cssi/SciSciGPT-SciSciNet huggingface dataset:
Cultural Cognition at Work

Cultural cognition theory provides an anthropological and psychological-based theory about how values actually influence judicial decisionmaking. It suggests that values act as a subconscious influence on cognition rather than as a self-conscious motive of decision-making. Applying these insights to two controversial United States Supreme Court labor and employment decisions, this Article contends that judges in many instances are not fighting over ideology, but rather over legallyconsequential facts. This type of disagreement is particularly prevalent in labor and employment law cases where the factual issues that divide judges involve significant uncertainty and turn on inconclusive evidence. This distinction between ideology and cultural cognition is critical for two connected reasons. First, the identification of cultural worldviews, as opposed to partisan or legal bias, as a major influence on judicial decisionmaking assists in bringing legitimacy back to the judging function. Second, social science research indicates that techniques exist for judges to counteract their susceptibility to this form of biased decisionmaking.
Local banks, credit supply, and house prices

I study the effects of an increase in the supply of local mortgage credit on local house Prices and employment by exploiting a natural experiment from Switzerland. Losses in U.S. security holdings triggered a migration of dissatified retail customers from a large, universal bank (UBS) to homogenous local mortgage lenders in mid-2008. Mortgage lenders close to UBS branches experience larger inows of deposits, unrelated to their investment opportunities. Using variation in the geographic distance between UBS branches and local mortgage lenders as an instrument for deposit growth,I find that banks with an exogenous positive funding shock invest in strict accordance with their specialization (i.e. local mortgage lending). Consequently, house prices in neighborhoods around affected banks rise over 50% more than around unaffected banks. I also find an increase in the number of employees at small firms, reliant on real estate collateral, in these neighborhoods. My results show that local mortgage oriented banks affect house prices through the supply of credit and that bank specialization thereby plays an important role in the allocation of capital across sectors.
mRNA vaccine-elicited antibodies to SARS-CoV-2 and circulating variants.

Here we report on the antibody and memory B cell responses of a cohort of 20 volunteers who received the Moderna (mRNA-1273) or Pfizer–BioNTech (BNT162b2) vaccine against SARS-CoV-21–4. Eight weeks after the second injection of vaccine, volunteers showed high levels of IgM and IgG anti-SARS-CoV-2 spike protein (S) and receptor-binding-domain (RBD) binding titre. Moreover, the plasma neutralizing activity and relative numbers of RBD-specific memory B cells of vaccinated volunteers were equivalent to those of individuals who had recovered from natural infection5,6. However, activity against SARS-CoV-2 variants that encode E484K-, N501Y- or K417N/E484K/N501-mutant S was reduced by a small—but significant—margin. The monoclonal antibodies elicited by the vaccines potently neutralize SARS-CoV-2, and target a number of different RBD epitopes in common with monoclonal antibodies isolated from infected donors5–8. However, neutralization by 14 of the 17 most-potent monoclonal antibodies that we tested was reduced or abolished by the K417N, E484K or N501Y mutation. Notably, these mutations were selected when we cultured recombinant vesicular stomatitis virus expressing SARS-CoV-2 S in the presence of the monoclonal antibodies elicited by the vaccines. Together, these results suggest that the monoclonal antibodies in clinical use should be tested against newly arising variants, and that mRNA vaccines may need to be updated periodically to avoid a potential loss of clinical efficacy. The Moderna (mRNA-1273) and Pfizer–BioNTech (BNT162b2) vaccines elicit anti-RBD antibodies similar to those elicited through natural infection with SARS-CoV-2, but their potent neutralizing activity was reduced or abolished by new viral variants of concern.
The Problem of Trans-National Libel

