cssi/SciSciGPT-SciSciNet huggingface dataset:
Transposable elements contribute to cell and species-specific chromatin looping and gene regulation in mammalian genomes.

Chromatin looping is important for gene regulation, and studies of 3D chromatin structure across species and cell types have improved our understanding of the principles governing chromatin looping. However, 3D genome evolution and its relationship with natural selection remains largely unexplored. In mammals, the CTCF protein defines the boundaries of most chromatin loops, and variations in CTCF occupancy are associated with looping divergence. While many CTCF binding sites fall within transposable elements (TEs), their contribution to 3D chromatin structural evolution is unknown. Here we report the relative contributions of TE-driven CTCF binding site expansions to conserved and divergent chromatin looping in human and mouse. We demonstrate that TE-derived CTCF binding divergence may explain a large fraction of variable loops. These variable loops contribute significantly to corresponding gene expression variability across cells and species, possibly by refining sub-TAD-scale loop contacts responsible for cell-type-specific enhancer-promoter interactions. A fraction of mammalian CTCF binding sites fall within transposable elements (TEs) but their contribution to the evolution of 3D chromatin structure is unknown. Here the authors investigate the effect of TE-driven CTCF binding site expansions on chromatin looping in humans and mice, and provide evidence that TEs contribute to cell-specific and species-specific chromatin looping diversity and variable gene regulation in mammalian genomes.
Functional anomaly mapping reveals local and distant dysfunction caused by brain lesions

The lesion method has been important for understanding brain-behavior relationships in humans, but has previously used maps based on structural damage. Lesion measurement based on structural damage may label partly damaged but functional tissue as abnormal, and moreover, ignores distant dysfunction in structurally intact tissue caused by deafferentation, diaschisis, and other processes. A reliable method to map functional integrity of tissue throughout the brain would provide a valuable new approach to measuring lesions. Here, we use machine learning on four dimensional resting state fMRI data obtained from left-hemisphere stroke survivors in the chronic period of recovery and control subjects to generate graded maps of functional anomaly throughout the brain in individual patients. These functional anomaly maps identify areas of obvious structural lesions and are stable across multiple measurements taken months and even years apart. Moreover, the maps identify functionally anomalous regions in structurally intact tissue, providing a direct measure of remote effects of lesions on the function of distant brain structures. Multivariate lesion-behavior mapping using functional anomaly maps replicates classic behavioral localization, identifying inferior frontal regions related to speech fluency, lateral temporal regions related to auditory comprehension, parietal regions related to phonology, and the hand area of motor cortex and descending corticospinal pathways for hand motor function. Further, this approach identifies relationships between tissue function and behavior distant from the structural lesions, including right premotor dysfunction related to ipsilateral hand movement, and right cerebellar regions known to contribute to speech fluency. Brain-wide maps of the functional effects of focal lesions could have wide implications for lesion-behavior association studies and studies of recovery after brain injury.
Steady-State Analysis of the Join-the-Shortest-Queue Model in the Halfin–Whitt Regime

This paper studies the steady-state properties of the join-the-shortest-queue model in the Halfin–Whitt regime. We focus on the process tracking the number of idle servers and the number of servers...
Estimating Mixed Memberships With Sharp Eigenvector Deviations

We consider the problem of estimating community memberships of nodes in a network, where every node is associated with a vector determining its degree of membership in each community. Existing prov...
Exploring the Magnetic Properties of the Largest Single-Molecule Magnets.

The giant {Mn70} and {Mn84} wheels are the largest nuclearity single-molecule magnets synthesized to date, and understanding their magnetic properties poses a challenge to theory. Starting from fir...
A New Urban Front for Shareholder Primacy

The hundredth anniversary of Dodge v. Ford marks an occasion to reflect upon what, if anything, has changed about shareholder primacy in a century. Seizing this opportunity, this Article analyzes new local laws and ordinances that mandate stakeholder governance and engagement, which seek to protect the interests of non-shareholder constituencies such as workers, the environment, and the communities in which corporations operate, among others. In doing so, this Article argues that such local laws meaningfully differ from traditional stakeholder protections in the ways that they weaken managerial accountability to shareholders. The emergence of these city laws creates a new urban front for shareholder primacy, with practical implications for the community benefits movement and theoretical implications for our understanding of the nature of corporate law itself.
Governing knowledge in the scientific community: Exploring the role of retractions in biomedicine

