cssi/SciSciGPT-SciSciNet huggingface dataset:
Introduction: Religious Law In the 21st Century

Professor Helfand introduces this symposium on Religious Law in the 21st Century. Helfand notes that a recurring theme in recent debates over the relationship between law and religion is the unique challenge of reconciling conflicts not just between law and religion, but between the law of the nation-state and “religious legal communities” -- that is, communities that primarily experience their religious norms through the prism of legal rules. Muslim and Jewish communities serve as prime examples of such religious legal communities, and the challenges faced by these communities often parallel each other in important ways. Thus, an important subset of contemporary religious controversies -- from circumcision bans to anti-Sharia laws -- emerge as not only conflicts between law and religion, but as conflicts between law and law. And it is to this unique set of questions that the jointly-sponsored program of the Islamic Law and Jewish Law Sections of the American Association of Law Schools was addressed. The program was split into two thematic panels, and the articles in this symposium reflect those themes. The first -- titled “Religious Law in U.S. Courts” -- considered the various contexts in which U.S. courts have been asked to address religious questions that touch upon religious law. The second -- titled “Religious Law in the Secular State” -- considered contemporary issues related to the practice and implementation of religious law in secular democracies. Together, these papers bring new insight to these questions and serve as a springboard for discussion and debate about how religious law will fit into the ever-evolving landscape of the 21st century.
A Note on the Archaeological Report

Archaeological reports provide a record of what is excavated as well as the context in which the remains are found. I have often read archaeological reports for information on the conservation practices and materials used by archaeologists and to find data bearing on the identification and exhibition of artifacts. I have studied them to discover the nature of the published literature on writing archaeological reports, and have found them exceedingly varied in content and structure. This article will introduce the history and theory of the report and will compare a selection of reports, using the descriptive time periods of Willey and Sabloff (1974). I will also examine published literature on report writing by American and British authors.
Vernacular Models of Development: An Analysis of Indonesia Under the “New Order”

Abstract Official development discourses often differ from the everyday discussions and activities of development agents. If the former reflect an official model of development, then the latter could be said to reflect a “vernacular” model. The latter model is the focus of this analysis, taking as a case study development in Indonesia during the “New Order” regime (1966–98) of former President Suharto. Resource relations between the Indonesian state center and rural periphery are examined along with the norms that govern the flow of resources as well as information on resource-use. This vernacular model of resource and information flows helps to explain the economic, political, and environmental crisis that befell Indonesia in the late 1990s and is relevant to understanding the development dynamics of a number of other nations.
Numerical convergence of nonlinear nonlocal continuum models to local elastodynamics

We quantify the numerical error and modeling error associated with replacing a nonlinear nonlocal bond-based peridynamic model with a local elasticity model or a linearized peridynamics model away from the fracture set. The nonlocal model treated here is characterized by a double well potential and is a smooth version of the peridynamic model introduced in n Silling (J Mech Phys Solids 48(1), 2000). The solutions of nonlinear peridynamics are shown to converge to the solution of linear elastodynamics at a rate linear with respect to the length scale $\epsilon$ of non local interaction. This rate also holds for the convergence of solutions of the linearized peridynamic model to the solution of the local elastodynamic model. For local linear Lagrange interpolation the consistency error for the numerical approximation is found to depend on the ratio between mesh size $h$ and $\epsilon$. More generally for local Lagrange interpolation of order $p\geq 1$ the consistency error is of order $h^p/\epsilon$. A new stability theory for the time discretization is provided and an explicit generalization of the CFL condition on the time step and its relation to mesh size $h$ is given. Numerical simulations are provided illustrating the consistency error associated with the convergence of nonlinear and linearized peridynamics to linear elastodynamics.
Criminal madness: cultural iconography and insanity.

Law relies on a well-developed and constantly evolving iconography to tell its stories. Like lawyers and judges, legal scholars typically rely upon official legal sources to flesh out the implicit meaning of the law’s language. But “official law,” with its stress on statutory language, legislative intent, and case precedent, is quite plainly an insufficient source for understanding the texture and nuance of legal language. To better understand law’s implicit meaning, readers of law need to mine unofficial as well as official sources of law. These unofficial sources often provide insight into, and occasionally substance for, law’s official meaning. Popular culture is one important source of legal meaning. In a myriad of ways, popular culture influences the making, interpretation, and application of law. By illuminating and contextualizing problems, creating certain types of narrative, or favoring some narrative constructions over others, popular culture frequently determines what kind of law is made. My primary interest in this paper revolves around the iconography of crime, mental illness, and insanity. These concepts not only go to the heart of the legal understanding of human responsibility, they also have long provided an unending well of narratives to feed the human hunger for meaning-making stories. This Article attempts to trace the iconography of criminal madness in popular cinema and to link it with the law’s development over the same span. Part I provides some prefatory observations about the relation of film and culture to law. Part II explores the depiction of criminal madness in the 1930s, primarily through the monster movies of the era. Part III describes the growing embrace of psychological and psychiatric theories in midcentury cinema, which occurred precisely during a period in which the insanity defense was liberalized and constitutional checks on the state’s power to institutionalize mad criminals were recognized. Finally, Part IV examines dramatic post-1970s changes in cinematic portrayals of criminals, the criminal justice system, and mad criminals, and explores ways in which the new iconography of criminal madness contributed to a dramatic shrinkage of the rights of mentally ill offenders.
Society-in-the-loop: programming the algorithmic social contract

