cssi/SciSciGPT-SciSciNet huggingface dataset:
Is there a constitutional right to clone

Recent scientific innovations, and proposed legislation, have raised questions about the nature of the constitutional right to reproductive freedom, and in particular about whether there is a constitutional “right to clone.” This essay urges that as a matter of substantive due process, rationality review is probably appropriate, and that restrictions on both reproductive and therapeutic cloning would and should survive constitutional scrutiny. At the same time, many of the arguments for banning both forms of cloning are based on ignorance, myths, and speculation. It is extremely important to distinguish between reproductive and nonreproductive cloning, and it is equally important to distinguish among the various rationales for banning each. Some of those rationales have some, but others, including some of the most influential, are exceedingly weak. As currently interpreted, the Constitution protects a range of rights involving marriage, bodily integrity, and reproduction. Does the Constitution guarantee the right to clone? The question is not as fanciful as it might appear. Cloning is a possible method for “reproduction.” For some people, cloning would undoubtedly be the preferred choice. Among this group, some would choose cloning on the ground that it is the only way to produce children with some genetic connection to them. For such people, the potential value of cloning should not be understated. And because reproduction is involved, the individual right to choose might well be thought to fall within the doctrinal protection given by Roe v. Wade and other cases. At the same time, it is not clear that the government can offer constitutionally adequate grounds for interfering with a presumptively protected right. Some of the most common justifications seem ignorant or speculative, and others could be taken care of through narrower means, falling short of a flat ban on cloning. Indeed, the justifications for banning cloning might well seem, at first glance, to be weaker than the justifications for banning abortion, which involves the intentional destruction of what many people consider innocent human life. If government cannot ban the intentional destruction of * Karl N. Llewellyn Distinguished Service Professor of Jurisprudence, Law School and Department of Political Science, University of Chicago. This essay is a written version of the keynote luncheon address for the Hastings Law Journal symposium on human cloning, held in January 2002. 1 See John Robertson, Liberty, Identity, and Human Cloning, 76 Tex. L. Rev. 1371, 1441-42 (1998). 2 410 U.S. 113 (1973). 3 See, e.g., Griswold v. Connecticut, 381 US 479 (1965); Eisenstadt v. Baird, 405 US 438 (1972); Carey v. Population Servs.. 431 US 113 (1973).
Efficient Scene Compression for Visual-based Localization

Estimating the pose of a camera with respect to a 3D reconstruction or scene representation is a crucial step for many mixed reality and robotics applications. Given the vast amount of available data nowadays, many applications constrain storage and/or bandwidth to work efficiently. To satisfy these constraints, many applications compress a scene representation by reducing its number of 3D points. While state-of-the-art methods use K-cover-based algorithms to compress a scene, they are slow and hard to tune. To enhance speed and facilitate parameter tuning, this work introduces a novel approach that compresses a scene representation by means of a constrained quadratic program (QP). Because this QP resembles a one-class support vector machine, we derive a variant of the sequential minimal optimization to solve it. Our approach uses the points corresponding to the support vectors as the subset of points to represent a scene. We also present an efficient initialization method that allows our method to converge quickly. Our experiments on publicly available datasets show that our approach compresses a scene representation quickly while delivering accurate pose estimates.
Wind- and gravity-forced drop depinning

Contact-angle hysteresis enables drops to pin to surfaces in the presence of wind or gravity forcing. Under what combined forcing conditions do drops depin and run back along a surface? On noninclined surfaces, drops depin at a constant critical Weber number across a wide range of Bond numbers. On inclined surfaces, two regimes of wind- and gravity-dominated forcing are observed, but a simple correlation may still describe critical depinning conditions.
Neural-adjoint method for the inverse design of all-dielectric metasurfaces

All-dielectric metasurfaces exhibit exotic electromagnetic responses, similar to those obtained with metal-based metamaterials. Research in all-dielectric metasurfaces currently uses relatively simple unit-cell designs, but increased geometrical complexity may yield even greater scattering states. Although machine learning has recently been applied to the design of metasurfaces with impressive results, the much more challenging task of finding a geometry that yields a desired spectra remains largely unsolved. We propose and demonstrate a method capable of finding accurate solutions to ill-posed inverse problems, where the conditions of existence and uniqueness are violated. A specific example of finding the metasurface geometry which yields a radiant exitance matching the external quantum efficiency of gallium antimonide is demonstrated. We also show how the neural-adjoint method can intelligently grow the design search space to include designs that increasingly and accurately approximate the desired scattering response. The neural-adjoint method is not restricted to the case demonstrated and may be applied to plasmonics, photonic crystal, and other artificial electromagnetic materials.
Probabilistic Semantic Mapping for Urban Autonomous Driving Applications

