cssi/SciSciGPT-SciSciNet huggingface dataset:
A chromosomal assembly of the soybean cyst nematode genome.

The soybean cyst nematode (Heterodera glycines) is a sedentary plant parasite that exceeds billion USD annually in yield losses. This problem is exacerbated by H. glycines populations overcoming the limited sources of natural resistance in soybean and by the lack of effective and safe alternative treatments. Although there are genetic determinants that render soybeans resistant to nematode genotypes, resistant soybeans are increasingly ineffective because their multiyear usage has selected for virulent H. glycines populations. Successful H. glycines infection relies on the comprehensive re-engineering of soybean root cells into a syncytium, as well as the long-term suppression of host defences to ensure syncytial viability. At the forefront of these complex molecular interactions are effectors, the proteins secreted by H. glycines into host root tissues. The mechanisms that control genomic effector acquisition, diversification, and selection are important insights needed for the development of essential novel control strategies. As a foundation to obtain this understanding, we created a nine-scaffold, 158 Mb pseudomolecule assembly of the H. glycines genome using PacBio, Chicago, and Hi-C sequencing. A Mikado consensus gene prediction produced an annotation of 22,465 genes using short- and long-read expression data. To evaluate assembly and annotation quality, we cross-examined synteny among H. glycines assemblies, and compared BUSCO across related species. To describe the predicted proteins involved in H. glycines' secretory pathway, we contrasted expression between preparasitic and parasitic stages with functional gene information. Here, we present the results from our assembly and annotation of the H. glycines genome and contribute this resource to the scientific community.
Coded Computing for Boolean Functions

The growing size of modern datasets necessitates splitting a large scale computation into smaller computations and operate in a distributed manner for improving overall performance. However, adversarial servers in a distributed computing system deliberately send erroneous data in order to affect the computation for their benefit. Computing Boolean functions is the key component of many applications of interest, e.g., classification problem, verification functions in the blockchain and the design of cryptographic algorithm. In this paper, we consider the problem of computing a Boolean function in which the computation is carried out distributively across several workers with particular focus on security against Byzantine workers. We note that any Boolean function can be modeled as a multivariate polynomial which can have high degree in general. Hence, the recently proposed Lagrange Coded Computing (LCC) can be used to simultaneously provide resiliency, security, and privacy. However, the security threshold (i.e., the maximum number of adversarial workers that can be tolerated) provided by LCC can be extremely low if the degree of the polynomial is high. Our goal is to design an efficient coding scheme which achieves the optimal security threshold. We propose two novel schemes called coded Algebraic normal form (ANF) and coded Disjunctive normal form (DNF). Instead of modeling the Boolean function as a general polynomial, the key idea of the proposed schemes is to model it as the concatenation of some linear functions and threshold functions. The proposed coded ANF and coded DNF outperform LCC by providing the security threshold which is independent of the polynomial’s degree.
Discovering interesting subgraphs in social media networks

Social media data are often modeled as heterogeneous graphs with multiple types of nodes and edges. We present a discovery algorithm that first chooses a "background" graph based on a user's analytical interest and then automatically discovers subgraphs that are structurally and content-wise distinctly different from the background graph. The technique combines the notion of a group-by operation on a graph and the notion of subjective interestingness, resulting in an automated discovery of interesting subgraphs. Our experiments on a socio-political database show the effectiveness of our technique.
Equilibria in infinite games of incomplete information

The notion of communication equilibrium extends Aumann’s (J Math Econ 1:67–96, 1974, https://doi.org/10.1016/0304-4068(74)90037-8 ) correlated equilibrium concept for complete information games to the case of incomplete information. This paper shows that this solution concept has the following property: for the class of incomplete information games with compact metric type and action spaces, and with payoff functions jointly measurable and continuous in actions, limits of Bayes-Nash equilibria of finite approximations to an infinite game are communication equilibria (and, in general, not Bayes-Nash equilibria) of the limit game. Stinchcombe’s (J Econ Theory 146:638–655, 2011b, https://doi.org/10.1016/j.jet.2010.12.006 ) extension of Aumann’s (J Math Econ 1:67–96, 1974, https://doi.org/10.1016/0304-4068(74)90037-8 ) solution concept to the case of incomplete information fails to satisfy this condition.
Identifying homeless population needs in the Emergency Department using Community-Based Participatory Research.