Forum shopping in trans-national libel cases – “libel tourism” – has a chilling effect on journalism, academic scholarship, and scientific criticism. The United States and Britain (the most popular venue for such cases) have recently attempted to address the issue legislatively. In 2010, the U.S. passed the SPEECH Act, which prohibits recognition and enforcement of libel judgments from jurisdictions applying law less protective than the First Amendment. On March 15, 2011, the British Ministry of Justice proposed a draft Defamation Act 2011 with provisions designed, inter alia, to discourage libel tourism. This Article questions the extent to which the SPEECH Act and the proposed Defamation Act 2011 will accomplish their stated aims. The SPEECH Act provides little protection for hard-hitting investigative and accountability journalism by professional news organizations with global assets. The proposed British bill has important substantive limits and, controversial in Britain, may well not be adopted. Even if Parliament approves it, the site of libel tourism may shift to other claimant-friendly jurisdictions. Global harmonization of libel law is neither realistically feasible nor desirable. Instead, this Article proposes a two-fold approach. On the legal front, it supports the procedural focus of Britain’s proposed bill, but also calls for foreign courts to apply a governmental interest analysis to choice of law in trans-national defamation cases threatening core political speech in the United States. On the policy front, it calls for: 1) measures to improve the way in which the press does its job in order to reduce the number of trans-national libel cases; and 2) new approaches to help defend the claims when they are brought. The recommended press-improvement measures include expanded access to, and efficient use of, documents, journalistic self-criticism, and bestpractices education. The defense measures explored include the development of alternative, community-based support for libel defense funds; the formation of pro bono libel review consortia; and the promotion of the availability of libel insurance by means designed to help insurers more accurately assess libel risk. * Professor of Law, University of Miami School of Law. Many thanks to Caroline Bradley, Adam Candeub, Mary Doyle, Bernie Oxman, Jan Paulsson, Robin Schard, Steve Schnably, Ralph Shalom and Nicolo Trocker for extremely helpful conversations and comments, and to Pam Lucken for last-minute research help. I am also indebted to Derek Bambauer and Anupam Chander for thoughtful questions at the Bits Without Borders Conference at MSU in October 2010 where I presented early thoughts on the subject. All errors are my own.
Social media information sharing for natural disaster response

Social media has become an essential channel for posting disaster-related information, which provides governments and relief agencies real-time data for better disaster management. However, research in this field has not received sufficient attention, and extracting useful information is still challenging. This paper aims to improve disaster relief efficiency via mining and analyzing social media data like public attitudes toward disaster response and public demands for targeted relief supplies during different types of disasters. We focus on different natural disasters based on properties such as types, durations, and damages, which contains a total of 41,993 tweets. In this paper, public perception is assessed qualitatively by manually classified tweets, which contain information like the demand for targeted relief supplies, satisfactions of disaster response, and public fear. Public attitudes to natural disasters are studied via a quantitative analysis using eight machine learning models. To better provide decision-makers with the appropriate model, the comparison of machine learning models based on computational time and prediction accuracy is conducted. The change of public opinion during different natural disasters and the evolution of peoples’ behavior of using social media for disaster relief in the face of the identical type of natural disasters as Twitter continues to evolve are studied. The results in this paper demonstrate the feasibility and validation of the proposed research approach and provide relief agencies with insights into better disaster management.
Self-training with improved regularization for sample-efficient chest x-ray classification

Automated diagnostic assistants in healthcare necessitate accurate AI models that can be trained with limited labeled data, can cope with severe class imbalances and can support simultaneous prediction of multiple disease conditions. To this end, we present a deep learning framework that utilizes a number of key components to enable robust modeling in such challenging scenarios. Using an important use-case in chest X-ray classification, we provide several key insights on the effective use of data augmentation, self-training via distillation and confidence tempering for small data learning in medical imaging. Our results show that using 85% lesser labeled data, we can build predictive models that match the performance of classifiers trained in a large-scale data setting.
CellWalker integrates single-cell and bulk data to resolve regulatory elements across cell types in complex tissues

Single-cell and bulk genomics assays have complementary strengths and weaknesses, and alone neither strategy can fully capture regulatory elements across the diversity of cells in complex tissues. We present CellWalker, a method that integrates single-cell open chromatin (scATAC-seq) data with gene expression (RNA-seq) and other data types using a network model that simultaneously improves cell labeling in noisy scATAC-seq and annotates cell type-specific regulatory elements in bulk data. We demonstrate CellWalker’s robustness to sparse annotations and noise using simulations and combined RNA-seq and ATAC-seq in individual cells. We then apply CellWalker to the developing brain. We identify cells transitioning between transcriptional states, resolve regulatory elements to cell types, and observe that autism and other neurological traits can be mapped to specific cell types through their regulatory elements.
$J/\psi$-$p$ Scattering Length from the Total and Differential Photoproduction Cross Sections

The $$J/\psi $$ -p scattering length, $$\alpha _{J/\psi p}$$ , can be extracted from the $$J/\psi $$ photoproduction cross section near threshold using the Vector Meson Dominance (VMD) model to relate the reaction $$\gamma p \rightarrow J/\psi p$$ to $$J/\psi p \rightarrow J/\psi p$$ . Such estimates based on experimental data result in values for $$|\alpha _{J/\psi p}|$$ , which are much lower than most of the theoretical predictions. In this work, we study the relations between the different results, depending on the use of the total or the differential cross sections, and the method of extrapolating the data to threshold in the case of a low-statistics data sample, such as the near threshold $$J/\psi $$ photoproduction dataset. We estimate a range for $$|\alpha _{J/\psi p}|$$ of 0.003–0.025 fm as extracted from experimental data within the VMD model and discuss possible reasons for such lower values compared to the theoretical results.
On BBW parabolics for simple classical Lie superalgebras