Recent rapid advances in Artificial Intelligence (AI) and Machine Learning have raised many questions about the regulatory and governance mechanisms for autonomous machines. Many commentators, scholars, and policy-makers now call for ensuring that algorithms governing our lives are transparent, fair, and accountable. Here, I propose a conceptual framework for the regulation of AI and algorithmic systems. I argue that we need tools to program, debug and maintain an algorithmic social contract, a pact between various human stakeholders, mediated by machines. To achieve this, we can adapt the concept of human-in-the-loop (HITL) from the fields of modeling and simulation, and interactive machine learning. In particular, I propose an agenda I call society-in-the-loop (SITL), which combines the HITL control paradigm with mechanisms for negotiating the values of various stakeholders affected by AI systems, and monitoring compliance with the agreement. In short, `SITL = HITL + Social Contract.'
Isaiah Berlin's anti-reductionism: The move from semantic to normative perspectives

Against the standard reading of Isaiah Berlin's thought that drives a wedge between his early and subsequent work, this article suggests that his late normative anti-reductionism has roots in the early writings on meaning, semantics and truth. Berlin's anti-reductionist objection to logical positivists in the realm of semantics evince a sensitivity to reductionism, a recognition of the irreducibility of propositional meaning, a plea for the embededness of language in a temporal continuum, an anti-dualist call, and a celebration of the plural and open-ended character of propositions, all of which keep broad affinity with his mature value pluralism in ethics and politics. The unity of concerns in Berlin's work brings about two implications. First, it sheds light on the fact that Berlin's ethical and political pluralism rests on views about semantics and ontology as well. Second, it offers a basis on which to deflect some current moral realist readings of his value pluralism, offering instead a history-laden understanding of his pluralism that provides more compelling grounds to understand the relationship between the latter and liberalism in Berlin's work.
Gross substitutes and complements: A simple generalization

We extend the gross substitutes and complements framework (Sun and Yang, 2006). Competitive equilibrium with indivisible goods exists under significantly weaker, intuitive and interpretable conditions. A generalized dynamic double-track procedure (Sun and Yang, 2008, 2009) finds the competitive equilibrium outcome.
Blacklisted Businesses: Social Activists’ Challenges and the Disruption of Corporate Political Activity

This paper explores whether and how social activists’ challenges affect politicians’ willingness to associate with targeted firms. We study the effect of public protest on corporate political activ...
Bubbles, Crashes and Efficiency with Double Auction Mechanisms

We provide a quantitative boundary on the stepsizes of bid and ask of a double auction (DA) mechanism to answer two questions, when the DA mechanism is efficient and when it creates bubbles and crashes. The main result is that the ratio of the two stepsizes and their spread are the key factors for the DA mechanism to be efficient. Sentiment that leads to a swing in the spread and the ratio of the two stepsizes can result in prices to deviate from the intrinsic value equilibrium. These results are derived from a theoretical analysis of the DA mechanism built on the incremental subgradient method in Nedic and Bertsekas (2001).
MASSIVE ELEMENTARY PARTICLES AND BLACK HOLE PHYSICS IN RESUMMED QUANTUM GRAVITY

We use exact results in a new approach to quantum gravity to discuss some issues in black hole physics.
How Volatile and Unpredictable Are Aid Flows, and What Are the Policy Implications?

This paper examines empirical evidence on the volatility and uncertainty of aid flows, and the main policy implications. Aid is found to be more volatile than fiscal revenues—particularly in highly aid-dependent countries—and mildly procyclical in relation to activity in the recipient country. These findings imply that the current pattern of aid disbursements is welfare-reducing. We also find that uncertainty about aid disbursements is large and that the information content of commitments made by donors is either very small or statistically insignificant. Policies to cope with these features of aid, as well as broader international efforts to reduce the volatility and procyclicality of aid, are briefly discussed.
Trade Credit, Relationship-specific Investment, and Product Market Power

We rely on a model with incomplete contracts and bargaining power to argue that trade credit (TC) can serve as a commitment device for making relationship-specific investments (RSIs). Unlike existing theories, we explain within a single theoretical frame- work why TC is affected by firms' bargaining power and by the specialized nature of transacted goods. Using a large panel of publicly listed firms and innovation-based proxies for RSI, we find strong support for the model's predictions: TC increases in upstream firm's RSI and downstream firm's market power. Endogeneity concerns are ad- dressed by using the passage of innovation-increasing state laws to instrument for RSI and import penetration to instrument for bargaining power.
Offshore activities and financial vs operational hedging

