cssi/SciSciGPT-SciSciNet huggingface dataset:
MODELING UNCERTAINTY IN TAX LAW

Each year, the government faces a massive shortfall in tax collections: the annual difference between the amount taxpayers owe the government and the amount the government actually receives is nearly $400 billion dollars. The questions of when and why taxpayers choose to comply with the tax law are thus pressing ones for scholars and policymakers. Many legal scholars rely on economic models better to understand these issues. However, none of the models that legal scholars use can accommodate the reality that taxpayers face unknown probabilities when they decide whether to comply. A taxpayer does not know, for example, the probability that he will be selected for audit, the probability that the government will identify a particular questionable position on his tax return, or the probability that the IRS or a court will strike the position down. This Article presents a formal model of tax compliance that, unlike other models of tax compliance used in legal scholarship, takes unknown probabilities into account. The model presented incorporates both the extent of a taxpayer’s uncertainty and the taxpayer’s attitude toward uncertainty, and thus provides new insights into problems as disparate as how the government should reveal information about its approach to audits, whether the government should use antiabuse rules to attack tax shelters, and whether tax professionals should be subject to penalties for providing certain kinds of tax advice.
Brazil's Agricultural Trade War: Success and Failure on the Southern Route to Antarctica

On September 8, 2004, the World Trade Organization ("WTO") issued a report documenting Brazil's landmark victory in its trade battle against U.S. cotton subsidies.1 The Report concluded that certain U.S. government subsidies to cotton producers caused "serious prejudice" to Brazilian cotton farmers by encouraging excessive U.S. production of cotton, thus depressing world cotton prices.2 This decision, the first ever by the WTO on agricultural subsidies, dealt the United States its most serious trade defeat in recent history. Exultant Brazilian officials vowed: "This is a war that must
The (Non-)Right to Sex

What is the relationship between the battle for marriage equality and the expansion of sexual liberty? Some see access to marriage as a quintessentially progressive project—the recognition of the equality and dignity of gay and lesbian couples. For others, promoting marriage or marital-like relationships reinforces bias against individuals making alternative intimate decisions. With powerful policy arguments on either side, there appears to be no clear way to advance the discussion.
Investor learning about analyst predictive ability

Abstract Bayesian learning implies decreasing weights on prior beliefs and increasing weights on the accuracy of the analyst's past forecast record, as the number of forecast errors comprising her forecast record (its length) increases. Consistent with this model of investor learning, empirical tests show that investors’ reactions to forecast news are increasing in the product of the accuracy and length of analysts’ forecast records. Moreover, the Bayesian learning predicted by our model is more descriptive of investor reactions than is a static model which predicts that investors’ responses condition only on the prior accuracy of the analyst.
The Turkish “model” of civil–military relations

As Egypt underwent a tumultuous military-led transition from autocracy to democracy beginning in 2011, a chorus of commentators advocated a “Turkish model” for civil–military relations in Egypt’s nascent democracy. That term is frequently invoked in both popular and academic discourse, but rarely defined. This article takes up the task of giving content to that elusive phrase. It begins by analyzing the composition, structure, and ob jectives of the Turkish military beginning with the Ottoman Empire. It then turns to May 1960, when the Turkish military staged its first direct intervention in republican politics by toppling an authoritarian government and installing democratically elected leaders after seventeen months of interim military rule.The article shows that the military played a crucial role in Turkish modernization and democratization during the coup and in its immediate aftermath—a role that has been largely obscured by the current portrayal of the Turkish military as a hegemonic and repressive institution. The article then explores why, following its initial democratization role after the 1960 coup, the military failed to retreat to the barracks and began to present impediments to democracy. It argues that the plethora of counter-majoritarian institutions established in the 1961 Constitution, drafted under military supervision following the 1960 coup, sparked frequent power vacuums in Turkey, prompting the military to stage political interventions to ensure stability. It further argues that the military’s focus on domestic policy matters with its institutionalization in the National Security Council in the 1961 Constitution ensnared the military in domestic disputes, providing an impetus for the military to stage further interventions. The article then explains the recent exodus of the Turkish military from politics with the ascension to power of stable civilian governments and Turkey’s accession process to the European Union. It concludes by offering observations and lessons for other nations seeking to normalize their civil–military relations.
Reflections on the Supreme Court's 1988 Term: The Employment Discrimination Decisions and the Abandonment of the Second Reconstruction