Abstract In this paper the authors introduce a class of parabolic subalgebras for classical simple Lie superalgebras associated to the detecting subalgebras introduced by Boe, Kujawa and Nakano. These parabolic subalgebras are shown to have good cohomological properties governed by the Bott-Borel-Weil theorem involving the zero component of the Lie superalgebra in conjunction with the odd roots. These results are later used to verify an open conjecture given by Boe, Kujawa and Nakano pertaining to the equality of various support varieties.
Union-Find Decoders For Homological Product Codes

Homological product codes are a class of codes that can have improved distance while retaining relatively low stabilizer weight. We show how to build union-find decoders for these codes, using a union-find decoder for one of the codes in the product and a brute force decoder for the other code. We apply this construction to the specific case of the product of a surface code with a small code such as a $[[4,2,2]]$ code, which we call an augmented surface code. The distance of the augmented surface code is the product of the distance of the surface code with that of the small code, and the union-find decoder, with slight modifications, can decode errors up to half the distance. We present numerical simulations, showing that while the threshold of these augmented codes is lower than that of the surface code, the low noise performance is improved.
Apollonian Packings in Seven and Eight Dimensions

In an earlier work, we proposed a generalization for the Apollonian packing in arbitrary dimensions and showed that the resulting object in four, five, and six dimensions have properties consistent with the Apollonian circle and sphere packings in two and three dimensions. In this work, we investigate the generalization in seven and eight dimensions and show that they too have many of the properties of those in lower dimensions. In particular, the hyperspheres are tangent or do not intersect; they fill the hyperspace; the object includes a maximal cluster of mutually tangent hyperspheres; and there exists a perspective where all hyperspheres in the object have integer curvatures.
Vortex sheet turbulence as solvable string theory

We study steady vortex sheet solutions of the Navier–Stokes in the limit of vanishing viscosity at fixed energy flow. We refer to this as the turbulent limit. These steady flows correspond to a min...
The Aliment to Bodily Condition knowledgebase (ABCkb): a database connecting plants and human health.

Overconsumption of processed foods has led to an increase in chronic diet-related diseases such obesity and type 2 diabetes. Although diets high in fresh fruits and vegetables are linked with healthier outcomes, the specific mechanisms for these relationships are poorly understood. Experiments examining plant phytochemical production and breeding programs, or separately on the health effects of nutritional supplements have yielded results that are sparse, siloed, and difficult to integrate between the domains of human health and agriculture. To connect plant products to health outcomes through their molecular mechanism an integrated computational resource is necessary. We created the Aliment to Bodily Condition Knowledgebase (ABCkb) to connect plants to human health by creating a stepwise path from plant $$\rightarrow$$ plant product $$\rightarrow$$ human gene $$\rightarrow$$ pathways $$\rightarrow$$ indication. ABCkb integrates 11 curated sources as well as relationships mined from Medline abstracts by loading into a graph database which is deployed via a Docker container. This new resource, provided in a queryable container with a user-friendly interface connects plant products with human health outcomes for generating nutritive hypotheses. All scripts used are available on github ( https://github.com/atrautm1/ABCkb ) along with basic directions for building the knowledgebase and a browsable interface is available ( https://abckb.charlotte.edu ).
The effect of the 2016 United States presidential election on employment discrimination

We examine whether employment discrimination increased after the 2016 presidential election in the United States. We submitted fictitious applications to publicly advertised positions using resumes that are manipulated on perceived race and ethnicity (Somali American, African American, and white American). Prior to the 2016 election, employers contacted Somali American applicants slightly less than white applicants but more than African American applicants. After the election, the difference between white and Somali American applicants increased by 8 percentage points. The increased discrimination predominantly occurred in occupations involving interaction with customers. We continued data collection from July 2017 to March 2018 to test for seasonality in discrimination; there was no substantial increase in discrimination after the 2017 local election.
Auditor Integration of IT Specialist Input on Internal Control Issues: How a Weaker Team Identity Can Be Beneficial

I investigate how auditors integrate information technology (IT) specialist input into internal control over financial reporting (ICFR) issue classifications. Given the ill-structured nature of evaluating ICFR issues and the impact of these issues on audit quality, combining knowledge from different perspectives is likely beneficial. Drawing on social identity theory, I predict and find that a weaker one-team identity between auditors and IT specialists yields benefits. Auditors with a weaker versus stronger team identity place more weight on IT specialist input for IT-related issues and differentially weight higher and lower quality input for non-IT issues. I also find that more severe ICFR issues drive the predicted results. My study provides insight into how team identity influences auditor integration of input from specialists. The implications of my study are of interest to researchers, regulators, and practitioners, especially as recent firm initiatives encourage a one-team view for auditors and IT specialists.
Local Economy, Asset Location and REIT Firm Growth

