cssi/SciSciGPT-SciSciNet huggingface dataset:
Surface residues and non-additive interactions stabilize a consensus homeodomain protein.

Abstract Despite the widely reported success of consensus design in producing highly stabilized proteins, little is known about the physical mechanisms underlying this stabilization. Here we explore the potential sources of stabilization by performing a systematic analysis of the 29 substitutions that we previously found to collectively stabilize a consensus homeodomain compared to an extant homeodomain. By separately introducing groups of consensus substitutions that alter or preserve charge state, occur at varying degrees of residue burial, and occur at positions of varying degrees of conservation, we determine the extent to which these three features contribute to the consensus stability enhancement. Surprisingly, we find that the largest total contribution to stability comes from consensus substitutions on the protein surface and that the largest per-substitution contributions come from substitutions that maintain charge state. This finding suggests that although consensus proteins are often enriched in charged residues, consensus stabilization does not result primarily from interactions involving charged residues. Although consensus substitutions at strongly conserved positions also contribute disproportionately to stabilization, significant stabilization is also contributed from substitutions at weakly conserved positions. Furthermore, we find that identical consensus substitutions show larger stabilizing effects when introduced into the consensus background than when introduced into an extant homeodomain, indicating that synergistic, stabilizing interactions among the consensus residues contribute to consensus stability enhancement of the homeodomain. By measuring DNA binding affinity for the same set of variants, we find that although consensus design of the homeodomain increases both affinity and folding stability, it does so using a largely non-overlapping set of substitutions.
Low-rank Matrix Completion in a General Non-orthogonal Basis

Abstract This paper considers theoretical analysis of recovering a low rank matrix given a few expansion coefficients with respect to any basis. The current approach generalizes the existing analysis for the low-rank matrix completion problem with sampling under entry sensing or with respect to a symmetric orthonormal basis. The analysis is based on dual certificates using a dual basis approach. We introduce a condition on the basis called the correlation condition. This condition can be computed in time O ( n 3 ) and holds for many cases of deterministic basis. If the correlation condition holds and the underlying low rank matrix obeys the coherence condition with parameter ν, under additional mild assumptions, our main result shows that the true matrix can be recovered with very high probability from O ( n r ν log 2 ⁡ n ) uniformly random expansion coefficients.
DebiNet: Debiasing Linear Models with Nonlinear Overparameterized Neural Networks

Recent years have witnessed strong empirical performance of over-parameterized neural networks on various tasks and many advances in the theory, e.g. the universal approximation and provable convergence to global minimum. In this paper, we incorporate over-parameterized neural networks into semi-parametric models to bridge the gap between inference and prediction, especially in the high dimensional linear problem. By doing so, we can exploit a wide class of networks to approximate the nuisance functions and to estimate the parameters of interest consistently. Therefore, we may offer the best of two worlds: the universal approximation ability from neural networks and the interpretability from classic ordinary linear model, leading to both valid inference and accurate prediction. We show the theoretical foundations that make this possible and demonstrate with numerical experiments. Furthermore, we propose a framework, DebiNet, in which we plug-in arbitrary feature selection methods to our semi-parametric neural network. DebiNet can debias the regularized estimators (e.g. Lasso) and perform well, in terms of the post-selection inference and the generalization error.
Training a Single Bandit Arm

In several applications of the stochastic multi-armed bandit problem, the traditional objective of maximizing the expected sum of rewards obtained can be inappropriate. Motivated by the problem of optimizing job assignments to train novice workers of unknown quality in labor platforms, we consider a new objective in the classical setup. Instead of maximizing the expected total reward from $T$ pulls, we consider the vector of cumulative rewards earned from the $K$ arms at the end of $T$ pulls, and aim to maximize the expected value of the highest cumulative reward across the $K$ arms. This corresponds to the objective of training a single, highly skilled worker using a limited supply of training jobs. For this new objective, we show that any policy must incur an instance-dependent asymptotic regret of $\Omega(\log T)$ (with a higher instance-dependent constant compared to the traditional objective) and an instance-independent regret of $\Omega(K^{1/3}T^{2/3})$. We then design an explore-then-commit policy, featuring exploration based on appropriately tuned confidence bounds on the mean reward and an adaptive stopping criterion, which adapts to the problem difficulty and achieves these bounds (up to logarithmic factors). Our numerical experiments demonstrate the efficacy of this policy compared to several natural alternatives in practical parameter regimes.
Viability of Optical Coherence Tomography for Iris Presentation Attack Detection

