cssi/SciSciGPT-SciSciNet huggingface dataset:
The classical average-cost inventory models of Iglehart and Veinott-Wagner revisited

This paper revisits the classical papers of Iglehart (Ref. 1) and Veinott and Wagner (Ref. 2) devoted to stochastic inventory problems with the criterion of long-run average cost minimization. We indicate some of the assumptions that are used implicitly without verification in their stationary distribution approach to the problems and provide the missing (nontrivial) verification. In addition to completing their analysis, we examine the relationship between the stationary distribution approach and the dynamic programming approach to the average-cost stochastic inventory problems.
Co-opted Boards

We develop two measures of board composition to investigate whether directors appointed by the CEO have allegiance to the CEO and decrease their monitoring. Co-option is the fraction of the board comprised of directors appointed after the CEO assumed office. As Co-option increases, board monitoring decreases: turnover-performance sensitivity diminishes, pay increases (without commensurate increase in pay-performance sensitivity), and investment increases. Non-Co-opted Independence�the fraction of directors who are independent and were appointed before the CEO�has more explanatory power for monitoring effectiveness than the conventional measure of board independence. Our results suggest that not all independent directors are effective monitors.
Locking the Doors to Discovery? Assessing the Effects of Twombly and Iqbal on Access to Discovery

Many observers believe the Supreme Court's Twombly and Iqbal opinions have curtailed access to civil justice. But previous empirical studies looking only at Rule 12(b)(6) grant rates have failed to capture the full effect of these cases because they have not accounted for party selection—changes in party behavior that can be expected following changes in pleading standards. In this Note, I show how party selection can be expected to undermine the empirical usefulness of simple grant-rate comparisons. I then use a conceptual model of party behavior that allows me to derive an adjusted measure of Twombly/Iqbal's impact and show how to estimate a lower bound on this measure using data from recent studies by the Federal Judicial Center. My empirical results suggest that, depending on the nature of the suit in question, Twombly and Iqbal have negatively affected plaintiffs in at least 15% to 21% of cases that faced Rule 12(b)(6) motions in the post-Iqbal data window. Again depending on the nature of the suit, these figures represent between one-fourth and two-fifths of the cases that fail to reach discovery on at least some claims in the post-Iqbal data window.
Scholarship Against Desire

How do ego-driven fears and ambitions influence intellectual life in our law schools? How do law review placements, promotion applications, and faculty workshops skew the questions we law professors ask and the conclusions we reach? In my own case, they led me to frame my last project before tenure — which at its heart is about intimate relationships — through a tax policy analysis. Instead of writing about “sex against desire” I wrote about “preglimony.” I stand behind the result, but the exercise also left me feeling incomplete. This article reflects on the price of strategically motivated scholarship and articulates a vision for what a more authentic ethos may bring to students, to the profession, and to the world we help shape.
Diversification potential of ADRs, country funds and underlying stocks across economic conditions

We study the relative diversification potential of American Depository Receipts (ADRs) as compared to the underlying shares as well as the relative diversification potential of closed-end country funds as compared to the foreign market indexes across various economic conditions. We find that, based on daily return correlations, direct access to foreign stocks is most advantageous in bad times. Specifically, we construct several measures of the US stock market's and the US economy's effect on the benefits of including ADRs and country funds in equity portfolios. For all measures, we find that the underlying shares are more useful for diversification purposes than ADRs and country funds when the US stock market returns are low and when the US economy is underperforming. However, there is no evidence of differential benefits of relative diversification when we examine measures based on monthly Sharpe ratios. We discuss potential reasons for the discrepancies between our correlation-based and Sharpe ratio-bas...
Case Study of Apple, Inc. for Business Law Students: How Apple's Business Model Controls Digital Content Through Legal and Technological Means

This article explores how Apple's business model makes use of content licensed from third-party music labels that is in digital format, and how it delivers the service to consumers over the internet. Control of content and profits are the two top goals of the music labels - yet the goals of the music labels' customers are in stark opposition to those goals, since users wish to use the content in different ways and formats, and they have become very averse to paying for content, which becomes easy when content is digital. Since the music labels have in effect conceded distribution of their products to Apple, for one, Apple has found itself in the middle, at once adhering to its contractual duties under agreements with the music labels, while enticing customers and profiting from its user-friendly interface and platform. Apple must both control content - to avoid lawsuits, and sell the content - to continue its iTunes service.
The Effect of FASB Statement No. 123R on Stock Repurchases: An Empirical Examination of Management Incentives

This study examines management’s response to the change in accounting for stock option-based compensation imposed by SFAS No. 123R, whose implementation is expected to reduce reported income. To cope with this impact, management may be motivated to decrease the use of stock options as part of compensating employees and engage in stock repurchases in an attempt to increase the value of outstanding employee stock options. Our findings demonstrate a significant negative relation between stock options granted and shares repurchased in the aftermath of SFAS No. 123R, particularly for the S&P 500 firms known for their heavy use of employee stock options. Furthermore, evidence of a contemporaneous increase in repurchases and leverage in the post SFAS 123R period may suggest that some of the buybacks may have been funded with debt. Our findings are robust to the inclusion of traditional determinants of share repurchases.
FIFA, Forced Arbitration, and the U.S. Soccer Lawsuits

