cssi/SciSciGPT-SciSciNet huggingface dataset:
Theory of Weak Identification in Semiparametric Models

We provide general formulation of weak identification in semiparametric models and an efficiency concept. Weak identification occurs when a parameter is weakly regular, i.e., when it is locally homogeneous of degree zero. When this happens, consistent or equivariant estimation is shown to be impossible. We then show that there exists an underlying regular parameter that fully characterizes the weakly regular parameter. While this parameter is not unique, concepts of sufficiency and minimality help pin down a desirable one. If estimation of minimal sufficient underlying parameters is inefficient, it introduces noise in the corresponding estimation of weakly regular parameters, whence we can improve the estimators by local asymptotic Rao-Blackwellization. We call an estimator weakly efficient if it does not admit such improvement. New weakly efficient estimators are presented in linear IV and nonlinear regression models. Simulation of a linear IV model demonstrates how 2SLS and optimal IV estimators are improved.
A New Martian Crater Chronology: Implications for Jezero Crater

Crater chronologies are a fundamental tool to assess relative and absolute ages of planetary surfaces when direct radiometric dating is not available. Martian crater chronologies are derived from lunar crater spatial densities on terrains with known radiometric ages, and thus they critically depend on the extrapolation Moon to Mars. This extrapolation requires knowledge of the time evolution of the impact flux, including contributions from various impactor populations, factors that are not trivially connected to the dynamical evolution of the early Solar System. In this paper, we will present a new martian crater chronology based on current dynamical models, and consider the main sources of uncertainties. The new martian crater chronology is discussed using two interesting applications: Jezero crater's dark terrain (relevant to the NASA Mars 2020 mission) and the southern heavily cratered highlands. [abridged]
Impact of Electron-Phonon Interaction on Thermal Transport: A Review

A thorough understanding of the microscopic picture of heat conduction in solids is critical to a broad range of applications, from thermal management of microelectronics to more efficient thermoel...
Distributed linear regression by averaging

Distributed statistical learning problems arise commonly when dealing with large datasets. In this setup, datasets are partitioned over machines, which compute locally, and communicate short messages. Communication is often the bottleneck. In this paper, we study one-step and iterative weighted parameter averaging in statistical linear models under data parallelism. We do linear regression on each machine, send the results to a central server and take a weighted average of the parameters. Optionally, we iterate, sending back the weighted average and doing local ridge regressions centered at it. How does this work compared to doing linear regression on the full data? Here, we study the performance loss in estimation and test error, and confidence interval length in high dimensions, where the number of parameters is comparable to the training data size. We find the performance loss in one-step weighted averaging, and also give results for iterative averaging. We also find that different problems are affected differently by the distributed framework. Estimation error and confidence interval length increases a lot, while prediction error increases much less. We rely on recent results from random matrix theory, where we develop a new calculus of deterministic equivalents as a tool of broader interest.
Rule of law and purpose of the corporation

Research Question: What is the purpose of the modern corporation? How is the rule of law related to the purpose of the modern corporation? Research Insights: We study the role of rule of law in enabling corporations to enhance economic prosperity and diminish income inequality across the globe. Theoretical/Academic Implications: First, we provide empirical evidence on the role of rule of law in enhancing economic prosperity and diminishing income inequality across the globe. Next, we draw on the law and finance literature, and the finance and growth literature to highlight the role of rule of law in enabling corporations to be the major driver of a country’s economic growth. Specifically, rule of law is necessary for a citizenry’s belief in secure private property rights, which gives the citizenry confidence to invest in physical capital, human capital, and innovation – the three catalysts of economic growth. Also, the rule of law allows for an effective judiciary that can enforce legal contracts. Shareholder reliance on limited liability, and debtholder rights originate from the legal contracts among shareholders, debtholders, and other stakeholders. This highlights the importance of rule of law in enabling companies to raise equity and debt financing, leading to financial development. Given this background on the role of the rule of law in the issuance of equity capital to provide financial resources to corporations, we analyze the current debate among policymakers, corporate leaders, institutional investors, and social activists on the purpose of the modern corporation. We conclude that the modern corporation should maximize long-term shareholder value, while conforming to the law of the land. Practitioner/Policy Implications: At the national and international level, policymakers should focus on improving rule of law – this enhances economic prosperity and diminishes income inequality. At the individual country level, we suggest steps to align shareholder wealth maximization with stakeholder interests: First, antitrust public policies should be vigorously enforced to maintain and enhance competition in product markets and labor markets. Second, management and board compensation should be reformed to focus on creating and sustaining long-term shareholder value. Finally, for many of society’s more serious problems, corporations do not represent the appropriate level of action. Climate change, for example, poses significant challenges for societies and businesses. But significant changes to combat climate change require public policy changes in the United States and abroad. Turning more to corporations because the political process seems broken won’t do.
Collaborative Localization for Micro Aerial Vehicles