Eight decisions of the 1988 Term effectively rewrote Title VII of the Civil Rights Act of 1964 and threatened to undo the significant gains in equal employment opportunity that had been achieved since its enactment. Established modes of proof, theories of recovery, entitlement to attorneys’ fees, and the enforceability of affirmative action decrees were all significantly altered in ways that advantaged employers and burdened plaintiffs. Congress would later respond with the Civil Rights Act of 1991, restoring and even extending the protections for minorities and women.
Community Discovery in Dynamic Networks: A Survey

Several research studies have shown that complex networks modeling real-world phenomena are characterized by striking properties: (i) they are organized according to community structure, and (ii) their structure evolves with time. Many researchers have worked on methods that can efficiently unveil substructures in complex networks, giving birth to the field of community discovery. A novel and fascinating problem started capturing researcher interest recently: the identification of evolving communities. Dynamic networks can be used to model the evolution of a system: nodes and edges are mutable, and their presence, or absence, deeply impacts the community structure that composes them. This survey aims to present the distinctive features and challenges of dynamic community discovery and propose a classification of published approaches. As a “user manual,” this work organizes state-of-the-art methodologies into a taxonomy, based on their rationale, and their specific instantiation. Given a definition of network dynamics, desired community characteristics, and analytical needs, this survey will support researchers to identify the set of approaches that best fit their needs. The proposed classification could also help researchers choose in which direction to orient their future research.
EXPLAINING THE PRICE OF VOLUNTARY CARBON OFFSETS

This paper identifies factors that explain the large variability in the price of voluntary carbon offsets. We estimate hedonic price functions using a variety of provider- and project-level characteristics as explanatory variables. We find that providers located in Europe sell offsets at prices that are approximately 30% higher than providers located in either North America or Australasia. Contrary to what one might expect, offset prices are generally higher, by roughly 20%, when projects are located in developing or least-developed nations. But this result does not hold for forestry-based projects. We find evidence that forestry-based offsets sell at lower prices, and the result is particularly strong when projects are located in developing or least-developed nations. Offsets that are certified under the Clean Development Mechanism or the Gold Standard, and therefore qualify for emission reductions under the Kyoto Protocol, sell at a premium of more than 30%; however, third-party certification from the Voluntary Carbon Standard, one of the popular certifiers, is associated with a price discount. Variables that have no effect on offset prices are the number of projects that a provider manages and a provider's status as for-profit or not-for-profit.
Estimates of Income and Income Inequality in the United States and in Each of the Fifty States: 1988–1999

Estimates of income levels and income inequality in the United States in 1988, 1995, and 1999 are presented based on a dataset created by a statistical match of Internal Revenue Service and decennial Census data. In addition, estimates are presented of income levels and income distribution in each of the fifty states of the United States during those same years. Trends in national and regional income inequality and income levels over the period are analyzed also.
Belonging and Trust: Divorce and Social Capital

In other work, I have discussed at some length how trust functions within ongoing marriages and families. Marriages, I claim, “are viewed as good when the spouses trust each other. They founder when trust is no longer there.” When a wife, typically, loses trust in her husband’s acting unselfishly and for the benefit of the marriage, she may file for divorce. Meanwhile, trust is the foundation for teaching children about love—love of parents for each other, love they have for you, and how God loves.
The Emerging Consensus for Cutting the Corporate Income Tax Rate

Contemporary tax policy circles are characterized by healthy debates on a range of policy measures. However, a consensus has emerged among tax policy experts on one point: the United States should lower its statutory corporate income tax (“CIT”) rate.1 Doing so would produce a number of benefits, both internationally and domestically. This short Article summarizes some of the chief benefits that are driving the consensus for a reduced CIT rate. It also describes the surprising degree of political agreement that has quietly emerged on this point.
Interpersonal Authority in a Theory of the Firm

This paper develops a theory of the flrm in which a flrm’s centralized asset ownership and low-powered incentives give a manager ‘interpersonal authority’ over employees (in a world with difiering priors). The paper derives such interpersonal authority as an equilibrium phenomenon. One key result is that a manager’s control over critical assets { through its efiect on the level of outside options { allows the manager to order employees what to do. The paper thus provides microfoundations for the idea that bringing a project inside a flrm gives the manager authority over that project, while { in the process { explaining concentrated asset ownership, low-powered incentives, and centralized authority as typical characteristics of flrms. It also leads to a new perspective on the flrm as a legal entity and, building on the insights of a parallel paper, to a new theory for flrm boundaries based on the idea of break-up. A key feature of the latter theory is that flrm boundaries matter even though both ex-ante investments and ex-post actions are perfectly contractible.
AUTOMATIC CLASSIFICATION OF KEPLER PLANETARY TRANSIT CANDIDATES