Abstract A key question is why many multinational firms forgo foreign exchange derivative (FX) hedging and instead use operational hedging. We propose an explanation based on illiquidity and the unique advantages of operational hedges. We use 10-K filings to construct dynamically updated text-based measures of the offshore sale of output, purchase of input, and ownership of assets. We find that firms use FX derivatives when they are liquid and generally available. Otherwise, they often favor purchasing input from the same nations they sell output to, an operational hedge. Quasi-natural experiments based on new derivative product launches suggest a likely causal relation.
Toric double determinantal varieties

We examine Li’s double determinantal varieties in the special case that they are toric. We recover from the general double determinantal varieties case, via a more elementary argument, that they ar...
Shared Developmental Trajectories for Fractional Reasoning and Fine Motor Ability in 4 and 5 Year Olds

We investigated preschool-aged children’s understanding of early fractional tasks and how that performance correlates with fine motor skills and use of gestures while counting. Participants were 33 preschoolers aged 4 to 5 in two Southeastern public elementary schools. Children were tested individually in an interview-like setting. Mathematics tasks were presented in a paper and pencil format and the Grooved Pegboard test assessed fine motor skills. Finally, utilization of gestures was evaluated by taking a behavioral rating of the child’s hand morphology, accuracy of gestures, and synchrony of gestures and spoken word while performing a counting task. Results indicate that performance on fractional reasoning tasks significantly predicts both fine motor ability and accuracy of gestures.
Embedding cosmology and gravity

I start with a scenario where the universe is an abstract space $${\mathcal {M}}$$ having d dimensions. There is a two dimensional surface embedded in it. Embedding is a map from the embedded surface to $${\mathcal {M}}$$ that has a field theory described by Sigma model. I take d directions of $${\mathcal {M}}$$ to be the generators of a symmetry group SU(n) of the Lagrangian of the embedding. This means embedding has n flavors. Then I introduce spontaneous symmetry breaking in the theory and define the direction along which the symmetry breaking occurs as time. Next I write down the modified Einstein’s equation including the embedding. Then I discuss embedding’s relation to the expansion of the universe. After that I construct an inflationary scenario with embedding as inflaton and discuss its connection to Starobinsky $$R^{2}$$ model. Finally, I discuss the effect of inflation on the non-commutativity of the spacetime.
Frequency and profile of objective cognitive deficits in hospitalized patients recovering from COVID-19.

Early reports and case series suggest cognitive deficits occurs in some patients with COVID-19. We evaluated the frequency, severity, and profile of cognitive dysfunction in patients recovering from prolonged COVID-19 hospitalization who required acute inpatient rehabilitation prior to discharge. We analyzed cross-sectional scores from the Brief Memory and Executive Test (BMET) in a cohort of N = 57 COVID-19 patients undergoing inpatient rehabilitation, calculating the frequency of impairment based on neuropsychologist diagnosis and by age-normed BMET subtests. In total, 43 patients (75%) were male, 35 (61%) were non-white, and mean age was 64.5 (SD = 13.9) years. In total, 48 (84%) were previously living at home independently. Two patients had documented preexisting cognitive dysfunction; none had known dementia. Patients were evaluated at a mean of 43.2 (SD = 19.2) days after initial admission. In total, 50 patients (88%) had documented hypoxemic respiratory failure and 44 (77%) required intubation. Forty-six patients (81%) had cognitive impairment, ranging from mild to severe. Deficits were common in working memory (26/47 [55%] of patients), set-shifting (21/44 [47%]), divided attention (18/39 [46%]), and processing speed (14/35 [40%]). Executive dysfunction was not significantly associated with intubation length or the time from extubation to assessment, psychiatric diagnosis, or preexisting cardiovascular/metabolic disease. Attention and executive functions are frequently impaired in COVID-19 patients who require acute rehabilitation prior to discharge. Though interpretation is limited by lack of a comparator group, these results provide an early benchmark for identifying and characterizing cognitive difficulties after COVID-19. Given the frequency and pattern of impairment, easy-to-disseminate interventions that target attention and executive dysfunctions may be beneficial to this population.
Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modeling