In this paper, we propose the use of Optical Coherence Tomography (OCT) imaging for the problem of iris presentation attack (PA) detection. We assess its viability by comparing its performance with respect to traditional iris imaging modalities, viz., near-infrared (NIR) and visible spectrum. OCT imaging provides a cross-sectional view of an eye, whereas traditional imaging provides 2D iris textural information. PA detection is performed using three state-of-the-art deep architectures (VGG19, ResNet50 and DenseNet121) to differentiate between bonafide and PA samples for each of the three imaging modalities. Experiments are performed on a dataset of 2,169 bonafide, 177 Van Dyke eyes and 360 cosmetic contact images acquired using all three imaging modalities under intra-attack (known PAs) and cross-attack (unknown PAs) scenarios. We observe promising results demonstrating OCT as a viable solution for iris presentation attack detection.
Designing an Interactive Visualization System for Monitoring Participant Compliance in a Large-Scale, Longitudinal Study

Frequent monitoring of participant compliance is necessary when conducting large-scale, longitudinal studies to ensure that the collected data is of sufficiently high quality. While the need for achieving high compliance has been underscored and there are discussions on incentives and factors affecting compliance, little is shared about the actual processes and tools used for monitoring compliance in such studies. Monitoring participant compliance with respect to multi-modal data can be a tedious process, especially if there are only a few personnel involved. In this case study, we describe the iterative design of an interactive visualization system we developed for monitoring compliance and refined based on changing requirements in an ongoing study. We find that the visualization system, leveraging the digital medium, both facilitates the exploratory tasks of monitoring participant compliance and supports asynchronous collaboration among non-co-located researchers. Our documented requirements for checking participant compliance as well as the design of the visualization system can help inform the compliance-monitoring process in future studies.
Chemotactic migration of bacteria in porous media

Chemotactic migration of bacteria-their ability to direct multicellular motion along chemical gradients-is central to processes in agriculture, the environment, and medicine. However, current understanding of migration is based on studies performed in bulk liquid, despite the fact that many bacteria inhabit tight porous media such as soils, sediments, and biological gels. Here, we directly visualize the chemotactic migration of Escherichia coli populations in well-defined 3D porous media in the absence of any other imposed external forcing (e.g., flow). We find that pore-scale confinement is a strong regulator of migration. Strikingly, cells use a different primary mechanism to direct their motion in confinement than in bulk liquid. Furthermore, confinement markedly alters the dynamics and morphology of the migrating population-features that can be described by a continuum model, but only when standard motility parameters are substantially altered from their bulk liquid values to reflect the influence of pore-scale confinement. Our work thus provides a framework to predict and control the migration of bacteria, and active matter in general, in complex environments.
Real talk: saturated sites of violence in CS education

Despite numerous CS education pedagogical interventions, the pipeline of Black women in Computing has not increased, which illustrates the need to address structural issues (such as racism, sexism, power, and privilege) that impact Black women's intersectional identities. Without honest conversations about power relations within the field of Computing, one cannot expect to engender social change that equates to equity for all CS students. Leveraging intersectionality as a critical framework, we interview 18 Black women about their experiences navigating the computing education ecosystem. Intersectional analysis of Black women's experiences reveals that CS education consists of saturated sites of violence in which interconnected systems of power converge to enact oppression. Findings reveal three saturated sites of violence within CS education: 1. traditional K-12 classrooms; 2. predominantly White institutions; and 3. internships as supplementary learning experiences.
Spheres to jets tuning event shapes with 5d simplified models

Hidden sectors could give rise to a wide variety of events at the LHC. Confining hidden sectors are known to engender events with a small number of jets when they are weakly-coupled at high energies, and quasi-spherical soft unclustered energy patterns (SUEPs) when they are very strongly-coupled (large ‘t Hooft coupling) at high energies. The intermediate regime is murky, and could give rise to signals hiding from existing search strategies. While the intermediate coupling regime is not calculable, it is possible to pursue a phenomenological approach in which one creates signals that are intermediate between spherical and jetty. We propose a strategy for generating events of this type using simplified models in extra dimensions. The degree to which the event looks spherical is related to the number of decays produced near kinematic threshold. We provide an analytic understanding of how this is determined by parameters of the model. To quantify the shape of events produced with this model, we use a recently proposed observable — event isotropy — which is a better probe of the spherical regime than earlier event shape observables.
Sex differences in the impact of childhood socioeconomic status on immune function.