In the first three years of operation, the Kepler mission found 3697 planet candidates (PCs) from a set of 18,406 transit-like features detected on more than 200,000 distinct stars. Vetting candidate signals manually by inspecting light curves and other diagnostic information is a labor intensive effort. Additionally, this classification methodology does not yield any information about the quality of PCs; all candidates are as credible as any other. The torrent of exoplanet discoveries will continue after Kepler, because a number of exoplanet surveys will have an even broader search area. This paper presents the application of machine-learning techniques to the classification of the exoplanet transit-like signals present in the Kepler light curve data. Transit-like detections are transformed into a uniform set of real-numbered attributes, the most important of which are described in this paper. Each of the known transit-like detections is assigned a class of PC; astrophysical false positive; or systematic, instrumental noise. We use a random forest algorithm to learn the mapping from attributes to classes on this training set. The random forest algorithm has been used previously to classify variable stars; this is the first time it has been used for exoplanet classification. We are able to achieve an overall error rate of 5.85% and an error rate for classifying exoplanets candidates of 2.81%.
The Role of Organizational Scope and Governance in Strengthening Private Monitoring

Governments and other organizations often outsource activities to achieve cost savings from market competition. Yet such benefits are often accompanied by poor quality resulting from moral hazard, which can be particularly onerous when outsourcing the monitoring and enforcement of government regulation. In this paper, we argue that the considerable moral hazard associated with private regulatory monitoring can be mitigated by understanding conflicts of interest in the monitoring organizations’ product/service portfolios and by the effects of their private governance mechanisms. These organizational characteristics affect the stringency of monitoring through reputation, customer loyalty, differential impacts of government sanctions, and the standardization and internal monitoring of operations. We test our theory in the context of vehicle emissions testing in a state in which the government has outsourced these inspections to the private sector. Analyzing millions of emissions tests, we find empirical support for our hypotheses that particular product portfolios and forms of governance can mitigate moral hazard. Our results have broad implications for regulation, financial auditing, and private credit and quality rating agencies in financial markets.
Poverty and Property in Kant's System of Rights

The civil union marks the transition to public right from the property regime of the state of nature. Immanuel Kant’s theory of property rights necessitates not only this transition but also — as part of it — the people’s duty to the poor. Although Kant’s notion of property completely conforms to corrective justice, it generates the distributive justice that consists in the alleviation of poverty through taxation. Kant’s inclusion of the public duty to support the poor among the effects of the civil condition suggests that the agreement of all would be impossible unless the state assumed this duty. This chapter views that the relief of poverty is not as something from which the state might contingendy benefit, but as a duty of the people that the state assumes, like all duties Kant describes, this duty presumably reflects a normative necessity rather than a prudential option.
Corporate governance post-Enron: Effective reforms, or closing the stable door?

Abstract We examine Enron's collapse to provide insights as to the efficacy of recent governance reforms. In doing so, we explore two main issues. First, if recently mandated governance changes had been in place earlier, would they have constrained actions by Enron's management? Second, and more generally, which of the recent governance changes might act to constrain governance failures going forward? Although many aspects of corporate governance failed at Enron, the firm's viability ultimately rested on an inherently risky business strategy, a strategy that the board and others apparently failed to understand. However, it is not apparent that increasing board independence would have changed Enron's strategic direction, or prevented the firm's collapse. From this perspective, many recent reforms, including those mandating specific board structures likely move firms away from their optimal governance structure and are tantamount to closing the stable door after the horse has bolted. We assert that, ceteris paribus, stronger internal controls coupled with reduced potential for conflicts of interest on the part of the external auditor might have constrained management's ability to hide the firm's true financial condition and are likely to constrain aspects of fraudulent behavior going forward.
Federal Sentencing during the Interregnum: Defense Practice as the Blakely Dust Settles

Although the long term impact of Blakely v. Washington is not yet clear, no one can doubt that the case raises a host of immediate, significant and perplexing practical questions for federal criminal defense attorneys. The Supreme Court has granted certiorari in a pair of cases raising Blakely issues and oral argument is scheduled for October 4, 2004. It seems likely that the Supreme Court will offer some guidance by Thanksgiving. Until the Court rules, uncertainty will continue as the lower courts interpret Blakely in disparate ways. Once the Court does rule, many hard questions may remain unanswered. This article suggests how defense counsel can effectively represent clients during this period of uncertainty.
Optimal Task-Invariant Energetic Control for a Knee-Ankle Exoskeleton