This paper empirically examines the extent to which and how local economic growth and asset location affect firm growth based on a sample of US equity real estate investment trusts (REITs) from 2001 to 2016. Using the GDP growth rate by MSA and individual property data of REITs, we construct an aggregated measure of local economic growth for each REIT based on its asset locations in different metropolitan areas. We find that REIT firm growth (measured using both book value and market value of assets) is positively correlated with the lagged firm-level economic growth measure, indicating that REITs allocating assets in areas with higher economic growth tend to experience higher firm growth. Moreover, local economic growth enhances REIT growth mainly through the growth of equity (not through the growth of debt), as REITs with more assets in higher economic growth areas provide higher stock returns to shareholders. These findings suggest that local economic conditions have a significant impact on REIT firm growth and a REIT’s asset allocation strategy can play an important role in its long-term prospects.
Monopoly without a monopolist : An economic analysis of the bitcoin payment system

Bitcoin provides its users with transaction-processing services which are similar to those of traditional payment systems. This paper models the novel economic structure implied by Bitcoin's innovative decentralized design, which allows the payment system to be reliably operated by unrelated parties called miners. We find that this decentralized design protects users from monopoly pricing. Competition among service providers within the platform and free entry imply no entity can profitably affect the level of fees paid by users. Instead, a market for transaction-processing determines the fees users pay to gain priority and avoid transaction-processing delays. The paper derives closed-form formulas of the fees and waiting times and studies their properties; compares pricing under the Bitcoin Payment System to that under a traditional payment system operated by a profit-maximizing firm; and suggests protocol design modifications to enhance the platform’s efficiency. The appendix describes and explains the main attributes of Bitcoin and the underlying blockchain technology.
Density-Matrix Model for Photon-Driven Transport in Quantum Cascade Lasers

We develop a time-dependent density-matrix model to study photon-assisted (PA) electron transport in quantum cascade lasers. The Markovian equation of motion for the density matrix in the presence of an optical field is solved for an arbitrary field amplitude. Level-broadening terms emerge from microscopic Hamiltonians and supplant the need for empirical parameters that are often employed in related approaches. We show that, in quantum cascade lasers with diagonal design, photon resonances have a pronounced impact on electron dynamics around and above the lasing threshold, an effect that stems from the large spatial separation between the upper and lower lasing states. With the inclusion of PA tunneling, the calculated current density and output power are in good agreement with experiment.
Covid-19 Thrives in Larger Cities, Not Denser Ones

The findings introduced in this longitudinal study of cumulative Covid-19 cases and deaths are based on reported data for 384 US Metropolitan Statistical Areas (MSAs) for 20 weeks, starting on Marc...
Congenital Sideroblastic Anemias: Iron and Heme Lost in Mitochondrial Translation

The congenital sideroblastic anemias (CSAs) are an uncommon, diverse class of inherited hematopoietic disorders characterized by pathological deposition of iron in the mitochondria of erythroid precursors. In recent years, the genetic causes of several clinically distinctive forms of CSA have been elucidated, which has revealed common themes in their pathogenesis. In particular, most, if not all, can be attributed to disordered mitochondrial heme synthesis, iron-sulfur cluster biogenesis or pathways related to mitochondrial protein synthesis. This presentation will summarize the clinical features, molecular genetics, and pathophysiology of the CSAs in the context of these pathways with particular emphasis on new disease entities and how characterization these phenotypes can contribute to CSA diagnostics and therapeutics. Disclosures No relevant conflicts of interest to declare.
Loss of coordinated expression between ribosomal and mitochondrial genes revealed by comprehensive characterization of a large family with a rare Mendelian disorder.

Abstract Non-canonical intronic variants are a poorly characterized yet highly prevalent class of alterations associated with Mendelian disorders. Here, we report the first RNA expression and splicing analysis from a family whose members carry a non-canonical splice variant in an intron of RPL11 (c.396 + 3A > G). This mutation is causative for Diamond Blackfan Anemia (DBA) in this family despite incomplete penetrance and variable expressivity. Our analyses revealed a complex pattern of disruptions with many novel junctions of RPL11. These include an RPL11 transcript that is translated with a late stop codon in the 3′ untranslated region (3’UTR) of the main isoform. We observed that RPL11 transcript abundance is comparable among carriers regardless of symptom severity. Interestingly, both the small and large ribosomal subunit transcripts were significantly overexpressed in individuals with a history of anemia in addition to congenital abnormalities. Finally, we discovered that coordinated expression between mitochondrial components and RPL11 was lost in all carriers, which may lead to variable expressivity. Overall, this study highlights the importance of RNA splicing and expression analyses in families for molecular characterization of Mendelian diseases.
Tunable quantum two-photon interference with reconfigurable metasurfaces using phase-change materials

