, dark environment. We expect this isolate's low light, psychrophilic niche to shrink as future warming-induced stratification increases both light and temperature levels experienced by high latitude marine phytoplankton.
Non-interactive Batch Arguments for NP from Standard Assumptions

We study the problem of designing non-interactive batch arguments for \(\mathsf {NP}\). Such an argument system allows an efficient prover to prove multiple \(\mathsf {NP}\) statements, with size smaller than the combined witness length.
Using machine learning to improve survival prediction after heart transplantation.

BACKGROUND This study investigates the use of modern machine learning (ML) techniques to improve prediction of survival after orthotopic heart transplantation (OHT). METHODS Retrospective study of adult patients undergoing primary, isolated OHT between 2000 and 2019 as identified in the United Network for Organ Sharing (UNOS) registry. The primary outcome was 1-year post-transplant survival. Patients were randomly divided into training (80%) and validation (20%) sets. Dimensionality reduction and data re-sampling were employed during training. Multiple machine learning algorithms were combined into a final ensemble ML model. The discriminatory capability was assessed using the area under receiver-operating-characteristic curve (AUROC), net reclassification index (NRI), and decision curve analysis (DCA). RESULTS A total of 33,657 OHT patients were evaluated. One-year mortality was 11% (n = 3738). In the validation cohort, the AUROC of singular logistic regression was 0.649 (95% CI, 0.628-0.670) compared to 0.691 (95% CI, 0.671-0.711) with random forest, 0.691 (95% CI, 0.671-0.712) with deep neural network, and 0.653 (95% CI, 0.632-0.674) with Adaboost. A final ensemble ML model was created that demonstrated the greatest improvement in AUROC: 0.764 (95% CI, 0.745-0.782) (p < .001). The ensemble ML model improved predictive performance by 72.9% ±3.8% (p < .001) as assessed by NRI compared to logistic regression. DCA showed the final ensemble method improved risk prediction across the entire spectrum of predicted risk as compared to all other models (p < .001). CONCLUSIONS Modern ML techniques can improve risk prediction in OHT compared to traditional approaches. This may have important implications in patient selection, programmatic evaluation, allocation policy, and patient counseling and prognostication.
Hidden Markov model based approach for diagnosing cause of alarm signals

When a fault occurs in a process, it slowly propagates within the system and affects the measurements triggering a sequence of alarms in the control room. The operators are required to diagnose the cause of alarms and take necessary corrective measures. The idea of representing the alarm sequence as the fault propagation path and using the propagation path to diagnose the fault is explored. A diagnoser based on hidden Markov model is built to identify the cause of the alarm signals. The proposed approach is applied to an industrial case study: Tennessee Eastman process. The results show that the proposed approach is successful in determining the probable cause of alarms generated with high accuracy. The model was able to identify the cause accurately, even when tested with short alarm sub-sequences. This allows for early identification of faults, providing more time to the operator to restore the system to normal operation.