Vast amount of data generated from networks of sensors, wearables, and the Internet of Things (IoT) devices underscores the need for advanced modeling techniques that leverage the spatio-temporal structure of decentralized data due to the need for edge computation and licensing (data access) issues. While federated learning (FL) has emerged as a framework for model training without requiring direct data sharing and exchange, effectively modeling the complex spatio-temporal dependencies to improve forecasting capabilities still remains an open problem. On the other hand, state-of-the-art spatio-temporal forecasting models assume unfettered access to the data, neglecting constraints on data sharing. To bridge this gap, we propose a federated spatio-temporal model -- Cross-Node Federated Graph Neural Network (CNFGNN) -- which explicitly encodes the underlying graph structure using graph neural network (GNN)-based architecture under the constraint of cross-node federated learning, which requires that data in a network of nodes is generated locally on each node and remains decentralized. CNFGNN operates by disentangling the temporal dynamics modeling on devices and spatial dynamics on the server, utilizing alternating optimization to reduce the communication cost, facilitating computations on the edge devices. Experiments on the traffic flow forecasting task show that CNFGNN achieves the best forecasting performance in both transductive and inductive learning settings with no extra computation cost on edge devices, while incurring modest communication cost.
CRAC: Checkpoint-Restart Architecture for CUDA with Streams and UVM

The share of the top 500 supercomputers with NVIDIA GPUs is now over 25% and continues to grow. While fault tolerance is a critical issue for supercomputing, there does not currently exist an efficient, scalable solution for CUDA applications on NVIDIA GPUs. CRAC (Checkpoint-Restart Architecture for CUDA) is a new checkpoint-restart solution for fault tolerance that supports the full range of CUDA applications. CRAC combines: low runtime overhead (approximately 1% or less); fast checkpoint-restart; support for scalable CUDA streams (for efficient usage of all of the thousands of GPU cores); and support for the full features of Unified Virtual Memory (eliminating the programmer’s burden of migrating memory between device and host). CRAC achieves its flexible architecture by segregating application code (checkpointed) and its external GPU communication via non-reentrant CUDA libraries (not checkpointed) within a single process’s memory. This eliminates the high overhead of inter-process communication in earlier approaches, and has fewer limitations.
Unique Exams: Designing Assessments for Integrity and Fairness

During the COVID-19 pandemic, many educators have had to rethink their methodology for summative assessment. Are timed and proctored exams appropriate---or even feasible---in this new open-internet, online learning environment? In this experience paper, we discuss our unique exams framework: our tool for generating exams that are uniquely identifiable but conceptually identical. In our university-level Probability for Computer Scientists Course, students completed unique exams generated from a common exam skeleton, with unique numeric variations per problem. With few deviations from the creation, administration, and grading processes of a traditional exam, our framework can provide a layer of security for both students and instructors about exam reliability for any classroom environment---in-person or online. In addition to sharing our experience designing unique exams, in this paper we also present a simple end-to-end tool and example question templates for different CS subjects that other instructors can adapt to their own courses.
Deep learning-based super-resolution fluorescence microscopy on small datasets

Fluorescence microscopy has enabled a dramatic development in modern biology by visualizing biological organ- isms with micrometer scale resolution. However, due to the diffraction limit, sub-micron/nanometer features are difficult to resolve. While various super-resolution techniques are developed to achieve nanometer-scale resolu- tion, they often either require expensive optical setup or specialized fluorophores. In recent years, deep learning has shown potentials to reduce the technical barrier and obtain super-resolution from diffraction-limited images. For accurate results, conventional deep learning techniques require thousands of images as a training dataset. Obtaining large datasets from biological samples is not often feasible due to photobleaching of fluorophores, phototoxicity, and dynamic processes occurring within the organism. Therefore, achieving deep learning-based super-resolution using small datasets is challenging. We address this limitation with a new convolutional neural network based approach that is successfully trained with small datasets and achieves super-resolution images. We captured 750 images in total from 15 different field-of-views as the training dataset to demonstrate the technique. In each FOV, a single target image is generated using the super-resolution radial fluctuation method. As expected, this small dataset failed to produce a usable model using traditional super-resolution architecture. However, using the new approach, a network can be trained to achieve super-resolution images from this small dataset. This deep learning model can be applied to other biomedical imaging modalities such as MRI and X-ray imaging, where obtaining large training datasets is challenging.
Avoiding Disparity Amplification under Different Worldviews

We mathematically compare four competing definitions of group-level nondiscrimination: demographic parity, equalized odds, predictive parity, and calibration. Using the theoretical framework of Friedler et al., we study the properties of each definition under various worldviews, which are assumptions about how, if at all, the observed data is biased. We argue that different worldviews call for different definitions of fairness, and we specify the worldviews that, when combined with the desire to avoid a criterion for discrimination that we call disparity amplification, motivate demographic parity and equalized odds. We also argue that predictive parity and calibration are insufficient for avoiding disparity amplification because predictive parity allows an arbitrarily large inter-group disparity and calibration is not robust to post-processing. Finally, we define a worldview that is more realistic than the previously considered ones, and we introduce a new notion of fairness that corresponds to this worldview.
Implementing a telemedicine curriculum for internal medicine residents during a pandemic: the Cleveland Clinic experience.