Recent advancements in statistical learning and computational abilities have enabled autonomous vehicle technology to develop at a much faster rate. While many of the architectures previously introduced are capable of operating under highly dynamic environments, many of these are constrained to smaller-scale deployments, require constant maintenance due to the associated scalability cost with highdefinition (HD) maps, and involve tedious manual labeling. As an attempt to tackle this problem, we propose to fuse image and pre-built point cloud map information to perform automatic and accurate labeling of static landmarks such as roads, sidewalks, crosswalks, and lanes. The method performs semantic segmentation on 2D images, associates the semantic labels with point cloud maps to accurately localize them in the world, and leverages the confusion matrix formulation to construct a probabilistic semantic map in bird’s eye view from semantic point clouds. Experiments from data collected in an urban environment show that this model is able to predict most road features and can be extended for automatically incorporating road features into HD maps with potential future work directions.
Line Drawing in the Dark

The law inevitably draws lines. These lines distinguish, for example, whether certain conduct reﬂects ordinary recklessness constituting manslaughter or more extreme recklessness constituting murder. There is no way to meaningfully draw such lines, however, absent shared ways of representing amounts of recklessness or at least knowledge of the consequences of drawing lines in particular places. Yet legal actors frequently draw lines in the dark, establishing cutoffs along a spectrum with little or none of the information required to do so in a way that suits the law’s goals. For example, jurors must decide whether some conduct constitutes extreme recklessness without knowing prior precedent nor the sentencing consequences of drawing cutoffs in particular places. Judges and lawyers cite line drawing precedents from other jurisdictions without considering whether the lines drawn in prior cases had the same consequences as those in the case at bar. And scholars argue about how to classify conduct without making clear what consequences they believe ought to attach once the classification is made, leaving it hard to tell when scholars have substantive or simply superficial disagreements. In this Article, I discuss some line drawing problems and brieﬂy suggest ways we can add meaning to cutoffs. More generally, I argue, we can “smooth” certain features of the law to both reduce our vulnerability to line drawing in the dark and improve the fit between the law and what our best theories of law recommend. Even when we cannot easily smooth the law, thinking about the law in a smoother fashion can help reduce the jurisprudential pathologies I describe.
Prediction of high-performance computing input/output variability and its application to optimization for system configurations

Performance variability is an important measure for a reliable high performance computing (HPC) system. Performance variability is affected by complicated interactions between numerous factors, suc...
Tied links in the solid torus

We introduce the concept of tied links in the solid torus, which generalizes naturally the concept of tied links in S3 previously introduced by Aicardi and Juyumaya. We also define an invariant of ...
Comparative Population Genetics in the Human Gut Microbiome

Genetic variation in the human gut microbiome is responsible for conferring a number of crucial phenotypes like the ability to digest food and metabolize drugs. Yet, our understanding of how this variation arises and is maintained remains relatively poor. Thus, the microbiome remains a largely untapped resource, as the large number of co-existing species in the microbiome presents a unique opportunity to compare and contrast evolutionary processes across species to identify universal trends and deviations. Here we outline features of the human gut microbiome that, while not unique in isolation, as an assemblage make it a system with unparalleled potential for comparative population genomics studies. We consciously take a broad view of comparative population genetics, emphasizing how sampling a large number of species allows researchers to identify universal evolutionary dynamics in addition to new genes, which can then be leveraged to identify exceptional species that deviate from general patterns. To highlight the potential power of comparative population genetics in the microbiome, we re-analyze patterns of purifying selection across ∼40 prevalent species in the human gut microbiome to identify intriguing trends which highlight functional categories in the microbiome that may be under more or less constraint.
The evolutionary stability of optimism, pessimism, and complete ignorance

We seek an evolutionary explanation for why in some situations humans maintain either optimistic or pessimistic attitudes toward uncertainty and are ignorant to relevant aspects of their environment. Players in strategic games face Knightian uncertainty about opponents’ actions and maximize individually their Choquet expected utility with respect to neo-additive capacities (Chateauneuf et al. 2007) allowing for both an optimistic or pessimistic attitude toward uncertainty as well as ignorance to strategic dependencies. An optimist (resp. pessimist) overweighs good (resp. bad) outcomes. A complete ignorant never reacts to opponents’ changes of actions. We focus on sub- and supermodular aggregative games and provide monotone comparative statics w.r.t. optimism/pessimism. With qualifications, we show that in finite populations, optimistic (resp. pessimistic) complete ignorance is evolutionary stable and yields a strategic advantage in submodular (resp. supermodular) games with aggregate externalities. Moreover, this evolutionary stable preference leads to Walrasian behavior in these classes of games.
Real-Time Nonrigid Mosaicking of Laparoscopy Images