American soccer has been besieged by lawsuits. In the last two years alone, the United States Soccer Federation (“U.S. Soccer”) has been hit with two antitrust lawsuits, two Equal Pay Act and Title VII gender discrimination lawsuits, and a trademark lawsuit, while two of its professional league members are engaged in their own trademark lawsuit. One threshold question that has received scant attention in the media is whether these disputes should be in federal court at all. Under the Statutes and Regulations of the Federation Internationale de Football Association (“FIFA”), soccer’s global governing organization, all disputes are required to be arbitrated. Taking a dispute to an ordinary court of law is potentially subject to sanction, which could include suspension or even expulsion. Given this forced arbitration rule, this article considers several possible explanations for why there has been no push to arbitrate the disputes in most of the lawsuits: (1) The enforceability of FIFA’s arbitration requirement has been called into question by recent rulings against forced arbitration clauses; (2) FIFA focuses the enforcement of its arbitration requirement on certain types of cases; (3) FIFA does not consider certain types of claims subject to arbitration; and (4) U.S. Soccer’s bylaws do not impose the arbitration requirement in such a way as it would apply to these types of cases. Although none of these entirely resolve the matter in a satisfactory way, in the aggregate they may help to define the emerging limits to arbitration for sports governing bodies in the U.S. and elsewhere.
Another Hair Piece: Exploring New Strands of Analysis Under Title VII

* Professor of Law, Charles M. and Marion J. Kierscht Scholar, University of Iowa. J.D., University of Michigan Law School; B.A., Grinnell College. E-mail: angela-onwuachi@uiowa.edu. © 2010, Angela Onwuachi-Willig. Thanks to Dean Carolyn Jones and Charles M. and Marion J. Kierscht for their research support. My research assistants Christie Canales and Matt McMurrer provided invaluable assistance. I especially thank Professor Paulette Caldwell for beginning this conversation about black women, hair, and grooming codes with her article, A Hair Piece: Perspectives on the Intersection of Race and Gender, 1991 DUKE L.J. 365. I also thank Michelle Adams, Rachel Anderson, Mario Barnes, Shanna Benjamin, Randy Bezanson, Arthur Bonfield, Devon Carbado, Bob Chang, Miriam Cherry, Carin Crain, Marion Crain, Angela Davis, Tai Duncan, Karla Erickson, Catherine Fisk, Tom Gallanis, Tristin Green, Wendy Greene, Aya Gruber, Lani Guinier, Mitu Gulati, Don Herzog, Kevin Johnson, Trina Jones, Robin Lenhardt, Nancy Levit, Natasha Martin, Marcia McCormick, Ann McGinley, Rachel Moran, Melissa Murray, Richard Primus, Daria Roithmayr, Bertrall Ross, Paul Secunda, Jessica Silbey, Catherine Smith, Peggie Smith, Rose Cuison Villazor, Adrien Wing, Rachel Wortman, and Kimberly Yuracko for their helpful comments and support on this Essay. Finally, I give special thanks to my husband, Jacob Willig-Onwuachi, and our children, Elijah, Bethany, and Solomon for their constant love and support. This project gained much from comments at the Nineteenth Annual Midwestern People of Color Legal Scholarship Conference at the University of Iowa College of Law, the Lutie A. Lytle Black Women Law Faculty Workshop at Seattle University School of Law, the 2009 Southeastern Association of Law Schools (SEALS) Conference, the Iowa Legal Studies Workshop, and the University of Michigan Law School Legal Theory Workshop.
Altruism in Anonymous Dictator Games

We conduct double-anonymous dictator experiments to explore the role of altruism in motivating subjects' behavior. We vary the extent to which an anonymous recipient is deserving of aid and investigate its effect on the allocation of a fixed pie by student subjects. This is accomplished by including as treatments: (1) an anonymous student subject and (2) an established charity. We find that a significant increase in donations occurs when we increase the extent to which a donation goes to a recipient generally agreed to be “deserving.” We conclude that subjects are rational in the way they incorporate fairness into their decisions.Journal of Economic LiteratureClassification Numbers: A13, C91, D64.
Dynamic Resource Allocation in the Cloud with Near-Optimal Efficiency

Motivated by maximizing spot instances in cloud shared systems, in this work, we consider the problem of taking advantage of unused resources in highly dynamic cloud environments while preserving u...
TATA and paused promoters active in differentiated tissues have distinct expression characteristics