We present a framework for performing collaborative localization for groups of micro aerial vehicles (MAV) that use vision-based sensing. The vehicles are each assumed to be equipped with a monocular camera, and to be capable of communicating with each other. This collaborative localization approach is developed as a decentralized algorithm and built in a distributed fashion where individual and relative pose estimation techniques are combined for the group to localize against surrounding environments. The MAVs initially detect and match salient features between each other to create a sparse reconstruction of the observed environment, which acts as a global map. Once a map is available, each MAV individually performs feature detection and tracking with a robust outlier rejection process to estimate its own pose in 6 degrees of freedom. When needed, one or more MAVs can compute poses for another MAV through relative measurements, which is achieved by exploiting multiple view geometry concepts. These relative measurements are then fused with individual measurements in a consistent fashion to result in more accurate pose estimates. We present the results of the algorithm on image data from MAV flights both in simulation and real life, and discuss the advantages of collaborative localization in improving pose estimation accuracy.
On Koopman mode decomposition and tensor component analysis

Koopman mode decomposition and tensor component analysis [also known as CANDECOMP (canonical decomposition)/PARAFAC (parallel factorization)] are two popular approaches of decomposing high dimensional datasets into modes that capture the most relevant features and/or dynamics. Despite their similar goal, the two methods are largely used by different scientific communities and are formulated in distinct mathematical languages. We examine the two together and show that, under certain conditions on the data, the theoretical decomposition given by the tensor component analysis is the same as that given by Koopman mode decomposition. This provides a "bridge" with which the two communities should be able to more effectively communicate. Our work provides new possibilities for algorithmic approaches to Koopman mode decomposition and tensor component analysis and offers a principled way in which to compare the two methods. Additionally, it builds upon a growing body of work showing that dynamical systems theory and Koopman operator theory, in particular, can be useful for problems that have historically made use of optimization theory.
Heterogeneous Graphlets

In this article, we introduce a generalization of graphlets to heterogeneous networks called typed graphlets. Informally, typed graphlets are small typed induced subgraphs. Typed graphlets generalize graphlets to rich heterogeneous networks as they explicitly capture the higher-order typed connectivity patterns in such networks. To address this problem, we describe a general framework for counting the occurrences of such typed graphlets. The proposed algorithms leverage a number of combinatorial relationships for different typed graphlets. For each edge, we count a few typed graphlets, and with these counts along with the combinatorial relationships, we obtain the exact counts of the other typed graphlets in o(1) constant time. Notably, the worst-case time complexity of the proposed approach matches the time complexity of the best known untyped algorithm. In addition, the approach lends itself to an efficient lock-free and asynchronous parallel implementation. While there are no existing methods for typed graphlets, there has been some work that focused on computing a different and much simpler notion called colored graphlet. The experiments confirm that our proposed approach is orders of magnitude faster and more space-efficient than methods for computing the simpler notion of colored graphlet. Unlike these methods that take hours on small networks, the proposed approach takes only seconds on large networks with millions of edges. Notably, since typed graphlet is more general than colored graphlet (and untyped graphlets), the counts of various typed graphlets can be combined to obtain the counts of the much simpler notion of colored graphlets. The proposed methods give rise to new opportunities and applications for typed graphlets.
Swivel: Hardening WebAssembly against Spectre.

We describe Swivel, a new compiler framework for hardening WebAssembly (Wasm) against Spectre attacks. Outside the browser, Wasm has become a popular lightweight, in-process sandbox and is, for example, used in production to isolate different clients on edge clouds and function-as-a-service platforms. Unfortunately, Spectre attacks can bypass Wasm's isolation guarantees. Swivel hardens Wasm against this class of attacks by ensuring that potentially malicious code can neither use Spectre attacks to break out of the Wasm sandbox nor coerce victim code-another Wasm client or the embedding process-to leak secret data. We describe two Swivel designs, a software-only approach that can be used on existing CPUs, and a hardware-assisted approach that uses extension available in Intel 11th generation CPUs. For both, we evaluate a randomized approach that mitigates Spectre and a deterministic approach that eliminates Spectre altogether. Our randomized implementations impose under 10.3% overhead on the Wasm-compatible subset of SPEC 2006, while our deterministic implementations impose overheads between 3.3% and 240.2%. Though high on some benchmarks, Swivel's overhead is still between 9x and 36.3x smaller than existing defenses that rely on pipeline fences.
It Takes Two to Tango: Combining Visual and Textual Information for Detecting Duplicate Video-Based Bug Reports

