cssi/SciSciGPT-SciSciNet huggingface dataset:
Spectrum Preserving Short Cycle Removal on Regular Graphs.

We describe a new method to remove short cycles on regular graphs while maintaining spectral bounds (the nontrivial eigenvalues of the adjacency matrix), as long as the graphs have certain combinatorial properties. These combinatorial properties are related to the number and distance between short cycles and are known to happen with high probability in uniformly random regular graphs. Using this method we can show two results involving high girth spectral expander graphs. First, we show that given d ⩾ 3 and n, there exists an explicit distribution of d-regular Θ(n)-vertex graphs where with high probability its samples have girth Ω(log_{d-1} n) and are e-near-Ramanujan; i.e., its eigenvalues are bounded in magnitude by 2√{d-1} + e (excluding the single trivial eigenvalue of d). Then, for every constant d ⩾ 3 and e > 0, we give a deterministic poly(n)-time algorithm that outputs a d-regular graph on Θ(n)-vertices that is e-near-Ramanujan and has girth Ω(√{log n}), based on the work of [Mohanty et al., 2020].
Grant Review Feedback: Appropriateness and Usefulness

The primary goal of the peer review of research grant proposals is to evaluate their quality for the funding agency. An important secondary goal is to provide constructive feedback to applicants for their resubmissions. However, little is known about whether review feedback achieves this goal. In this paper, we present a multi-methods analysis of responses from grant applicants regarding their perceptions of the effectiveness and appropriateness of peer review feedback they received from grant submissions. Overall, 56-60% of applicants determined the feedback to be appropriate (fair, well-written, and well-informed), although their judgments were more favorable if their recent application was funded. Importantly, independent of funding success, women found the feedback better written than men, and more white applicants found the feedback to be fair than non-white applicants. Also, perceptions of a variety of biases were specifically reported in respondents' feedback. Less than 40% of applicants found the feedback to be very useful in informing their research and improving grantsmanship and future submissions. Further, negative perceptions of the appropriateness of review feedback were positively correlated with more negative perceptions of feedback usefulness. Importantly, respondents suggested that highly competitive funding pay-lines and poor inter-panel reliability limited the usefulness of review feedback. Overall, these results suggest that more effort is needed to ensure that appropriate and useful feedback is provided to all applicants, bolstering the equity of the review process and likely improving the quality of resubmitted proposals.
An extension of the Siegel-Walfisz theorem

We extend the Siegel-Walfisz theorem to a family of integer sequences that are characterized by constraints on the size of the prime factors. Besides prime powers, this family includes smooth numbers, almost primes and practical numbers.
Law, policy, and the participation of transgender athletes in the United States

This article provides an overview of U.S. law and public policy governing the rights of transgender athletes to participate in sports programs and how sports organizers should take the law, or its ...
Stopping and Reversing Sound via Dynamic Dispersion Tuning in a Phononic Metamaterial

Slowing down, stopping, and reversing a signal is a core functionality for information processing. Here, we show that this functionality can be realized by tuning the dispersion of a periodic system through a dispersionless, or flat, band. Specifically, we propose a phononic metamaterial based on plate resonators, in which the phonon band dispersion can be modified from an acousticlike to an optical character by modulating a uniform prestress. The switch is enabled by the change in sign of an effective coupling between fundamental modes, which generically leads to a nearly dispersion-free band at the transition point. We demonstrate how adiabatic tuning of the band dispersion can immobilize and reverse the propagation of a sound pulse in simulations of a one-dimensional resonator chain. Our study relies on the basic principles of thin-plate elasticity independently of any specific material, making our results applicable across varied length scales and experimental platforms. More broadly, our approach could be replicated for signal manipulation in photonic metamaterials and electronic heterostructures.
The Influence of the COVID-19 Pandemic on Pediatric Firearm Injuries.

* Abbreviations: COVID-19 — : coronavirus disease 2019 NICS — : National Instant Criminal Background Check System In this issue of Pediatrics, Cohen et al1 address the epidemic of pediatric firearm injuries through the lens of the coronavirus disease 2019 (COVID-19) pandemic. The authors used the National Instant Criminal Background Check System (NICS) to identify new gun purchases and the Gun Violence Archive to identify firearm injuries in children <12 years of age. The authors conclude that there has “been a surge in firearm injuries in young children and inflicted by young children” during the first 6 months of the COVID-19 pandemic. The authors also report these incidents correlate with an increase in new firearm ownership. Finally, the authors suggest that temporary national policies should be enacted to address firearm safety for children in the context of the pandemic. In the study, Cohen et al1 assume that NICS background checks are a proxy for new firearm ownership. In other studies, researchers have found that the increase in NICS background checks was likely in part due to purchases made by first time gun owners; however, it is difficult to determine what percentage of firearm purchases were made by new gun … Address correspondence to Teresa M. Bell, PhD, Departments of Surgery and Population Health Sciences, School of Medicine, University of Utah, 30 N 1900 E, Room 2C430, Salt Lake City, UT 84132. E-mail: teresa.bell{at}hsc.utah.edu
GRIT: Generative Role-filler Transformers for Document-level Event Entity Extraction