Core promoter types differ in the extent to which RNA polymerase II (Pol II) pauses after initiation, but how this affects their tissue-specific gene expression characteristics is not well understood. While promoters with Pol II pausing elements are active throughout development, TATA promoters are highly active in differentiated tissues. We therefore used a genomics approach on late-stage Drosophila embryos to analyze the properties of promoter types. Using tissue-specific Pol II ChIP-seq, we found that paused promoters have high levels of paused Pol II throughout the embryo, even in tissues where the gene is not expressed, while TATA promoters only show Pol II occupancy when the gene is active. The promoter types are associated with different chromatin accessibility in ATAC-seq data and have different expression characteristics in single-cell RNA-seq data. The two promoter types may therefore be optimized for different properties: paused promoters show more consistent expression when active, while TATA promoters have lower background expression when inactive. We propose that tissue-specific genes have evolved to use two different strategies for their differential expression across tissues.
A Deep Learning Algorithm for Objective Assessment of Hypernasality in Children With Cleft Palate

Objectives: Evaluation of hypernasality requires extensive perceptual training by clinicians and extending this training on a large scale internationally is untenable; this compounds the health disparities that already exist among children with cleft. In this work, we present the objective hypernasality measure (OHM), a speech-based algorithm that automatically measures hypernasality in speech, and validate it relative to a group of trained clinicians. Methods: We trained a deep neural network (DNN) on approximately 100 hours of a publicly-available healthy speech corpus to detect the presence of nasal acoustic cues generated through the production of nasal consonants and nasalized phonemes in speech. Importantly, this model does not require any clinical data for training. The posterior probabilities of the deep learning model were aggregated at the sentence and speaker-levels to compute the OHM. Results: The results showed that the OHM was significantly correlated with perceptual hypernasality ratings from the Americleft database ( r = 0.797, p r = 0.713, p Significance: The results indicate that the OHM is able to measure the severity of hypernasality on par with Americleft-trained clinicians on thisdataset.
Tripping through time: Efficient Localization of Activities in Videos

Localizing moments in untrimmed videos via language queries is a new and interesting task that requires the ability to accurately ground language into video. Previous works have approached this task by processing the entire video, often more than once, to localize relevant activities. In the real world applications of this approach, such as video surveillance, efficiency is a key system requirement. In this paper, we present TripNet, an end-to-end system that uses a gated attention architecture to model fine-grained textual and visual representations in order to align text and video content. Furthermore, TripNet uses reinforcement learning to efficiently localize relevant activity clips in long videos, by learning how to intelligently skip around the video. It extracts visual features for few frames to perform activity classification. In our evaluation over Charades-STA, ActivityNet Captions and the TACoS dataset, we find that TripNet achieves high accuracy and saves processing time by only looking at 32-41% of the entire video.
Budget-Management Strategies in Repeated Auctions

In online advertising, advertisers purchase ad placements by participating in a long sequence of repeated auctions. One of the most important features advertising platforms often provide, and advertisers often use, is budget management, which allows advertisers to control their cumulative expenditures. Advertisers typically declare the maximum daily amount they are willing to pay, and the platform adjusts allocations and payments to guarantee that cumulative expenditures do not exceed budgets. There are multiple ways to achieve this goal, and each one, when applied to all budget-constrained advertisers simultaneously, steers the system toward a different equilibrium. While previous research focused on online stochastic optimization techniques or game-theoretic equilibria of such settings, our goal in this paper is to compare the ``system equilibria'' of a range of budget management strategies in terms of the seller's profit and buyers' utility. In particular, we consider six different budget management strategies including probabilistic throttling, thresholding, bid shading, reserve pricing, and multiplicative boosting. We show these methods admit a system equilibrium in a rather general setting, and prove dominance relations between them in a simplified setting. Our study sheds light on the impact of budget management strategies on the tradeoff between the seller's profit and buyers' utility. Finally, we also empirically compare the system equilibria of these strategies using real ad auction data in sponsored search and randomly generated bids. The empirical study confirms our theoretical findings about the relative performances of budget management strategies.
Recent progress on intrinsic charm

Over the past ∼10 years, the topic of the nucleon’s nonperturbative or intrinsic charm (IC) content has enjoyed something of a renaissance, largely motivated by theoretical developments involving quark modelers and PDF-fitters. In this talk I will briefly describe the importance of intrinsic charm to various issues in high-energy phenomenology, and survey recent progress in constraining its overall normalization and contribution to the momentum sum rule of the nucleon. I end with the conclusion that progress on the side of calculation has now placed the onus on experiment to unambiguously resolve the proton’s intrinsic charm component.
Entity-level Factual Consistency of Abstractive Text Summarization.