The ability to extend the field of view of laparoscopy images can help the surgeons to obtain a better understanding of the anatomical context. However, due to tissue deformation, complex camera motion and significant three-dimensional (3D) anatomical surface, image pixels may have non-rigid deformation and traditional mosaicking methods cannot work robustly for laparoscopy images in real-time. To solve this problem, a novel two-dimensional (2D) non-rigid simultaneous localization and mapping (SLAM) system is proposed in this paper, which is able to compensate for the deformation of pixels and perform image mosaicking in real-time. The key algorithm of this 2D non-rigid SLAM system is the expectation maximization and dual quaternion (EMDQ) algorithm, which can generate smooth and dense deformation field from sparse and noisy image feature matches in real-time. An uncertainty-based loop closing method has been proposed to reduce the accumulative errors. To achieve real-time performance, both CPU and GPU parallel computation technologies are used for dense mosaicking of all pixels. Experimental results on in vivo and synthetic data demonstrate the feasibility and accuracy of our non-rigid mosaicking method.
The matching problem has no small symmetric SDP

Yannakakis (Proceedings of the STOC, pp 223–228, 1988; J Comput Syst Sci 43(3):441–466, 1991. doi: 10.1016/0022-0000(91)90024-Y ) showed that the matching problem does not have a small symmetric linear program. Rothvos (Proceedings of the STOC, pp 263–272, 2014) recently proved that any, not necessarily symmetric, linear program also has exponential size. In light of this, it is natural to ask whether the matching problem can be expressed compactly in a framework such as semidefinite programming (SDP) that is more powerful than linear programming but still allows efficient optimization. We answer this question negatively for symmetric SDPs: any symmetric SDP for the matching problem has exponential size. We also show that an O(k)-round Lasserre SDP relaxation for the asymmetric metric traveling salesperson problem yields at least as good an approximation as any symmetric SDP relaxation of size $$n^{k}$$ . The key technical ingredient underlying both these results is an upper bound on the degree needed to derive polynomial identities that hold over the space of matchings or traveling salesperson tours.
Synteny-Based Genome Assembly for 16 Species of Heliconius Butterflies, and an Assessment of Structural Variation across the Genus.

Heliconius butterflies (Lepidoptera: Nymphalidae) are a group of 48 neotropical species widely studied in evolutionary research. Despite the wealth of genomic data generated in past years, chromosomal level genome assemblies currently exist for only two species, Heliconius melpomene and Heliconius erato, each a representative of one of the two major clades of the genus. Here, we use these reference genomes to improve the contiguity of previously published draft genome assemblies of 16 Heliconius species. Using a reference-assisted scaffolding approach, we place and order the scaffolds of these genomes onto chromosomes, resulting in 95.7-99.9% of their genomes anchored to chromosomes. Genome sizes are somewhat variable among species (270-422 Mb) and in one small group of species (Heliconius hecale, Heliconius elevatus, and Heliconius pardalinus) expansions in genome size are driven mainly by repetitive sequences that map to four small regions in the H. melpomene reference genome. Genes from these repeat regions show an increase in exon copy number, an absence of internal stop codons, evidence of constraint on nonsynonymous changes, and increased expression, all of which suggest that at least some of the extra copies are functional. Finally, we conducted a systematic search for inversions and identified five moderately large inversions fixed between the two major Heliconius clades. We infer that one of these inversions was transferred by introgression between the lineages leading to the erato/sara and burneyi/doris clades. These reference-guided assemblies represent a major improvement in Heliconius genomic resources that enable further genetic and evolutionary discoveries in this genus.
Scaling effect of mechanical fastening on thick composite structures

There are obvious advantages in utilizing very thick composites for structures. The joining of these components to substructure or other components must be, to a large degree, by mechanical fastening in order to gain high loading-carrying capability and ease of structural disassembly. In most studies on mechanical joints, the interactions between pin diameter (D) and composite dimensions such as width (W) and distance from hole center to composite end (e), i.e. W/D and e/D ratios, were the primary concerns. However, as composite thickness increases, the contact interaction between the pin and composite becomes more complicated. This study investigated the mechanical joint efficiency of a composite material made of woven glass fabric and phenolic matrix. Sixteen joint configurations based on four composite thicknesses and four pin diameters were examined. Both experimental method and finite element analysis were performed in this study. Results showed that thick composites with small pins and thin composites with large pins had lower efficiencies of joint stiffness and joint strength than those having similar dimensions between pin diameter and composite thickness.
On the Complexity of the Tiden-Arnborg Algorithm for Unification modulo One-Sided Distributivity

We prove that the Tiden and Arnborg algorithm for equational unification modulo one-sided distributivity is not polynomial time bounded as previously thought. A set of counterexamples is developed that demonstrates that the algorithm goes through exponentially many steps.
Microfluidic guillotine reveals multiple timescales and mechanical modes of wound response in Stentor coeruleus