We revisit the classic problem of document-level role-filler entity extraction (REE) for template filling. We argue that sentence-level approaches are ill-suited to the task and introduce a generative transformer-based encoder-decoder framework (GRIT) that is designed to model context at the document level: it can make extraction decisions across sentence boundaries; is implicitly aware of noun phrase coreference structure, and has the capacity to respect cross-role dependencies in the template structure. We evaluate our approach on the MUC-4 dataset, and show that our model performs substantially better than prior work. We also show that our modeling choices contribute to model performance, e.g., by implicitly capturing linguistic knowledge such as recognizing coreferent entity mentions.
Particle resonances and trapping of direct laser acceleration in a laser-plasma channel

As one of the leading acceleration mechanisms in laser-driven underdense plasmas, direct laser acceleration (DLA) is capable of producing high-energy-density electron beams in a plasma channel for many applications. However, the mechanism relies on highly nonlinear particle-laser resonances, rendering its modeling and control to be very challenging. Here, we report on novel physics of the particle resonances and, based on that, define a potential path toward more controlled DLA. Key findings are acquired by treating the electron propagation angle independently within a comprehensive model. This approach uncovers the complete particle resonances over broad propagation angles, the physical regimes under which paraxial/nonparaxial dynamics dominates, a unified picture for different harmonics, and crucially, the physical accessibility to these particle resonances. These new insights can have important implications where we address the basic issue of particle trapping as an example. We show how the uncovered trapping parameter space can lead to better acceleration control. More implications for the development of this basic type of acceleration are discussed.
Sharp Analysis of a Simple Model for Random Forests

Random forests have become an important tool for improving accuracy in regression and classification problems since their inception by Leo Breiman in 2001. In this paper, we revisit a historically important random forest model originally proposed by Breiman in 2004 and later studied by Gerard Biau in 2012, where a feature is selected at random and the splits occurs at the midpoint of the node along the chosen feature. If the regression function is Lipschitz and depends only on a small subset of $ S $ out of $ d $ features, we show that, given access to $ n $ observations and properly tuned split probabilities, the mean-squared prediction error is $ O((n(\log n)^{(S-1)/2})^{-\frac{1}{S\log2+1}}) $. This positively answers an outstanding question of Biau about whether the rate of convergence for this random forest model could be improved. Furthermore, by a refined analysis of the approximation and estimation errors for linear models, we show that this rate cannot be improved in general. Finally, we generalize our analysis and improve extant prediction error bounds for another random forest model in which each tree is constructed from subsampled data and the splits are performed at the empirical median along a chosen feature.
Input-Independent Homeostasis of Developing Thalamocortical Activity.

The isocortex of all mammals studied to date shows a progressive increase in the amount and continuity of background activity during early development. In humans the transition from a discontinuous (mostly silent, intermittently bursting) cortex to one that is continuously active is complete soon after birth and is a critical prognostic indicator. In the visual cortex of rodents this switch from discontinuous to continuous background activity occurs during the two days before eye-opening, driven by activity changes in relay thalamus. The factors that regulate the timing of continuity development, which enables mature visual processing, are unknown. Here we test the role of the retina, the primary input, in the development of continuous spontaneous activity in the visual cortex of mice using depth electrode recordings from Enucleated mice in vivo. Bilateral enucleation at postnatal day (P)6, one week prior to the onset of continuous activity, acutely silences cortex, yet firing rates and early oscillations return to normal within two days and show a normal developmental trajectory through P12. Enucleated animals showed differences in silent period duration and continuity on P13 that resolved on P16, and an increase in low frequency power that did not. Our results show that the timing of cortical activity development is not determined by the major driving input to the system. Rather, even during a period of rapid increase in firing rates and continuity, neural activity in the visual cortex is under homeostatic control that is largely robust to the loss of the primary input. SIGNIFICANCE STATEMENT Uncovering the mechanistic underpinnings of EEG development is critical to increasing the diagnostic potential of this cheap and portable methodology. An important component of this maturation is the acquisition of activity that is continuous, i.e. lacking silent periods. Here we used background activity in the visual cortex of developing unanesthetized mice to show that the primary sensory input plays little role in the development of continuity and normal firing rates, which instead appear to be regulated by mechanism internal to thalamus and cortex. These findings suggest that damage to driving thalamic inputs will be difficult to detect by EEG, and point to the importance of firing rate homeostasis in regulating even early development.
Tacit collusion and price dispersion in the presence of Southwest Airlines