A key challenge for abstractive summarization is ensuring factual consistency of the generated summary with respect to the original document. For example, state-of-the-art models trained on existing datasets exhibit entity hallucination, generating names of entities that are not present in the source document. We propose a set of new metrics to quantify the entity-level factual consistency of generated summaries and we show that the entity hallucination problem can be alleviated by simply filtering the training data. In addition, we propose a summary-worthy entity classification task to the training process as well as a joint entity and summary generation approach, which yield further improvements in entity level metrics.
Neuropathy severity at the time of oxaliplatin treatment alteration in patients with colon cancer (Alliance A151912)

Clinical guidelines recommend altering chemotherapy treatment by decreasing, delaying, or discontinuing dosing in patients who are experiencing chemotherapy-induced peripheral neuropathy. There are few data available on the clinical use of treatment alteration including the severity of CIPN at the time of treatment alteration. This was a retrospective analysis of patients receiving oxaliplatin on the NCCTG N08CB trial. Neuropathy severity was assessed at each cycle by clinicians and patients. Patients were classified as (1) completed treatment without alteration, (2) dose reduction or delay due to neuropathy, (3) discontinuation due to neuropathy, (4) discontinuation for other toxicity, or (5) discontinuation for another reason (5). Comparisons focused primarily on patients with alteration due to neuropathy (groups 2 and/or 3) compared with patients who completed treatment without alteration (group 1). In 350 participants, 135 (39%) completed treatment without alteration, 70 (20%) had a dose reduction or delay due to neuropathy, and 35 (10%) discontinued early due to neuropathy. Clinician-assessed neuropathy severity was greater in patients at the time of dose reduction or delay compared with severity at the end of treatment in patients without alteration (p < 0.0001). Patient-reported neuropathy severity at cycle 4 was worse in patients who eventually had a reduction or delay as compared with patients who completed treatment without alteration (p = 0.017). Treatment alterations due to neuropathy are common in patients receiving oxaliplatin for colon cancer and are associated with clinician-assessed neuropathy severity. Rapid increases in patient-reported neuropathy severity indicate a potential need for monitoring and intervention. Clinicaltrials.gov Identifier: NCT01099449 (NCCTG N08CB)
Demographic Representation and Collective Storytelling in the Me Too Twitter Hashtag Activism Movement

The #MeToo movement on Twitter has drawn attention to the pervasive nature of sexual harassment and violence. While #MeToo has been praised for providing support for self-disclosures of harassment or violence and shifting societal response, it has also been criticized for exemplifying how women of color have been discounted for their historical contributions to and excluded from feminist movements. Through an analysis of over 600,000 tweets from over 256,000 unique users, we examine online #MeToo conversations across gender and racial/ethnic identities and the topics that each demographic emphasized. We found that tweets authored by white women were overrepresented in the movement compared to other demographics, aligning with criticism of unequal representation. We found that intersected identities contributed differing narratives to frame the movement, co-opted the movement to raise visibility in parallel ongoing movements, employed the same hashtags both critically and supportively, and revived and created new hashtags in response to pivotal moments. Notably, tweets authored by black women often expressed emotional support and were critical about differential treatment in the justice system and by police. In comparison, tweets authored by white women and men often highlighted sexual harassment and violence by public figures and weaved in more general political discussions. We discuss the implications of this work for digital activism research and design, including suggestions to raise visibility by those who were under-represented in this hashtag activism movement. Content warning: this article discusses issues of sexual harassment and violence.
A Novel Hybrid Monte Carlo Algorithm for Sampling Path Space

To sample from complex, high-dimensional distributions, one may choose algorithms based on the Hybrid Monte Carlo (HMC) method. HMC-based algorithms generate nonlocal moves alleviating diffusive behavior. Here, I build on an already defined HMC framework, hybrid Monte Carlo on Hilbert spaces (Beskos, et al. Stoch. Proc. Applic. 2011), that provides finite-dimensional approximations of measures π, which have density with respect to a Gaussian measure on an infinite-dimensional Hilbert (path) space. In all HMC algorithms, one has some freedom to choose the mass operator. The novel feature of the algorithm described in this article lies in the choice of this operator. This new choice defines a Markov Chain Monte Carlo (MCMC) method that is well defined on the Hilbert space itself. As before, the algorithm described herein uses an enlarged phase space Π having the target π as a marginal, together with a Hamiltonian flow that preserves Π. In the previous work, the authors explored a method where the phase space π was augmented with Brownian bridges. With this new choice, π is augmented by Ornstein–Uhlenbeck (OU) bridges. The covariance of Brownian bridges grows with its length, which has negative effects on the acceptance rate in the MCMC method. This contrasts with the covariance of OU bridges, which is independent of the path length. The ingredients of the new algorithm include the definition of the mass operator, the equations for the Hamiltonian flow, the (approximate) numerical integration of the evolution equations, and finally, the Metropolis–Hastings acceptance rule. Taken together, these constitute a robust method for sampling the target distribution in an almost dimension-free manner. The behavior of this novel algorithm is demonstrated by computer experiments for a particle moving in two dimensions, between two free-energy basins separated by an entropic barrier.
BiSulfite Bolt: A bisulfite sequencing analysis platform