When a bug manifests in a user-facing application, it is likely to be exposed through the graphical user interface (GUI). Given the importance of visual information to the process of identifying and understanding such bugs, users are increasingly making use of screenshots and screen-recordings as a means to report issues to developers. However, when such information is reported en masse, such as during crowd-sourced testing, managing these artifacts can be a time-consuming process. As the reporting of screen-recordings in particular becomes more popular, developers are likely to face challenges related to manually identifying videos that depict duplicate bugs. Due to their graphical nature, screen-recordings present challenges for automated analysis that preclude the use of current duplicate bug report detection techniques. To overcome these challenges and aid developers in this task, this paper presents Tango, a duplicate detection technique that operates purely on video-based bug reports by leveraging both visual and textual information. Tango combines tailored computer vision techniques, optical character recognition, and text retrieval. We evaluated multiple configurations of Tango in a comprehensive empirical evaluation on 4,860 duplicate detection tasks that involved a total of 180 screen-recordings from six Android apps. Additionally, we conducted a user study investigating the effort required for developers to manually detect duplicate video-based bug reports and compared this to the effort required to use Tango. The results reveal that Tango's optimal configuration is highly effective at detecting duplicate video-based bug reports, accurately ranking target duplicate videos in the top-2 returned results in 83% of the tasks. Additionally, our user study shows that, on average, Tango can reduce developer effort by over 60%, illustrating its practicality.
Analyzing The Spatiotemporal Interaction And Propagation Of Atn Biomarkers In Alzheimer’s Disease Using Longitudinal Neuroimaging Data

Three major biomarkers: $\beta$-amyloid (A), pathologic tau (T), and neurodegeneration (N), are recognized as valid proxies for neuropathologic changes of Alzheimer’s disease. While there are extensive studies on cerebrospinal fluids biomarkers (amyloid, tau), the spatial propagation pattern across brain is missing and their interactive mechanisms with neurodegeneration are still unclear. To this end, we aim to analyze the spatiotemporal associations between ATN biomarkers using large-scale neuroimaging data. We first investigate the temporal appearances of amyloid plaques, tau tangles, and neuronal loss by modeling the longitudinal transition trajectories. Second, we propose linear mixed-effects models to quantify the pathological interactions and propagation of ATN biomarkers at each brain region. Our analysis of the current data shows that there exists a temporal latency in the build-up of amyloid to the onset of tau pathology and neurodegeneration. The propagation pattern of amyloid can be characterized by its diffusion along the topological brain network. Our models provide sufficient evidence that the progression of pathological tau and neurodegeneration share a strong regional association, which is different from amyloid.
Progressive failure of a unidirectional fiber-reinforced composite using the method of cells: Discretization objective computational results

Abstract The smeared crack band theory is implemented within the high-fidelity generalized method of cells micromechanics model to capture progressive failure within the constituents of a composite material while retaining objectivity with respect to the size of the discretization elements used in the model. Orientation of the crack band is determined using the maximum principal stress. When oriented perpendicular to the maximum principle stress the faces of the cracks in the crack band are subjected to only normal tractions and grow under pure mode I conditions. The traction–separation law governing the behavior of the crack band is related to the mode I fracture toughness, and formation of the crack band is initiated with a maximum stress criterion. Conversely, if the direction of the principal stress with the largest magnitude is compressive, it is assumed that the cracks within the crack band are constrained from growing in mode I. Instead, it is assumed that mode II cracks form within the crack band oriented along the plane of maximum shear stress. A Mohr–Coulomb initiation criterion is utilized to incorporate the effects of the normal tractions acting on the crack faces, and an effective shear traction is defined accordingly. The effective shear traction versus mode II separation law is a function of the mode II fracture toughness. A repeating unit cell containing 13 randomly arranged fibers is modeled and subjected to a combination of transverse tension/compression and transverse shear loading. The implementation is verified against experimental data and an equivalent finite element model that utilizes the same implementation of the crack band theory. Additionally, a sensitivity study is also performed on the effect of the size of the RUC on the stiffness and strength of the RUC.
Bilevel Optimization: Convergence Analysis and Enhanced Design

Bilevel optimization has arisen as a powerful tool for many machine learning problems such as meta-learning, hyperparameter optimization, and reinforcement learning. In this paper, we investigate the nonconvex-strongly-convex bilevel optimization problem. For deterministic bilevel optimization, we provide a comprehensive convergence rate analysis for two popular algorithms respectively based on approximate implicit differentiation (AID) and iterative differentiation (ITD). For the AID-based method, we orderwisely improve the previous convergence rate analysis due to a more practical parameter selection as well as a warm start strategy, and for the ITD-based method we establish the first theoretical convergence rate. Our analysis also provides a quantitative comparison between ITD and AID based approaches. For stochastic bilevel optimization, we propose a novel algorithm named stocBiO, which features a sample-efficient hypergradient estimator using efficient Jacobian- and Hessian-vector product computations. We provide the convergence rate guarantee for stocBiO, and show that stocBiO outperforms the best known computational complexities orderwisely with respect to the condition number $\kappa$ and the target accuracy $\epsilon$. We further validate our theoretical results and demonstrate the efficiency of bilevel optimization algorithms by the experiments on meta-learning and hyperparameter optimization.
Sharp endpoint estimates for eigenfunctions restricted to submanifolds of codimension 2