Abstract Although the validity of knowledge is critical to scientific progress, substantial concerns exist regarding the governance of knowledge production. While research errors are as relevant to the knowledge economy as defects are to the manufacturing economy, mechanisms to identify and signal “defective” or false knowledge are poorly understood. In this paper, we investigate one such institution – the system of scientific retractions. We analyze the universe of peer-reviewed scientific articles retracted from the biomedical literature between 1972–2006 and comparing with a matched control sample in order to identify the correlates, timing, and causal impact of scientific retractions. This effort provides insight into the workings of a distributed, peer-based system for the governance of validity in scientific knowledge. Our findings suggest that attention is a key predictor of retraction – retracted articles arise most frequently among highly-cited articles. The retraction system is expeditious in uncovering knowledge that is ever determined to be false (the mean time to retraction is less than two years) and democratic (retraction is not systematically affected by author prominence). Lastly, retraction causes an immediate, severe, and long-lived decline in future citations. Conditional on the obvious limitation that we cannot measure the absolute amount of false science in circulation, these results support the view that distributed governance systems can be designed to uncover false knowledge relatively swiftly and to mitigate the costs that false knowledge for future generations of producers.
The Transformation of Hunger: The Demand for Calories Past and Present

According to conventional income measures, American and British industrial workers in the late nineteenth century were two to four times as wealthy as those in developing countries today. Estimated calorie expenditure elasticities of American and British industrial workers based on the 1888 Cost of Living Survey are greater than calorie elasticity estimates for developing countries today, which suggest that yesterday's wealthy workers were hungrier than today's poor. The result is robust to numerous criticisms. The finding implies an extraordinary improvement in nutritional well-being among the poor in the last century that has not been captured by our income estimates.
Managers, investors, and crises: mutual fund strategies in emerging markets

The authors address the trading strategies of mutual funds in emerging markets. The data set they develop permits analyses of these strategies at the level of individual portfolios. A methodologically novel feature of their analysis: they disentangle the behavior of fund managers from that of investors. For both managers and investors, they strongly reject the null hypothesis of no momentum trading. Funds' momentum trading is positive: they systematically buy winners and sell losers. Contemporaneous momentum trading (buying current winners and selling current losers) is stronger during crises, and stronger for fund investors than for fund managers. Lagged momentum trading (buying past winners and selling past losers) is stronger during noncrises, and stronger for fund managers. Investors also engage in contagion trading-selling assets from one country when asset prices fall in another. These findings are based on data about mutual funds that represent only 10 percent of the market capitalization in the countries considered. Were it a larger share of the market, finding counterparties for their trades (the investors who buy when they sell and sell when they buy) would be difficult-and the premise that funds respond to contemporaneous returns rather than causing them would become tenuous.
PDE Traffic Observer Validated on Freeway Data

This article develops a boundary observer for the estimation of congested freeway traffic states based on the Aw–Rascle–Zhang (ARZ) partial differential equations (PDEs) model. Traffic state estimation refers to the acquisition of traffic state information from partially observed traffic data. This problem is relevant for freeway due to its limited accessibility to real-time traffic information. We propose a model-driven approach in which the estimation of aggregated traffic states in a freeway segment is obtained simply from the boundary measurement of flow and velocity without knowledge of the initial states. The macroscopic traffic dynamics is represented by the ARZ model, a $2 \times 2$ coupled nonlinear hyperbolic PDEs for traffic density and velocity. Using the PDE backstepping method, we construct a boundary observer consisting of a copy of the nonlinear plant with output injections from boundary measurement errors. The exponential stability of the estimation error system in the $L^{2}$ norm and finite-time convergence to zero is guaranteed. Numerical simulation and data validation are conducted to validate the boundary observer design with vehicle trajectory data.
Optimizing Weighted Ensemble Sampling of Steady States

We propose parameter optimization techniques for weighted ensemble sampling of Markov chains in the steady-state regime. Weighted ensemble consists of replicas of a Markov chain, each carrying a we...
A Dynamic Analysis of the Market for Wide-Bodied Commercial Aircraft

This paper uses an empirical dynamic oligopoly model of the commercial aircraft industry to analyse industry pricing, industry performance, and optimal industry policy. A novel feature of the model with respect to the previous literature is that entry, exit, prices, and quantities are endogenously determined in Markov perfect equilibrium (MPE). We find that many unusual aspects of the aircraft data, such as high concentration and persistent pricing below static marginal cost, are explained by this model. We also find that the unconstrained MPE is quite efficient from a social perspective, providing only 10% less welfare on average than a social planner would obtain. Finally, we provide simulation evidence that an anti-trust policy in the form of a concentration restriction would be welfare reducing.
Chapter 2 – Clinical Pharmacokinetics

Publisher Summary This chapter discusses various aspects of clinical pharmacokinetics (PK). PK is an important tool when conducting both basic and applied research and is an essential component of the drug development process. PK is a valuable adjunct for prescribing and evaluating drug therapy. The rationale for measuring concentrations of drugs in plasma, serum, or blood is that concentration–response relationships are often less variable than dose–response relationships. Additional clinical information is often necessary to interpret drug-concentration measurements that are otherwise equivocal. Drug-concentration monitoring is most helpful for the drugs that have a low therapeutic index and that have no clinically observable effects that can be easily monitored to guide dose adjustment. PK provides the scientific basis of dose selection, and the process of dose regimen design can be used to illustrate with a single-compartment model the basic concepts of apparent distribution volume, elimination half-life, and elimination clearance. Laplace transform methods are also helpful in PK when convolution/deconvolution methods are used to characterize drug absorption processes.
Consumer Preferences for Second-Generation Bioethanol