Telemedicine training was not a substantial element of most residency programmes prior to the COVID-19 pandemic. Social distancing measures changed this. The Cleveland Clinic Internal Medicine Residency Programme (IMRP) is one of the largest programmes in the USA, which made the task of implementing a telemedicine curriculum more complex. Here we describe our experience implementing an effective, expedited telemedicine curriculum for our ambulatory resident clinics. This study was started in April 2020 when we implemented a resident-led curriculum and training programme for providing ambulatory telemedicine care. The curriculum was finalised in less than 5 weeks. It entailed introducing a formal training programme for residents, creating a resource guide for different video communication tools and training preceptors to safely supervise care in this new paradigm. Residents were surveyed before the curriculum to assess prior experience with telemedicine, and then afterward to assess the curriculum's effectiveness. We also created a mini-CEX assessment for residents to solicit feedback on their performance during virtual appointments. Over 2000 virtual visits were performed by residents in a span of 10 weeks. Of 148 residents, 38% responded to the pre-participation survey. A majority had no prior telemedicine experience and expressed only slight comfort with the modality. Through collaboration with experienced residents and faculty, we expeditiously deployed an enhancement to our ambulatory care curriculum to teach residents how to provide virtual care and help faculty with supervision. We share our insights on this experience for other residency programmes to use.
Prenatal methadone exposure disrupts behavioral development and alters motor neuron intrinsic properties and local circuitry.

The far-reaching opioid crisis extends to babies born to mothers who take prescription or illicit opioids during pregnancy. Opioids such as oxycodone and methadone can freely cross the placenta from mother to baby. With the rising misuse of and addiction to opioids, the number of babies born physically dependent on opioids has risen sharply over the last decade. Although these infants are only passively exposed to opioids in the womb, they can still experience withdrawal symptoms at birth. This withdrawal is characterized by irritability, excessive crying, body shakes, problems with feeding, fevers and diarrhea. While considerable attention has been given to treating opioid withdrawal in newborn babies, little is known about how these children develop in their first years of life. This is, in part, because it is difficult for researchers to separate drug-related effects from other factors in a child’s home environment that can also disrupt their development. In addition, the biological mechanisms underpinning opioid-related impairments to infant development also remain unclear. Animal models have been used to study the effects of opioid exposure during pregnancy (termed prenatal exposure) on infants. These models, however, could be improved to better replicate the typical pattern of opioid use among pregnant women. Recognizing this gap, Grecco et al. have developed a mouse model of prenatal methadone exposure where female mice that were previously dependent on oxycodone were treated with methadone throughout their pregnancy. Methadone is an opioid drug commonly prescribed for treating opioid use disorder in pregnant women and was found to accumulate at high levels in the fetal brain of mice, which fell quickly after birth. The offspring also experienced withdrawal symptoms. Grecco et al. then examined the physical, behavioral and brain development of mice born to opioid-treated mothers. These included assessments of the animals’ motor skills, sensory reflexes and behavior in their first four weeks of life. Additional experiments tested the properties of nerve cells in the brain to examine cell-level changes. The assessments showed that methadone exposure in the womb impaired the physical growth of offspring and this persisted into ‘adolescence’. Prenatal methadone exposure also delayed progress towards key developmental milestones and led to hyperactivity in three-week-old mice. Moreover, Grecco et al. found that these mice had reduced neuron density and cell-to-cell connectivity in the part of the brain which controls movement. These findings shed light on the potential consequences of prenatal methadone exposure on physical, behavioral and brain development in infants. This model could also be used to study new potential treatments or intervention strategies for offspring exposed to opioids during pregnancy.
Accuracy of Difference Schemes in Electromagnetic Applications: A Trefftz Analysis

The article examines local approximation errors of finite-difference schemes in electromagnetic analysis. Despite a long history of the subject, several accuracy-related issues have been overlooked and/or remain controversial. For example, conflicting claims have been made in the literature about the order of Yee-like schemes in the vicinity of slanted or curved material interfaces. Two novel practical methods for comparison of local accuracy of difference schemes are proposed: one makes use of Trefftz test matrices , and the other one relies on a new measure of approximation accuracy in scheme-exact Trefftz subspaces . One particular conclusion is that a loss of accuracy for Yee-like schemes at slanted material boundaries is unavoidable.
Third-Party Consequences of Changes in Managerial Fiduciary Duties: The Case of Auditors’ Going Concern Opinions

This study examines the effect of managerial fiduciary duties on the likelihood of firms receiving going concern (GC) opinions from their auditors. We exploit an influential 1991 legal ruling that ...
The Dynamics of Seller Reputation: Theory and Evidence from eBay