Abstract Burq-Gerard-Tzvetkov [10] and Hu [26] established L p estimates ( 2 ≤ p ≤ ∞ ) for the restriction of eigenfunctions to submanifolds. The estimates are sharp, except for the log loss at the endpoint L 2 estimates for submanifolds of codimension 2. It has long been believed that the log loss at the endpoint can be removed in general, while the problem is still open. So this paper is devoted to the study of sharp endpoint restriction estimates for eigenfunctions in this case. Chen-Sogge [17] removed the log loss for the geodesics on 3-dimensional manifolds. In this paper, we generalize their result to higher dimensions and prove that the log loss can be removed for totally geodesic submanifolds of codimension 2. Moreover, on 3-dimensional manifolds, we can remove the log loss for curves with nonvanishing geodesic curvatures, and more general finite type curves. The problem in 3D is essentially related to Hilbert transforms along curves in the plane and a class of singular oscillatory integrals studied by Phong-Stein [35] , Ricci-Stein [38] , Pan [32] , Seeger [40] , Carbery-Perez [15] .
Assortativity measures for weighted and directed networks

Assortativity measures the tendency of a vertex in a network being connected by other vertexes with respect to some vertex-specific features. Classical assortativity coefficients are defined for unweighted and undirected networks with respect to vertex degree. We propose a class of assortativity coefficients that capture the assortative characteristics and structure of weighted and directed networks more precisely. The vertex-to-vertex strength correlation is used as an example, but the proposed measure can be applied to any pair of vertex-specific features. The effectiveness of the proposed measure is assessed through extensive simulations based on prevalent random network models in comparison with existing assortativity measures. In application World Input-Ouput Networks,the new measures reveal interesting insights that would not be obtained by using existing ones. An implementation is publicly available in a R package "wdnet".
VC density of definable families over valued fields

We prove a tight bound on the number of realized $0/1$ patterns (or equivalently on the Vapnik-Chervonenkis codensity) of definable families in models of the theory of algebraically closed valued fields with a non-archimedean valuation. Our result improves the best known result in this direction proved by Aschenbrenner, Dolich, Haskell, Macpherson and Starchenko, who proved a weaker bound in the restricted case where the characteristics of the field $K$ and its residue field are both assumed to be $0$. The bound obtained here is optimal and without any restriction on the characteristics. We obtain the aforementioned bound as a consequence of another result on bounding the Betti numbers of semi-algebraic subsets of certain Berkovich analytic spaces, mirroring similar results known already in the case of o-minimal structures and for real closed, as well as, algebraically closed fields. The latter result is the first result in this direction and is possibly of independent interest. Its proof relies heavily on recent results of Hrushovski and Loeser on the topology of semi-algebraic subsets of Berkovich analytic spaces.
Single View Geocentric Pose in the Wild

Current methods for Earth observation tasks such as semantic mapping, map alignment, and change detection rely on near-nadir images; however, often the first available images in response to dynamic world events such as natural disasters are oblique. These tasks are much more difficult for oblique images due to observed object parallax. There has been recent success in learning to regress an object’s geocentric pose, defined as height above ground and orientation with respect to gravity, by training with airborne lidar registered to satellite images. We present a model for this novel task that exploits affine invariance properties to outperform state of the art performance by a wide margin. We also address practical issues required to deploy this method in the wild for real-world applications. Our data and code are publicly available 1.
Accurate localization microscopy by intrinsic aberration calibration.

A standard paradigm of localization microscopy involves extension from two to three dimensions by engineering information into emitter images, and approximation of errors resulting from the field dependence of optical aberrations. We invert this standard paradigm, introducing the concept of fully exploiting the latent information of intrinsic aberrations by comprehensive calibration of an ordinary microscope, enabling accurate localization of single emitters in three dimensions throughout an ultrawide and deep field. To complete the extraction of spatial information from microscale bodies ranging from imaging substrates to microsystem technologies, we introduce a synergistic concept of the rigid transformation of the positions of multiple emitters in three dimensions, improving precision, testing accuracy, and yielding measurements in six degrees of freedom. Our study illuminates the challenge of aberration effects in localization microscopy, redefines the challenge as an opportunity for accurate, precise, and complete localization, and elucidates the performance and reliability of a complex microelectromechanical system. The authors demonstrate accurate localization in three dimensions by comprehensive calibration of an ordinary microscope, exploiting the latent information of intrinsic aberrations. Rigid transformation of the emitter positions tests the method and enables measurements in six degrees of freedom.
Flow and noise from septa nozzles

Flow and noise fields are explored for the concept of distributed propulsion. A model-scale experiment is performed with an 8:1 aspect ratio rectangular nozzle that is divided into six passages by ...
Robust Fairness Under Covariate Shift.