Early life stress increases one’s risk for health problems later in life, and many studies find that these effects are sex-differentiated. Here, we examined relationships between multiple sources of early life stress and adult immune function in humans across several functional assays. Adult participants provided retrospective information about their childhood (a) socioeconomic status, (b) household unpredictability, and (c) exposure to adverse experiences. Participants’ peripheral blood mononuclear cells (PBMCs) were then isolated for use in functional assays of immune performance: (a) tumor cell lysis by natural killer cells, (b) phagocytosis of Escherichia coli bioparticles, and (c) mitogen-induced leukocyte proliferation and cytokine release. In men, lower childhood socioeconomic status predicted decrements in immunological performance across functional assays, along with greater spontaneous cytokine release from PBMCs. These changes co-occurred with elevations in plasma testosterone levels. Similar effects were not observed for other sources of stress, nor were they found in women (with the exception of spontaneous cytokine release). These findings provide evidence that low childhood socioeconomic status has a lasting negative impact on multiple aspects of immune function, particularly in men.
pSONIC: Ploidy-aware Syntenic Orthologous Networks Identified via Collinearity

With the rapid rise in availability of high-quality genomes for closely related species, methods for orthology inference that incorporate synteny are increasingly useful. Polyploidy perturbs the 1:1 expected frequencies of orthologs between two species, complicating the identification of orthologs. Here we present a method of ortholog inference, Ploidy-aware Syntenic Orthologous Networks Identified via Collinearity (pSONIC). We demonstrate the utility of pSONIC using four species in the cotton tribe (Gossypieae), including one allopolyploid, and place between 75% and 90% of genes from each species into nearly 32,000 orthologous groups, 97% of which consist of at most singletons or tandemly duplicated genes-58.8% more than comparable methods that do not incorporate synteny. We show that 99% of singleton gene groups follow the expected tree topology and that our ploidy-aware algorithm recovers 97.5% identical groups when compared to splitting the allopolyploid into its two respective subgenomes, treating each as separate "species."
Central moment lattice Boltzmann method on a rectangular lattice

Simulating inhomogeneous flows with different characteristic scales in different coordinate directions using the collide-and-stream based lattice Boltzmann methods (LBMs) can be accomplished efficiently using rectangular lattice grids. We develop and investigate a new rectangular central moment LBM based on a non-orthogonal moment basis and involving multiple relaxation times. The equilibria to which the central moments relax under collision in this approach are obtained from matching with those corresponding to the continuous Maxwell distribution. A Chapman–Enskog analysis is performed to derive the correction terms to the second order moment equilibria involving the grid aspect ratio and velocity gradients that restore the isotropy of the viscous stress tensor and eliminate the non-Galilean invariant cubic velocity terms in the resulting hydrodynamical equations. A special case of this rectangular formulation involving the raw moments is also constructed. The resulting schemes represent a considerable simplification, especially for the transformation matrices and isotropy corrections, and improvement over the existing lattice Boltzmann schemes based on raw moments on rectangular lattice grids that use orthogonal moment basis. Numerical validation study of both the proposed rectangular LBMs for a variety of benchmark flows is performed, which shows good accuracy at various grid aspect ratios. The ability of our proposed schemes to simulate flows at relatively low grid aspect ratios and higher Reynolds numbers than considered in prior approaches is demonstrated. Furthermore, simulations reveal the superior stability characteristics of the rectangular central moment LBM over that based on raw moments in handling shear flows at lower viscosities and/or higher characteristic velocities. In addition, computational advantages of using our rectangular LB formulation in lieu of that based on the square lattice are shown.
Probing Ionomer Interactions with Electrocatalyst Particles in Solution