We study the impact of tacit collusion on price dispersion in the U.S. airline industry. We find that tacit collusion driven by multimarket contact has a positive effect on prices, but a negative effect on price dispersion. Our empirical results suggest that airfares throughout the price distribution increases, yet the price distribution becomes more compressed since 10th percentile airfares increase by a larger amount than 90th percentile airfares. Moreover, we also find that this pricing phenomenon does not exist if Southwest Airlines is present on the route. Thus, route-level price competition is softened when the same airlines directly compete more frequently, except when Southwest Airlines services that route. As such, our empirical analysis provides evidence that the presence of Southwest Airlines exhibits an anti-collusive effect.
Extremal Numbers of Cycles Revisited

We give a simple geometric interpretation of an algebraic construction of Wenger that gives n-vertex graphs with no cycle of length 4, 6, or 10 and close to the maximum number of edges.
An additivity theorem for cobordism categories

Using methods inspired from algebraic K–theory, we give a new proof of the Genauer fibration sequence, relating the cobordism categories of closed manifolds with cobordism categories of manifolds with boundaries, and of the Bokstedt–Madsen delooping of the cobordism category. Unlike the existing proofs, this approach generalizes to other cobordism-like categories of interest. Indeed we argue that the Genauer fibration sequence is an analogue, in the setting of cobordism categories, of Waldhausen’s additivity theorem in algebraic K–theory.
Localization from Incomplete Noisy Distance Measurements

We consider the problem of positioning a cloud of points in the Euclidean space ℝ d , using noisy measurements of a subset of pairwise distances. This task has applications in various areas, such as sensor network localization and reconstruction of protein conformations from NMR measurements. It is also closely related to dimensionality reduction problems and manifold learning, where the goal is to learn the underlying global geometry of a data set using local (or partial) metric information. Here we propose a reconstruction algorithm based on semidefinite programming. For a random geometric graph model and uniformly bounded noise, we provide a precise characterization of the algorithm’s performance: in the noiseless case, we find a radius r 0 beyond which the algorithm reconstructs the exact positions (up to rigid transformations). In the presence of noise, we obtain upper and lower bounds on the reconstruction error that match up to a factor that depends only on the dimension d, and the average degree of the nodes in the graph.
Improving the accuracy and efficiency of quantum connected moments expansions

The still-maturing noisy intermediate-scale quantum (NISQ) technology faces strict limitations on the algorithms that can be implemented efficiently. In quantum chemistry, the variational quantum eigensolver (VQE) algorithm has become ubiquitous, using the functional form of the ansatz as a degree of freedom, whose parameters are found variationally in a feedback loop between the quantum processor and its conventional counterpart. Alternatively, a promising new avenue has been unraveled by the quantum variants of techniques grounded on expansions of the moments of the Hamiltonian, among which two stand out: the connected moments expansion (CMX) [Phys. Rev. Lett. 58, 53 (1987)] and the Peeters-Devreese-Soldatov (PDS) functional [J. Phys. A 17, 625 (1984); Int. J. Mod. Phys. B 9, 2899], the latter based on the standard moments . Contrasting with VQE-based methods and provided the quantum circuit prepares a state with non-vanishing overlap with the true ground state, CMX often converges to the ground state energy, while PDS is guaranteed to converge by virtue of being variational. However, for a finite CMX/PDS order, the circuit may significantly impact the energy accuracy. Here we use the ADAPT-VQE algorithm to test shallow circuit construction strategies that are not expected to impede their implementation in the present quantum hardware while granting sizable accuracy improvement in the computed ground state energies. We also show that we can take advantage of the fact that the terms in the connected moments are highly recurring in different powers, incurring a sizable reduction in the number of necessary measurements. By coupling this measurement caching with a threshold that determines whether a given term is to be measured based on its associated scalar coefficient, we observe a further reduction in the number of circuit implementations while allowing for tunable accuracy.
Higher concentrations of bacterial enveloped virus Phi6 can protect the virus from environmental decay.