Annually 1.5 million Americans face housing insecurity, and compared to their domiciled counterparts are three times more likely to utilize the Emergency Department (ED). Community Based Participatory Research (CBPR) methods have been employed in underserved populations, but use in the ED has been limited. We employed CBPR in an urban American hospital with a primary goal of improved linkage to care, reduced ED recidivism, and improved homeless health care. A needs analysis was performed using semi-structured individual interviews with participants experiencing homelessness as well as with stakeholders. Results were analyzed using principles of grounded theory. At the end of the interviews, respondents were invited to join the “CBPR team”. At CBPR team meetings, results from interviews were expounded upon and discussions on intervention development were conducted. Twenty-five stakeholders were interviewed including people experiencing housing insecurity, ED staff, inpatient staff, and community shelters and services. Three themes emerged from the interviews. First, the homeless population lack access to basic needs, thus management of medical needs must be managed alongside social ones. Second, specific challenges to address homeless needs in the ED include episodic care, inability to recognize housing insecurity, timely involvement of ancillary staff, and provider attitudes towards homeless patients affecting quality of care. Lastly, improved discharge planning and communication with outside resources is essential to improving homeless health and decreasing ED overutilization. A limitation of results is difficulty for participants experiencing homelessness to commit to regular CBPR meetings, as well as possible bias towards social networks influencing included stakeholders. CBPR is a promising approach to address gaps in homeless health care as it provides a comprehensive view incorporating various critical perspectives. Key ED-based interventions addressing recidivism include improved identification of housing insecurity, reinforced relationships between ED and community resources, and better discharge planning.
Two-fluid model of rf current condensation in magnetic islands

The stabilization of tearing modes with rf waves is subject to a nonlinear effect, termed rf current condensation, that has the potential to greatly enhance and localize current driven within magnetic islands. Here we extend previous investigations of this effect with a two fluid model that captures the balance of diffusive and thermal equilibration processes within the island. We show that the effective power and resulting strength of the condensation effect can be greatly enhanced by avoiding collisional heat loss to the ions. The relative impact of collisions on the overall power balance within the island depends on the ratio of the characteristic diffusion timescale and the electron–ion equilibration time, rather than the latter alone. Although relative heat loss to ions increases with island size, the heating efficiency does as well. In particular, we show that the latter safely dominates for large deposition profiles, as is typically the case for lower hybrid current drive. This supports the possibility of passive stabilization of neoclassical tearing modes without the precise aiming of the rf waves required for electron cyclotron current drive stabilization.
Personalization Paradox in Behavior Change Apps: Lessons from a Social Comparison-Based Personalized App for Physical Activity

Social comparison-based features are widely used in social computing apps. However, most existing apps are not grounded in social comparison theories and do not consider individual differences in social comparison preferences and reactions. This paper is among the first to automatically personalize social comparison targets. In the context of an m-health app for physical activity, we use artificial intelligence (AI) techniques of multi-armed bandits. Results from our user study (n=53) indicate that there is some evidence that motivation can be increased using the AI-based personalization of social comparison. The detected effects achieved small-to-moderate effect sizes, illustrating the real-world implications of the intervention for enhancing motivation and physical activity. In addition to design implications for social comparison features in social apps, this paper identified the personalization paradox, the conflict between user modeling and adaptation, as a key design challenge of personalized applications for behavior change. Additionally, we propose research directions to mitigate this Personalization Paradox.
Do Aquatic Sports Make Much of a Splash

The sports of water polo, swimming, and diving have not received as much attention as other high-profile sports like football. Yet, top athletes in these sports earn sizeable incomes and the organizing bodies of these sports exercise considerable power over economic outcomes. What follows is an introduction to some of the interesting economic questions involving aquatic sport—focusing on water polo and swim.
Iron activates microglia and directly stimulates indoleamine-2,3-dioxygenase activity in the N171-82Q mouse model of Huntington's disease.

Huntington’s disease (HD) is a neurodegenerative disorder caused by a dominant CAG-repeat expansion in the huntingtin gene. Microglial activation is a key feature of HD pathology, and is present before clinical disease onset. The kynurenine pathway (KP) of tryptophan degradation is activated in HD, and is thought to contribute to disease progression. Indoleamine-2,3-dioxygenase (IDO) catalyzes the first step in this pathway; this and other pathway enzymes reside with microglia. While HD brain microglia accumulate iron, the role of iron in promoting microglial activation and KP activity is unclear. Here we utilized the neonatal iron supplementation model to investigate the relationship between iron, microglial activation and neurodegeneration in adult HD mice. We show in the N171-82Q mouse model of HD microglial morphologic changes consistent with immune activation. Neonatal iron supplementation in these mice promoted neurodegeneration and resulted in additional microglial activation in adults as determined by increased soma volume and decreased process length. We further demonstrate that iron activates IDO, both in brain lysates and purified recombinant protein (EC50 = 1.24 nM). Brain IDO activity is increased by HD. Neonatal iron supplementation further promoted IDO activity in cerebral cortex, altered KP metabolite profiles, and promoted HD neurodegeneration as measured by brain weights and striatal volumes. Our results demonstrate that dietary iron is an important activator of microglia and the KP pathway in this HD model, and that this occurs in part through a direct effect on IDO. The findings are relevant to understanding how iron promotes neurodegeneration in HD.
Corporate venture capital, disclosure, and financial reporting