Author(s): Farrell, Colin; Thompson, Michael; Tosevska, Anela; Oyetunde, Adewale; Pellegrini, Matteo | Abstract: BackgroundBisulfite sequencing is commonly used to measure DNA methylation. Processing bisulfite sequencing data is often challenging owing to the computational demands of mapping a low-complexity, asymmetrical library and the lack of a unified processing toolset to produce an analysis-ready methylation matrix from read alignments. To address these shortcomings, we have developed BiSulfite Bolt (BSBolt), a fast and scalable bisulfite sequencing analysis platform. BSBolt performs a pre-alignment sequencing read assessment step to improve efficiency when handling asymmetrical bisulfite sequencing libraries.FindingsWe evaluated BSBolt against simulated and real bisulfite sequencing libraries. We found that BSBolt provides accurate and fast bisulfite sequencing alignments and methylation calls. We also compared BSBolt to several existing bisulfite alignment tools and found BSBolt outperforms Bismark, BSSeeker2, BISCUIT, and BWA-Meth based on alignment accuracy and methylation calling accuracy.ConclusionBSBolt offers streamlined processing of bisulfite sequencing data through an integrated toolset that offers support for simulation, alignment, methylation calling, and data aggregation. BSBolt is implemented as a Python package and command line utility for flexibility when building informatics pipelines. BSBolt is available at https://github.com/NuttyLogic/BSBolt under an MIT license.
Integrated Visualization Editing via Parameterized Declarative Templates

Interfaces for creating visualizations typically embrace one of several common forms. Textual specification enables fine-grained control, shelf building facilitates rapid exploration, while chart choosing promotes immediacy and simplicity. Ideally these approaches could be unified to integrate the user- and usage-dependent benefits found in each modality, yet these forms remain distinct. We propose parameterized declarative templates, a simple abstraction mechanism over JSON-based visualization grammars, as a foundation for multimodal visualization editors. We demonstrate how templates can facilitate organization and reuse by factoring the more than 160 charts that constitute Vega-Lite’s example gallery into approximately 40 templates. We exemplify the pliability of abstracting over charting grammars by implementing—as a template—the functionality of the shelf builder Polestar (a simulacra of Tableau) and a set of templates that emulate the Google Sheets chart chooser. We show how templates support multimodal visualization editing by implementing a prototype and evaluating it through an approachability study.
Machine Learning-Assisted Beam Alignment for mmWave Systems

Beam alignment is a challenging and time-consuming process for millimeter wave (mmWave) systems, particularly as they trend towards higher carrier frequencies which require ever narrower beams. We propose a beam alignment method that is assisted by machine learning (ML), where we train ML models to predict the optimal access point (AP) and beam – or the best few candidates – for a user equipment (UE) given just its GPS coordinates, which can be fed back by the UE or estimated by the network using emerging localization techniques. We train and evaluate the models with data generated by a state-of-the-art commercial ray-tracing software in a realistic urban topology. Even with dynamic scatterers and imperfect UE coordinates, our proposed method can greatly reduce the search space (number of candidates) for finding the optimal AP and beam. For example, in a 28 GHz scenario with 64 beams, our method reduces the search space by approximately 4x for AP selection and over 10x for beam selection while achieving over 95% accuracy. We provide our dataset and models for ease of reproducing and extending our results, which suggest that UE localization coupled with suitably trained ML models can significantly speed up current beam alignment procedures.
Is Cash the Panacea of the COVID-19 Pandemic: Evidence from Corporate Performance

Abstract This study investigates the impact of COVID-19 crisis on corporate investment and financing policies. Using a difference-in-difference approach, I find while firms suffer from a real negative shock from the pandemic on average, firms with an abundant cash reserve prior to the crisis outperform firms without. Consistent with the precautionary motive behind corporate cash holdings, this paper demonstrates the effect of cash holdings is meaningful to mitigate adverse effect of the aggregate market. My finding also highlights the difficulty in estimating the optimal cash level when rare market condition is considered.
Robust Entry Vehicle Guidance with Sampling-Based Invariant Funnels

Managing uncertainty is a fundamental and critical issue in spacecraft entry guidance. This paper presents a novel approach for uncertainty propagation during entry, descent and landing that relies on a new sum-of-squares robust verification technique. Unlike risk-based and probabilistic approaches, our technique does not rely on any probabilistic assumptions. It uses a set-based description to bound uncertainties and disturbances like vehicle and atmospheric parameters and winds. The approach leverages a recently developed sampling-based version of sum-of-squares programming to compute regions of finite time invariance, commonly referred to as “invariant funnels”. We apply this approach to a three-degree-of-freedom entry vehicle model and test it using a Mars Science Laboratory reference trajectory. We compute tight approximations of robust invariant funnels that are guaranteed to reach a goal region with increased landing accuracy while respecting realistic thermal constraints.
Millimeter Wave Massive MIMO Downlink Per-Group Communications With Hybrid Linear Precoding