Abstract This study investigates the consumer responses toward fuel from second-generation, nature-inspired lignocellulose processing systems. Data was collected via in-person consumer surveys across in three major U.S. cities with two different information treatments. A dichotomous-choice contingent valuation methodology is utilized to estimate consumers' willingness to pay for this product and analyze factors that affect consumer choice. The results suggest that the average respondent was willing to pay an 11% premium for second-generation nature-inspired bioethanol compared to conventional fuel. The willingness to pay was the highest in Portland, Oregon (17%), followed by Minneapolis (9%) and then Boston (8%). Driving distance was found to have a negative effect on consumer willingness to pay. Consumers who purchase more organic foods were more willing to pay a premium for the product. Risk attitude had heterogeneous effects in different locations. The effect of information regarding the second-generation, nature-inspired lignocellulose process was found to be significantly positive.
Rolling stones gather moss: movement and longevity of moss balls on an Alaskan glacier

Glaciers support diverse ecosystems that are largely comprised of microbial life. However, at larger, macroscopic scales, glacier moss balls (sometimes called “glacier mice”) can develop from impurities on ice surfaces and represent a relatively rare biological phenomenon. These ovoid-shaped conglomerations of dirt and moss are only found on some glacier surfaces and provide key habitats for invertebrate colonization. Yet, despite their development and presence being widely reported, no studies of their movement and persistence across years have been conducted. This knowledge gap is particularly important when considering the degree to which glacier moss balls may represent viable, long-term biotic habitats on glaciers, perhaps complete with their own ecological succession dynamics. Here, we describe the movement and persistence of glacier moss balls on the Root Glacier in southcentral Alaska, USA. We show that glacier moss balls move an average of 2.5 cm per day in herd-like fashion initially to the south and later towards the southwest, and their movements are positively correlated with glacier ablation. Surprisingly, the dominant moss ball movement direction does not align with the prevailing wind or downslope directions, nor with the dominant direction of solar radiation. After attaining a mature size, glacier moss balls persist for many years, likely in excess of 6 years. Finally, we observed moss ball formation on the Root Glacier to occur within a narrow, low albedo stripe downwind of a nunatak, a potential key source of moss spores and/or fine-grained sediment that interact to promote their formation.
Social Security reform: three Rawlsian options

To maintain long-run solvency in light of changing demographics, the US Social Security system needs to be reformed. We present three reform options that protect the retirement benefits of the economically vulnerable while also balancing the Social Security budget. We refer to these three options as Rawlsian reforms because, with each reform option, the Social Security benefits of those at the low end of the income distribution are left intact. Two of our reforms break the link between the benefit cap and the tax cap by lowering the benefit cap. We explore the effect of each reform on ex-ante expected utility, the distribution of private savings, and the distribution of lifetime income in a life-cycle model.
Using Noisy Extractions to Discover Causal Knowledge.

Knowledge bases (KB) constructed through information extraction from text play an important role in query answering and reasoning. In this work, we study a particular reasoning task, the problem of discovering causal relationships between entities, known as causal discovery. There are two contrasting types of approaches to discovering causal knowledge. One approach attempts to identify causal relationships from text using automatic extraction techniques, while the other approach infers causation from observational data. However, extractions alone are often insufficient to capture complex patterns and full observational data is expensive to obtain. We introduce a probabilistic method for fusing noisy extractions with observational data to discover causal knowledge. We propose a principled approach that uses the probabilistic soft logic (PSL) framework to encode well-studied constraints to recover long-range patterns and consistent predictions, while cheaply acquired extractions provide a proxy for unseen observations. We apply our method gene regulatory networks and show the promise of exploiting KB signals in causal discovery, suggesting a critical, new area of research.
Stack-sorting, set partitions, and Lassalle's sequence

Abstract We exhibit a bijection between recently-introduced combinatorial objects known as valid hook configurations and certain weighted set partitions. When restricting our attention to set partitions that are matchings, we obtain three new combinatorial interpretations of Lassalle's sequence. One of these interpretations involves permutations that have exactly one preimage under the (West) stack-sorting map. We prove that the sequences obtained by counting these permutations according to their first entries are symmetric, and we conjecture that they are log-concave. We also obtain new recurrence relations involving Lassalle's sequence and the sequence that enumerates valid hook configurations. We end with several suggestions for future work.
Radiative Processes and Jet Modification at the EIC