Making predictions that are fair with regard to protected attributes (race, gender, age, etc.) has become an important requirement for classification algorithms. Existing techniques derive a fair model from sampled labeled data relying on the assumption that training and testing data are identically and independently drawn (iid) from the same distribution. In practice, distribution shift can and does occur between training and testing datasets as the characteristics of individuals interacting with the machine learning system change. We investigate fairness under covariate shift, a relaxation of the iid assumption in which the inputs or covariates change while the conditional label distribution remains the same. We seek fair decisions under these assumptions on target data with unknown labels. We propose an approach that obtains the predictor that is robust to the worst-case testing performance while satisfying target fairness requirements and matching statistical properties of the source data. We demonstrate the benefits of our approach on benchmark prediction tasks.
Mission opportunities for the flight validation of the kinetic impactor concept for asteroid deflection

Abstract The kinetic impactor technique for deflecting near-Earth objects (NEOs), whereby a spacecraft is directed to collide with a NEO to alter its orbit via momentum transfer, is one of several proposed methods for defending Earth against hazardous NEOs (asteroids and comets). In this paper we present detailed mission design concepts for a feasible and affordable kinetic impactor flight validation mission deployed to a currently known near-Earth asteroid (NEA). Several filter steps are devised that utilize relevant criteria to optimally balance key parameters, such as approach phase angle, estimated NEA diameter, relative velocity at intercept, and current NEA orbit knowledge, and produce refined lists of the most promising candidate target NEAs.
A BAYESIAN NONPARAMETRIC MODEL FOR INFERRING SUBCLONAL POPULATIONS FROM STRUCTURED DNA SEQUENCING DATA

There are distinguishing features or “hallmarks” of cancer that are found across tumors, individuals and types of cancer, and these hallmarks can be driven by specific genetic mutations. Yet within a single tumor there is often extensive genetic heterogeneity as evidenced by single-cell and bulk DNA sequencing data. The goal of this work is to jointly infer the underlying genotypes of tumor subpopulations and the distribution of those subpopulations in individual tumors by integrating single-cell and bulk sequencing data. Understanding the genetic composition of the tumor at the time of treatment is important in the personalized design of targeted therapeutic combinations and monitoring for possible recurrence after treatment. We propose a hierarchical Dirichlet process mixture model that incorporates the correlation structure induced by a structured sampling arrangement, and we show that this model improves the quality of inference. We develop a representation of the hierarchical Dirichlet process prior as a Gamma–Poisson hierarchy, and we use this representation to derive a fast Gibbs sampling inference algorithm using the augment-and-marginalize method. Experiments with simulation data show that our model outperforms standard numerical and statistical methods for decomposing admixed count data. Analyses of real acute lymphoblastic leukemia cancer sequencing dataset shows that our model improves upon state-of-the-art bioinformatic methods. An interpretation of the results of our model on this real dataset reveals comutated loci across samples.
Demonstration of the DC-Kerr effect in silicon-rich nitride.

We demonstrate the DC-Kerr effect in plasma enhanced chemical vapor deposition (PECVD) silicon-rich nitride (SRN) and use it to demonstrate a third order nonlinear susceptibility, χ(3), as high as (6±0.58)×10−19m2/V2. We employ spectral shift versus applied voltage measurements in a racetrack resonator as a tool to characterize the nonlinear susceptibilities of these films. In doing so, we demonstrate a χ(3) larger than that of silicon and argue that PECVD SRN can provide a versatile platform for employing optical phase shifters while maintaining a low thermal budget using a deposition technique readily available in CMOS process flows.
Dirichlet $L$-functions of quadratic characters of prime conductor at the central point

We prove that more than nine percent of the central values $L(\frac{1}{2},\chi_p)$ are non-zero, where $p\equiv 1 \pmod{8}$ ranges over primes and $\chi_p$ is the real primitive Dirichlet character of conductor $p$. Previously, it was not known whether a positive proportion of these central values are non-zero. As a by-product, we obtain the order of magnitude of the second moment of $L(\frac{1}{2},\chi_p)$, and conditionally we obtain the order of magnitude of the third moment. Assuming the Generalized Riemann Hypothesis, we show that our lower bound for the second moment is asymptotically sharp.
Collision Free Navigation with Interacting, Non-Communicating Obstacles