The interaction between ionomer (ion-conducting polymer) and catalyst particles in porous electrodes of electrochemical energy-conversion devices is a critical yet poorly understood phenomenon that controls device performance. This interaction stems from that in the electrode precursor inks, which also governs porous-electrode morphology during formation. In this letter, we probe the origin of this interaction in solution to unravel the ionomer/particle agglomeration process. Quartz-crystal microbalance studies detail ionomer adsorption (with a range of charge densities) to model surfaces under a variety of solvent environments, and isothermal-titration-calorimetry experiments extract thermodynamic binding information to platinum- and carbon-black nanoparticles. Results reveal that under the conditions tested, ionomer binding to platinum is similar to carbon, suggesting that adsorption to platinum-on-carbon catalyst particles in inks is likely dictated mostly by hydrophobic interactions with the carbon surface. Furthermore, water-rich solvents (relative to propanol) promote ionomer adsorption. Finally, ionomer dispersions change with time, yielding dynamic binding interactions.
High-Dimensional Mixed-Frequency IV Regression

This article introduces a high-dimensional linear IV regression for the data sampled at mixed frequencies. We show that the high-dimensional slope parameter of a high-frequency covariate can be ide...
Joint Biomedical Entity and Relation Extraction with Knowledge-Enhanced Collective Inference

Compared to the general news domain, information extraction (IE) from biomedical text requires much broader domain knowledge. However, many previous IE methods do not utilize any external knowledge during inference. Due to the exponential growth of biomedical publications, models that do not go beyond their fixed set of parameters will likely fall behind. Inspired by how humans look up relevant information to comprehend a scientific text, we present a novel framework that utilizes external knowledge for joint entity and relation extraction named KECI (Knowledge-Enhanced Collective Inference). Given an input text, KECI first constructs an initial span graph representing its initial understanding of the text. It then uses an entity linker to form a knowledge graph containing relevant background knowledge for the the entity mentions in the text. To make the final predictions, KECI fuses the initial span graph and the knowledge graph into a more refined graph using an attention mechanism. KECI takes a collective approach to link mention spans to entities by integrating global relational information into local representations using graph convolutional networks. Our experimental results show that the framework is highly effective, achieving new state-of-the-art results in two different benchmark datasets: BioRelEx (binding interaction detection) and ADE (adverse drug event extraction). For example, KECI achieves absolute improvements of 4.59% and 4.91% in F1 scores over the state-of-the-art on the BioRelEx entity and relation extraction tasks
A variational phase-field model For ductile fracture with coalescence dissipation

A novel phase-field model for ductile fracture is presented. The model is developed within a consistent variational framework in the context of finite-deformation kinematics. A novel coalescence dissipation introduces a new coupling mechanism between plasticity and fracture by degrading the fracture toughness as the equivalent plastic strain increases. The proposed model is compared with a recent alternative where plasticity and fracture are strongly coupled. Several representative numerical examples motivate specific modeling choices. In particular, a linear crack geometric function provides an “unperturbed” ductile response prior to crack initiation, and Lorentz-type degradation functions ensure that the critical fracture strength remains independent of the phase-field regularization length. In addition, the response of the model is demonstrated to converge with a vanishing phase-field regularization length. The model is then applied to calibrate and simulate a three-point bending experiment of an aluminum alloy specimen with a complex geometry. The effect of the proposed coalescence dissipation coupling on simulations of the experiment is first investigated in a two-dimensional plane strain setting. The calibrated model is then applied to a three-dimensional calculation, where the calculated load-deflection curves and the crack trajectory show excellent agreement with experimental observations. Finally, the model is applied to simulate crack nucleation and growth in a specimen from a recent Sandia Fracture Challenge.
Upward pricing pressure in mergers of capacity‐constrained firms

Merging firms regularly argue that mergers involving capacity-constrained firms are unlikely to be anticompetitive, because a capacity-constrained firm does not represent a meaningful competitive constraint on its rivals. We construct a modified notion of upward pricing pressure called ccGUPPI, or capacity-constrained GUPPI, which accounts for upward pricing pressure from binding capacity constraints, in addition to standard merger effects. We show that the pricing pressure terms underlying ccGUPPI, calculated using pre-merger data, are sufficient to determine whether a merger of capacity-constrained firms will increase price, irrespective of the functional form of demand. Further, using Monte Carlo simulation, we show that ccGUPPI is generally a useful proxy for actual price effects, with lower informational requirements than full merger simulation.
No evidence for a single oscillator underlying discrete visual percepts.