A U.S.-based Electron-Ion Collider will provide the ultimate capability to determine both the structure and properties of nucleons and nuclei, as well as how matter and energy can be transported through a strongly interacting quantum mechanical environment. The production and propagation of long-lived heavy subatomic particles is a unique and critical part of this planned decade-long research program. In these proceedings we report the derivation of all branching processes in nuclei that lead to a modification of semi-inclusive hadron production, jet cross sections, and jet substructure when compared to the vacuum. This work allows for their evaluation to any desired order in opacity. As an example, we show an application to the modification of light hadron and open heavy flavor fragmentation functions at the EIC. We discuss how this observable can shed light on the physics of hadronization and parton energy loss in large nuclei.
Late-season surveys to document seed rain potential of Palmer amaranth (Amaranthus palmeri) and waterhemp (Amaranthus tuberculatus) in Texas cotton

Weed escapes are often present in large production fields prior to harvest, contributing to seed rain and species persistence. Late-season surveys were conducted in cotton (Gossypium hirsutum L.) fields in Texas in 2016 and 2017 to identify common weed species present as escapes and estimate seed rain potential of Palmer amaranth (Amaranthus palmeri S. Watson) and waterhemp [A. tuberculatus (Moq.) J.D. Sauer], two troublesome species with high fecundity. A total of 400 cotton fields across four major cotton-producing regions in Texas [High Plains (HP), Gulf Coast (GC), Central Texas, and Blacklands] were surveyed. Amaranthus palmeri, Texas millet [Urochloa texana (Buckley) R. Webster], A. tuberculatus, ragweed parthenium (Parthenium hysterophorus L.), and barnyardgrass [Echinochloa crus-galli (L.) P. Beauv.] were reported as the top five weed escapes in cotton fields. Amaranthus palmeri was the most prevalent weed in the HP and Lower GC regions, whereas A. tuberculatus escapes were predominantly observed in the Upper GC and Blacklands regions. On average, 9.4% of an individual field was infested with A. palmeri escapes in the Lower GC region; however, 5.1 to 8.1% of a field was infested in the HP region. Average A. palmeri density ranged from 405 (Central Texas) to 3,543 plants ha-1 (Lower GC). The greatest seed rain potential by A. palmeri escapes was observed in the upper HP region (13.9 million seeds ha-1), whereas the seed rain potential of A. tuberculatus escapes was the greatest in the Blacklands (12.9 million seeds ha-1) and the upper GC regions (9.8 million seeds ha-1). Seed rain from late-season A. palmeri and A. tuberculatus escapes is significant in Texas cotton, and effective management of these escapes is imperative for minimizing seedbank inputs and impacting weed species persistence.
Does Partisanship Shape Investor Beliefs? Evidence from the COVID-19 Pandemic

We use party-identifying language â€“ like â€œLiberal Mediaâ€ and â€œMAGAâ€â€“ to identify Republican users on the investor social platform StockTwits Using a difference
Generalized Evolutionary Point Processes: Model Specifications and Model Comparison

Generalized evolutionary point processes offer a class of point process models that allows for either excitation or inhibition based upon the history of the process. In this regard, we propose modeling which comprises generalization of the nonlinear Hawkes process. Working within a Bayesian framework, model fitting is implemented through Markov chain Monte Carlo. This entails discussion of computation of the likelihood for such point patterns. Furthermore, for this class of models, we discuss strategies for model comparison. Using simulation, we illustrate how well we can distinguish these models from point pattern specifications with conditionally independent event times, e.g., Poisson processes. Specifically, we demonstrate that these models can correctly identify true relationships (i.e., excitation or inhibition/control). Then, we consider a novel extension of the log Gaussian Cox process that incorporates evolutionary behavior and illustrate that our model comparison approach prefers the evolutionary log Gaussian Cox process compared to simpler models. We also examine a real dataset consisting of violent crime events from the 11th police district in Chicago from the year 2018. This data exhibits strong daily seasonality and changes across the year. After we account for these data attributes, we find significant but mild self-excitation, implying that event occurrence increases the intensity of future events.
Variational analysis of landscape elevation and drainage networks

Landscapes evolve towards surfaces with complex networks of channels and ridges in response to climatic and tectonic forcing. Here, we analyse variational principles giving rise to minimalist model...
Uniqueness of solutions of the KdV-hierarchy via Dubrovin-type flows

Abstract We consider the Cauchy problem for the KdV hierarchy – a family of integrable PDEs with a Lax pair representation involving one-dimensional Schrodinger operators – under a local in time boundedness assumption on the solution. For reflectionless initial data, we prove that the solution stays reflectionless. For almost periodic initial data with absolutely continuous spectrum, we prove that under Craig-type conditions on the spectrum, Dirichlet data evolve according to a Lipschitz Dubrovin-type flow, so the solution is uniquely recovered by a trace formula. This applies to algebro-geometric (finite gap) solutions; more notably, we prove that it applies to small quasiperiodic initial data with analytic sampling functions and Diophantine frequency. This also gives a uniqueness result for the Cauchy problem on the line for periodic initial data, even in the absence of Craig-type conditions.
Toward a discursive approach to growth models: Social blocs in the politics of digital transformation