Phage Phi6 is an enveloped virus considered a possible nonpathogenic surrogate for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and other viral pathogens in transmission studies. Larger input amounts of bacteriophage Phi6 are shown to delay and protect the phage from environmental decay, both when the phages are dried in plastic tubes and when they are stored in saline solution at 4°C. In contrast, when bacteriophage Phi6 is placed in LB (Luria-Bertani) growth medium (instead of saline) prior to placement on the plastic surface, the influence of the starting concentration on viral recovery is negligible. Protection is reflected in the phage half-lives at higher concentrations being longer than the half-lives at lower concentrations. Because experiments supporting the possibility of fomite transmission of SARS-CoV-2 and other viruses rely upon the survival of infectious virus following inoculation onto various surfaces, large initial amounts of input virus on a surface may generate artificially inflated survival times compared to realistic lower levels of virus that a subject would normally encounter. This is not only because there are extra half-lives to go through at higher concentrations but also because the half-lives themselves are extended at higher virus concentrations. It is important to design surface drying experiments for pathogens with realistic levels of input virus and to consider the role of the carrier and matrix if the results are to be clinically relevant. IMPORTANCE During the coronavirus disease 2019 (COVID-19) pandemic, much attention has been paid to the environmental decay of SARS-CoV-2 due to the proposed transmission of the virus via fomites. However, published experiments have commenced with inocula with very high virus titers, an experimental design not representative of real-life conditions. The study described here evaluated the impact of the initial virus titer on the environmental decay of an enveloped virus, using a nonpathogenic surrogate for the transmission of SARS-CoV-2, enveloped bacteriophage Phi6. We establish that higher concentrations of virus can protect the virus from environmental decay, depending on conditions. This has important implications for stability studies of SARS-CoV-2 and other viruses. Our results point to a limitation in the fundamental methodology that has been used to attribute fomite transmission for almost all respiratory viruses.
torchdistill: A Modular, Configuration-Driven Framework for Knowledge Distillation

While knowledge distillation (transfer) has been attracting attentions from the research community, the recent development in the fields has heightened the need for reproducible studies and highly generalized frameworks to lower barriers to such high-quality, reproducible deep learning research. Several researchers voluntarily published frameworks used in their knowledge distillation studies to help other interested researchers reproduce their original work. Such frameworks, however, are usually neither well generalized nor maintained, thus researchers are still required to write a lot of code to refactor/build on the frameworks for introducing new methods, models, datasets and designing experiments. In this paper, we present our developed open-source framework built on PyTorch and dedicated for knowledge distillation studies. The framework is designed to enable users to design experiments by declarative PyYAML configuration files, and helps researchers complete the recently proposed ML Code Completeness Checklist. Using the developed framework, we demonstrate its various efficient training strategies, and implement a variety of knowledge distillation methods. We also reproduce some of their original experimental results on the ImageNet and COCO datasets presented at major machine learning conferences such as ICLR, NeurIPS, CVPR and ECCV, including recent state-of-the-art methods. All the source code, configurations, log files and trained model weights are publicly available at https://github.com/yoshitomo-matsubara/torchdistill.
Defining an abrasion index for lunar surface systems as a function of dust interaction modes and variable concentration zones

Abstract Unexpected issues were encountered during the Apollo era of lunar exploration due to detrimental abrasion of materials upon exposure to the fine-grained, irregular shaped dust on the surface of the Moon. For critical design features involving contact with the lunar surface and for astronaut safety concerns, operational concepts and dust tolerance must be considered in the early phases of mission planning. To systematically define material selection criteria, dust interaction can be characterized by two-body or three-body abrasion testing, and sub-categorically by physical interactions of compression, rolling, sliding, and bending representing specific applications within the system. Two-body abrasion occurs when a single particle or asperity slides across a given surface removing or displacing material. Three-body abrasion occurs when multiple particles interact with a solid surface, or in between two surfaces, allowing the abrasives to freely rotate and interact with the material(s), leading to removal or displacement of mass. Different modes of interaction are described in this paper along with corresponding types of tests that can be utilized to evaluate each configuration. In addition to differential modes of abrasion, variable concentrations of dust in different zones can also be considered for a given system design and operational protocol. These zones include (1) outside the habitat where extensive dust exposure occurs, (2) in a transitional zone such as an airlock or suitport, and (3) inside the habitat or spacesuit with a low particle count. These zones can be used to help define dust interaction frequencies, and corresponding risks to the systems and/or crew can be addressed by appropriate mitigation strategies. An abrasion index is introduced that includes the level of risk, R , the hardness of the mineralogy, H , the severity of the abrasion mode, S , and the frequency of particle interactions, F .
Information Processing Costs and Firms' Investment Efficiency: An Examination of Channels of the XBRL Effect

We examine the channels through which a reduction in information processing costs improves firm-level investment efficiency. We do so by exploiting the SEC’s eXtensible Business Reporting Language (XBRL) mandate, which serves as a natural experiment. We provide empirical evidence suggesting potential channels including (1) the use of standardized official elements versus customized extension elements in XBRL-filings and (2) the role of the external auditors. Specifically, we find that, while the use of standardized official elements facilitates investment efficiency, the use of customized extension elements hinders it. In addition, we show that firms with Big 4 auditors experience lesser improvement in investment efficiency. We also investigate ex post investment performance and find that firms’ return on assets and Tobin’s q increase following XBRL adoption, suggesting improvements in the effectiveness of investments in addition to improved investment efficiency. Furthermore, insofar as reduced information processing costs improve firms’ capital investment efficiency, we extend this line of research to investments in human capital. Notably, we show that labor investment efficiency is significantly improved in the post-XBRL era. This finding adds another dimension to the growing knowledge about the impact of accounting information on firm-level investment decisions.
Causal Effects of Linguistic Properties