We propose a basic theoretical model of eBay's reputation mechanism, derive a series of implications and empirically test their validity. Our theoretical model features both adverse selection and moral hazard. We show that when a seller receives a negative rating for the first time his reputation decreases and so does his effort level. This implies a decline in sales and price; and an increase in the rate of arrival of subsequent negative feedback. Our model also suggests that sellers with worse records are more likely to exit (and possibly re-enter under a new identity), whereas better sellers have more to gain from buying a reputation' by building up a record of favorable feedback through purchases rather than sales. Our empirical evidence, based on a panel data set of seller feedback histories and cross-sectional data on transaction prices collected from eBay is broadly consistent with all of these predictions. An important conclusion of our results is that eBay's reputation system gives way to strategic responses from both buyers and sellers.
Materials challenges for trapped-ion quantum computers

Trapped-ion quantum information processors store information in atomic ions maintained in position in free space by electric fields. Quantum logic is enacted through manipulation of the ions’ internal and shared motional quantum states using optical and microwave signals. Although trapped ions show great promise for quantum-enhanced computation, sensing and communication, materials research is needed to design traps that allow for improved performance by means of integration of system components, including optics and electronics for ion-qubit control, while minimizing the near-ubiquitous electric-field noise produced by trap-electrode surfaces. In this Review, we consider the materials requirements for such integrated systems, with a focus on problems that hinder current progress towards practical quantum computation. We give suggestions for how materials scientists and trapped-ion technologists can work together to develop materials-based integration and noise-mitigation strategies to enable the next generation of trapped-ion quantum computers. Trapped-ion qubits have great potential for quantum computation, but materials improvements are needed. This Review surveys materials opportunities to improve the performance of trapped-ion qubits, from understanding the surface science that leads to electric-field noise to developing methods for building ion traps with integrated optics and electronics.
Electric energies of a charged sphere surrounded by electrolyte

By using the recently generalized version of Newton's Shell Theorem [6] analytical equations are derived to calculate the electric potential energy needed to build up a charged sphere, and the field and polarization energy of the electrolyte inside and around the sphere. These electric energies are calculated as a function of the electrolyte's ion concentration and the radius of the charged sphere. The work needed to build up the charged sphere, ECC (i.e. the total charge-charge interaction energy) decreases with increasing ion concentration of the electrolyte because of the electrolyte ions' increasing screening effect on the charge-charge interaction. The work needed to build up the charged sphere appears as a sum of the field and polarization energy of the electrolyte. At zero ion concentration the electrolyte's field energy is equal with ECC while the polarization energy is zero. At high electrolyte ion concentrations ( C > 10 mol / m 3) 50% of ECC appears as the polarization energy of the electrolyte, 25% as the electrolyte's field energy inside the sphere and 25% as the electrolyte's field energy around the sphere.
Perfection Not Required? Human-AI Partnerships in Code Translation

Generative models have become adept at producing artifacts such as images, videos, and prose at human-like levels of proficiency. New generative techniques, such as unsupervised neural machine translation (NMT), have recently been applied to the task of generating source code, translating it from one programming language to another. The artifacts produced in this way may contain imperfections, such as compilation or logical errors. We examine the extent to which software engineers would tolerate such imperfections and explore ways to aid the detection and correction of those errors. Using a design scenario approach, we interviewed 11 software engineers to understand their reactions to the use of an NMT model in the context of application modernization, focusing on the task of translating source code from one language to another. Our three-stage scenario sparked discussions about the utility and desirability of working with an imperfect AI system, how acceptance of that system’s outputs would be established, and future opportunities for generative AI in application modernization. Our study highlights how UI features such as confidence highlighting and alternate translations help software engineers work with and better understand generative NMT models.
HULK: An Energy Efficiency Benchmark Platform for Responsible Natural Language Processing

Computation-intensive pretrained models have been taking the lead of many natural language processing benchmarks such as GLUE. However, energy efficiency in the process of model training and inference becomes a critical bottleneck. We introduce HULK, a multi-task energy efficiency benchmarking platform for responsible natural language processing. With HULK, we compare pretrained models’ energy efficiency from the perspectives of time and cost. Baseline benchmarking results are provided for further analysis. The fine-tuning efficiency of different pretrained models can differ significantly among different tasks, and fewer parameter number does not necessarily imply better efficiency. We analyzed such a phenomenon and demonstrated the method for comparing the multi-task efficiency of pretrained models. Our platform is available at https://hulkbenchmark.github.io/ .
A compiler infrastructure for accelerator generators

We present Calyx, a new intermediate language (IL) for compiling high-level programs into hardware designs. Calyx combines a hardware-like structural language with a software-like control flow representation with loops and conditionals. This split representation enables a new class of hardware-focused optimizations that require both structural and control flow information which are crucial for high-level programming models for hardware design. The Calyx compiler lowers control flow constructs using finite-state machines and generates synthesizable hardware descriptions. We have implemented Calyx in an optimizing compiler that translates high-level programs to hardware. We demonstrate Calyx using two DSL-to-RTL compilers, a systolic array generator and one for a recent imperative accelerator language, and compare them to equivalent designs generated using high-level synthesis (HLS). The systolic arrays are 4.6× faster and 1.11× larger on average than HLS implementations, and the HLS-like imperative language compiler is within a few factors of a highly optimized commercial HLS toolchain. We also describe three optimizations implemented in the Calyx compiler.
Microbial diversity of co-occurring heterotrophs in cultures of marine picocyanobacteria