The growth models perspective analyzes the role of social blocs in crafting countries’ economic policies, but its treatment of business power as purely structural prevents it from addressing an imp...
Mechanisms for outsourcing computation via a decentralized market

As the number of personal computing and IoT devices grows rapidly, so does the amount of computational power that is available at the edge. Many of these devices are often idle and constitute an untapped resource which could be used for outsourcing computation. Existing solutions for harnessing this power, such as volunteer computing (e.g., BOINC), are centralized platforms in which a single organization or company can control participation and pricing. By contrast, an open market of computational resources, where resource owners and resource users trade directly with each other, could lead to greater participation and more competitive pricing. To provide an open market, we introduce MODiCuM, a decentralized system for outsourcing computation. MODiCuM deters participants from misbehaving---which is a key problem in decentralized systems---by resolving disputes via dedicated mediators and by imposing enforceable fines. However, unlike other decentralized outsourcing solutions, MODiCuM minimizes computational overhead since it does not require global trust in mediation results. We provide analytical results proving that MODiCuM can deter misbehavior, and we evaluate the overhead of MODiCuM using experimental results based on an implementation of our platform.
Polymorphic mobile element insertions contribute to gene expression and alternative splicing in human tissues

Mobile elements are a major source of structural variants in the human genome, and some mobile elements can regulate gene expression and transcript splicing. However, the impact of polymorphic mobile element insertions (pMEIs) on gene expression and splicing in diverse human tissues has not been thoroughly studied. The multi-tissue gene expression and whole genome sequencing data generated by the Genotype-Tissue Expression (GTEx) project provide a great opportunity to systematically evaluate the role of pMEIs in regulating gene expression in human tissues. Using the GTEx whole genome sequencing data, we identify 20,545 high-quality pMEIs from 639 individuals. Coupling pMEI genotypes with gene expression profiles, we identify pMEI-associated expression quantitative trait loci (eQTLs) and splicing quantitative trait loci (sQTLs) in 48 tissues. Using joint analyses of pMEIs and other genomic variants, pMEIs are predicted to be the potential causal variant for 3522 eQTLs and 3717 sQTLs. The pMEI-associated eQTLs and sQTLs show a high level of tissue specificity, and these pMEIs are enriched in the proximity of affected genes and in regulatory elements. Using reporter assays, we confirm that several pMEIs associated with eQTLs and sQTLs can alter gene expression levels and isoform proportions, respectively. Overall, our study shows that pMEIs are associated with thousands of gene expression and splicing variations, indicating that pMEIs could have a significant role in regulating tissue-specific gene expression and transcript splicing. Detailed mechanisms for the role of pMEIs in gene regulation in different tissues will be an important direction for future studies.
SARS-CoV-2 detection in different respiratory sites: A systematic review and meta-analysis.

Abstract Background The accurate detection of SARS-CoV-2 through respiratory sampling is critical for the prevention of further transmission and the timely initiation of treatment for COVID-19. There is a diverse range of SARS-CoV-2 detection rates in reported studies, with uncertainty as to the optimal sampling strategy for COVID-19 diagnosis and monitoring. Methods We performed a systematic review and meta-analysis of studies comparing respiratory sampling strategies for the detection of SARS-CoV-2 RNA. The inclusion criteria were studies that assessed at least two respiratory sampling sites (oropharyngeal swab, nasopharyngeal swab, and sputum) in participants with COVID-19. The percentage positive tests were compared between sampling modalities by constructing a Z-test assuming independence and using the standard errors obtained from the random effects meta-analysis. Findings From 1039 total studies, we identified 11 studies that met our inclusion criteria, with SARS-CoV-2 testing results from a total of 3442 respiratory tract specimens. Compared to nasopharyngeal swab sampling, sputum testing resulted in significantly higher rates of SARS-CoV-2 RNA detection while oropharyngeal swab testing had lower rates of viral RNA detection. Earlier sampling after symptom onset was associated with improved detection rates, but the differences in SARS-CoV-2 RNA detection by sampling method was consistent regardless of the duration of symptoms. Interpretation The results support sputum sampling as a valuable method of COVID-19 diagnosis and monitoring, and highlight the importance of early testing after symptom onset to increase the rates of COVID-19 diagnosis. Funding This study was funded in part by the NIH grants U01AI106701 and by the Harvard University for AIDS Research (NIAID 5P30AI060354).
Energy-Efficient Node Deployment in Wireless Ad-hoc Sensor Networks