We consider the problem of using observational data to estimate the causal effects of linguistic properties. For example, does writing a complaint politely lead to a faster response time? How much will a positive product review increase sales? This paper addresses two technical challenges related to the problem before developing a practical method. First, we formalize the causal quantity of interest as the effect of a writer’s intent, and establish the assumptions necessary to identify this from observational data. Second, in practice, we only have access to noisy proxies for the linguistic properties of interest—e.g., predictions from classifiers and lexicons. We propose an estimator for this setting and prove that its bias is bounded when we perform an adjustment for the text. Based on these results, we introduce TextCause, an algorithm for estimating causal effects of linguistic properties. The method leverages (1) distant supervision to improve the quality of noisy proxies, and (2) a pre-trained language model (BERT) to adjust for the text. We show that the proposed method outperforms related approaches when estimating the effect of Amazon review sentiment on semi-simulated sales figures. Finally, we present an applied case study investigating the effects of complaint politeness on bureaucratic response times.
Deadlock detection in communicating finite state machines by even reachability analysis

A network of communicating finite state machines (CFSM) consists of a set of finite state machines which communicate asynchronously with each other over (potentially) unbounded FIFO channels by sending and receiving typed messages. As a concurrency model, CFSMs has been widely used to specify and validate communications protocols. CFSMs is also powerful and suitable for modeling mobile communication systems—a CFSM can naturally model a mobile station in a wireless communication system. The unbounded FIFO channels are ideal for modeling the communication behavior among mobile stations. Fair reachability is a very useful technique in detecting errors of deadlocks and unspecified receptions in networks of (CFSMs) consisting of two machines. The paper extends the classical fair reachability technique, which is only applicable to the class of two-machine CFSMs, to the general class of CFSMs. For bounded CFSMs, the extended fair reachability technique reduces by more than one half the total number of reachable global states that have to be searched in verifying freedom from deadlocks. The usefulness of the new reachability technique, called even reachability, is demonstrated through two examples.
Unsteady Aerodynamic Flow Phenomena in a Transonic Compressor Stage

A three-dimensional unsteady, viscous aerodynamic analysis has been developed for the flow inside a transonic, high-through-flow, single stage compressor. The compressor stage is comprised of a low-aspect-ratio rotor and a closely coupled stator. The analysis is based on a numerical method for solving the three-dimensional Navier-Stokes equation for unsteady viscous flow through multiple turbomachinery blade rows. The method solves the fully three-dimensional Navier-Stokes equation with an implicit scheme. A two-equation turbulence model with a low-Reynolds-number modification is applied for the turbulence closure. A third-order accurate upwinding scheme is used to approximate convection terms while a second-order accurate central difference scheme is used for the discretization of the viscous terms. A second-order accurate scheme is employed for the temporal discretization. The numerical method is applied to study the unsteady flow field inside a transonic, high-through-flow, axial compressor stage. The numerical results are compared with available experimental data.
Dietary patterns and hypertension among Chinese adults: a nationally representative cross‐sectional study

Several healthful dietary patterns appear to be effective at lowering blood pressure and preventing hypertension. However, the relationship between dietary patterns and hypertension among a representative Chinese population sample is unclear. A nationally representative sample of 23 671 participants aged 18-59 years were recruited by the 2002 China National Nutrition and Health Survey. All participants had their blood pressure measured with standardized mercury sphygmomanometers. Hypertension was defined as systolic blood pressure ≥140 mmHg and/or diastolic blood pressure ≥90 mmHg. We conducted factor analysis using dietary information from a validated food frequency questionnaire to derive dietary patterns. Information of participants on physical activities, education level, annual household income, smoking status and family history of hypertension was collected by interviewer-administrated questionnaires. Three major dietary patterns, defined as 'Western', 'traditional northern', and 'traditional southern', were identified. Participants with the highest quartile for the score of the Western pattern had significantly higher blood pressure comparing with counterparts in the lowest quartile. In contrast, participants in the top quartile for the score of the traditional southern pattern presented significantly lower blood pressure comparing with counterparts in the lowest quartile. In multivariate analyses the traditional northern pattern score was associated with an odds ratio (OR) of 1.30 (95% confidence interval (CI) 1.11-1.53, P for trend = 0.0001) comparing with the lowest quartile. The OR for the top quartile of score for the traditional southern pattern was 0.73 (95% CI, 0.59-0.89, P for trend = 0.0040) compared with the lowest quartile of traditional southern pattern score. However, the significant association between the traditional northern pattern and prevalence of hypertension disappeared after further adjusting for body mass index (BMI) (P for trend = 0.3), whereas the association between the traditional southern pattern and prevalence of hypertension persisted after further adjusting for BMI (P for trend = 0.01). We observed a positive relationship between the traditional northern pattern and hypertension that was mediated through differences in BMI. In addition, the traditional southern pattern was significantly associated with lower odds of presenting with hypertension.
District-Fair Participatory Budgeting.