Task-invariant control methods for powered exoskeletons provide flexibility in assisting humans across multiple activities and environments. Energy shaping control serves this purpose by altering the human body’s dynamic characteristics in closed loop. Our previous work on potential energy shaping alters the gravitational vector to reduce the user’s perceived gravity, but this method cannot provide velocity-dependent assistance. The interconnection and damping assignment passivity-based control (IDA-PBC) method provides more freedom to shape a dynamical system’s energy through the interconnection structure of a port-controlled Hamiltonian system model. This letter derives a novel energetic control strategy based on IDA-PBC for a backdrivable knee-ankle exoskeleton. The control law provides torques that depend on various basis functions related to gravitational and gyroscopic terms. We optimize a set of constant weighting parameters for these basis functions to obtain a control law that produces able-bodied joint torques during walking on multiple ground slopes. We perform experiments with an able-bodied human subject wearing a knee-ankle exoskeleton to demonstrate reduced activation in certain lower-limb muscles.
Conserved arginine residues in synaptotagmin 1 regulate fusion pore expansion through membrane contact.

Synaptotagmin 1 is a vesicle-anchored membrane protein that functions as the Ca2+ sensor for synchronous neurotransmitter release. In this work, an arginine containing region in the second C2 domain of synaptotagmin 1 (C2B) is shown to control the expansion of the fusion pore and thereby the concentration of neurotransmitter released. This arginine apex, which is opposite the Ca2+ binding sites, interacts with membranes or membrane reconstituted SNAREs; however, only the membrane interactions occur under the conditions in which fusion takes place. Other regions of C2B influence the fusion probability and kinetics but do not control the expansion of the fusion pore. These data indicate that the C2B domain has at least two distinct molecular roles in the fusion event, and the data are consistent with a model where the arginine apex of C2B positions the domain at the curved membrane surface of the expanding fusion pore. Synaptotagmin 1 (Syt1) is the calcium sensor for fast synchronous neurotransmitter release but the mechanism by which it functions is still under debate. Here, the authors combine EPR measurements and functional studies and observe that different faces of the Syt1 C2B domain play different roles in regulating neurotransmitter release and they show that the expansion of the fusion pore is mediated by membrane contact of the C2B arginine apex.
Avoiding Occupancy Detection From Smart Meter Using Adversarial Machine Learning

More and more conventional electromechanical meters are being replaced with smart meters because of their substantial benefits such as providing faster bi-directional communication between utility services and end users, enabling direct load control for demand response, energy saving and so on. However, the fine-grained usage data provided by smart meter brings additional vulnerabilities from users to companies. Occupancy detection is one such example which causes privacy violation of smart meter users. Detecting the occupancy of a home is straightforward with time of use information as there is a strong correlation between occupancy and electricity usage. In this work, our major contributions are twofold. First, we validate the viability of an occupancy detection attack based on a machine learning technique called Long Short Term Memory (LSTM) method and demonstrate improved results. In addition, we introduce an Adversarial Machine Learning Occupancy Detection Avoidance (AMLODA) framework as a counter attack in order to prevent abuse of energy consumption. Essentially, the proposed privacy-preserving framework is designed to mask real-time or near real-time electricity usage information using calculated optimum noise without compromising users’ billing systems functionality. The results show that without the use of the proposed AMLODA approach, our occupancy detection attack models using LSTM achieve a high detection rate with Matthews Correlation Coefficient (MCC) value of 0.89 on average for the five different households energy consumption data under investigation captured during the winter and summer seasons. With the proposed AMLODA approach working to protect consumers’ privacy, occupancy detection attacks are demonstrated to be mitigated with the MCC values of the attack models converging to zero with no significant change over the actual consumption data and thus protecting needed functionalities of the utility companies.
Freemium Pricing in Digital Games with Virtual Currency

Providers of free-to-play games often gain revenue by monetizing players’ playtime, for example, through in-game advertising and by selling premium modules of the game. One emerging strategy to sel...
Information transfer as a framework for optimized phase imaging

In order to efficiently image a non-absorbing sample (a phase object), dedicated phase contrast optics are required. Typically, these optics are designed with the assumption that the sample is weakly scattering, implying a linear relation between a sample’s phase and its transmission function. In the strongly scattering, nonlinear case, the standard optics are ineffective, and the transfer functions used to characterize them are uninformative. We use the Fisher information (FI) to assess the efficiency of various phase imaging schemes and to calculate an information transfer function (ITF). We show that a generalized version of Zernike phase contrast is efficient given sufficient prior knowledge of the sample. We show that with no prior knowledge, a random sensing measurement yields a significant fraction of the available information. Finally, we introduce a generalized approach to common path interferometry that can be optimized to prioritize sensitivity to particular sample features. Each of these measurements can be performed using Fourier lenses and phase masks.
Visual Finance: The Pervasive Effects of Red on Investor Behavior