Author(s): Guo, Jun; Karimi-Bidhendi, Saeed; Jafarkhani, Hamid; IEEE | Abstract: We study a wireless ad-hoc sensor network (WASN) where $N$ sensors gather data from the surrounding environment and transmit their sensed information to $M$ fusion centers (FCs) via multi-hop wireless communications. This node deployment problem is formulated as an optimization problem to make a trade-off between the sensing uncertainty and energy consumption of the network. Our primary goal is to find an optimal deployment of sensors and FCs to minimize a Lagrange combination of the sensing uncertainty and energy consumption. To support arbitrary routing protocols in WASNs, the routing-dependent necessary conditions for the optimal deployment are explored. Based on these necessary conditions, we propose a routing-aware Lloyd algorithm to optimize node deployment. Simulation results show that, on average, the proposed algorithm outperforms the existing deployment algorithms.
Solving a steady-state PDE using spiking networks and neuromorphic hardware

The widely parallel, spiking neural networks of neuromorphic processors can enable computationally powerful formulations. While recent interest has focused on primarily machine learning tasks, the space of appropriate applications is wide and continually expanding. Here, we leverage the parallel and event-driven structure to solve a steady state heat equation using a random walk method. The random walk can be executed fully within a spiking neural network using stochastic neuron behavior, and we provide results from both IBM TrueNorth and Intel Loihi implementations. Additionally, we position this algorithm as a potential scalable benchmark for neuromorphic systems.
Diffraction gratings with two‐orders‐of‐magnitude‐enhanced dispersion rates for sub‐meV resolution soft X‐ray spectroscopy

Diffraction gratings with large angular dispersion rates are central to obtaining high spectral resolution in grating spectrometers operating over a broad spectral range from infrared to soft X-ray domains. The greatest challenge is of course to achieve large dispersion rates in the short-wavelength X-ray domain. Here it is shown that crystals in non-coplanar asymmetric X-ray Bragg diffraction can function as high-reflectance broadband soft X-ray diffraction gratings with dispersion rates that are at least two orders of magnitude larger than those that are possible with state-of-the-art man-made gratings. This opens new opportunities to design and implement soft X-ray resonant inelastic scattering (RIXS) spectrometers with spectral resolutions that are up to two orders of magnitude higher than what is currently possible, to further advance a very dynamic field of RIXS spectroscopy, and to make it competitive with inelastic neutron scattering. Examples of large-dispersion-rate crystal diffraction gratings operating near the 930 eV L3 absorption edge in Cu and of the 2.838 keV L3-edge in Ru are presented.
SIGAR: Inferring Features of Genome Architecture and DNA Rearrangements by Split-Read Mapping.

Ciliates are microbial eukaryotes with distinct somatic and germline genomes. Post-zygotic development involves extensive remodeling of the germline genome to form somatic chromosomes. Ciliates therefore offer a valuable model for studying the architecture and evolution of programmed genome rearrangements. Current studies usually focus on a few model species, where rearrangement features are annotated by aligning reference germline and somatic genomes. While many high-quality somatic genomes have been assembled, a high quality germline genome assembly is difficult to obtain due to its smaller DNA content and abundance of repetitive sequences. To overcome these hurdles, we propose a new pipeline SIGAR (Split-read Inference of Genome Architecture and Rearrangements) to infer germline genome architecture and rearrangement features without a germline genome assembly, requiring only short germline DNA sequencing reads. As a proof of principle, 93% of rearrangement junctions identified by SIGAR in the ciliate Oxytricha trifallax were validated by the existing germline assembly. We then applied SIGAR to six diverse ciliate species without germline genome assemblies, including Ichthyophthirius multifilii, a fish pathogen. Despite the high level of somatic DNA contamination in each sample, SIGAR successfully inferred rearrangement junctions, short eliminated sequences and potential scrambled genes in each species. This pipeline enables pilot surveys or exploration of DNA rearrangements in species with limited DNA material access, thereby providing new insights into the evolution of chromosome rearrangements.
Membrane Association and Functional Mechanism of Synaptotagmin-1 in Triggering Vesicle Fusion.

Abstract Upon Ca2+ influx, synaptic vesicles fuse with the presynaptic plasma membrane (PM) to release neurotransmitters. Membrane fusion is triggered by synaptotagmin-1, a transmembrane protein in the vesicle membrane (VM), but the mechanism is under debate. Synaptotagmin-1 contains a single transmembrane helix (TM) and two tandem C2 domains (C2A and C2B). This study aimed to use molecular dynamics simulations to elucidate how Ca2+-bound synaptotagmin-1, by simultaneously associating with VM and PM, brings them together for fusion. Although C2A stably associates with VM via two Ca2+-binding loops, C2B has a propensity to partially dissociate. Importantly, an acidic motif in the TM-C2A linker competes with VM for interacting with C2B, thereby flipping its orientation to face PM. Subsequently, C2B readily associates with PM via a polybasic cluster and a Ca2+-binding loop. The resulting mechanistic model for the triggering of membrane fusion by synaptotagmin-1 reconciles many experimental observations.
Biased permutative equivariant categories