Participatory budgeting is a method used by city governments to select public projects to fund based on residents' votes. Many cities use participatory budgeting at a district level. Typically, a budget is divided among districts proportionally to their population, and each district holds an election over local projects and then uses its budget to fund the projects most preferred by its voters. However, district-level participatory budgeting can yield poor social welfare because it does not necessarily fund projects supported across multiple districts. On the other hand, decision making that only takes global social welfare into account can be unfair to districts: A social-welfare-maximizing solution might not fund any of the projects preferred by a district, despite the fact that its constituents pay taxes to the city. Thus, we study how to fairly maximize social welfare in a participatory budgeting setting with a single city-wide election. We propose a notion of fairness that guarantees each district at least as much welfare as it would have received in a district-level election. We show that, although optimizing social welfare subject to this notion of fairness is NP-hard, we can efficiently construct a lottery over welfare-optimal outcomes that is fair in expectation. Moreover, we show that, when we are allowed to slightly relax fairness, we can efficiently compute a fair solution that is welfare-maximizing, but which may overspend the budget.
Improving Automated Evaluation of Open Domain Dialog via Diverse Reference Augmentation

Multiple different responses are often plausible for a given open domain dialog context. Prior work has shown the importance of having multiple valid reference responses for meaningful and robust automated evaluations. In such cases, common practice has been to collect more human written references. However, such collection can be expensive, time consuming, and not easily scalable. Instead, we propose a novel technique for automatically expanding a human generated reference to a set of candidate references. We fetch plausible references from knowledge sources, and adapt them so that they are more fluent in context of the dialog instance in question. More specifically, we use (1) a commonsense knowledge base to elicit a large number of plausible reactions given the dialog history (2) relevant instances retrieved from dialog corpus, using similar past as well as future contexts. We demonstrate that our automatically expanded reference sets lead to large improvements in correlations of automated metrics with human ratings of system outputs for DailyDialog dataset.
Selective Replay Enhances Learning in Online Continual Analogical Reasoning

In continual learning, a system learns from non-stationary data streams or batches without catastrophic forgetting. While this problem has been heavily studied in supervised image classification and reinforcement learning, continual learning in neural networks designed for abstract reasoning has not yet been studied. Here, we study continual learning of analogical reasoning. Analogical reasoning tests such as Raven's Progressive Matrices (RPMs) are commonly used to measure non-verbal abstract reasoning in humans, and recently offline neural networks for the RPM problem have been proposed. In this paper, we establish experimental baselines, protocols, and forward and backward transfer metrics to evaluate continual learners on RPMs. We employ experience replay to mitigate catastrophic forgetting. Prior work using replay for image classification tasks has found that selectively choosing the samples to replay offers little, if any, benefit over random selection. In contrast, we find that selective replay can significantly outperform random selection for the RPM task1.
Learning by Watching

When in a new situation or geographical location, human drivers have an extraordinary ability to watch others and learn maneuvers that they themselves may have never performed. In contrast, existing techniques for learning to drive preclude such a possibility as they assume direct access to an instrumented ego-vehicle with fully known observations and expert driver actions. However, such measurements cannot be directly accessed for the non-ego vehicles when learning by watching others. Therefore, in an application where data is regarded as a highly valuable asset, current approaches completely discard the vast portion of the training data that can be potentially obtained through indirect observation of surrounding vehicles. Motivated by this key insight, we propose the Learning by Watching (LbW) framework which enables learning a driving policy without requiring full knowledge of neither the state nor expert actions. To increase its data, i.e., with new perspectives and maneuvers, LbW makes use of the demonstrations of other vehicles in a given scene by (1) transforming the egovehicle’s observations to their points of view, and (2) inferring their expert actions. Our LbW agent learns more robust driving policies while enabling data-efficient learning, including quick adaptation of the policy to rare and novel scenarios. In particular, LbW drives robustly even with a fraction of available driving data required by existing methods, achieving an average success rate of 92% on the original CARLA benchmark with only 30 minutes of total driving data and 82% with only 10 minutes.
Accelerator performance analysis of the Fermilab Muon Campus

Fermilab is dedicated to hosting world-class experiments in search of new physics that will operate in the coming years. The Muon g-2 Experiment is one such experiment that will determine with unprecedented precision the muon anomalous magnetic moment, which offers an important test of the Standard Model. We describe in this study the accelerator facility that will deliver a muon beam to this experiment. We first present the lattice design that allows for efficient capture, transport, and delivery of polarized muon beams. We then numerically examine its performance by simulating pion production in the target, muon collection by the downstream beam line optics, as well as transport of muon polarization. We finally establish the conditions required for the safe removal of unwanted secondary particles that minimizes contamination of the final beam.
Neonatal thyroxine activation modifies epigenetic programming of the liver.