Theories of perception based on discrete sampling posit that visual consciousness is reconstructed based on snapshot-like perceptual moments, as opposed to being updated continuously. According to a model proposed by Schneider (2018), discrete sampling can explain both the flash-lag and the Frohlich illusion, whereby a lag in the conscious updating of a moving stimulus alters its perceived spatial location in comparison to stationary stimulus. The alpha-band frequency, which is associated with phasic modulation of stimulus detection and the temporal resolution of perception, has been proposed to reflect the duration of perceptual moments. The goal of this study was to determine whether a single oscillator (e.g., alpha) is underlying the duration of perceptual moments, which would predict that the point of subjective equality (PSE) in the flash-lag and Frohlich illusions are positively correlated across individuals. Although our displays induced robust flash-lag and Frohlich effects, virtually zero correlation was seen between the PSE in the two illusions, indicating that the illusion magnitudes are unrelated across observers. These findings suggest that, if discrete sampling theory is true, these illusory percepts either rely on different oscillatory frequencies or not on oscillations at all. Alternatively, discrete sampling may not be the mechanism underlying these two motion illusions or our methods were ill-suited to test the theory.
Versatile, high brightness, cryogenic photoinjector electron source

A cryogenically cooled rf photoinjector capable of meeting the brightness demands of next-generation electron beam applications.
Plane partitions of shifted double staircase shape

Abstract We give a product formula for the number of shifted plane partitions of shifted double staircase shape with bounded entries. This is the first new example of a family of shapes with a plane partition product formula in many years. The proof is based on the theory of lozenge tilings; specifically, we apply the “free boundary” Kuo condensation due to Ciucu.
A Game-Theoretic Model for Co-Adaptive Brain-Machine Interfaces

Co-adaptation in brain-machine interfaces (BMIs) can improve performance and facilitate user learning. We propose and analyze a mathematical model for co-adaptation in BMIs. We model the brain and the decoder as strategic agents who seek to minimize their individual cost functions, leading to a game-theoretic formulation of interaction. We frame our BMI model as a potential game to identify stationary points (Nash equilibria) of the brain-decoder interactions, which correspond to points at which both the brain and the decoder stop adapting. Assuming the brain and the decoder adapt using gradient-based schemes, we analytically show how convergence to these equilibria depends on agent learning rates. This theoretical framework presents a basis for modeling co-adaptation using dynamic game theory and can be extended to tasks with multiple dimensions and to different decoder models. This framework can ultimately be used to develop robust adaptive decoder designs to shape brain learning and optimize BMI performance.
Closed-loop pilot vehicle analysis of the approach and landing task

Optimal-control-theoretic modeling and frequency-domain analysis is the methodology proposed to evaluate analytically the handling qualities of higher-order manually controlled dynamic systems. Fundamental to the methodology is evaluating the interplay between pilot workload and closed-loop pilot/vehicle performance and stability robustness. The model-based metric for pilot workload is the required pilot phase compensation. Pilot/vehicle performance and loop stability is then evaluated using frequency-domain techniques. When these techniques were applied to the flight-test data for thirty-two highly-augmented fighter configurations, strong correlation was obtained between the analytical and experimental results.
Ternary and Quaternary Curves of Small Fixed Genus and Gonality With Many Rational Points

We extend the computations from our previous paper arXiv:2005.07054 to determine the maximum number of rational points on a curve over $\mathbb{F}_3$ and $\mathbb{F}_4$ with fixed gonality and small genus. We find, for example, that there is no curve of genus 5 and gonality 6 over a finite field. We propose two conjectures based on our data. First, an optimal curve of genus $g$ has gonality at most $\lfloor \frac{g+3}{2} \rfloor$. Second, a curve of gonality $\gamma$ and large genus over $\mathbb{F}_q$ has $\gamma(q+1)$ rational points.
Hydride Conductivity in Nitride Hydrides