Corporate venture capital (CVC) is one of the most important avenues for corporate innovation today, yet there can be unintended consequences related to anticompetitive practices. Recent scrutiny from regulators and policymakers underscores their growing desire for more information about firms’ CVC investment activities, even of those previously believed to be too small to matter. Using a comprehensive sample of 115 publicly-listed U.S. parent firms that owned 133 CVC firms, we document that for almost half of the firm-years in our sample, parent firms do not disclose any information about their CVC program. Among the parent firms that do disclose their CVC activities, we find that they disclose less when they make investments in industries outside of their core industry. Firms with a CVC program, relative to similar firms without a CVC program, tend to make more future acquisitions and report less future goodwill asset write-downs. This study documents the current state of affairs with respect to the amount of publicly available CVC information for a large sample of firms, an important starting point for regulators and policymakers to conduct an informed debate about whether disclosure requirements should be expanded.
Entry Dispersion Analysis for the Genesis Sample Return Capsule

Genesis will be the first mission to return samples from beyond the Earth-Moon system. The spacecraft will be inserted into a halo orbit about the L1 (Sun-Earth) libration point where it will remain for two years collecting solar wind particles. Upon Earth return, the sample return capsule, which is passively controlled, will descend under parachute to Utah. The present study describes the analysis of the entry, descent, and landing scenario of the returning sample capsule. The robustness of the entry sequence is assessed through a Monte Carlo dispersion analysis where the impact of off-nominal conditions is ascertained. The dispersion results indicate that the capsule attitude excursions near peak heating and drogue chute deployment are within Genesis mission limits. Additionally, the size of the resulting 3-sigma landing ellipse is 47.8 km in downrange by 15.2 km in crossrange, which is within the Utah Test and Training Range boundaries.
CD45 Pre-Exclusion from the Tips of Microvilli Establishes a Phosphatase-Free Zone for Early TCR Triggering

The tyrosine phosphatase CD45 is a major gatekeeper for restraining T cell activation. Its exclusion from the immunological synapse (IS) is crucial for TCR signal transduction. Here, we used expansion super-resolution microscopy to reveal that CD45 is pre-excluded from the tips of microvilli on primary T cells prior to antigen encounter. This pre-exclusion was diminished by depleting cholesterol or by engineering the transmembrane domain of CD45 to increase its membrane integration length, but was independent of the CD45 extracellular domain. We further show that brief microvilli-mediated contacts can induce Ca 2+ influx in mouse antigen-specific T cells engaged by antigen-pulsed APCs. We propose that the absence of CD45 phosphatase activity at the tips of microvilli enables or facilitates TCR triggering from brief T cell-APC contacts before formation of a stable IS, and that these microvilli-mediated contacts represent the earliest step in the initiation of a T cell adaptive immune response.
Model-based Reinforcement Learning for Continuous Control with Posterior Sampling

Balancing exploration and exploitation is crucial in reinforcement learning (RL). In this paper, we study model-based posterior sampling for reinforcement learning (PSRL) in continuous state-action spaces theoretically and empirically. First, we show the first regret bound of PSRL in continuous spaces which is polynomial in the episode length to the best of our knowledge. With the assumption that reward and transition functions can be modeled by Bayesian linear regression, we develop a regret bound of $\tilde{O}(H^{3/2}d\sqrt{T})$, where $H$ is the episode length, $d$ is the dimension of the state-action space, and $T$ indicates the total time steps. This result matches the best-known regret bound of non-PSRL methods in linear MDPs. Our bound can be extended to nonlinear cases as well with feature embedding: using linear kernels on the feature representation $\phi$, the regret bound becomes $\tilde{O}(H^{3/2}d_{\phi}\sqrt{T})$, where $d_\phi$ is the dimension of the representation space. Moreover, we present MPC-PSRL, a model-based posterior sampling algorithm with model predictive control for action selection. To capture the uncertainty in models, we use Bayesian linear regression on the penultimate layer (the feature representation layer $\phi$) of neural networks. Empirical results show that our algorithm achieves the state-of-the-art sample efficiency in benchmark continuous control tasks compared to prior model-based algorithms, and matches the asymptotic performance of model-free algorithms.
Linear Semantics in Generative Adversarial Networks