The type 2 deiodinase (D2) in the neonatal liver accelerates local thyroid hormone triiodothyronine (T3) production and expression of T3-responsive genes. Here we show that this surge in T3 permanently modifies hepatic gene expression. Liver-specific Dio2 inactivation (Alb-D2KO) transiently increases H3K9me3 levels during post-natal days 1–5 (P1–P5), and results in methylation of 1,508 DNA sites (H-sites) in the adult mouse liver. These sites are associated with 1,551 areas of reduced chromatin accessibility (RCA) within core promoters and 2,426 within intergenic regions, with reduction in the expression of 1,363 genes. There is strong spatial correlation between density of H-sites and RCA sites. Chromosome conformation capture (Hi-C) data reveals a set of 81 repressed genes with a promoter RCA in contact with an intergenic RCA ~300 Kbp apart, within the same topologically associating domain (χ2 = 777; p < 0.00001). These data explain how the systemic hormone T3 acts locally during development to define future expression of hepatic genes. Whether thyroid hormones affect gene expression via DNA methylation is not well known. Here the authors show that type 2 deiodinase (D2) converts T4 to produce T3, which prevents DNA methylation of discrete areas in the neonatal liver. In the absence of D2, DNA methylation occurs and is associated with reduced chromatin accessibility in promoters and enhancers and affects gene expression.
Spatio-Temporal Graph Dual-Attention Network for Multi-Agent Prediction and Tracking

An effective understanding of the environment and accurate trajectory prediction of surrounding dynamic obstacles are indispensable for intelligent mobile systems (e.g., autonomous vehicles, social robots) to achieve safe and high-quality planning when they navigate in highly interactive and crowded scenarios. Due to the existence of frequent interactions and uncertainty in the scene evolution, it is desired for the prediction system to enable relational reasoning on different entities and provide a distribution of future trajectories for each agent. In this paper, we propose a generic generative neural system (called STG-DAT) for multi-agent trajectory prediction involving heterogeneous agents. The system takes a step forward to explicit interaction modeling by incorporating relational inductive biases with a dynamic graph representation and leverages both trajectory and scene context information. We also employ an efficient kinematic constraint layer applied to vehicle trajectory prediction. The constraint not only ensures physical feasibility but also enhances model performance. Moreover, the proposed prediction model can be easily adopted by multi-target tracking frameworks. The tracking accuracy proves to be improved by empirical results. The proposed system is evaluated on three public benchmark datasets for trajectory prediction, where the agents cover pedestrians, cyclists and on-road vehicles. The experimental results demonstrate that our model achieves better performance than various baseline approaches in terms of prediction and tracking accuracy.
Toward reproducible, scalable, and robust data analysis across multiplex tissue imaging platforms.

Summary The emergence of megascale single-cell multiplex tissue imaging (MTI) datasets necessitates reproducible, scalable, and robust tools for cell phenotyping and spatial analysis. We developed open-source, graphics processing unit (GPU)-accelerated tools for intensity normalization, phenotyping, and microenvironment characterization. We deploy the toolkit on a human breast cancer (BC) tissue microarray stained by cyclic immunofluorescence and present the first cross-validation of breast cancer cell phenotypes derived by using two different MTI platforms. Finally, we demonstrate an integrative phenotypic and spatial analysis revealing BC subtype-specific features.
Improved handling of repeats and jumps in audio-sheet image synchronization

This paper studies the problem of automatically generating piano score following videos given an audio recording and raw sheet music images. Whereas previous works focus on synthetic sheet music where the data has been cleaned and preprocessed, we instead focus on developing a system that can cope with the messiness of raw, unprocessed sheet music PDFs from IMSLP. We investigate how well existing systems cope with real scanned sheet music, filler pages and unrelated pieces or movements, and discontinuities due to jumps and repeats. We find that a significant bottleneck in system performance is handling jumps and repeats correctly. In particular, we find that a previously proposed Jump DTW algorithm does not perform robustly when jump locations are unknown a priori. We propose a novel alignment algorithm called Hierarchical DTW that can handle jumps and repeats even when jump locations are not known. It first performs alignment at the feature level on each sheet music line, and then performs a second alignment at the segment level. By operating at the segment level, it is able to encode domain knowledge about how likely a particular jump is. Through carefully controlled experiments on unprocessed sheet music PDFs from IMSLP, we show that Hierarachical DTW significantly outperforms Jump DTW in handling various types of jumps.
Investigating the exchange of Ising chains on a digital quantum computer