Nitride hydrides are a largely unexplored class of materials with promising applications in solid-state hydrogen fuel cells. Here, we use first-principles calculations to characterize defects and ionic mobility in Sr$_2$LiH$_2$N (SLHN), a nitride hydride with high hydride conductivity. Calculating defect formation energies, we find that SLHN contains high concentrations of hydrogen interstitials (H$_i$). H$_i^-$ migrates with very low energetic barriers, which, together with its low formation energy, implies that SLHN will have excellent hydride kinetics, potentially surpassing those of other known hydride electrolytes. Oxygen contamination is a concern, meaning that encapsulation will be critical. By direct analogy to the La/Sr-based oxyhydrides, which have similar crystal structures, we also investigate the La-based nitride hydride La$_2$LiHN$_2$ but find that it will be significantly less conductive, and thus not as technologically useful. Our findings buttress the exploration of SLHN and similar nitride hydrides for use in solid-state hydrogen fuel cells.
Dual-Mandate Patrols: Multi-Armed Bandits for Green Security

Conservation efforts in green security domains to protect wildlife and forests are constrained by the limited availability of defenders (i.e., patrollers), who must patrol vast areas to protect from attackers (e.g., poachers or illegal loggers). Defenders must choose how much time to spend in each region of the protected area, balancing exploration of infrequently visited regions and exploitation of known hotspots. We formulate the problem as a stochastic multi-armed bandit, where each action represents a patrol strategy, enabling us to guarantee the rate of convergence of the patrolling policy. However, a naive bandit approach would compromise short-term performance for long-term optimality, resulting in animals poached and forests destroyed. To speed up performance, we leverage smoothness in the reward function and decomposability of actions. We show a synergy between Lipschitz-continuity and decomposition as each aids the convergence of the other. In doing so, we bridge the gap between combinatorial and Lipschitz bandits, presenting a no-regret approach that tightens existing guarantees while optimizing for short-term performance. We demonstrate that our algorithm, LIZARD, improves performance on real-world poaching data from Cambodia.
Durable Top-K Instant-Stamped Temporal Records with User-Specified Scoring Functions

A way of finding interesting or exceptional records from instant-stamped temporal data is to consider their "durability, " or, intuitively speaking, how well they compare with other records that arrived earlier or later, and how long they retain their supremacy. For example, people are naturally fascinated by claims with long durability, such as: "On January 22, 2006, Kobe Bryant dropped 81 points against Toronto Raptors. Since then, this scoring record has yet to be broken." In general, given a sequence of instant-stamped records, suppose that we can rank them by a user-specified scoring function f, which may consider multiple attributes of a record to compute a single score for ranking. This paper studies durable top-k queries, which find records whose scores were within top-k among those records within a "durability window" of given length, e.g., a 10-year window starting/ending at the timestamp of the record. The parameter k, the length of the durability window, and parameters of the scoring function (which capture user preference) can all be given at the query time. We illustrate why this problem formulation yields more meaningful answers in some practical situations than other similar types of queries considered previously. We propose new algorithms for solving this problem, and provide a comprehensive theoretical analysis on the complexities of the problem itself and of our algorithms. Our algorithms vastly outperform various baselines (by up to two orders of magnitude on real and synthetic datasets).
Contact with Child Protective Services is pervasive but unequally distributed by race and ethnicity in large US counties.

This article provides county-level estimates of the cumulative prevalence of four levels of Child Protective Services (CPS) contact using administrative data from the 20 most populous counties in the United States. Rates of CPS investigation are extremely high in almost every county. Racial and ethnic inequality in case outcomes is large in some counties. The total median investigation rate was 34.5%; the risk for Black, Hispanic, and White children exceeded 10% in all counties. Risks of having a CPS investigation were highest for Black children (32.9 to 62.8%). Black children also experienced high rates of later-stage CPS contact, with rates often above 20% for confirmed maltreatment, 10% for foster care placement, and 2% for termination of parental rights (TPR). The only other children who experienced such extreme rates of later-stage CPS interventions were American Indian/Alaska Native children in Middlesex, MA; Hispanic children in Bexar, TX; and all children except Asian/Pacific Islander children in Maricopa, AZ. The latter has uniquely high rates of late-stage CPS interventions. In some jurisdictions, such as New York, NY, (0.2%) and Cook, IL (0.2%), very few children experienced TPR. These results show that early CPS interventions are ubiquitous in large counties but with marked variation in how CPS systems respond to these investigations.
End-to-End Constrained Optimization Learning: A Survey