We address the problem of analyzing and classifying into groups the downlink channel environment in a dense millimeter-wavelength cell, accounting for path loss, multipath fading, and User Equipment (UE) blocking, by employing a hybrid propagation and multipath fading model, thus using accurate inter-group interference modeling. The base station (BS) employs a large Uniform Planar Array (UPA) to facilitate massive Multiple-Input, Multiple-Output (MIMO) communications with high efficiency. UEs are equipped with a single antenna and are distributed uniformly within the cell. The key problem is analyzing and defining groups toward efficient precoding. Because balanced throughput is desired between groups, up to four separate frequency sub-carriers are employed in an Orthogonal Frequency Division Multiple Access (OFDMA) system. We show that by employing three or four OFDMA subcarriers, depending on the number of UEs in the cell and the cell range, the UEs can be efficiently separated into high-throughput groups, with each group employing Virtual Channel Model Beams (VCMB) based inner precoding, followed by efficient Multi-User Multiple-Input Multiple-Output (MU-MIMO) outer precoders. For each group, we study two different sub-grouping methods offering different advantages. We show that the improvement offered by Zero-Forcing Per-Group Precoding (ZF-PGP) over Matched Filter Precoding (MFP) and Zero-Forcing Precoding (ZFP) is very high when finite-alphabet inputs are used. We also show ZF-PGP with finite-alphabet inputs significantly outperforms MFP with Gaussian inputs in certain realistic setups. Finally, a new technique for power allocation among different UEs is proposed, called Optimized Per-Group Power Allocation (OPGPA), which achieves much higher power efficiency jointly with equal throughput among all UEs in each group.
Investigation of Communications Involved in Near-Term UAM Operations

The present research explored whether the implementation of a letter of agreement (LOA), or pre-established written terms of engagement, would reduce controller communication associated workload in a HITL study simulating a near-term UAM infrastructure with varying traffic levels. Current helicopter routes, including modified versions, and communication procedures were outlined in the LOA. Time spent communicating was reduced under both conditions featuring a LOA, for current and modified routes, compared to present day procedures without a LOA. Results suggest that utilizing current-day helicopter routes and implementing a LOA may prove beneficial for near-term low-density and low-tempo UAM operations.
A multifaceted role of progranulin in regulating amyloid-beta dynamics and responses.

Haploinsufficiency of progranulin (PGRN) is a leading cause of frontotemporal lobar degeneration (FTLD). PGRN polymorphisms are associated with Alzheimer’s disease. PGRN is highly expressed in the microglia near Aβ plaques and influences plaque dynamics and microglial activation. However, the detailed mechanisms remain elusive. Here we report that PGRN deficiency reduces human APP and Aβ levels in the young male but not female mice. PGRN-deficient microglia exhibit increased expression of markers associated with microglial activation, including CD68, galectin-3, TREM2, and GPNMB, specifically near Aβ plaques. In addition, PGRN loss leads to up-regulation of lysosome proteins and an increase in the nuclear localization of TFE3, a transcription factor involved in lysosome biogenesis. Cultured PGRN-deficient microglia show enhanced nuclear translocation of TFE3 and inflammation in response to Aβ fibril treatment. Taken together, our data revealed a sex- and age-dependent effect of PGRN on APP metabolism and a role of PGRN in regulating lysosomal activities and inflammation in plaque-associated microglia.
Generating bug-fixes using pretrained transformers

Detecting and fixing bugs are two of the most important yet frustrating parts of the software development cycle. Existing bug detection tools are based mainly on static analyzers, which rely on mathematical logic and symbolic reasoning about the program execution to detect common types of bugs. Fixing bugs is typically left out to the developer. In this work we introduce DeepDebug: a data-driven program repair approach which learns to detect and fix bugs in Java methods mined from real-world GitHub repositories. We frame bug-patching as a sequence-to-sequence learning task consisting of two steps: (i) denoising pretraining, and (ii) supervised finetuning on the target translation task. We show that pretraining on source code programs improves the number of patches found by 33% as compared to supervised training from scratch, while domain-adaptive pretraining from natural language to code further improves the accuracy by another 32%. We refine the standard accuracy evaluation metric into non-deletion and deletion-only fixes, and show that our best model generates 75% more non-deletion fixes than the previous state of the art. In contrast to prior work, we attain our best results when generating raw code, as opposed to working with abstracted code that tends to only benefit smaller capacity models. Finally, we observe a subtle improvement from adding syntax embeddings along with the standard positional embeddings, as well as with adding an auxiliary task to predict each token's syntactic class. Despite focusing on Java, our approach is language agnostic, requiring only a general-purpose parser such as tree-sitter.
Cassini Attitude Control Operations: Flight Rules and How They are Enforced