The visual communication of financial information is commonplace and involves numerous facets, including the use of color. At the same time, perception of color influences behavior. Through eight e...
Genome-Wide Screens Identify Lineage- and Tumor-Specific Genes Modulating MHC-I and MHC-II Immunosurveillance in Human Lymphomas

Tumors frequently subvert MHC class I (MHC-I) peptide presentation to evade CD8+ T cell immunosurveillance. To better define the regulatory networks controlling antigen presentation, we employed genome-wide screening in human diffuse large B cell lymphomas (DLBCLs). This approach revealed dozens of novel genes that positively and negatively modulate MHC-I cell surface levels. Identified genes cluster in multiple pathways including cytokine signaling, mRNA processing, endosomal trafficking, and protein metabolism. Many genes exhibit lymphoma subtype- or tumor-specific MHC-I regulation, and a majority of primary DLBCL tumors display genetic alterations in multiple regulators. We establish that the HSP90 co-chaperone SUGT1 is a major positive regulator of both MHC-I and MHC-II cell surface expression. Further, pharmacological inhibition of two negative regulators of antigen presentation, EZH2 and thymidylate synthase, enhances DLBCL MHC-I presentation. These and other genes represent potential targets for manipulating MHC-I immunosurveillance in cancers, infectious diseases, and autoimmunity.
The Federal Reserve's response to the COVID-19 contraction: An initial appraisal

Abstract We provide an initial assessment of the Federal Reserve's policy response to the COVID-19 contraction We briefly review the historical episode and consider the standard textbook treatment of a pandemic on the macroeconomy We summarize and then evaluate the Fed's monetary and emergency lending policies through the end of 2020 We credit the Fed with promoting monetary stability while maintaining that it could have done more We argue that the Fed could have achieved stability without employing its emergency lending facilities Although some facilities likely helped to promote general liquidity, others were primarily intended to allocate credit, which blurs the line between monetary and fiscal policy These credit allocation facilities were unwarranted and unwise
Global study of separable pairing interaction in covariant density functional theory

A systematic global investigation of pairing properties based on all available experimental data on pairing indicators has been performed for the first time in the framework of covariant density functional theory. It is based on the separable pairing interaction of Tian, Ma, and Ring [Phys. Lett. B 676, 44 (2009)]. The optimization of the scaling factors of this interaction to experimental data clearly reveals its isospin dependence in the neutron subsystem. However, the situation is less certain in the proton subsystem since similar accuracy of the description of pairing indicators can be achieved both with isospin-dependent and mass-dependent scaling factors. The differences in the functional dependencies of scaling factors lead to uncertainties in the prediction of proton and neutron pairing properties which are especially pronounced at high isospin and could have a significant impact on some physical observables. For a given part of the nuclear chart the scaling factors for spherical nuclei are smaller than those for deformed ones; this feature exists also in nonrelativistic density functional theories. Its origin is traced back to particle-vibration coupling in odd-$A$ nuclei which is missing in all existing global studies of pairing. Although the present investigation is based on the NL5(E) covariant energy density functional (CEDF), its general conclusions are expected to be valid also for other CEDFs built at the Hartree level.
Nonprehensile Riemannian Motion Predictive Control

Nonprehensile manipulation involves long horizon underactuated object interactions and physical contact with different objects that can inherently introduce a high degree of uncertainty. In this work, we introduce a novel Real-to-Sim reward analysis technique, called Riemannian Motion Predictive Control (RMPC), to reliably imagine and predict the outcome of taking possible actions for a real robotic platform. Our proposed RMPC benefits from Riemannian motion policy and second order dynamic model to compute the acceleration command and control the robot at every location on the surface. Our approach creates a 3D object-level recomposed model of the real scene where we can simulate the effect of different trajectories. We produce a closed-loop controller to reactively push objects in a continuous action space. We evaluate the performance of our RMPC approach by conducting experiments on a real robot platform as well as simulation and compare against several baselines. We observe that RMPC is robust in cluttered as well as occluded environments and outperforms the baselines.
Complex Network Analysis of North American Institutions of Higher Education on Twitter

North American institutions of higher education (IHEs): universities, 4- and 2-year colleges, and trade schools—are heavily present and followed on Twitter. An IHE Twitter account, on average, has 20,000 subscribers. Many of them follow more than one IHE, making it possible to construct an IHE network, based on the number of co-followers. In this paper, we explore the structure of a network of 1,435 IHEs on Twitter. We discovered significant correlations between the network attributes: various centralities and clustering coefficients—and IHEs’ attributes, such as enrollment, tuition, and religious/racial/gender affiliations. We uncovered the community structure of the network linked to homophily—such that similar followers follow similar colleges. Additionally, we analyzed the followers’ self-descriptions and identified twelve overlapping topics that can be traced to the followers’ group identities.
Dynamical Tides in Jupiter as Revealed by Juno