Generative Adversarial Networks (GANs) are able to generate high-quality images, but it remains difficult to explicitly specify the semantics of synthesized images. In this work, we aim to better understand the semantic representation of GANs, and thereby enable semantic control in GAN’s generation process. Interestingly, we find that a well-trained GAN encodes image semantics in its internal feature maps in a surprisingly simple way: a linear transformation of feature maps suffices to extract the generated image semantics. To verify this simplicity, we conduct extensive experiments on various GANs and datasets; and thanks to this simplicity, we are able to learn a semantic segmentation model for a trained GAN from a small number (e.g., 8) of labeled images. Last but not least, leveraging our finding, we propose two few-shot image editing approaches, namely Semantic-Conditional Sampling and Semantic Image Editing. Given a trained GAN and as few as eight semantic annotations, the user is able to generate diverse images subject to a user-provided semantic layout, and control the synthesized image semantics. We have made the code publicly available1.
Cooling of High-Pressure Rocket Thrust Chambers with Liquid Oxygen

An experimental program using hydrogen and oxygen as the propellants and supercritical liquid oxygen (LOX) as the coolant was conducted at 4.14 and 8.274 MN/square meters (600 and 1200 psia) chamber pressure. Data on the following are presented: the effect of LOX leaking into the combustion region through small cracks in the chamber wall; and verification of the supercritical oxygen heat transfer correlation developed from heated tube experiments; A total of four thrust chambers with throat diameters of 0.066 m were tested. Of these, three were cyclically tested to 4.14 MN/square meters (600 psia) chamber pressure until a crack developed. One had 23 additional hot cycles accumulated with no apparent metal burning or distress. The fourth chamber was operated at 8.274 MN/square meters (1200 psia) pressure to obtain steady state heat transfer data. Wall temperature measurements confirmed the heat transfer correlation.
An Improved Model for Voicing Silent Speech

In this paper, we present an improved model for voicing silent speech, where audio is synthesized from facial electromyography (EMG) signals. To give our model greater flexibility to learn its own input features, we directly use EMG signals as input in the place of hand-designed features used by prior work. Our model uses convolutional layers to extract features from the signals and Transformer layers to propagate information across longer distances. To provide better signal for learning, we also introduce an auxiliary task of predicting phoneme labels in addition to predicting speech audio features. On an open vocabulary intelligibility evaluation, our model improves the state of the art for this task by an absolute 25.8%.
Intent Classification and Slot Filling for Privacy Policies

Understanding privacy policies is crucial for users as it empowers them to learn about the information that matters to them. Sentences written in a privacy policy document explain privacy practices, and the constituent text spans convey further specific information about that practice. We refer to predicting the privacy practice explained in a sentence as intent classification and identifying the text spans sharing specific information as slot filling. In this work, we propose PolicyIE, an English corpus consisting of 5,250 intent and 11,788 slot annotations spanning 31 privacy policies of websites and mobile applications. PolicyIE corpus is a challenging real-world benchmark with limited labeled examples reflecting the cost of collecting large-scale annotations from domain experts. We present two alternative neural approaches as baselines, (1) intent classification and slot filling as a joint sequence tagging and (2) modeling them as a sequence-to-sequence (Seq2Seq) learning task. The experiment results show that both approaches perform comparably in intent classification, while the Seq2Seq method outperforms the sequence tagging approach in slot filling by a large margin. We perform a detailed error analysis to reveal the challenges of the proposed corpus.
Target-normal single spin asymmetries measured with positrons

Two-photon exchange and the larger class of hadronic box diagrams are difficult to calculate without a large degree of model-dependence. At the same time, these processes are significant radiative corrections in parity-violating electron scattering, in neutron decay, and may even be responsible for the proton’s form factor ratio discrepancy. New kinds of experimental data are needed to help constrain models and guide future box-diagram calculations. The target-normal single spin asymmetry, $$A_n$$ , formed with an unpolarized beam scattering from a target that is polarized normal to the scattering plane, is sensitive to the imaginary part of the two-photon exchange amplitude, and can provide a valuable constraint. A measurement with both electrons and positrons can reduce sources of experimental error, and distinguish between the effects of two-photon exchange and those of time-reversal symmetry violation. This article describes a proposed experiment in Hall A, using the new Super Big-Bite Spectrometer that can cover a momentum transfer range in the critical zone of uncertainty between where hadronic calculations and those based on partonic degrees of freedom are expected to be accurate.
An Exhaustive Study of Using Commercial LTE Network for UAV Communication in Rural Areas

Unmanned aerial vehicles (UAVs) have been increasingly used in a wide area of military and civilian applications such as data collection and monitoring. A reliable network for command and control, communication, and data transfer is crucial, not only for mission purposes but also for safety concerns. The already deployed cellular networks are appropriate candidates for UAV communication given the solid security and wide coverage of these networks. However, the reliability of such networks needs a comprehensive investigation. In this paper, we use the long-term evolution (LTE) network as the infrastructure for drone communication and data transfer, in a rural area. We study the communication characteristics of an LTE-connected drone during low-altitude flights, for different altitudes and UAV speeds. We show that, in such areas, the higher elevation benefits from a better signal quality and experiences a fewer number of handover processes. Higher speed flights also slightly degrade the communication performance 1.
Molecular Evolutionary Dynamics of Energy Limited Microorganisms.