The Cassini spacecraft was launched on October 15, 1997 and arrived at Saturn on June 30, 2004. It has performed detailed observations and remote sensing of Saturn, its rings, and its satellites since that time. Cassini deployed the European-built Huygens probe which descended through the Titan atmosphere and landed on its surface on January 14, 2005. Operating the Cassini spacecraft is a complex scientific, engineering, and management job. In order to safely operate the spacecraft, a large number of flight rules were developed. These flight rules must be enforced throughout the lifetime of the Cassini spacecraft. Flight rules are defined as any operational limitation imposed by the spacecraft system design, hardware, and software, violation of which would result in spacecraft damage, loss of consumables, loss of mission objectives, loss and/or degradation of science, and less than optimal performance. Flight rules require clear description and rationale. Detailed automated methods have been developed to insure the spacecraft is continuously operated within these flight rules. An overview of all the flight rules allocated to the Cassini Attitude Control and Articulation Subsystem and how they are enforced is presented in this paper.
CREATe: Clinical Report Extraction and Annotation Technology

Clinical case reports are written descriptions of the unique aspects of a particular clinical case, playing an essential role in sharing clinical experiences about atypical disease phenotypes and new therapies. However, to our knowledge, there has been no attempt to develop an end-to-end system to annotate, index, or otherwise curate these reports. In this paper, we propose a novel computational resource platform, CREATe, for extracting, indexing, and querying the contents of clinical case reports. CREATe fosters an environment of sustainable resource support and discovery, enabling researchers to overcome the challenges of information science. An online video of the demonstration can be viewed at https://youtu.be/Q8owBQYTjDc.
Exact Logit-Based Product Design.

The share-of-choice product design (SOCPD) problem is to find the product, as defined by its attributes, that maximizes market share arising from a collection of customer types or segments. When customers follow a logit model of choice, the market share is given by a weighted sum of logistic probabilities, leading to the logit-based share-of-choice product design problem. At first glance, this problem appears hopeless: one must optimize an objective function that is neither convex nor concave, over an exponentially-sized discrete set of attribute combinations. In this paper, we develop an exact methodology for solving this problem based on modern integer, convex and conic optimization. At the heart of our methodology is the surprising result that the logit-based SOCPD problem can be exactly reformulated as a mixed-integer convex program. Using both synthetic problem instances and instances derived from real conjoint data sets, we show that our methodology can solve large instances to provable optimality or near optimality in operationally feasible time frames.
Static and dynamic quantile preferences

This paper axiomatizes static and dynamic quantile preferences. Static quantile preferences specify that a prospect should be preferred if it has a higher $$\tau $$ -quantile, for some $$\tau \in (0,1)$$ , while its dynamic counterpart extends this to take into account a sequence of decisions and information disclosure. An important motivation for the axiomatization that leads to this preference is the separation of tastes and beliefs. We first axiomatize quantile preferences for the static case with finite state space and then extend the axioms to the dynamic context. The dynamic preferences induce an additively separable quantile model with standard discounting, that is, the recursive equation is characterized by the sum of the current period utility function and the discounted value of the certainty equivalent, which is a quantile function. These preferences are time consistent and have a simple quantile recursive representation, which gives the model the analytical tractability needed in several fields in financial and economic applications. Finally, we study the notion of risk attitude in both the static and recursive quantile models. In quantile models, the risk attitude is completely captured by the quantile $$\tau $$ , a single-dimensional parameter. This is simpler than in expected utility models, where in general the risk attitude is determined by a function.
Conformance Constraint Discovery: Measuring Trust in Data-Driven Systems

The reliability of inferences made by data-driven systems hinges on the data's continued conformance to the systems' initial settings and assumptions. When serving data (on which we want to apply inference) deviates from the profile of the initial training data, the outcome of inference becomes unreliable. We introduce conformance constraints, a new data profiling primitive tailored towards quantifying the degree of non-conformance, which can effectively characterize if inference over that tuple is untrustworthy. Conformance constraints are constraints over certain arithmetic expressions (called projections) involving the numerical attributes of a dataset, which existing data profiling primitives such as functional dependencies and denial constraints cannot model. Our key finding is that projections that incur low variance on a dataset construct effective conformance constraints. This principle yields the surprising result that low-variance components of a principal component analysis, which are usually discarded for dimensionality reduction, generate stronger conformance constraints than the high-variance components. Based on this result, we provide a highly scalable and efficient technique--linear in data size and cubic in the number of attributes--for discovering conformance constraints for a dataset. To measure the degree of a tuple's non-conformance with respect to a dataset, we propose a quantitative semantics that captures how much a tuple violates the conformance constraints of that dataset. We demonstrate the value of conformance constraints on two applications: trusted machine learning and data drift. We empirically show that conformance constraints offer mechanisms to (1) reliably detect tuples on which the inference of a machine-learned model should not be trusted, and (2) quantify data drift more accurately than the state of the art.
The Krein–von Neumann extension revisited

We revisit the Krein-von Neumann extension in the case where the underlying symmetric operator is strictly positive and apply this to derive the explicit form of the Krein-von Neumann extension for singular, general (i.e., three-coefficient) Sturm-Liouville operators on arbitrary intervals. In particular, the boundary conditions for the Krein-von Neumann extension of the strictly positive minimal Sturm-Liouville operator are explicitly expressed in terms of generalized boundary values adapted to the (possible) singularity structure of the coefficients near an interval endpoint.
Blockchain Based Accounts Payable Platform for Goods Trade