The ability of phase-change materials to reversibly and rapidly switch between two stable phases has driven their use in a number of applications such as data storage and optical modulators. Incorporating such materials into metasurfaces enables new approaches to the control of optical fields. In this article we present the design of novel switchable metasurfaces that enable the control of the nonclassical two-photon quantum interference. These structures require no static power consumption, operate at room temperature, and have high switching speed. For the first adaptive metasurface presented in this article, tunable nonclassical two-photon interference from −97.7% (anti-coalescence) to 75.48% (coalescence) is predicted. For the second adaptive geometry, the quantum interference switches from −59.42% (anti-coalescence) to 86.09% (coalescence) upon a thermally driven crystallographic phase transition. The development of compact and rapidly controllable quantum devices is opening up promising paths to brand-new quantum applications as well as the possibility of improving free space quantum logic gates, linear-optics bell experiments, and quantum phase estimation systems.
Attractor dynamics gate cortical information flow during decision-making.

Decisions are held in memory until enacted, which makes them potentially vulnerable to distracting sensory input. Gating of information flow from sensory to motor areas could protect memory from interference during decision-making, but the underlying network mechanisms are not understood. Here, we trained mice to detect optogenetic stimulation of the somatosensory cortex, with a delay separating sensation and action. During the delay, distracting stimuli lost influence on behavior over time, even though distractor-evoked neural activity percolated through the cortex without attenuation. Instead, choice-encoding activity in the motor cortex became progressively less sensitive to the impact of distractors. Reverse engineering of neural networks trained to reproduce motor cortex activity revealed that the reduction in sensitivity to distractors was caused by a growing separation in the neural activity space between attractors that encode alternative decisions. Our results show that communication between brain regions can be gated via attractor dynamics, which control the degree of commitment to an action. The flow of information in the brain is regulated over space and time. The authors show that mice can adaptively filter stimuli originating in the sensory cortex. The stimuli are gated by attractor dynamics in the frontal cortex, revealing a mechanism of gating of neural information.
Consistency of Empirical Bayes And Kernel Flow For Hierarchical Parameter Estimation

Hierarchical modeling and learning has proven very powerful in the field of Gaussian process regression and kernel methods, especially for machine learning applications and, increasingly, within the field of inverse problems more generally. The classical approach to learning hierarchical information is through Bayesian formulations of the problem, implying a posterior distribution on the hierarchical parameters or, in the case of empirical Bayes, providing an optimization criterion for them. Recent developments in the machine learning literature have suggested new criteria for hierarchical learning, based on approximation theoretic considerations that can be interpreted as variants of cross-validation, and exploiting approximation consistency in data splitting. The purpose of this paper is to compare the empirical Bayesian and approximation theoretic approaches to hierarchical learning, in terms of large data consistency, variance of estimators, robustness of the estimators to model misspecification, and computational cost. Our analysis is rooted in the setting of Matern-like Gaussian random field priors, with smoothness, amplitude and inverse lengthscale as hierarchical parameters, in the regression setting. Numerical experiments validate the theory and extend the scope of the paper beyond the Matern setting.
The political economy of health epidemics: Evidence from the Ebola outbreak

Abstract This paper investigates whether political incentives affect the government's response during a health epidemic and the subsequent effects on citizens' voting behavior. Leveraging novel data, I study this question in the context of the 2014 Ebola outbreak in Liberia. The national incumbent government appropriately prioritized the allocation of resources to villages affected by the epidemic. By building a spatiotemporal epidemiological model that estimates the ex-ante optimal allocation of relief efforts, there is also evidence that resources were misallocated toward electoral swing villages. Instead, no resources were diverted toward core supporters or co-ethnic villages. Voters, in turn, reacted by rewarding the national incumbent party in areas where additional resources were misallocated.
Battery-aware routing for streaming data transmissions in wireless sensor networks