The Juno orbiter continues to collect data on Jupiter's gravity field with unprecedented precision since 2016, recently reporting a non-hydrostatic component in the tidal response of the planet. At the mid-mission perijove 17, Juno registered a Love number $k_2=0.565\pm0.006$ that is $-4\pm1\%$ ($1\sigma$) from the theoretical hydrostatic $k_2^{(hs)}=0.590$. Here we assess whether the aforementioned departure of tides from hydrostatic equilibrium represents the neglected gravitational contribution of dynamical tides. We employ perturbation theory and simple tidal models to calculate a fractional dynamical correction $\Delta k_2$ to the well-known hydrostatic $k_2$. Exploiting the analytical simplicity of a toy uniform-density model, we show how the Coriolis acceleration motivates the negative sign in the $\Delta k_2$ observed by Juno. By simplifying Jupiter's interior into a core-less, fully-convective, and chemically-homogeneous body, we calculate $\Delta k_2$ in a model following an $n=1$ polytrope equation of state. Our numerical results for the $n=1$ polytrope qualitatively follow the behaviour of the uniform-density model, mostly because the main component of the tidal flow is similar in each case. Our results indicate that the gravitational effect of the Io-induced dynamical tide leads to $\Delta k_2=-4\pm1\%$, in agreement with the non-hydrostatic component reported by Juno. Consequently, our results suggest that Juno obtained the first unambiguous detection of the gravitational effect of dynamical tides in a gas giant planet. These results facilitate a future interpretation of Juno tidal gravity data with the purpose of elucidating the existence of a dilute core in Jupiter.
Predictive modeling of a lithium vapor box divertor in NSTX-U using SOLPS-ITER

Abstract The unmitigated heat flux in attached operation of a fusion power plant is predicted to be destructive to any solid divertor surface. Detachment, whereby the plasma pressure drops significantly before reaching the divertor target thus greatly reducing the heat flux and sputtering, will be necessary to ensure adequate lifetime of plasma facing components (PFCs). The lithium vapor box divertor aims to detach the divertor plasma via evaporating and condensing lithium surfaces. By evaporating lithium near or at the divertor plate and condensing it closer to the main chamber, a lithium vapor density gradient can be created. This density gradient ties energy losses to poloidal distance between the target and the detachment point. The radiation zone is then prevented from reaching the X-point as the lithium ionization rate decreases when the detachment front moves away from the divertor target. Here we present Scrape Off Layer Plasma Simulator (SOLPS) simulations of a lithium vapor box divertor using an NSTX-U equilibrium and PFC geometry. The parameters for the core boundary conditions, gas puff intensity, and heat and particle transport coefficients are chosen to match experimental values. Acceptable agreement with experimental Scrape-Off Layer (SOL) widths is found, indicating a reasonable choice of transport coefficients. In predictive simulations, lithium is added via evaporation at the target. Predictions for peak heat fluxes and upstream impurity concentration are given for a variety of evaporation rates. Target electron temperature is predicted to be able to be reduced to recombination levels (below 1 eV) for lithium evaporation rates of 1 ⋅ 1 0 23 Li/s, indicating detachment. Peak heat flux at the lower outer target could be reduced by as much as a factor of six while maintaining upstream lithium fractions below 2%. The prevention of lithium from reaching the midplane is shown to be due to an increase in frictional forces acting on the lithium from a deuterium gas puff. Lithium is also shown to be redeposited close to the evaporator which is favorable for initial tests and future capillary porous systems.
Virtual Reality Therapy for Depression and Mood in Long-Term Care Facilities.

Virtual reality (VR) describes a family of technologies which immerse users in sensorily-stimulating virtual environments. Such technologies have increasingly found applications in the treatment of neurological and mental health disorders. Depression, anxiety, and other mood abnormalities are of concern in the growing older population—especially those who reside in long-term care facilities (LTCFs). The transition from the familiar home environment to the foreign LTCF introduces a number of stressors that can precipitate depression. However, recent studies reveal that VR therapy (VRT) can promote positive emotionality and improve cognitive abilities in older people, both at home and in LTCFs. VR thus holds potential in allowing older individuals to gradually adapt to their new environments—thereby mitigating the detrimental effects of place attachment and social exclusion. Nevertheless, while the current psychological literature is promising, the implementation of VR in LTCFs faces many challenges. LTCF residents must gain trust in VR technologies, care providers require training to maximize the positive effects of VRT, and decision makers must evaluate both the opportunities and obstacles in adopting VR. In this review article, we concisely discuss the implications of depression related to place attachment in LTCFs, and explore the potential therapeutic applications of VR.
Nonlinear Decoding of Natural Images From Large-Scale Primate Retinal Ganglion Recordings