Wound healing is one of the defining features of life and is seen not only in tissues but also within individual cells. Understanding wound response at the single-cell level is critical for determining fundamental cellular functions needed for cell repair and survival. This understanding could also enable the engineering of single-cell wound repair strategies in emerging synthetic cell research. One approach is to examine and adapt self-repair mechanisms from a living system that already demonstrates robust capacity to heal from large wounds. Towards this end, Stentor coeruleus, a single-celled free-living ciliate protozoan, is a unique model because of its robust wound healing capacity. This capacity allows one to perturb the wounding conditions and measure their effect on the repair process without immediately causing cell death, thereby providing a robust platform for probing the self-repair mechanism. Here we used a microfluidic guillotine and a fluorescence-based assay to probe the timescales of wound repair and of mechanical modes of wound response in Stentor. We found that Stentor requires ~ 100–1000 s to close bisection wounds, depending on the severity of the wound. This corresponds to a healing rate of ~ 8–80 μm2/s, faster than most other single cells reported in the literature. Further, we characterized three distinct mechanical modes of wound response in Stentor: contraction, cytoplasm retrieval, and twisting/pulling. Using chemical perturbations, active cilia were found to be important for only the twisting/pulling mode. Contraction of myonemes, a major contractile fiber in Stentor, was surprisingly not important for the contraction mode and was of low importance for the others. While events local to the wound site have been the focus of many single-cell wound repair studies, our results suggest that large-scale mechanical behaviors may be of greater importance to single-cell wound repair than previously thought. The work here advances our understanding of the wound response in Stentor and will lay the foundation for further investigations into the underlying components and molecular mechanisms involved.
Fish on First

In this short essay I examine Stanley Fish's views on freedom of speech and particularly the relation of freedom of speech to the curricula and practices of public universities. Although I agree with most of what Fish asserts, I find that speech by governmental entities such as public universities reveals a deep paradox, one that pervades liberalism itself.
A Posteriori Error Estimates for Numerical Solutions to Hyperbolic Conservation Laws

This paper is concerned with a posteriori error bounds for a wide class of numerical schemes, for $$n\times n$$ hyperbolic conservation laws in one space dimension. These estimates are achieved by a “post-processing algorithm”, checking that the numerical solution retains small total variation, and computing its oscillation on suitable subdomains. The results apply, in particular, to solutions obtained by the Godunov or the Lax–Friedrichs scheme, backward Euler approximations, and the method of periodic smoothing. Some numerical implementations are presented.
Guillain Barre Syndrome as a Complication of COVID-19: A Systematic Review.

BACKGROUND: In January 2020, the first case of Guillain Barre syndrome (GBS) due to COVID-19 was documented in China. GBS is known to be postinfectious following several types of infections. Although causality can only be proven through large epidemiological studies, we intended to study this association by a thorough review of the literature. METHODS: We searched PubMed, EMBASE, and Google scholar and included all papers with English or Spanish full text and original data of patients with GBS and recent COVID infection. Variables of interest were demographics, diagnostic investigations, and the latency between arboviral and neurological symptoms. Further variables were pooled to identify GBS clinical and electrophysiological variants, used treatments, and outcomes. The certainty of GBS diagnosis was verified using Brighton criteria. RESULTS: We identified a total of 109 GBS cases. Ninety-nine cases had confirmed COVID-19 infection with an average age of 56.07 years. The average latency period between the arboviral symptoms and neurologic manifestations for confirmed COVID-19 cases was 12.2 d. The predominant GBS clinical and electromyography variants were the classical sensorimotor GBS and acute demyelinating polyneuropathy respectively. Forty cases required intensive care, 33 cases required mechanical ventilation, and 6 cases were complicated by death. CONCLUSIONS: Studies on COVID-19-related GBS commonly reported sensorimotor demyelinating GBS with frequent facial palsy. The time between the onset of infectious and neurological symptoms suggests a postinfectious mechanism. Early diagnosis of GBS in COVID-19 patients is important as it might be associated with a severe disease course requiring intensive care and mechanical ventilation.
Religion and Mortgage Misrepresentation

We investigate whether religion acts as a deterrent to the types of mortgage misrepresentation that played a significant role in the recent housing boom and bust. Using a large sample of mortgages originated from 2000 to 2007, we provide evidence that local religious adherence (religiosity) is associated with a lower likelihood of home appraisal overstatement and owner occupancy misreporting. The evidence on borrower income misrepresentation is mixed. Religiosity does not appear to reduce the incidence of income misrepresentation; however, it seems to restrain the degree to which income is misrepresented. Our results are generally consistent with the hypothesis that religion, as a set of social norms, fosters ethical behavior, and possibly risk aversion, in the mortgage market.
Interfacial Tension Modulation of Liquid Metal via Electrochemical Oxidation