Recent technological advances have made it possible to support long lifetime and large volume streaming data transmissions in sensor networks. A major challenge is to maximize the lifetime of battery-powered sensors to support such transmissions. Battery, as the power provider of the sensors, therefore emerges as the key factor for achieving high performance in such applications. Recent study in battery technology reveals that the behavior of battery discharging is more complex than we used to think. Battery powered sensors might waste a huge amount of energy if we do not carefully schedule and budget their discharging. In this paper we study the effect of battery behavior on routing for streaming data transmissions in wireless sensor networks. We first give an on-line computable energy model to mathematically model battery discharge behavior. We show that the model can capture and describe battery behavior accurately at low computational complexity and thus is suitable for on-line battery capacity computation. Based on this battery model we then present a battery-aware routing (BAR) protocol to schedule the routing in wireless sensor networks. The routing protocol is sensitive to the battery status of routing nodes and avoids energy loss. We use the battery data from actual sensors to evaluate the performance of our protocol. The results show that the battery-aware protocol proposed in this paper performs well and can save a significant amount of energy compared to existing routing protocols for streaming data transmissions. Network lifetime is also prolonged with maximum data throughput. As far as we know, this is the first work considering battery-awareness with an accurate analytical on-line computable battery model in sensor network routing. We believe the battery model can be used to explore other energy efficient schemes for wireless networks as well.
Continuous wavelet analysis as an aid in the representation and interpretation of electrogastrographic signals

This paper presents an application of the continuous wavelet transform (CWT) in the analysis of electrogastrographic (EGG) signals. Due to the nonstationary nature of EGG signals, the CWT method, which uses multiresolution scaled windows, gives a better time-frequency resolution than the short-time Fourier transform, which uses a fixed window. Spike activity due to gastric contraction was investigated through experiments on dogs. During spike activity we observed an increase in magnitude of the slow wave and the appearance of a low frequency component with half the frequency of the slow wave. Studies of the EGG signals from the small intestine are also presented to investigate the hypothesis that its slow wave might be confounded with spike activity in the stomach due to the similarity of their frequency ranges.
Sparse, Dense, and Attentional Representations for Text Retrieval

Dual encoders perform retrieval by encoding documents and queries into dense low-dimensional vectors, scoring each document by its inner product with the query. We investigate the capacity of this architecture relative to sparse bag-of-words models and attentional neural networks. Using both theoretical and empirical analysis, we establish connections between the encoding dimension, the margin between gold and lower-ranked documents, and the document length, suggesting limitations in the capacity of fixed-length encodings to support precise retrieval of long documents. Building on these insights, we propose a simple neural model that combines the efficiency of dual encoders with some of the expressiveness of more costly attentional architectures, and explore sparse-dense hybrids to capitalize on the precision of sparse retrieval. These models outperform strong alternatives in large-scale retrieval.
Bulk high-entropy hexaborides

Abstract For the first time, a group of CaB6-typed cubic rare earth high-entropy hexaborides have been successfully fabricated into dense bulk pellets (>98.5 % in relative densities). The specimens are prepared from elemental precursors via in-situ metal-boron reactive spark plasma sintering. The sintered bulk pellets are determined to be single-phase without any detectable oxides or other secondary phases. The homogenous elemental distributions have been confirmed at both microscale and nanoscale. The Vickers microhardness are measured to be 16−18 GPa at a standard indentation load of 9.8 N. The nanoindentation hardness and Young’s moduli have been measured to be 19−22 GPa and 190−250 GPa, respectively, by nanoindentation test using a maximum load of 500 mN. The material work functions are determined to be 3.7–4.0 eV by ultraviolet photoelectron spectroscopy characterizations, which are significantly higher than that of LaB6.
Synthetic promoter designs enabled by a comprehensive analysis of plant core promoters.

Targeted engineering of plant gene expression holds great promise for ensuring food security and for producing biopharmaceuticals in plants. However, this engineering requires thorough knowledge of cis-regulatory elements to precisely control either endogenous or introduced genes. To generate this knowledge, we used a massively parallel reporter assay to measure the activity of nearly complete sets of promoters from Arabidopsis, maize and sorghum. We demonstrate that core promoter elements—notably the TATA box—as well as promoter GC content and promoter-proximal transcription factor binding sites influence promoter strength. By performing the experiments in two assay systems, leaves of the dicot tobacco and protoplasts of the monocot maize, we detect species-specific differences in the contributions of GC content and transcription factors to promoter strength. Using these observations, we built computational models to predict promoter strength in both assay systems, allowing us to design highly active promoters comparable in activity to the viral 35S minimal promoter. Our results establish a promising experimental approach to optimize native promoter elements and generate synthetic ones with desirable features. A massively parallel reporter assay was used to measure the activity of nearly complete sets of promoters from Arabidopsis, maize and sorghum in two assay systems, uncovering the sequence features affecting promoter strength and facilitating promoter strength prediction and synthetic design.
Transient Characteristics of Pool Boiling Heat Transfer in Nanofluids