Microorganisms have the unique ability to survive extended periods of time in environments with extremely low levels of exploitable energy. To determine the extent that energy limitation affects microbial evolution, we examined the molecular evolutionary dynamics of a phylogenetically diverse set of taxa over the course of 1,000 days. We found that periodic exposure to energy limitation affected the rate of molecular evolution, the accumulation of genetic diversity, and the rate of extinction. We then determined the degree that energy limitation affected the spectrum of mutations as well as the direction of evolution at the gene level. Our results suggest that the initial depletion of energy altered the direction and rate of molecular evolution within each taxon, though after the initial depletion the rate and direction did not substantially change. However, this consistent pattern became diminished when comparisons were performed across phylogenetically distant taxa, suggesting that although the dynamics of molecular evolution under energy limitation are highly generalizable across the microbial tree of life, the targets of adaptation are specific to a given taxon.
Improving Match Rates in Dating Markets through Assortment Optimization

Motivated by our collaboration with a major US online dating company, we study how a platform should dynamically select the set of potential partners to show to each user in each period to maximize the expected number of matches in a time horizon, considering that a match is formed only after two users like each other, possibly in different periods. Increasing match rates is a prevalent objective among online platforms. We provide insights on how to leverage users? preferences and behavior towards this end. Our proposed algorithm was piloted by our collaborator in major cities in the US. We introduce a model of a dynamic matching market mediated by a platform. The platform hosts a set of users and must decide, in each period, what subset of profiles to show to each user to maximize the overall expected number of matches. Each period, users log in with some time-dependent probability and, conditional on logging in, observe a set of profiles-an assortment-that satisfies the constraints imposed by the platform. Then, users decide whether to like or not like each profile in their assortment based on their preferences. If two users mutually like each other, possibly in different periods, a match is generated. Our goal is to find an algorithm to maximize the total expected number of matches generated by the platform over an entire time horizon. We show that the platform's problem is computationally hard.
Zika virus infection of pregnant ifnar1-/- mice triggers strain-specific differences in fetal outcomes

Zika virus (ZIKV) is a flavivirus that causes a constellation of adverse fetal outcomes collectively termed congenital Zika syndrome (CZS). However, not all pregnancies exposed to ZIKV result in an infant with apparent defects. During the 2015 to 2016 American outbreak of ZIKV, CZS rates varied by geographic location. The underlying mechanisms responsible for this heterogeneity in outcomes have not been well defined. Therefore, we sought to characterize and compare the pathogenic potential of multiple Asian-/American-lineage ZIKV strains in an established Ifnar1-/- pregnant mouse model. Here, we show significant differences in the rate of fetal demise following maternal inoculation with ZIKV strains from Puerto Rico, Panama, Mexico, Brazil, and Cambodia. Rates of fetal demise broadly correlated with maternal viremia but were independent of fetus and placenta virus titer, indicating that additional underlying factors contribute to fetal outcome. Our results, in concert with those from other studies, suggest that subtle differences in ZIKV strains may have important phenotypic impacts. With ZIKV now endemic in the Americas, greater emphasis needs to be placed on elucidating and understanding the underlying mechanisms that contribute to fetal outcome. IMPORTANCE Zika virus (ZIKV) transmission has been reported in 87 countries and territories around the globe. ZIKV infection during pregnancy is associated with adverse fetal outcomes, including birth defects, microcephaly, neurological complications, and even spontaneous abortion. Rates of adverse fetal outcomes vary between regions, and not every pregnancy exposed to ZIKV results in birth defects. Not much is known about how or if the infecting ZIKV strain is linked to fetal outcomes. Our research provides evidence of phenotypic heterogeneity between Asian-/American-lineage ZIKV strains and provides insight into the underlying causes of adverse fetal outcomes. Understanding ZIKV strain-dependent pathogenic potential during pregnancy and elucidating underlying causes of diverse clinical sequelae observed during human infections is critical to understanding ZIKV on a global scale.
Monetary transmission in money markets: The not-so-elusive missing piece of the puzzle