This progress report summarizes recent studies of electrochemical oxidation to modulate the interfacial tension of gallium-based alloys. These alloys, which are liquid at ambient conditions, have the largest interfacial tension of any liquid at room temperature. The ability to modulate the tension offers the possibility to create forces that change the shape and position of the metal. It has been known since the late 1800s that electrocapillarity-the use of potential to modulate the electric double layer on the surface of metals in electrolyte-lowers the interfacial tension of liquid metal. Yet, this phenomenon can only achieve modest changes in interfacial tension since it is limited to potential windows that avoid reactions. A recent discovery suggests that reactions driven by the electrochemical oxidation of gallium alloys cause the interfacial tension to decrease from ~500 mN/m at 0 V to ~0 mN/m at ~0.8 V, a change in tension that goes well beyond what is possible via conventional electrocapillarity or surfactants. The changes in tension are reversible; reductive potentials return the metal back to a state of high interfacial tension. This report aims to summarize key work and introduce beginners to this field by including electrochemistry basics while addressing misconceptions. We discuss applications that utilize modulations in interfacial tension of liquid metal and conclude with remaining opportunities and challenges that need further investigation.
Ultrasound Confidence Maps Of Intensity And Structure Based On Directed Acyclic Graphs And Artifact Models

Ultrasound imaging has been improving, but continues to suffer from inherent artifacts that are challenging to model, such as attenuation, shadowing, diffraction, speckle, etc. These artifacts can potentially confuse image analysis algorithms unless an attempt is made to assess the certainty of individual pixel values. Our novel confidence algorithms analyze pixel values using a directed acyclic graph based on acoustic physical properties of ultrasound imaging. We demonstrate unique capabilities of our approach and compare it against previous confidence-measurement algorithms for shadow-detection and image-compounding tasks.
Amazon SageMaker Clarify: Machine Learning Bias Detection and Explainability in the Cloud

Understanding the predictions made by machine learning (ML) models and their potential biases remains a challenging and labor-intensive task that depends on the application, the dataset, and the specific model. We present Amazon SageMaker Clarify, an explainability feature for Amazon SageMaker that launched in December 2020, providing insights into data and ML models by identifying biases and explaining predictions. It is deeply integrated into Amazon SageMaker, a fully managed service that enables data scientists and developers to build, train, and deploy ML models at any scale. Clarify supports bias detection and feature importance computation across the ML lifecycle, during data preparation, model evaluation, and post-deployment monitoring. We outline the desiderata derived from customer input, the modular architecture, and the methodology for bias and explanation computations. Further, we describe the technical challenges encountered and the tradeoffs we had to make. For illustration, we discuss two customer use cases. We present our deployment results including qualitative customer feedback and a quantitative evaluation. Finally, we summarize lessons learned, and discuss best practices for the successful adoption of fairness and explanation tools in practice.
Topological gaps by twisting

Twisted bilayered systems such as bilayered graphene exhibit remarkable properties such as superconductivity at magic angles and topological insulating phases. For generic twist angles, the bilayers are truly quasiperiodic, a fact that is often overlooked and that has consequences which are largely unexplored. Herein, we uncover that twisted n-layers host intrinsic higher dimensional topological phases, and that those characterized by second Chern numbers can be found in twisted bi-layers. We employ phononic lattices with interactions modulated by a second twisted lattice and reveal Hofstadter-like spectral butterflies in terms of the twist angle, which acts as a pseudo magnetic field. The phason provided by the sliding of the layers lives on 2n-tori and can be used to access and manipulate the edge states. Our work demonstrates how multi-layered systems are virtual laboratories for studying the physics of higher dimensional quantum Hall effect, and can be employed to engineer topological pumps via simple twisting and sliding. All aperiodic systems display an intrinsic degree of freedom, called the phason, and here it is shown that the phason space for twisted bilayered systems is a 2-torus. As a consequence, these systems host the physics of 4-dimensional integer quantum Hall effect, which can be accessed by simply sliding the layers relative to each other.
Mathematical modeling to inform vaccination strategies and testing approaches for COVID-19 in nursing homes.

Background Nursing home residents and staff were included in the first phase of COVID-19 vaccination in the United States. Because the primary trial endpoint was vaccine efficacy (VE) against symptomatic disease, there are limited data on the extent to which vaccines protect against SARS-CoV-2 infection and the ability to infect others (infectiousness). Assumptions about VE against infection and infectiousness have implications for changes to infection prevention guidance for vaccinated populations, including testing strategies. Methods We use a stochastic agent-based SEIR model of a nursing home to simulate SARS-CoV-2 transmission. We model three scenarios, varying VE against infection, infectiousness, and symptoms, to understand the expected impact of vaccination in nursing homes, increasing staff vaccination coverage, and different screening testing strategies under each scenario. Results Increasing vaccination coverage in staff decreases total symptomatic cases in the nursing home (among staff and residents combined) in each VE scenario. In scenarios with 50% and 90% VE against infection and infectiousness, increasing staff coverage reduces symptomatic cases among residents. If vaccination only protects against symptoms, and asymptomatic cases remain infectious, increased staff coverage increases symptomatic cases among residents. However, this is outweighed by the reduction in symptomatic cases among staff. Higher frequency testing-more than once weekly-is needed to reduce total symptomatic cases if the vaccine has lower efficacy against infection and infectiousness, or only protects against symptoms. Conclusions Encouraging staff vaccination is not only important for protecting staff, but might also reduce symptomatic cases in residents if a vaccine confers at least some protection against infection or infectiousness.
Dynamic Language Models for Continuously Evolving Content