The cyanobacteria Prochlorococcus and Synechococcus are responsible for around 10% of global net primary productivity, serving as part of the foundation of marine food webs. Heterotrophic bacteria are often co-isolated with these picocyanobacteria in seawater enrichment cultures that contain no added organic carbon; heterotrophs grow on organic carbon supplied by the photolithoautotrophs. For examining the selective pressures shaping autotroph/heterotroph interactions, we have made use of unialgal enrichment cultures of Prochlorococcus and Synechococcus maintained for hundreds to thousands of generations in the lab. We examine the diversity of heterotrophs in 74 enrichment cultures of these picocyanobacteria obtained from diverse areas of the global oceans. Heterotroph community composition differed between clades and ecotypes of the autotrophic ‘hosts’ but there was significant overlap in heterotroph community composition across these cultures. Collectively, the cultures were comprised of many shared taxa, even at the genus level. Yet, observed differences in community composition were associated with time since isolation, location, depth, and methods of isolation. The majority of heterotrophs in the cultures are rare in the global ocean, but enrichment conditions favor the opportunistic outgrowth of these rare bacteria. However, we found a few examples, such as bacteria in the family Rhodobacteraceae, of heterotrophs that were ubiquitous and abundant in cultures and in the global oceans. We found their abundance in the wild is also positively correlated with that of picocyanobacteria. Particular conditions surrounding isolation have a persistent effect on long-term culture composition, likely from bottlenecking and selection that happen during the early stages of enrichment for the picocyanobacteria. We highlight the potential for examining ecologically relevant relationships by identifying patterns of distribution of culture-enriched organisms in the global oceans.
Viscosity of the earth's core

Estimates of the coefficient of kinematical viscosity ν of the Earth's liquid metallic core that are given in the geophysical literature range from ≈ 10−3 cm2/s, the viscosity of molten iron at ordinary pressures, to ⪅ 108 cm2/s, based on the observation that compressional waves traverse the core without suffering serious attenuation (see Gans, 1972). Bumps on the core-mantle boundary with typical horizontal dimensions up to a few thousand km and vertical dimensions h of a few km would produce the “topographic” coupling between the core and mantle that is evidently implied by the observed “decade” variations in the length of the day (unless the coupling is due to the presence of rapidly fluctuating magnetic fields in the core). A further possible manifestation of the concomitant magnetohydrodynamic interactions between core motions and bumps on the core-mantle interface is the recently discovered correlation between global features of the Earth's gravitational and magnetic fields, and these interactions cannot occur unless h exceeds the thickness δ of the viscous boundary layer at the interface. Now δ = (νΩ)12ƒ(x) , where Ω is the Earth's angular speed of rotation and x ≡ σB22ρΩ , a dimensionless parameter, where σ is the electrical conductivity of the core (≈ 10−5.5±0.5 emu) and ρ its density (≈ 10 g/cm3), B is the vertical component of the geomagnetic field at the core-mantle interface (≈5 G), and ƒ(x) = 212(1+x2)−14sec(12arccot x) (≈1 for the core); hence (see Hide, 1971) ν ⪅ h2 Ω ≈ 106cm2/s
Beyond Expertise and Roles: A Framework to Characterize the Stakeholders of Interpretable Machine Learning and their Needs

To ensure accountability and mitigate harm, it is critical that diverse stakeholders can interrogate black-box automated systems and find information that is understandable, relevant, and useful to them. In this paper, we eschew prior expertise- and role-based categorizations of interpretability stakeholders in favor of a more granular framework that decouples stakeholders’ knowledge from their interpretability needs. We characterize stakeholders by their formal, instrumental, and personal knowledge and how it manifests in the contexts of machine learning, the data domain, and the general milieu. We additionally distill a hierarchical typology of stakeholder needs that distinguishes higher-level domain goals from lower-level interpretability tasks. In assessing the descriptive, evaluative, and generative powers of our framework, we find our more nuanced treatment of stakeholders reveals gaps and opportunities in the interpretability literature, adds precision to the design and comparison of user studies, and facilitates a more reflexive approach to conducting this research.
{PEARL}: Plausibly Deniable Flash Translation Layer using {WOM} coding