In this paper we consider the problem of navigation and motion control in an area densely populated with other agents. We propose an algorithm that, without explicit communication and based on the information it has, computes the best control action for all the agents and implements its own. Notably, the host agent (the agent executing the algorithm) computes the differences between the other agents' computed and observed control actions and treats them as known disturbances that are fed back into a robust control barrier function (RCBF) based quadratic program. A feedback loop is created because the computed control action for another agent depends on the previously used disturbance estimate. In the case of two interacting agents, stability of the feedback loop is proven and a performance guarantee in terms of constraint adherence is established. This holds whether the other agent executes the same algorithm or not.
Revisiting Priority k-Center: Fairness and Outliers

In the Priority k-Center problem, the input consists of a metric space (X,d), an integer k and for each point v ∈ X a priority radius r(v). The goal is to choose k-centers S ⊆ X to minimize max_{v ∈ X} 1/(r(v)) d(v,S). If all r(v)’s were uniform, one obtains the classical k-center problem. Plesnik [Jan Plesnik, 1987] introduced this problem and gave a 2-approximation algorithm matching the best possible algorithm for vanilla k-center. We show how the Priority k-Center problem is related to two different notions of fair clustering [Harris et al., 2019; Christopher Jung et al., 2020]. Motivated by these developments we revisit the problem and, in our main technical contribution, develop a framework that yields constant factor approximation algorithms for Priority k-Center with outliers. Our framework extends to generalizations of Priority k-Center to matroid and knapsack constraints, and as a corollary, also yields algorithms with fairness guarantees in the lottery model of Harris et al.
One semantic parser to parse them all: Sequence to sequence multi-task learning on semantic parsing datasets

Semantic parsers map natural language utterances to meaning representations. The lack of a single standard for meaning representations led to the creation of a plethora of semantic parsing datasets. To unify different datasets and train a single model for them, we investigate the use of Multi-Task Learning (MTL) architectures. We experiment with five datasets (Geoquery, NLMaps, TOP, Overnight, AMR). We find that an MTL architecture that shares the entire network across datasets yields competitive or better parsing accuracies than the single-task baselines, while reducing the total number of parameters by 68%. We further provide evidence that MTL has also better compositional generalization than single-task models. We also present a comparison of task sampling methods and propose a competitive alternative to widespread proportional sampling strategies.
Multiple memory formation in glassy landscapes

Cyclically sheared jammed packings form memories of the shear amplitude at which they were trained by falling into periodic orbits where each particle returns to the identical position in subsequent cycles. While simple models that treat clusters of rearranging particles as isolated two-state systems offer insight into this memory formation, they fail to account for the long training times and multiperiod orbits observed in simulated sheared packings. We show that adding interactions between rearranging clusters overcomes these deficiencies. In addition, interactions allow simultaneous encoding of multiple memories, which would not have been possible otherwise. These memories are different in an essential way from those found in other systems, such as multiple transient memories observed in sheared suspensions, and contain information about the strength of the interactions.
The complexity of counting edge colorings for simple graphs

Abstract We prove #P-completeness results for counting edge colorings on simple graphs. These strengthen the corresponding results on multigraphs from [4] . We prove that for any κ ≥ r ≥ 3 counting κ-edge colorings on r-regular simple graphs is #P-complete. Furthermore, we show that for planar r-regular simple graphs, where r ∈ { 3 , 4 , 5 } , counting edge colorings with κ colors for any κ ≥ r is also #P-complete. As there are no planar r-regular simple graphs for any r > 5 , these statements cover all interesting cases in terms of the parameters ( κ , r ) .
Hopf actions of some quantum groups on path algebras

Abstract Our first collection of results parametrize (filtered) actions of a quantum Borel U q ( b ) ⊂ U q ( sl 2 ) on the path algebra of an arbitrary (finite) quiver. When q is a root of unity, we give necessary and sufficient conditions for these actions to factor through corresponding finite-dimensional quotients, generalized Taft algebras T ( r , n ) and small quantum groups u q ( sl 2 ) . In the second part of the paper, we shift to the language of tensor categories. Here we consider a quiver path algebra equipped with an action of a Hopf algebra H to be a tensor algebra in the tensor category of representations H. Such a tensor algebra is generated by an algebra and bimodule in this tensor category. Our second collection of results describe the corresponding bimodule categories via an equivalence with categories of representations of certain explicitly described quivers with relations.
Harmonic analysis and statistics of the first Galois cohomology group

We utilize harmonic analytic tools to count the number of elements of the Galois cohomology group $$f\in H^1(K,T)$$ with discriminant-like invariant $$\text {inv}(f)\le X$$ as $$X\rightarrow \infty $$ . Specifically, Poisson summation produces a canonical decomposition for the corresponding generating series as a sum of Euler products for a very general counting problem. This type of decomposition is exactly what is needed to compute asymptotic growth rates using a Tauberian theorem. These new techniques allow for the removal of certain obstructions to known results and answer some outstanding questions on the generalized version of Malle’s conjecture for the first Galois cohomology group.
Enhancing Resilience of Distribution Networks by Coordinating Microgrids and Demand Response Programs in Service Restoration