Abstract We investigate the effects of U.S. monetary policy shocks from alternative policy indicators for a modern sample encompassing 1988–2020. The choice of the Wu and Xia (2016) shadow federal funds rate leads to persistent price puzzles. These puzzles arise despite inclusion of the usual suspect fixes such as commodity prices, federal funds futures and forward rate data. We find they occur at monthly and quarterly frequencies. We consider alternative indicators with the same broad monetary aggregates Keating et al. (2019) employed in their investigation of a historical sample. They provide a consistent resolution of the price puzzle and they do not require the ad hoc inclusion of commodity prices or futures data. This price puzzle correction is not a feature of our time-varying approach as it also obtains from constant parameter econometric estimation. Our analysis suggests monetary policy has transmitted substantial expansionary effects in money markets in the aftermath of the 2007 Financial Crisis and the decade that followed.
Kiobel's Broader Significance: Implications for International Legal Theory

The U.S. Supreme Court's decision in Kiobel v. Royal Dutch Petroleum Co. has ushered in a new era for human rights enforcement. Unanimously, the Court ended so–called foreigncubed human rights cases, that is, litigation where foreign plaintiffs sue foreign defendants for activity occurring abroad. The broadest form of universal civil jurisdiction that the Second Circuit's decision in Filartiga v.Pena–Irala once appeared to promise is over. Alien Tort Statute (ATS) litigation, while not foreclosed, has become more limited.
Age-Optimal Low-Power Status Update over Time-Correlated Fading Channel

In this paper, we consider transmission scheduling in a status update system, where updates are generated periodically and transmitted over a Gilbert-Elliott fading channel. The goal is to minimize the long-run average age of information (AoI) at the destination under an average energy constraint. The channel state is revealed by the feedback (Ack/Nack) of a transmission; while it remains unknown if there is no transmission. Thus, we have to design a scheduling policy that balances tradeoffs across energy, AoI, channel exploration, and channel exploitation. The problem is formulated as a constrained partially observable Markov decision process problem (POMDP). We show that the optimal policy is a randomized mixture of no more than two stationary deterministic policies each of which is of a threshold-type in the belief on the channel. We propose a finite-state approximation for our infinite-state belief MDP and show convergence. Based on the theoretical insights gained from studying this problem, we develop an optimal algorithm using the structure of the problem.
Invasive Grass Dominance over Native Forbs Is Linked to Shifts in the Bacterial Rhizosphere Microbiome

Rhizosphere microbiomes have received growing attention in recent years for their role in plant health, stress tolerance, soil nutrition, and invasion. Still, relatively little is known about how these microbial communities are altered under plant competition, and even less about whether these shifts are tied to competitive outcomes between native and invasive plants. We investigated the structure and diversity of rhizosphere bacterial and fungal microbiomes of native annual forbs and invasive annual grasses grown in a shade-house both individually and in competition using high-throughput amplicon sequencing of the bacterial 16S rRNA gene and the fungal ITS region. We assessed how differentially abundant microbial families correlate to plant biomass under competition. We find that bacterial diversity and structure differ between native forbs and invasive grasses, but fungal diversity and structure do not. Furthermore, bacterial community structures under competition are distinct from individual bacterial community structures. We also identified five bacterial families that varied in normalized abundance between treatments and that were correlated with plant biomass under competition. We speculate that invasive grass dominance over these natives may be partially due to effects on the rhizosphere community, with changes in specific bacterial families potentially benefiting invaders at the expense of natives.
In the Name of the Father? Fertility, Religion, and Child Naming in the Demographic Transition

This article shows that parents reveal information about their fertility behavior through how they name their children. I arrive at this finding from a detailed examination of the net fertility of 130,000 married couples in Ireland, a country known for its historically high fertility rate, circa 1911. After stringently accounting for couples' occupation, religion, and location, I find higher fertility rates among couples who chose distinctly Catholic names and traditional names for their children, with the latter being particularly important. Exposure to towns and cities lowered net fertility and weakened preferences for traditional and Catholic names. Cumulatively, these findings highlight the role of traditional rural norms over explicitly religious influences in driving high fertility rates in Ireland. The impact of towns and cities in reducing net fertility suggests that Ireland's sluggish urbanization was a key factor in its high historical fertility rate.
KNOW YOUR MEME AND THE HOMOGENIZATION OF WEB HISTORY

Know Your Meme (KYM) is a website devoted to compiling histories, definitions, and examples of internet memes. In the last decade, KYM has become popular among researchers, educators, and day-to-day Web users to understand memes and their meanings. As a result, it has become instrumental in establishing dominant histories of memes on the Web. This paper uses a discursive interface analysis of the KYM website along with the examples of Pepe the Frog, OK Boomer, and niche Facebook meme groups to demonstrate how the website constructs itself as a cultural authority to define and classify memes, and that an overreliance on KYM can have significant stakes. It may overlook entire uses of the meme, potentially downplay harmful ideologies, and generally imply the possibility for a meme to have a single primary meaning. I argue that an overreliance on KYM without acknowledging its limitations tends to overlook the essential plurality of the Web and instead implies a singular history of memes as an element of internet culture. However, KYM can still be a useful resource and to that end, ultimately, I conclude that we should move toward defining KYM as, “a curated collection of user-submitted meme instances and partially crowdsourced definitions.” While KYM is undeniably a useful resource, it is important that those of us who study the histories of the Web are mindful about how we lean upon this particular website and situate it within our work.
The Flaws of Policies Requiring Human Oversight of Government Algorithms.