The content on the web is in a constant state of flux. New entities,issues, and ideas continuously emerge, while the semantics of the existing conversation topics gradually shift. In recent years, pre-trained language models like BERT greatly improved the state-of-the-art for a large spectrum of content understanding tasks.Therefore, in this paper, we aim to study how these language models can be adapted to better handle continuously evolving web content.In our study, we first analyze the evolution of 2013 - 2019 Twitter data, and unequivocally confirm that a BERT model trained on past tweets would heavily deteriorate when directly applied to data from later years. Then, we investigate two possible sources of the deterioration: the semantic shift of existing tokens and the sub-optimal or failed understanding of new tokens. To this end, we both explore two different vocabulary composition methods, as well as propose three sampling methods which help in efficient incremental training for BERT-like models. Compared to a new model trained from scratch offline, our incremental training (a) reduces the training costs, (b) achieves better performance on evolving content, and (c)is suitable for online deployment. The superiority of our methods is validated using two downstream tasks. We demonstrate significant improvements when incrementally evolving the model from a particular base year, on the task of Country Hashtag Prediction, as well as on the OffensEval 2019 task.
The Effect of Moderation on Online Mental Health Conversations.

Many people struggling with mental health issues are unable to access adequate care due to high costs and a shortage of mental health professionals, leading to a global mental health crisis. Online mental health communities can help mitigate this crisis by offering a scalable, easily accessible alternative to in-person sessions with therapists or support groups. However, people seeking emotional or psychological support online may be especially vulnerable to the kinds of antisocial behavior that sometimes occur in online discussions. Moderation can improve online discourse quality, but we lack an understanding of its effects on online mental health conversations.In this work, we leveraged a natural experiment, occurring across 200,000 messages from 7,000 online mental health conversations, to evaluate the effects of moderation on online mental health discussions. We found that participation in group mental health discussions led to improvements in psychological perspective, and that these improvements were larger in moderated conversations. The presence of a moderator increased user engagement, encouraged users to discuss negative emotions more candidly, and dramatically reduced bad behavior among chat participants. Moderation also encouraged stronger linguistic coordination, which is indicative of trust building. In addition, moderators who remained active in conversations were especially successful in keeping conversations on topic. Our findings suggest that moderation can serve as a valuable tool to improve the efficacy and safety of online mental health conversations. Based on these findings, we discuss implications and trade-offs involved in designing effective online spaces for mental health support.
On complementing end-to-end human behavior predictors with planning

High capacity end-to-end approaches for human motion (behavior) prediction have the ability to represent subtle nuances in human behavior, but struggle with robustness to out of distribution inputs and tail events. Planning-based prediction, on the other hand, can reliably output decent-but-not-great predictions: it is much more stable in the face of distribution shift (as we verify in this work), but it has high inductive bias, missing important aspects that drive human decisions, and ignoring cognitive biases that make human behavior suboptimal. In this work, we analyze one family of approaches that strive to get the best of both worlds: use the end-to-end predictor on common cases, but do not rely on it for tail events / out-of-distribution inputs — switch to the planning-based predictor there. We contribute an analysis of different approaches for detecting when to make this switch, using an autonomous driving domain. We find that promising approaches based on ensembling or generative modeling of the training distribution might not be reliable, but that there very simple methods which can perform surprisingly well — including training a classifier to pick up on tell-tale issues in predicted trajectories.
Group Recruiting Events and Gender Stereotypes in Employee Selection

This paper reports the results of multiple studies that together provide converging evidence in support of theory that gender stereotypes bias employee selection during group recruiting events. Specifically, we predict and find that female (male) job candidates who exhibit stereotypically male behaviors receive lower (higher) evaluations during group recruiting events, particularly among male recruiters. Prior research suggests gender stereotypes do not bias employee selection during one-on-one interviews. However, our results suggest that evaluating job candidates in the more social context of group events can have important unintended consequences on employee selection, a key component of the accounting control environment. Given the importance of group recruiting events to inform hiring decisions across organizations such as investment banks and public accounting firms, our results contribute to a better understanding of survey and field evidence suggesting that entry-level male and female employees have different personalities at these organizations which appear to influence their career trajectories.
Estimation and decomposition of rack force for driving on uneven roads