This study shows the transient characteristics of the pool boiling curves using nanofluid as the boiling fluid. This time-dependency is in sharp contrast to a unique steady-state pool boiling curve that is typically obtained for a pure fluid. Past researches on nanofluids have provided several interesting information about the thermal characteristics for this potentially promising cooling fluid. Results from these studies have shown some extraordinary critical heat flux (CHF) values and thermal conductivity enhancement that is yet to be explained by existing theories and correlations. The nature of the pool boiling curve for a nanofluid is dependent on the nanoparticle concentration in the base fluid. Higher concentration nanofluids show a perceptible degradation in the boiling heat transfer coefficient but have exhibited an enhanced CHF value (up to ∼80%) when compared to the CHF value of the base fluid (water). Another key observation has been in the significant deposition of nanoparticles on the heater surface. This fouling of the heater surface by nanoparticles is widely viewed as a main contributor that modifies the pool boiling curve of the base liquid. The deposition of the nanoparticles on the heater surface is dynamic and this in turn makes the nanofluid pool boiling curve exhibit transient characteristics.Copyright © 2009 by ASME
Calculated Ionization Volumes for Batch, Continuous, and Selective Extraction Sources of Heavy Ions

Confinement time and ionization volume calculations have been made for batch, diffusive continuous, and selective extraction types of highly ionized, very heavy atom ion sources. For each source, the results are given for step‐by‐step only ionization by an energetic electron flux (1011 e/cm3 at 10 keV) and for ionization which includes the Auger effect. These are compared for loss conditions which vary from no loss to a loss cross section 0.05 times that of the 0 → 1+ionization cross section. For low loss, the ionization volume is least for the selective extraction source, while for a lossy source, the batch process becomes competitive. Volumes for the diffusive continuous source are the largest in all cases. Introduction of the Auger effect reduces the volumes by a factor of 2–10 in all but the diffusive continuous source. The ionization volumes required for an output of one particle microampere of Xe25+ are calculated for the different types of sources.
Machine learning approaches for feature engineering of the crystal structure: Application to the prediction of the formation energy of cubic compounds

In this study, we present an approach (along with the needed computational strategies) for efficient and scalable feature engineering of the crystal structure in compounds of different chemical compositions. This approach utilizes a versatile and extensible framework for the quantification of a three-dimensional voxelized crystal structure in the form of 2-point spatial correlations of multiple atomic attributes and performs principal component analysis to extract the low-dimensional features that could be used to build surrogate models for material properties of interest. An application of the proposed feature engineering framework is demonstrated on a case study involving the prediction of the formation energies of crystalline compounds using two vastly different surrogate model building strategies; local Gaussian process regression and neural networks. Specifically, it is shown that the top 25 features (i.e., principal component scores) identified by the proposed framework serve as good regressors for the formation energy of the crystalline substance for both model building strategies.
Scalable Epidemiological Workflows to Support COVID-19 Planning and Response

The COVID-19 global outbreak represents the most significant epidemic event since the 1918 influenza pandemic. Simulations have played a crucial role in supporting COVID-19 planning and response efforts. Developing scalable workflows to provide policymakers quick responses to important questions pertaining to logistics, resource allocation, epidemic forecasts and intervention analysis remains a challenging computational problem. In this work, we present scalable high performance computing-enabled workflows for COVID-19 pandemic planning and response. The scalability of our methodology allows us to run fine-grained simulations daily, and to generate county-level forecasts and other counterfactual analysis for each of the 50 states (and DC), 3140 counties across the USA. Our workflows use a hybrid cloud/cluster system utilizing a combination of local and remote cluster computing facilities, and using over 20,000 CPU cores running for 6-9 hours every day to meet this objective. Our state (Virginia), state hospital network, our university, the DOD and the CDC use our models to guide their COVID-19 planning and response efforts. We began executing these pipelines March 25, 2020, and have delivered and briefed weekly updates to these stakeholders for over 30 weeks without interruption.
Mining the Mind, Minding the Mine: Grand Challenges in Comprehension and Mining

The program comprehension and mining software repository communities are, in practice, two separate research endeavors. One is concerned with what's happening in a developer's mind, while the other is concerned with what's happening in a team. And yet, implicit in these fields is a common goal to make better software and the common approach of influencing developer decisions. In this keynote, I provide several examples of this overlap, suggesting several grand challenges in comprehension and mining.
An Empirical Study on Hyperparameter Optimization for Fine-Tuning Pre-trained Language Models

The performance of fine-tuning pre-trained language models largely depends on the hyperparameter configuration. In this paper, we investigate the performance of modern hyperparameter optimization methods (HPO) on fine-tuning pre-trained language models. First, we study and report three HPO algorithms’ performances on fine-tuning two state-of-the-art language models on the GLUE dataset. We find that using the same time budget, HPO often fails to outperform grid search due to two reasons: insufficient time budget and overfitting. We propose two general strategies and an experimental procedure to systematically troubleshoot HPO’s failure cases. By applying the procedure, we observe that HPO can succeed with more appropriate settings in the search space and time budget; however, in certain cases overfitting remains. Finally, we make suggestions for future work. Our implementation can be found in https://github.com/microsoft/FLAML/tree/main/flaml/nlp/
Unequal welfare costs of staying at home across socioeconomic and demographic groups