When adversaries are powerful enough to coerce users to reveal encryption keys, encryption alone becomes insufficient for data protection. Plausible deniability (PD) mechanisms resolve this by enabling users to hide the mere existence of sensitive data, often by providing plausible "cover texts" or "public data volumes" hosted on the same device. Unfortunately, with the increasing prevalence of (NAND) flash as a high-performance cost-effective storage medium, PD becomes even more challenging in the presence of realistic adversaries who can usually access a device at multiple points in time ("multi-snapshot"). This is because read/write operations to flash do not result in intuitive corresponding changes to the underlying device state. The problem is further compounded by the fact that this behavior is mostly proprietary. For example, in a majority of commercially-available flash devices, an issued delete or overwrite operation from the upper layers almost certainly won't result in an actual immediate erase of the underlying flash cells. To address these challenges, we designed a new class of write-once memory (WOM) codes to store hidden bits in the same physical locations as other public bits. This is made possible by the inherent nature of NAND flash and the possibility of issuing multiple writes to target cells that have not previous been written to in existing pages. We designed PEARL, a general-purpose Flash Translation Layer (FTL) that allows users to plausibly deniably store hidden data in NAND flash devices. We implemented and evaluated PEARL on a widely used simulator FlashSim (Kim et al. 2019). PEARL performs well on real-world workloads, comparably to non-PD baselines. PEARL is the first system that achieves strong plausible deniability for NAND flash devices, secure against realistic multi-snapshot adversaries.
Accelerating SARS-CoV-2 low frequency variant calling on ultra deep sequencing datasets

With recent advances in sequencing technology it has become affordable and practical to sequence genomes to very high depth-of-coverage, allowing researchers to discover low-frequency variants in the genome. However, due to the errors in sequencing it is an active area of research to develop algorithms that can separate noise from the true variants. LoFreq is a state of the art algorithm for low-frequency variant detection but has a relatively long runtime compared to other tools. In addition to this, the interface for running in parallel could be simplified, allowing for multithreading as well as distributing jobs to a cluster. In this work we describe some specific contributions to LoFreq that remedy these issues.
Meanline Analysis of Turbines with Choked Flow in the Object-Oriented Turbomachinery Analysis Code

The prediction of turbomachinery performance characteristics is an important part of the conceptual aircraft engine design process. During this phase, the designer must examine the effects of a large number of turbomachinery design parameters to determine their impact on overall engine performance and weight. The lack of detailed design information available in this phase necessitates the use of simpler meanline and streamline methods to determine the turbomachinery geometry characteristics and provide performance estimates prior to more detailed CFD (Computational Fluid Dynamics) analyses. While a number of analysis codes have been developed for this purpose, most are written in outdated software languages and may be difficult or impossible to apply to new, unconventional designs. The Object-Oriented Turbomachinery Analysis Code (OTAC) is currently being developed at NASA Glenn Research Center to provide a flexible meanline and streamline analysis capability in a modern object-oriented language. During the development and validation of OTAC, a limitation was identified in the code's ability to analyze and converge turbines as the flow approached choking. This paper describes a series of changes which can be made to typical OTAC turbine meanline models to enable the assessment of choked flow up to limit load conditions. Results produced with this revised model setup are provided in the form of turbine performance maps and are compared to published maps.
High leverage space transportation system technologies for human exploration missions to the moon and beyond

The feasibility of returning humans to the Moon by 2004, the 35th anniversary of the Apollo 11 landing, is examined assuming the use of existing launch vehicles (the Space Shuttle and Titan 4B), a near term, advanced technology space transportation system, and extraterrestrial propellant--specifically 'lunar-derived' liquid oxygen or LUNOX. The lunar transportation system (LTS) elements consist of an expendable, nuclear thermal rocket (NTR)-powered translunar injection (TLI) stage and a combination lunar lander/Earth return vehicle (LERV) using cryogenic liquid oxygen and hydrogen (LOX/LH2) chemical propulsion. The 'wet' LERV, carrying a crew of 2, is configured to fit within the Shuttle orbiter cargo bay and requires only modest assembly in low Earth orbit. After Earth orbit rendezvous and docking of the LERV with the Titan 4B-launched NTR TLI stage, the initial mass in low Earth orbit (IMLEO) is approx. 40 t. To maximize mission performance at minimum mass, the LERV carries no return LOX but uses approx. 7 t of LUNOX to 'reoxidize' itself for a 'direct return' flight to Earth followed by an 'Apollo-style' capsule recovery. Without LUNOX, mission capability is constrained and the total LTS mass approaches the combined Shuttle-Titan 4B IMLEO limit of approx. 45 t even with enhanced NTR and chemical engine performance. Key technologies are discussed, lunar mission scenarios described, and LTS vehicle designs and characteristics are presented. Mission versatility provided by using a small 'all LH2' NTR engine or a 'LOX-augmented' derivative, either individually or in clusters, for outer planet robotic orbiter, small Mars cargo, lunar 'commuter', and human Mars exploration class missions is also briefly discussed.