This paper surveys the recent attempts at leveraging machine learning to solve constrained optimization problems. It focuses on surveying the work on integrating combinatorial solvers and optimization methods with machine learning architectures. These approaches hold the promise to develop new hybrid machine learning and optimization methods to predict fast, approximate, solutions to combinatorial problems and to enable structural logical inference. This paper presents a conceptual review of the recent advancements in this emerging area.
Residual intersections and modules with Cohen-Macaulay Rees algebra

Abstract In this paper, we consider a finite, torsion-free module E over a Gorenstein local ring. We provide sufficient conditions for E to be of linear type and for the Rees algebra R ( E ) of E to be Cohen-Macaulay. Our results are obtained by constructing a generic Bourbaki ideal I of E and exploiting properties of the residual intersections of I.
Joint Universal Syntactic and Semantic Parsing

While numerous attempts have been made to jointly parse syntax and semantics, high performance in one domain typically comes at the price of performance in the other. This trade-off contradicts the large body of research focusing on the rich interactions at the syntax-semantics interface. We explore multiple model architectures which allow us to exploit the rich syntactic and semantic annotations contained in the Universal Decompositional Semantics (UDS) dataset, jointly parsing Universal Dependencies and UDS to obtain state-of-the-art results in both formalisms. We analyze the behaviour of a joint model of syntax and semantics, finding patterns supported by linguistic theory at the syntax-semantics interface. We then investigate to what degree joint modeling generalizes to a multilingual setting, where we find similar trends across 8 languages.
Vision-Based Guidance for Tracking Dynamic Objects

In this paper, we present a novel vision-based framework for tracking dynamic objects using guidance laws based on a rendezvous cone approach. These guidance laws enable an unmanned aircraft system equipped with a monocular camera to continuously follow a moving object within the sensor's field of view. We identify and classify feature point estimators for managing the occurrence of occlusions during the tracking process in an exclusive manner. Furthermore, we develop an open-source simulation environment and perform a series of simulations to show the efficacy of our methods.
Improving Weakly Supervised Sound Event Detection with Self-Supervised Auxiliary Tasks

While multitask and transfer learning has shown to improve the performance of neural networks in limited data settings, they require pretraining of the model on large datasets beforehand. In this paper, we focus on improving the performance of weakly supervised sound event detection in low data and noisy settings simultaneously without requiring any pretraining task. To that extent, we propose a shared encoder architecture with sound event detection as a primary task and an additional secondary decoder for a self-supervised auxiliary task. We empirically evaluate the proposed framework for weakly supervised sound event detection on a remix dataset of the DCASE 2019 task 1 acoustic scene data with DCASE 2018 Task 2 sounds event data under 0, 10 and 20 dB SNR. To ensure we retain the localisation information of multiple sound events, we propose a two-step attention pooling mechanism that provides a time-frequency localisation of multiple audio events in the clip. The proposed framework with two-step attention outperforms existing benchmark models by 22.3%, 12.8%, 5.9% on 0, 10 and 20 dB SNR respectively. We carry out an ablation study to determine the contribution of the auxiliary task and two-step attention pooling to the SED performance improvement.
Seawalls or social recovery? The role of policy networks and design in disaster recovery

Abstract This mixed-methods study examines the case of 30 advisory committees across 25 municipalities in Miyagi and Iwate Prefectures in northeast Japan after the 3/11 triple disasters. Drawing on synthetic control experiments, social network analysis, and case studies, we test whether the design of these committees’ policies or the traits of these committee networks improved the recovery trajectories of municipalities. We find that communities highly connected to this network of advisory committees saw better economic recovery than expected, especially when researchers, hyper-connected individuals, or plans for community centers were involved, controlling for disaster damage, infrastructure quality, social vulnerability, governance capacity, emergency services, and social capital. Our results bring with them a number of concrete policy recommendations for disaster managers, local residents, and decision makers.
On the Fokker–Planck approximation in the kinetic equation of multicomponent classical nucleation theory