Using daily census block group level data from the U.S., this paper investigates the welfare costs of staying at home due to COVID-19 across socioeconomic and demographic groups. The investigation ...
Identifying board of director network influence for firm characteristics

Abstract We utilize multiplex network analysis to evaluate the relationship between firms’ characteristics and board of director networks. Studying a sample of 384 board members of 20 largest US energy and utility firms, 10 from each sector, the firms are differentiated by a clustering analysis on their firm-level characteristics. The firms’ board member connections are represented in a multiplex network consisting of two layers, one for the board members’ direct connections and another for their indirect connections. Four additional sub-layers are identified for each major layer of the multiplex network to represent corporate, non-profit organization, education and government/military connections between the board members. Weights assigned to each layer of the multiplex network allow combining their effect in the resulting single layered directors’ network. The weights of the multiplex directors’ network are learned by optimally relating firms’ director network characteristics with firm clusters’ characteristics. We observe that director network displays significant connectivity at all multiplex network layers and firms belonging to the same cluster display similar director network characteristics, specifically enhanced by appropriate weighting of director network layers. The optimal value of network sub-layer weights indicate that, while corporate board member connections play the most significant role, the non-profit and military & government connections are also important, where non-profit connections lead to inferior firm outcomes.
Investigation of Minimum Operating Temperatures for Cryogenic Wind Tunnels

Total temperatures corresponding to the onset of condensation effects were determined for flow over a 0.137-m NACA 0012-64 airfoil mounted in the Langley 1/3-m transonic wind tunnel. Tests were carried out at a total pressure range from 1.2 to 4.5 atm and at free-stream Mach numbers of 0.75, 0.85, and 0.95. No condensation effects were found to occur until total temperatures were below those associated with free-stream saturation. Significant increases in Reynolds number may apparently be obtained by operation at wind tunnel temperatures below those associated with local saturation over the airfoil but above those where effects first occur. For the 0.85 and 0.95 Mach numbers the increase in Reynolds number was at least 15% over those achieved at local saturation conditions for the same pressure range. Higher pressures may have had some detrimental effect in the 0.95 Mach number test.
Building a Practical Natural Laminar Flow Design Capability

A preliminary natural laminar flow (NLF) design method that has been developed and applied to supersonic and transonic wings with moderate-to-high leading-edge sweeps at flight Reynolds numbers is further extended and evaluated in this paper. The modular design approach uses a knowledge-based design module linked with different flow solvers and boundary layer stability analysis methods to provide a multifidelity capability for NLF analysis and design. An assessment of the effects of different options for stability analysis is included using pressures and geometry from an NLF wing designed for the Common Research Model (CRM). Several extensions to the design module are described, including multiple new approaches to design for controlling attachment line contamination and transition. Finally, a modification to the NLF design algorithm that allows independent control of Tollmien-Schlichting (TS) and cross flow (CF) modes is proposed. A preliminary evaluation of the TS-only option applied to the design of an NLF nacelle for the CRM is performed that includes the use of a low-fidelity stability analysis directly in the design module.
Propaganda Warfare on the International Criminal Court

In an era dominated by constant news, propaganda and media influence have become an integral part of warfare. And while the tactical use of propaganda and disinformation has traditionally been reserved for military purposes, state leaders have increasingly adapted this strategy to delegitimize the International Criminal Court (ICC) in efforts to block prosecutions of state nationals. Because the ICC is built upon state cooperation, propaganda warfare has proven particularly disturbing to its vulnerable foundations. This aggressive use of propaganda compromises judicial impartiality and amplifies the apparent politicization of the ICC and international criminal justice more generally. Additionally, because those states who can utilize propaganda so effectively are those with the most geopolitical influence, anti-Court propaganda campaigns create further imbalance in an already inegalitarian system. This article thoroughly analyzes the impact of propaganda warfare on the ICC by examining how propaganda wars leveled against the Court by state leaders, such as those of Kenya and the United States, have influenced its judicial decisions and shaped its operations. To successfully defend against propaganda attacks, like those orchestrated by the Kenyan and United States Governments, the Court must adapt its approach to State relations to foster knowledge of, support for, and cooperation with the Court’s goals and prosecutorial strategies. This article ultimately seeks to identify workable proposals for the Court and the ICC Prosecutor to strengthen the Court’s perceived legitimacy, so as to minimize the detrimental impact posed by anti-Court propaganda wars.