For a finite group G, we introduce the complete suboperad $Q_G$ of the categorical G-Barratt-Eccles operad $P_G$. We prove that $P_G$ is not finitely generated, but $Q_G$ is finitely generated and is a genuine $E_\infty$ G-operad (i.e., it is $N_\infty$ and includes all norms). For G cyclic of order 2 or 3, we determine presentations of the object operad of $Q_G$ and conclude with a discussion of algebras over $Q_G$, which we call biased permutative equivariant categories.
Secure Distributed Storage: Rate-Privacy Trade-Off and XOR-Based Coding Scheme

We consider the problem of storing data in a distributed manner over T servers. We require the data (i) to be recoverable from the T servers, and (ii) to remain private from any T −1 colluding servers, where privacy is quantified in terms of mutual information between the data and all the information available at the T −1 colluding servers. For this model, we determine (i) the fundamental trade-off between storage size and the level of desired privacy, (ii) the optimal amount of local randomness necessary at the encoder, and (iii) an explicit low-complexity coding scheme that solely relies on XOR operations and that asymptotically (with the data size) matches the fundamental limits found.
Non-selective inhibition of the motor system following unexpected and expected infrequent events

Motor inhibition is a key control mechanism that allows humans to rapidly adapt their actions in response to environmental events. One of the hallmark signatures of rapidly exerted, reactive motor inhibition is the non-selective suppression of cortico-spinal excitability (CSE): unexpected sensory stimuli lead to a suppression of CSE across the entire motor system, even in muscles that are inactive. Theories suggest that this reflects a fast, automatic, and broad engagement of inhibitory control, which facilitates behavioral adaptations to unexpected changes in the sensory environment. However, it is an open question whether such non-selective CSE suppression is truly due to the unexpected nature of the sensory event, or whether it is sufficient for an event to be merely infrequent (but not unexpected). Here, we report data from two experiments in which human subjects experienced both unexpected and expected infrequent events during a two-alternative forced-choice reaction time task while CSE was measured from a task-unrelated muscle. We found that expected infrequent events can indeed produce non-selective CSE suppression—but only when they occur during movement initiation. In contrast, unexpected infrequent events produce non-selective CSE suppression relative to frequent, expected events even in the absence of movement initiation. Moreover, CSE suppression due to unexpected events occurs at shorter latencies compared to expected infrequent events. These findings demonstrate that unexpectedness and stimulus infrequency have qualitatively different suppressive effects on the motor system. They also have key implications for studies that seek to disentangle neural and psychological processes related to motor inhibition and stimulus detection.
Unidirectional Maxwellian Spin Waves

We develop a unified perspective of unidirectional topological edge waves in non-reciprocal media. We focus on the inherent role of photonic spin in non-reciprocal gy-roelectric media, ie. magnetized metals or magnetized insulators.
Learning to Scaffold the Development of Robotic Manipulation Skills

Learning contact-rich, robotic manipulation skills is a challenging problem due to the high-dimensionality of the state and action space as well as uncertainty from noisy sensors and inaccurate motor control. To combat these factors and achieve more robust manipulation, humans actively exploit contact constraints in the environment. By adopting a similar strategy, robots can also achieve more robust manipulation. In this paper, we enable a robot to autonomously modify its environment and thereby discover how to ease manipulation skill learning. Specifically, we provide the robot with fixtures that it can freely place within the environment. These fixtures provide hard constraints that limit the outcome of robot actions. Thereby, they funnel uncertainty from perception and motor control and scaffold manipulation skill learning. We propose a learning system that consists of two learning loops. In the outer loop, the robot positions the fixture in the workspace. In the inner loop, the robot learns a manipulation skill and after a fixed number of episodes, returns the reward to the outer loop. Thereby, the robot is incentivised to place the fixture such that the inner loop quickly achieves a high reward. We demonstrate our framework both in simulation and in the real world on three tasks: peg insertion, wrench manipulation and shallow- depth insertion. We show that manipulation skill learning is dramatically sped up through this way of scaffolding.
Adversarial Domain Adaptation Being Aware of Class Relationships.