Goods trade is a supply chain transaction that involves shippers buying goods from suppliers and carriers providing goods transportation. Shippers are issued invoices from suppliers and carriers. Shippers carry out goods receiving and invoice processing before payment processing of bills for suppliers and carriers, where invoice processing includes tasks like processing claims and adjusting the bill payments. Goods receiving involves verification of received goods by the Shipper’s receiving team. Invoice processing is carried out by the Shipper’s accounts payable team, which in turn is verified by the accounts receivable teams of suppliers and carriers. This paper presents a blockchain-based accounts payable system that generates claims for the deficiency in the goods received and accordingly adjusts the payment in the bills for suppliers and carriers. Primary motivations for these supply chain organizations to adopt blockchain-based accounts payable systems are to eliminate the process redundancies (accounts payable vs. accounts receivable), to reduce the number of disputes among the transacting participants, and to accelerate the accounts payable processes via optimizations in the claims generation and blockchain-based dispute reconciliation.
The Circumstances of Civil Recourse

What circumstances create the need for an institution that conforms to civil recourse theory? I consider polities that vary in the extent to which they instantiate justice and argue that only a moderately non-ideal polity has a need for such an institution. When a polity gets close to the ideal, the polity needs institutions of corrective justice. When the polity gets very far from the ideal, tort law is at best instrumentally justified. Somewhere in between those two extremes, a civil recourse conception has significant justificatory advantages over both corrective justice and instrumental accounts. Even under these conditions, however, it remains difficult to explain within the confines of civil recourse theory why enforcement of tort law’s primary norms should remain in the hands of the victim. I suggest a possible explanation, but it requires us to jettison the claim that the wrongs of tort law are genuine wrongs.
A two-level parameterized Model-Order Reduction approach for time-domain elastodynamics

Abstract We present a two-level parameterized Model Order Reduction (pMOR) technique for the linear hyperbolic Partial Differential Equation (PDE) of time-domain elastodynamics. In order to approximate the frequency-domain PDE, we take advantage of the Port-Reduced Reduced-Basis Component (PR-RBC) method to develop (in the offline stage) reduced bases for subdomains; the latter are then assembled (in the online stage) to form the global domains of interest. The PR-RBC approach reduces the effective dimensionality of the parameter space and also provides flexibility in topology and geometry. In the online stage, for each query, we consider a given parameter value and associated global domain. In the first level of reduction, the PR-RBC reduced bases are used to approximate the frequency-domain solution at selected frequencies. In the second level of reduction, these instantiated PR-RBC approximations are used as surrogate truth solutions in a Strong Greedy approach to identify a reduced basis space; the PDE of time-domain elastodynamics is then projected on this reduced space. We provide a numerical example to demonstrate the computational capability and assess the performance of the proposed two-level approach.
A deep learning study on osteosarcoma detection from histological images

Abstract In the U.S. 5–10% of new pediatric cases of cancer are primary bone tumors. The most common type of primary malignant bone tumor is osteosarcoma. The intention of the present work is to improve the detection and diagnosis of osteosarcoma using computer-aided detection (CAD) and diagnosis (CADx). Such tools as convolutional neural networks (CNNs) can significantly decrease the surgeon’s workload and make a better prognosis of patient conditions. CNNs need to be trained on a large amount of data in order to achieve a more trustworthy performance. In this study, transfer learning techniques, pre-trained CNNs, are adapted to a public dataset on osteosarcoma histological images to detect necrotic images from non-necrotic and healthy tissues. First, the dataset was preprocessed, and different classifications are applied. Then, Transfer learning models including VGG19 and Inception V3 are used and trained on Whole Slide Images (WSI) with no patches, to improve the accuracy of the outputs. Finally, the models are applied to different classification problems, including binary and multi-class classifiers. Experimental results show that the accuracy of the VGG19 has the highest, 96%, performance amongst all binary classes and multiclass classification. Our fine-tuned model demonstrates state-of-the-art performance on detecting malignancy of Osteosarcoma based on histologic images.
A Primal-Dual Method for Training Recurrent Neural Networks Constrained by the Echo-State Property

We present an architecture of a recurrent neural network (RNN) with a fully-connected deep neural network (DNN) as its feature extractor. The RNN is equipped with both causal temporal prediction and non-causal look-ahead, via auto-regression (AR) and moving-average (MA), respectively. The focus of this paper is a primal-dual training method that formulates the learning of the RNN as a formal optimization problem with an inequality constraint that provides a sufficient condition for the stability of the network dynamics. Experimental results demonstrate the effectiveness of this new method, which achieves 18.86% phone recognition error on the TIMIT benchmark for the core test set. The result approaches the best result of 17.7%, which was obtained by using RNN with long short-term memory (LSTM). The results also show that the proposed primal-dual training method produces lower recognition errors than the popular RNN methods developed earlier based on the carefully tuned threshold parameter that heuristically prevents the gradient from exploding.