In case of high-impact low-probability events, in order to restore the critical loads of the distribution network as much as possible, it is necessary to employ all available resources such as microgrids and distributed generations. This article presents a two-stage method for critical load restoration after an extreme event utilizing the coordination of all available distributed generations, microgrids, and demand response programs. In the first stage, the postdisaster reconfiguration of the network is determined, and in the second stage, the demand response outputs of the responsive loads and the restoration status of all critical loads are ascertained. In the first stage, an algorithm is proposed to determine electrical islands, and in the second stage, a new model based on the generalized Benders decomposition algorithm is proposed, which considers demand response programs and all operational constraints and aims to maximize the restored critical loads. The effectiveness of the proposed method is verified by simulating the 33- and 69-bus test systems.
The local topological free energy of proteins.

Abstract Protein folding, the process by which proteins attain a 3-dimensional conformation necessary for their function, remains an important unsolved problem in biology. A major gap in our understanding is how local properties of proteins relate to their global properties. In this manuscript, we use the Writhe and Torsion to introduce a new local topological/geometrical free energy that can be associated to 4 consecutive amino acids along the protein backbone. By analyzing a culled protein dataset from the PDB, our results show that high local topological free energy conformations are independent of sequence and may be involved in the rate limiting step in protein folding. By analyzing a set of 2-state single domain proteins, we find that the total local topological free energy of these proteins correlates with the experimentally observed folding rates reported in Plaxco et al. (2000).
Syn3 Gene Knockout Negatively Impacts Aspects of Reversal Learning Performance.

Behavioral flexibility enables the ability to adaptively respond to changes in contingency requirements to maintain access to desired outcomes, and deficits in behavioral flexibility have been documented in many psychiatric disorders. Previous research has shown a correlation between behavioral flexibility measured in a reversal learning test and Syn3, the gene encoding synapsin III, which negatively regulates phasic dopamine release. Syn3 expression in the hippocampus, striatum, and neocortex is reported to be negatively correlated with reversal learning performance, so here, we utilized a global knockout line to investigate reversal learning in mice homozygous wildtype, heterozygous null, and homozygous null for the Syn3 gene. Compared to wildtype animals, we found a reversal specific effect of genetic Syn3 deficiency that resulted in a greater proportional increase in trials required to reach a preset performance criteria during contingency reversal, despite no observed genotype effects on the ability to acquire the initial discrimination. Behavioral flexibility scores, which quantified the likelihood of switching subsequent choice behavior following positive or negative feedback, became significantly more negative in reversal only for Syn3 homozygous null mice, suggesting a substantial increase in perseverative behavior in the reversal phase. Syn3 ablation reduced the number of anticipatory responses made per trial, often interpreted as a measure of waiting impulsivity. Overall, Syn3 expression negatively affected behavioral flexibility in a reversal specific manner but may have reduced waiting impulsivity. Significance Statement Adaptations to changes in the environment are facilitated by behavioral flexibility, and inflexible behavior is observed in several mental health disorders. The Syn3 gene encodes synapsin III, a protein that negatively regulates phasic dopamine release by sequestering vesicles away from the ready-releasable pool. Previous research has shown a positive genetic correlation between Syn3 expression in brain and behavioral flexibility in a reversal learning task. Here, we show that mice carrying null alleles of the Syn3 gene exhibit less flexible responding following contingency reversal. These data reveal novel information about genetic mechanisms that may contribute to the impaired flexibility observed in multiple psychiatric conditions.
Mast cell function in prostate inflammation, fibrosis, and smooth muscle cell dysfunction.

LUTS-associated benign prostatic hyperplasia is derived from a combination of immune activation, extracellular matrix remodeling, hyperplasia, and smooth muscle cell contraction in prostates of men...
Photoreactive hydrogel stiffness influences volumetric muscle loss repair