Policymakers around the world are increasingly considering how to prevent government uses of algorithms from producing injustices. One mechanism that has become a centerpiece of global efforts to regulate government algorithms is to require human oversight of algorithmic decisions. Despite the widespread turn to human oversight, these policies rest on an uninterrogated assumption: that people are able to oversee algorithmic decision-making. In this article, I survey 40 policies that prescribe human oversight of government algorithms and find that they suffer from two significant flaws. First, evidence suggests that people are unable to perform the desired oversight functions. Second, as a result of the first flaw, human oversight policies legitimize government uses of faulty and controversial algorithms without addressing the fundamental issues with these tools. Thus, rather than protect against the potential harms of algorithmic decision-making in government, human oversight policies provide a false sense of security in adopting algorithms and enable vendors and agencies to shirk accountability for algorithmic harms. In light of these flaws, I propose a more stringent approach for determining whether and how to incorporate algorithms into government decision-making. First, policymakers must critically consider whether it is appropriate to use an algorithm at all in a specific context. Second, before deploying an algorithm alongside human oversight, agencies or vendors must conduct preliminary evaluations of whether people can effectively oversee the algorithm.
The local information of equivariant sheaves and elliptic difference equations

We study the singularities of algebraic difference equations on curves from the point of view of equivariant sheaves. We propose a definition for the formal local type of an equivariant sheaf at a point in the case of a reduced curve acted on by a group which is virtually the integers. We show that with this definition, equivariant sheaves can be glued from an “open cover”. Precisely, we show that an equivariant sheaf can be uniquely recovered from the following data: the restriction to the complement of a point, the local type at the point itself, and an isomorphism between the two over the punctured neighborhood of said point. We study symmetric elliptic difference equations (“elliptic equations” from now on) from this point of view. We consider several natural notions for an algebraic version of symmetric elliptic difference equations, i.e. symmetric elliptic difference modules (“elliptic modules”). We show that different versions are not equivalent, but we detail how they are related: all the versions embed fully faithfully into the same category of equivariant sheaves. This implies that we can use the theory for equivariant sheaves to study singularities of elliptic equations as well. One reason to study elliptic equations is that they generalize, and degenerate to, (q-)difference equations (i.e. equivariant sheaves) and differential equations (i.e. D-modules) on the projective line. We discuss this from the elliptic module point of view, which requires studying elliptic modules on singular curves. We study the relation between elliptic modules on singular curves and their normalization. We show that for modules which are flat at the singular points there is an equivalence and we give examples showing that this cannot be improved upon.
Fusing RGBD Tracking and Segmentation Tree Sampling for Multi-Hypothesis Volumetric Segmentation

Despite rapid progress in scene segmentation in recent years, 3D segmentation methods are still limited when there is severe occlusion. The key challenge is estimating the segment boundaries of (partially) occluded objects, which are inherently ambiguous when considering only a single frame. In this work, we propose Multihypothesis Segmentation Tracking (MST), a novel method for volumetric segmentation in changing scenes, which allows scene ambiguity to be tracked and our estimates to be adjusted over time as we interact with the scene. Two main innovations allow us to tackle this difficult problem: 1) A novel way to sample possible segmentations from a segmentation tree; and 2) A novel approach to fusing tracking results with multiple segmentation estimates. These methods allow MST to track the segmentation state over time and incorporate new information, such as new objects being revealed. We evaluate our method on several cluttered tabletop environments in simulation and reality. Our results show that MST outperforms baselines in all tested scenes.
AdaGrasp: Learning an Adaptive Gripper-Aware Grasping Policy