Decoding sensory stimuli from neural activity can provide insight into how the nervous system might interpret the physical environment, and facilitates the development of brain-machine interfaces. Nevertheless, the neural decoding problem remains a significant open challenge. Here, we present an efficient nonlinear decoding approach for inferring natural scene stimuli from the spiking activities of retinal ganglion cells (RGCs). Our approach uses neural networks to improve on existing decoders in both accuracy and scalability. Trained and validated on real retinal spike data from more than 1000 simultaneously recorded macaque RGC units, the decoder demonstrates the necessity of nonlinear computations for accurate decoding of the fine structures of visual stimuli. Specifically, high-pass spatial features of natural images can only be decoded using nonlinear techniques, while low-pass features can be extracted equally well by linear and nonlinear methods. Together, these results advance the state of the art in decoding natural stimuli from large populations of neurons.
Melting point depression and phase identification of sugar alcohols encapsulated in zif nanopores

Author(s): Dames, C; Urban, JJ; Kang, H | Abstract: Sugar alcohols (SAs) have attractive characteristics as phase-change materials, but their relatively high melting temperature limits their application in the real world. Nanoconfinement can be a useful parameter to reduce the melting temperature to pragmatic ranges. Using molecular dynamics simulations, we investigate the phases and behaviors of encapsulated SA in ZIF-8 and ZIF-11, which cannot be experimentally observed. Based on reliable partial charges for the zeolitic imidazolate framework (ZIF) structures calculated by a density functional theory, structural analysis shows that the SA's attractive interaction with the ZIF structure frustrates the SA crystallization and also elucidates the second-order phase transition between amorphous phases. A methodology is suggested to determine the phase transition temperature of confined materials and used to quantify the melting temperature depression of the ZIF-confined SAs. We also explored the thermal conductivity of SA-in-ZIF composites. Phonon frequency analysis verifies that the presence of SA molecules enhances the heat transfer by adding heat pathways between the nanoporous structure of ZIFs.
Stock merger activity and industry performance

Abstract We propose a merger activity variable (MAV) as an alternative to industry merger waves. Unlike discrete merger waves that separate periods of extreme activity from the rest, our continuous MAV utilizes information in the full range of stock merger activity. We rank Fama-French 12 industries by MAV each quarter and arrange them into 12 bucket portfolios. We examine their prior and subsequent three-year excess returns using calendar-time portfolio method. The prior returns are positively related to MAV ranks while the subsequent returns are negatively related to MAV ranks. This evidence suggests a build-up of misvaluation (undervaluation of relatively less stock merger active industries and overvaluation of relatively more active industries) followed by a correction. On average, the most active industry outperforms the least active industry by 13.46% during the prior period, but underperforms by 10.30% during the subsequent period. Industry operating performance results further support the industry misvaluation theory of stock merger activity.
The hybrid topological longitudinal transmon qubit

We introduce a new hybrid qubit consisting of a Majorana qubit interacting with a transmon longitudinally coupled to a resonator. To do so, we equip the longitudinal transmon qubit with topological quasiparticles, supported by an array of heterostructure nanowires, and derive charge- and phase-based interactions between the Majorana qubit and the resonator and transmon degrees of freedom. Inspecting the charge coupling, we demonstrate that the Majorana self-charging can be eliminated by a judicious choice of charge offset, thereby maintaining the Majorana degeneracy regardless of the quasiparticles spatial arrangement and parity configuration. We perform analytic and numerical calculations to derive the effective qubit-qubit interaction elements and discuss their potential utility for state readout and quantum error correction. Further, we find that select interactions depend strongly on the overall superconducting parity, which may provide a direct mechanism to characterize deleterious quasiparticle poisoning processes.
End-To-End Diarization for Variable Number of Speakers with Local-Global Networks and Discriminative Speaker Embeddings

We present an end-to-end deep network model that performs meeting diarization from single-channel audio recordings. End-to-end diarization models have the advantage of handling speaker overlap and enabling straightforward handling of discriminative training, unlike traditional clustering-based diarization methods. The proposed system is designed to handle meetings with unknown numbers of speakers, using variable-number permutation-invariant cross-entropy based loss functions. We introduce several components that appear to help with diarization performance, including a local convolutional network followed by a global self-attention module, multitask transfer learning using a speaker identification component, and a sequential approach where the model is refined with a second stage. These are trained and validated on simulated meeting data based on LibriSpeech and LibriTTS datasets; final evaluations are done using LibriCSS, which consists of simulated meetings recorded using real acoustics via loudspeaker playback. The proposed model performs better than previously proposed end-to-end diarization models on these data.
The Kids Are / Not / Sort of All Right*