Volumetric muscle loss (VML) injuries are characterized by permanent loss of muscle mass, structure, and function. Hydrogel biomaterials provide an attractive platform for skeletal muscle tissue engineering due to the ability to easily modulate their biophysical and biochemical properties to match a range of tissue characteristics. In this work we successfully developed a mechanically tunable hyaluronic acid (HA) hydrogel system to investigate the influence of hydrogel stiffness on VML repair. HA was functionalized with photoreactive norbornene groups to create hydrogel networks that rapidly crosslink via thiol-ene click chemistry with tailored mechanics. Mechanical properties were controlled by modulating the amount of matrix metalloproteinase (MMP)-degradable peptide crosslinker to produce hydrogels with increasing elastic moduli of 1.1 ± 0.002, 3.0 ± 0.002, and 10.6 ± 0.006 kPa mimicking a relevant range of developing and mature muscle stiffnesses. Functional muscle recovery was assessed following implantation of the HA hydrogels by in situ photopolymerization into rat latissimus dorsi (LD) VML defects at 12 and 24 weeks post-injury. After 12 weeks, muscles treated with medium stiffness (3.0 kPa) hydrogels produced maximum isometric forces most similar to contralateral healthy LD muscles. This trend persisted at 24 weeks post-injury, suggestive of sustained functional recovery. Histological analysis revealed a significantly larger zone of regeneration with more de novo muscle fibers following implantation of medium stiffness hydrogels in VML-injured muscles compared to other experimental groups. Lower (low and medium) stiffness hydrogels also appeared to attenuate the chronic inflammatory response characteristic of VML injuries, displaying similar levels of macrophage infiltration and polarization to healthy muscle. Together these findings illustrate the importance of hydrogel mechanical properties in supporting functional repair of VML injuries.
Evidence for a nuclear role for Drosophila Dlg as a regulator of the NURF complex

Proximity proteomics is used as an entry point toward identifying partners of the polarity-regulating tumor suppressor Discs-large. A nuclear pool of the protein associated with nucleosome remodeli...
Drell-Yan angular lepton distributions at small x from TMD factorization.

The Drell-Yan process is studied in the framework of TMD factorization in the Sudakov region s » Q2 » $$ {q}_{\perp}^2 $$ corresponding to recent LHC experiments with Q2 of order of mass of Z-boson and transverse momentum of DY pair ∼ few tens GeV. The DY hadronic tensors are expressed in terms of quark and quark-gluon TMDs with $$ \frac{1}{Q^2} $$ and $$ \frac{1}{N_c^2} $$ accuracy. It is demonstrated that in the leading order in Nc the higher-twist quark-quark-gluon TMDs reduce to leading-twist TMDs due to QCD equation of motion. The resulting hadronic tensors depend on two leading-twist TMDs: f1 responsible for total DY cross section, and Boer-Mulders function $$ {h}_1^{\perp } $$ . The corresponding qualitative and semi-quantitative predictions seem to agree with LHC data on five angular coefficients A0 − A4 of DY pair production. The remaining three coefficients A5 − A7 are determined by quark-quark-gluon TMDs multiplied by extra $$ \frac{1}{N_c} $$ so they appear to be relatively small in accordance with LHC results.
CoDERT: Distilling encoder representations with co-learning for transducer-based speech recognition

We propose a simple yet effective method to compress an RNN-Transducer (RNN-T) through the well-known knowledge distillation paradigm. We show that the transducer's encoder outputs naturally have a high entropy and contain rich information about acoustically similar word-piece confusions. This rich information is suppressed when combined with the lower entropy decoder outputs to produce the joint network logits. Consequently, we introduce an auxiliary loss to distill the encoder logits from a teacher transducer's encoder, and explore training strategies where this encoder distillation works effectively. We find that tandem training of teacher and student encoders with an inplace encoder distillation outperforms the use of a pre-trained and static teacher transducer. We also report an interesting phenomenon we refer to as implicit distillation, that occurs when the teacher and student encoders share the same decoder. Our experiments show 5.37-8.4% relative word error rate reductions (WERR) on in-house test sets, and 5.05-6.18% relative WERRs on LibriSpeech test sets.
Root Carbon Interaction with Soil Minerals Is Dynamic, Leaving a Legacy of Microbially Derived Residues

Minerals preserve the oldest, most persistent soil carbon, and mineral characteristics appear to play a critical role in the formation of soil organic matter (SOM) associations. To test the hypothesis that roots, and differences in carbon source and microbial communities, influence mineral SOM associations over short timescales, we incubated permeable mineral bags in soil microcosms with and without plants, inside a 13CO2 labeling chamber. Mineral bags contained quartz, ferrihydrite, kaolinite, or soil minerals isolated via density separation. Using 13C-nuclear magnetic resonance, Fourier transform ion cyclotron resonance mass spectrometry, and lipidomics, we traced carbon deposition onto minerals, characterizing total carbon, 13C enrichment, and SOM chemistry over three growth stages of Avena barbata. Carbon accumulation was rapid and mineral-dependent but slowed with time; the accumulated amount was not significantly affected by root presence. However, plant roots strongly shaped the chemistry of mineral-associated SOM. Minerals incubated in a plant rhizosphere were associated with a more diverse array of compounds (with different functional groups-carbonyl, aromatics, carbohydrates, and lipids) than minerals incubated in an unplanted bulk soil control. We also found that many of the lipids that sorbed to minerals were microbially derived, including many fungal lipids. Together, our data suggest that diverse rhizosphere-derived compounds may represent a transient fraction of mineral SOM, rapidly exchanging with mineral surfaces.