This paper aims to improve robots’ versatility and adaptability by allowing them to use a large variety of end- effector tools and quickly adapt to new tools. We propose AdaGrasp, a method to learn a single grasping policy that generalizes to novel grippers. By training on a large collection of grippers, our algorithm is able to acquire generalizable knowledge of how different grippers should be used in various tasks. Given a visual observation of the scene and the gripper, AdaGrasp infers the possible grasp poses and their grasp scores by computing the cross convolution between the shape encodings of the gripper and scene. Intuitively, this cross convolution operation can be considered as an efficient way of exhaustively matching the scene geometry with gripper geometry under different grasp poses (i.e., translations and orientations), where a good "match" of 3D geometry will lead to a successful grasp. We validate our methods in both simulation and real- world environments. Our experiment shows that AdaGrasp significantly outperforms the existing multi-gripper grasping policy method, especially when handling cluttered environments and partial observations. Code and Data are available at https://adagrasp.cs.columbia.edu.
A Simple and Effective Method To Eliminate the Self Language Bias in Multilingual Representations

Language agnostic and semantic-language information isolation is an emerging research direction for multilingual representations models. We explore this problem from a novel angle of geometric algebra and semantic space. A simple but highly effective method “Language Information Removal (LIR)” factors out language identity information from semantic related components in multilingual representations pre-trained on multi-monolingual data. A post-training and model-agnostic method, LIR only uses simple linear operations, e.g. matrix factorization and orthogonal projection. LIR reveals that for weak-alignment multilingual systems, the principal components of semantic spaces primarily encodes language identity information. We first evaluate the LIR on a cross-lingual question answer retrieval task (LAReQA), which requires the strong alignment for the multilingual embedding space. Experiment shows that LIR is highly effectively on this task, yielding almost 100% relative improvement in MAP for weak-alignment models. We then evaluate the LIR on Amazon Reviews and XEVAL dataset, with the observation that removing language information is able to improve the cross-lingual transfer performance.
An Assessment of Flap and Main Landing Gear Noise Abatement Concepts

A detailed assessment of the acoustic performance of several noise reduction concepts for aircraft flaps and landing gear is presented. Consideration is given to the best performing concepts within the suite of technologies that were evaluated in the NASA Langley Research Center 14- by 22-Foot Subsonic Tunnel using an 18 percent scale, semi-span, high-fidelity Gulfstream aircraft model as a test bed. Microphone array measurements were obtained with the model in a landing configuration (flap deflected 39 degrees and the main landing gear deployed or retracted). The effectiveness of each concept over the range of pitch angles, speeds, and directivity angles tested is presented. Comparison of the acoustic spectra, obtained from integration of the beamform maps between the untreated baseline and treated configurations, clearly demonstrates that the flap and gear concepts maintain noise reduction benefits over the entire range of the directivity angles tested.
Bond-Peierls polaron: Moderate mass enhancement and current-carrying ground state

We study polarons in the one-dimensional Bond-Peierls electron-phonon model in which phonons on bonds of a lattice modulate the hopping of electrons between lattice sites and contrast the results to those known for the breathing-mode Peierls problem. By inspecting the atomic limit, we show that polaronic dressing and mass enhancement of Bond-Peierls polarons depend on the momentum dependence of the phonons. For dispersionless phonons, Bond-Peierls polarons are perfectly localized in the atomic limit, unlike their breathing-mode counterparts because the carrier creates a string of phonon excitations that can only be annihilated via processes that retrace the carrier to its original site. However, inclusion of phonon dispersion leads to a nondivergent polaron mass even in the atomic limit and depending on the form of the phonon dispersion may lead to a transition to a non-zero-momentum ground state akin to that found in the breathing-mode Peierls model.
Multi-Agent Consensus Subject to Communication and Privacy Constraints

We consider a multi-agent consensus problem in the presence of adversarial agents. The adversaries are able to listen to the inter-agent communications and try to estimate the state of the agents. The agents have a limited bit-rate for communication and are required to quantize the transmitted signal in order to meet the bitrate constraint of the communication channel. We propose a consensus protocol that is protected against the adversaries, i.e., the expected mean-square error of the adversary state estimate is lower bounded. In order to deal with the bit-rate constraint, we propose a dynamic quantization scheme that guarantees protected consensus.
Navigating the Kaleidoscope of COVID-19 Misinformation Using Deep Learning

Irrespective of the success of the deep learning-based mixed-domain transfer learning approach for solving various Natural Language Processing tasks, it does not lend a generalizable solution for detecting misinformation from COVID-19 social media data. Due to the inherent complexity of this type of data, caused by its dynamic (context evolves rapidly), nuanced (misinformation types are often ambiguous), and diverse (skewed, fine-grained, and overlapping categories) nature, it is imperative for an effective model to capture both the local and global context of the target domain. By conducting a systematic investigation, we show that: (i) the deep Transformer-based pre-trained models, utilized via the mixed-domain transfer learning, are only good at capturing the local context, thus exhibits poor generalization, and (ii) a combination of shallow network-based domain-specific models and convolutional neural networks can efficiently extract local as well as global context directly from the target data in a hierarchical fashion, enabling it to offer a more generalizable solution.