We investigated changes in and factors affecting American adolescents’ subjective wellbeing during the early months (April – August 2020) of the coronavirus pandemic in the United States. Twenty-one teens (14-19 years) participated in interviews at the start and end of the study and completed ecological momentary assessments three times per week between the interviews. There was an aggregate trend toward increased wellbeing, with considerable variation within and across participants. Teens reported greater reliance on networked technologies as their unstructured time increased during lockdown. Using multilevel growth modeling, we found that how much total time teens spent with technology had less bearing on daily fluctuations in wellbeing than the satisfaction and meaning they derived from their technology use. Ultimately, teens felt online communication could not replace face-to-face interactions. We conducted two follow-up participatory design sessions with nine teens to explore these insights in greater depth and reflect on general implications for design to support teens’ meaningful technology experiences and wellbeing during disruptive life events.
Randomized Kaczmarz for tensor linear systems

Solving linear systems of equations is a fundamental problem in mathematics. When the linear system is so large that it cannot be loaded into memory at once, iterative methods such as the randomized Kaczmarz method excel. Here, we extend the randomized Kaczmarz method to solve multi-linear (tensor) systems under the tensor–tensor t-product. We present convergence guarantees for tensor randomized Kaczmarz in two ways: using the classical matrix randomized Kaczmarz analysis and taking advantage of the tensor–tensor t-product structure. We demonstrate experimentally that the tensor randomized Kaczmarz method converges faster than traditional randomized Kaczmarz applied to a naively matricized version of the linear system. In addition, we draw connections between the proposed algorithm and a previously known extension of the randomized Kaczmarz algorithm for matrix linear systems.
Entropy Stable Discontinuous Galerkin Methods for Nonlinear Conservation Laws on Networks and Multi-Dimensional Domains

We present a high-order entropy stable discontinuous Galerkin method for nonlinear conservation laws on both multi-dimensional domains and on networks constructed from one-dimensional domains. These methods utilize treatments of multi-dimensional interfaces and network junctions which retain entropy stability when coupling together entropy stable discretizations. Numerical experiments verify the stability of the proposed schemes, and comparisons with fully 2D implementations demonstrate the accuracy of each type of junction treatment.
Channel Scaling: A Scale-And-Select Approach For Transfer Learning

Transfer learning with pre-trained neural networks is a common strategy for training classifiers in medical image analysis. Without proper channel selections, this often results in unnecessarily large models that hinder deployment and explainability. In this paper, we propose a novel approach to efficiently build small and well performing networks by introducing the channel-scaling layers. A channel-scaling layer is attached to each frozen convolutional layer, with the trainable scaling weights inferring the importance of the corresponding feature channels. Unlike the fine-tuning approaches, we maintain the weights of the original channels and large datasets are not required. By imposing L1 regularization and thresholding on the scaling weights, this framework iteratively removes unnecessary feature channels from a pre-trained model. Using an ImageNet pre-trained VGG16 model, we demonstrate the capabilities of the proposed framework on classifying opacity from chest X-ray images. The results show that we can reduce the number of parameters by 95% while delivering a superior performance.
The asymmetric effect of warranty payments on firm value: The moderating role of advertising, R&D, and industry concentration

Abstract Changes in firms’ warranty payments are informative signals that enable investors to form timely expectations about potential changes in product quality. The authors’ survey shows that warranty payments affect potential investors’ product quality assessments and stock investment likelihood. Their quantitative analysis reveals an asymmetric stock market reaction: unanticipated increases in warranty payments (which signal quality “losses”) lower stock returns but unanticipated decreases do not affect stock returns. Two important factors moderate this relationship. First, boosting advertising spending attenuates the negative stock return effect of unanticipated increases in warranty payments. Second, unanticipated decreases in warranty payments, which signal quality “gains”, translate into higher stock returns when the industry has become less concentrated. Interestingly, changes in R&D spending do not moderate investors’ response to unanticipated increases or decreases in warranty payments. The authors advise firms to use advertising to lessen the harm from warranty payment increases and to strongly communicate warranty payment decreases in the face of intensified competition. The authors also caution that offering warranties in general does not ensure greater firm value as declining quality firms that myopically offer warranty programs experience lower firm value than those that do not provide warranties.