Abstract The force transmitted from the front tires to the steering rack of a vehicle, called the rack force, plays an important role in the function of Electric Power Steering (EPS) systems. Estimates of rack force can be used by EPS to reduce driver effort and enhance driving performance. Further, estimates of the components of rack force can be used to perform targeted compensation to enhance steering feel and vehicle safety. Existing rack force estimators either do not support the estimation of rack force components or are only applicable for driving on flat roads. This paper presents three vehicle and tire model-based rack force estimators that can estimate individual components of rack force as well as the total rack force for driving on uneven roads. The estimators use the same vehicle model but use tire models of differing complexities. The performance of the estimators was validated and compared by performing driving experiments with non-aggressive and aggressive steering maneuvers on roads with low and high frequency profile variations. The results show that the estimation accuracy increases with the complexity of tire model used, especially during aggressive steering maneuvers and high frequency road profile variations. Finally, results from a simulation study are presented to show that the estimators are also capable of estimating the components of rack force.
Thin-film smoothed particle hydrodynamics fluid

We propose a particle-based method to simulate thin-film fluid that jointly facilitates aggressive surface deformation and vigorous tangential flows. We build our dynamics model from the surface tension driven Navier-Stokes equation with the dimensionality reduced using the asymptotic lubrication theory and customize a set of differential operators based on the weakly compressible Smoothed Particle Hydrodynamics (SPH) for evolving pointset surfaces. The key insight is that the compressible nature of SPH, which is unfavorable in its typical usage, is helpful in our application to co-evolve the thickness, calculate the surface tension, and enforce the fluid incompressibility on a thin film. In this way, we are able to two-way couple the surface deformation with the in-plane flows in a physically based manner. We can simulate complex vortical swirls, fingering effects due to Rayleigh-Taylor instability, capillary waves, Newton's interference fringes, and the Marangoni effect on liberally deforming surfaces by presenting both realistic visual results and numerical validations. The particle-based nature of our system also enables it to conveniently handle topology changes and codimension transitions, allowing us to marry the thin-film simulation with a wide gamut of 3D phenomena, such as pinch-off of unstable catenoids, dripping under gravity, merging of droplets, as well as bubble rupture.
Cryogenic spray vaporization in high-velocity helium, argon and nitrogen gasflows

Effects of gas properties on cryogenic liquid-jet atomization in high-velocity helium, nitrogen, and argon gas flows were investigated. Volume median diameter, D(sub v.5e), data were obtained with a scattered-light scanning instrument. By calculating the change in spray drop size, -Delta D(sub v.5)(exp 2), due to droplet vaporization, it was possible to calculate D(sub v.5C). D(sub v.5C) is the unvaporized characteristic drop size formed at the fuel-nozzle orifice. This drop size was normalized with respect to liquid-jet diameter, D(sub O). It was then correlated with several dimensionless groups to give an expression for the volume median diameter of cryogenic LN2 sprays. This expression correlates drop size D(sub v.5c) with aerodynamic and liquid-surface forces so that it can be readily determined in the design of multiphase-flow propellant injectors for rocket combustors.
Discrete Adjoint-Based Design Optimization of Unsteady Turbulent Flows on Dynamic Unstructured Grids

An adjoint-based methodology for design optimization of unsteady turbulent flows on dynamic unstructured grids is described. The implementation relies on an existing unsteady three-dimensional unstructured grid solver capable of dynamic mesh simulations and discrete adjoint capabilities previously developed for steady flows. The discrete equations for the primal and adjoint systems are presented for the backward-difference family of time-integration schemes on both static and dynamic grids. The consistency of sensitivity derivatives is established via comparisons with complex-variable computations. The current work is believed to be the first verified implementation of an adjoint-based optimization methodology for the true time-dependent formulation of the Navier-Stokes equations in a practical computational code. Large-scale shape optimizations are demonstrated for turbulent flows over a tiltrotor geometry and a simulated aeroelastic motion of a fighter jet.
LCP-RIT at SemEval-2021 Task 1: Exploring Linguistic Features for Lexical Complexity Prediction

This paper describes team LCP-RIT’s submission to the SemEval-2021 Task 1: Lexical Complexity Prediction (LCP). The task organizers provided participants with an augmented version of CompLex (Shardlow et al., 2020), an English multi-domain dataset in which words in context were annotated with respect to their complexity using a five point Likert scale. Our system uses logistic regression and a wide range of linguistic features (e.g. psycholinguistic features, n-grams, word frequency, POS tags) to predict the complexity of single words in this dataset. We analyze the impact of different linguistic features on the classification performance and we evaluate the results in terms of mean absolute error, mean squared error, Pearson correlation, and Spearman correlation.
Functorial LCH for immersed Lagrangian cobordisms