Adversarial training is a useful approach to promote the learning of transferable representations across the source and target domains, which has been widely applied for domain adaptation (DA) tasks based on deep neural networks. Until very recently, existing adversarial domain adaptation (ADA) methods ignore the useful information from the label space, which is an important factor accountable for the complicated data distributions associated with different semantic classes. Especially, the inter-class semantic relationships have been rarely considered and discussed in the current work of transfer learning. In this paper, we propose a novel relationship-aware adversarial domain adaptation (RADA) algorithm, which first utilizes a single multi-class domain discriminator to enforce the learning of inter-class dependency structure during domain-adversarial training and then aligns this structure with the inter-class dependencies that are characterized from training the label predictor on source domain. Specifically, we impose a regularization term to penalize the structure discrepancy between the inter-class dependencies respectively estimated from domain discriminator and label predictor. Through this alignment, our proposed method makes the adversarial domain adaptation aware of the class relationships. Empirical studies show that the incorporation of class relationships significantly improves the performance on benchmark datasets.
Generic computability, Turing degrees, and asymptotic density

Generic decidability has been extensively studied in group theory, and we now study it in the context of classical computability theory. A set A of natural numbers is called generically computable if there is a partial computable function that agrees with the characteristic function of A on its domain D, and furthermore D has density 1, that is, lim n→∞ |{k<n: k∈D} |/n=1. A set A is called coarsely computable if there is a computable set R such that the symmetric difference of A and R has density 0. We prove that there is a computably enumerable (c.e.) set that is generically computable but not coarsely computable and vice versa. We show that every nonzero Turing degree contains a set that is neither generically computable nor coarsely computable. We prove that there is a c.e. set of density 1 that has no computable subset of density 1. Finally, we define and study generic reducibility.
The Sincerest Form of Flattery: Nationalist Emulation During the COVID-19 Pandemic

As COVID-19 rapidly spread across the globe, every government in the world has been forced to enact policies to slow the spread of the virus. While leaders often claim responses are based on the best available advice from scientists and public health experts, recent policy diffusion research suggests that countries are emulating the COVID-19 policies of their neighbors instead of responding to domestic conditions. Political and geographic considerations play a role in determining which countries imitate one another, but even among countries that are politically or geographically distant, nationalist regimes seem to favor certain approaches towards the pandemic. We investigate why this is the case by examining whether countries that embrace a nationalist ideology are more likely to emulate the COVID-19 policies of similarly nationalist regimes. We demonstrate that, even after controlling for domestic circumstances and linguistic, trade, geographic, and political connections, nationalist countries are emulating each other's responses. These results are robust and shed light not only on new mechanisms of policy diffusion but also on the growing international cooperation of nationalist regimes and leaders.
Trace decategorification of tensor product algebras

Abstract We show that in ADE type the trace of Webster's categorification of a tensor product of irreducibles for the quantum group is isomorphic to a tensor product of Weyl modules for the current algebra U ˙ ( g [ t ] ) . This extends a result of Beliakova, Habiro, Lauda, and Webster who showed that the trace of the categorified quantum group U ˙ ⁎ ( g ) is isomorphic to U ˙ ( g [ t ] ) , and the trace of a cyclotomic quotient of U ˙ ⁎ ( g ) , which categorifies a single irreducible for the quantum group, is isomorphic to a Weyl module for U ˙ ( g [ t ] ) . We use a deformation argument based on Webster's technique of unfurling 2-representations.
Scalar anomaly cancellation reveals the hidden superalgebraic structure of the quantum chiral SU(2/1) model of leptons and quarks.

At the classical level, the SU(2/1) superalgebra offers a natural description of the elementary particles: leptons and quarks massless states, graded by their chirality, fit the smallest irreducible representations of SU(2/1). Our new proposition is to pair the left/right space-time chirality with the superalgebra chirality and to study the model at the one-loop quantum level. If, despite the fact that they are non-Hermitian, we use the odd matrices of SU(2/1) to minimally couple an oriented complex Higgs scalar field to the chiral Fermions, novel anomalies occur. They affect the scalar propagators and vertices. However, these undesired new terms cancel out, together with the Adler-Bell-Jackiw vector anomalies, because the quarks compensate the leptons. The unexpected and striking consequence is that the scalar propagator must be normalized using the anti-symmetric super-Killing metric and the scalar-vector vertex must use the symmetric d_aij structure constants of the superalgebra. Despite this extraordinary structure, the resulting Lagrangian is actually Hermitian.
Curvature constraints in heterotic Landau-Ginzburg models

In this paper, we study a class of heterotic Landau-Ginzburg models. We show that the action can be written as a sum of BRST-exact and non-exact terms. The non-exact terms involve the pullback of the complexified Kahler form to the worldsheet and terms arising from the superpotential, which is a Grassmann-odd holomorphic function of the superfields. We then demonstrate that the action is invariant on-shell under supersymmetry transformations up to a total derivative. Finally, we extend the analysis to the case in which the superpotential is not holomorphic. In this case, we find that supersymmetry imposes a constraint which relates the nonholomorphic parameters of the superpotential to the Hermitian curvature. Various special cases of this constraint have previously been used to establish properties of Mathai-Quillen form analogues which arise in the corresponding heterotic Landau-Ginzburg models. There, it was claimed that supersymmetry imposes those constraints. Our goal in this paper is to support that claim. The analysis for the nonholomorphic case also reveals a constraint imposed by supersymmetry that we did not anticipate from studies of Mathai-Quillen form analogues.