The ferromagnetic state of an Ising chain can represent a twofold degenerate subspace or equivalently a logical qubit that is protected from excitations by an energy gap. Here, we study a braiding-like exchange operation through the movement of the ferromagnetic state in the qubit subspace, which resembles that of the localized edge modes in a Kitaev chain. The system consists of two Ising chains in a one-dimensional geometry where the operation is simulated through the adiabatic time evolution of the ground state. The time evolution is implemented via the Suzuki-Trotter expansion on basic single- and two-qubit quantum gates using IBM's Aer QASM simulator. The fidelity of the system is investigated as a function of the evolution and system parameters to obtain optimum efficiency and accuracy for different system sizes. Various aspects of the implementation, including the circuit depth, Trotterization error, and quantum gate errors pertaining to noisy intermediate-scale quantum (NISQ) hardware, are discussed as well. We demonstrate that the quantum gate errors, i.e., bit-flip, phase errors, are the dominating factor in determining the fidelity of the system as the Trotter error and the adiabatic condition are less restrictive even for modest values of Trotter time steps. We reach an optimum fidelity $g99%$ on systems of up to 11 sites per Ising chain. We show that running such simulations is far beyond the reach of current NISQ hardware with the most efficient implementation of a single braiding-like operation for a fidelity above $90%$ requiring a circuit depth of the order of $\ensuremath{\sim}{10}^{3}$ restricting the individual gate errors to be less than $\ensuremath{\sim}{10}^{\ensuremath{-}6}$.
Proper affine actions: a sufficient criterion

For a semisimple real Lie group G with a representation $$\rho $$ on a finite-dimensional real vector space V, we give a sufficient criterion on $$\rho $$ for existence of a group of affine transformations of V whose linear part is Zariski-dense in $$\rho (G)$$ and that is free, nonabelian and acts properly discontinuously on V. This new criterion is more general than the one given in Smilga (Groups Geom Dyn 12(2):449–528, 2018), insofar as it also deals with “swinging” representations. When G is split, almost all the irreducible representations of G that have 0 as a weight satisfy this criterion. We conjecture that it is actually a necessary and sufficient criterion.
The Structural Biology of Eastern Equine Encephalitis Virus, an Emerging Viral Threat

Alphaviruses are arboviruses that cause arthritis and encephalitis in humans. Eastern Equine Encephalitis Virus (EEEV) is a mosquito-transmitted alphavirus that is implicated in severe encephalitis in humans with high mortality. However, limited insights are available into the fundamental biology of EEEV and residue-level details of its interactions with host proteins. In recent years, outbreaks of EEEV have been reported mainly in the United States, raising concerns about public safety. This review article summarizes recent advances in the structural biology of EEEV based mainly on single-particle cryogenic electron microscopy (cryoEM) structures. Together with functional analyses of EEEV and related alphaviruses, these structural investigations provide clues to how EEEV interacts with host proteins, which may open avenues for the development of therapeutics.
The Effects of Parental and Sibling Incarceration: Evidence from Ohio

Every year, millions of Americans experience the incarceration of a family member. Using 30 years of administrative data from Ohio and exploiting differing incarceration propensities of randomly assigned judges, this paper provides the first quasi-experimental estimates of the effects of parental and sibling incarceration in the United States. Parental incarceration has beneficial effects on some important outcomes for children, reducing their likelihood of incarceration by 4.9 percentage points and improving their adult neighborhood quality. While estimates on academic performance and teen parenthood are imprecise, we reject large positive or negative effects. Sibling incarceration leads to similar reductions in criminal activity.
Medium amplitude parallel superposition (MAPS) rheology of a wormlike micellar solution

The weakly nonlinear rheology of a surfactant solution of wormlike micelles is investigated from both a modeling and experimental perspective using the framework of medium amplitude parallel superposition (MAPS) rheology. MAPS rheology defines material functions, such as the third-order complex compliance, which span the entire weakly nonlinear response space of viscoelastic materials to simple shear deformations. Three-tone oscillatory shear deformations are applied to obtain feature-rich data characterizing the third-order complex compliance with high data throughput. Here, data for a CPyCl solution are compared to the analytical solution for the MAPS response of a reptation-reaction constitutive model, which treats micelles as linear polymers that can break apart and recombine in solution. Regression of the data to the model predictions provides new insight into how these breakage and recombination processes are affected by shear, and demonstrates the importance of using information-rich data to infer precise estimates of model parameters.
Drug Discovery Approaches using Quantum Machine Learning

Traditional drug discovery pipeline takes several years and cost billions of dollars. Deep generative and predictive models are widely adopted to assist in drug development. Classical machines cannot efficiently produce atypical patterns of quantum computers which might improve the training quality of learning tasks. We propose a suite of quantum machine learning techniques e.g., generative adversarial network (GAN), convolutional neural network (CNN) and variational auto-encoder (VAE) to generate small drug molecules, classify binding pockets in proteins, and generate large drug molecules, respectively.