For $1$-dimensional Legendrian submanifolds of $1$-jet spaces, we extend the functorality of the Legendrian contact homology DG-algebra (DGA) from embedded exact Lagrangian cobordisms, as in \cite{EHK}, to a class of immersed exact Lagrangian cobordisms by considering their Legendrian lifts as conical Legendrian cobordisms. To a conical Legendrian cobordism $\Sigma$ from $\Lambda_-$ to $\Lambda_+$, we associate an immersed DGA map, which is a diagram $$\alg(\Lambda_+) \stackrel{f}{\rightarrow} \alg(\Sigma) \stackrel{i}{\hookleftarrow} \alg(\Lambda_-), $$ where $f$ is a DGA map and $i$ is an inclusion map. This construction gives a functor between suitably defined categories of Legendrians with immersed Lagrangian cobordisms and DGAs with immersed DGA maps. In an algebraic preliminary, we consider an analog of the mapping cylinder construction in the setting of DG-algebras and establish several of its properties. As an application we give examples of augmentations of Legendrian twist knots that can be induced by an immersed filling with a single double point but cannot be induced by any orientable embedded filling.
Optimizing Unlicensed Band Spectrum Sharing With Subspace-Based Pareto Tracing

To meet the ever-growing demands of data throughput for forthcoming and deployed wireless networks, new wireless technologies like Long-Term Evolution License-Assisted Access (LTE-LAA) operate in shared and unlicensed bands. However, the LAA network must co-exist with incumbent IEEE 802.11 Wi-Fi systems. We consider a coexistence scenario where multiple LAA and Wi-Fi links share an unlicensed band. We aim to improve this coexistence by maximizing the key performance indicators (KPIs) of these networks simultaneously via dimension reduction and multi-criteria optimization. These KPIs are network throughputs as a function of medium access control protocols and physical layer parameters. We perform an exploratory analysis of coexistence behavior by approximating active subspaces to identify low-dimensional structure in the optimization criteria, i.e., few linear combinations of parameters for simultaneously maximizing KPIs. We leverage an aggregate low-dimensional subspace parametrized by approximated active subspaces of throughputs to facilitate multi-criteria optimization. The low-dimensional subspace approximations inform visualizations revealing convex KPIs over mixed active coordinates leading to an analytic Pareto trace of near-optimal solutions.
Rapid deployment of a community engagement study and educational trial via social media: implementation of the UC-COVID study.

In response to the COVID-19 pandemic and associated adoption of scarce resource allocation (SRA) policies, we sought to rapidly deploy a novel survey to ascertain community values and preferences for SRA and to test the utility of a brief intervention to improve knowledge of and values alignment with a new SRA policy. Given social distancing and precipitous evolution of the pandemic, Internet-enabled recruitment was deemed the best method to engage a community-based sample. We quantify the efficiency and acceptability of this Internet-based recruitment for engaging a trial cohort and describe the approach used for implementing a health-related trial entirely online using off-the-shelf tools. We recruited 1971 adult participants (≥ 18 years) via engagement with community partners and organizations and outreach through direct and social media messaging. We quantified response rate and participant characteristics of our sample, examine sample representativeness, and evaluate potential non-response bias. Recruitment was similarly derived from direct referral from partner organizations and broader social media based outreach, with extremely low study entry from organic (non-invited) search activity. Of social media platforms, Facebook was the highest yield recruitment source. Bot activity was present but minimal and identifiable through meta-data and engagement behavior. Recruited participants differed from broader populations in terms of sex, ethnicity, and education, but had similar prevalence of chronic conditions. Retention was satisfactory, with entrance into the first follow-up survey for 61% of those invited. We demonstrate that rapid recruitment into a longitudinal intervention trial via social media is feasible, efficient, and acceptable. Recruitment in conjunction with community partners representing target populations, and with outreach across multiple platforms, is recommended to optimize sample size and diversity. Trial implementation, engagement tracking, and retention are feasible with off-the-shelf tools using preexisting platforms. ClinicalTrials.gov NCT04373135 . Registered on May 4, 2020
Understanding the Effect of Bias in Deep Anomaly Detection

Anomaly detection presents a unique challenge in machine learning, due to the scarcity of labeled anomaly data. Recent work attempts to mitigate such problems by augmenting training of deep anomaly detection models with additional labeled anomaly samples. However, the labeled data often does not align with the target distribution and introduces harmful bias to the trained model. In this paper, we aim to understand the effect of a biased anomaly set on anomaly detection. We formally state the anomaly detection problem as a supervised learning task, and focus on the anomaly detector’s recall at a given false positive rate as the main performance metric. Given two different anomaly score functions, we formally define their difference in performance as the relative scoring bias of the anomaly detectors. Along this line, our work provides two key contributions. We establish the first finite sample rates for estimating the relative scoring bias for deep anomaly detection, and empirically validate our theoretical results on both synthetic and real-world datasets. We also provide extensive empirical study on how a biased training anomaly set affects the anomaly score function and therefore the detection performance on different anomaly classes. Our study demonstrates scenarios in which the biased anomaly set can be useful or problematic, and provides a solid benchmark for future research.