Abstract We examine the validity of the Fokker–Planck equation with linear force coefficients as an approximation to the kinetic equation of nucleation in homogeneous isothermal multicomponent condensation. Starting with a discrete equation of balance governing the temporal evolution of the distribution function of an ensemble of multicomponent droplets and reducing it (by means of Taylor series expansions) to the differential form in the vicinity of the saddle point of the free energy surface, we have identified the parameters whereof the smallness is necessary for the resulting kinetic equation to have the form of the Fokker–Planck equation with linear (in droplet variables) force coefficients. The “non-smallness” of these parameters results either in the appearance of the third or higher order partial derivatives of the distribution function in the kinetic equation or in its force coefficients becoming non-linear functions of droplet variables, or both; this would render the conventional kinetic equation of multicomponent nucleation and its predictions inaccurate. As a numerical illustration, we carried out calculations for isothermal condensation in five binary systems of various non-ideality at T = 293 . 15 K: 1-butanol–1-hexanol, water–methanol, water–ethanol, water–1-propanol, water–1-butanol. Our results suggest that under typical experimental conditions the kinetic equation of binary nucleation of classical nucleation theory may require a two-fold modification and, hence, the conventional expression for the steady-state binary nucleation rate may not be adequate for the consistent comparison of theoretical predictions with experimental data.
Comparison of Ease of Use and Comfort in Fitness Trackers for Participants Impaired by Parkinson's Disease: An exploratory study

Parkinson's disease (PD) is an incurable, fatal neurodegenerative disease, and only available treatment is to minimize symptoms. Anecdotal evidence suggests whole body workout can help to reduce PD severity; however, it is challenging to quantify its effect on PD. The increased availability of fitness trackers can help in quantifying the effect of whole-body workout on PD. Before using any over the counter fitness tracker, we must study the ease of use of the fitness trackers in PD patients. We interviewed 32 PD patients with six over the counter fitness trackers and determined their perceptions and attitude towards the fitness trackers. Although none of the fitness trackers received perfect scores for ease of use or comfort due to the presence of tremors, two trackers performed significantly better than the others. Further study is warranted to understand the potential for fitness trackers to be used by PD patients.
Trial-to-Trial Variability of Spiking Delay Activity in Prefrontal Cortex Constrains Burst-Coding Models of Working Memory.

A hallmark neuronal correlate of working memory (WM) is stimulus-selective spiking activity of neurons in prefrontal cortex (PFC) during mnemonic delays. These observations have motivated an influential computational modeling framework in which WM is supported by persistent activity. Recently this framework has been challenged by arguments that observed persistent activity may be an artifact of trial-averaging, which potentially masks high variability of delay activity at the single-trial level. In an alternative scenario, WM delay activity could be encoded in bursts of selective neuronal firing which occur intermittently across trials. However, this alternative proposal has not been tested on single-neuron spike-train data. Here, we developed a framework for addressing this issue by characterizing the trial-to-trial variability of neuronal spiking quantified by Fano factor (FF). By building a doubly stochastic Poisson spiking model, we first demonstrated that the burst-coding proposal implies a significant increase in FF positively correlated with firing rate, and thus loss of stability across trials during the delay. Simulation of spiking cortical circuit WM models further confirmed that FF is a sensitive measure that can well dissociate distinct WM mechanisms. We then tested these predictions on datasets of single-neuron recordings from macaque prefrontal cortex during three WM tasks. In sharp contrast to the burst-coding model predictions, we only found a small fraction of neurons showing increased WM-dependent burstiness, and stability across trials during delay was strengthened in empirical data. Therefore, reduced trial-to-trial variability during delay provides strong constraints on the contribution of single-neuron intermittent bursting to WM maintenance. SIGNIFICANCE STATEMENT There are diverging classes of theoretical models explaining how information is maintained in working memory by cortical circuits. In an influential model class, neurons fire exhibit persistent elevated memorandum-selective firing, whereas a recently developed class of burst-coding models suggests that persistent activity is an artifact of trial-averaging, and spiking is sparse in each single trial, subserved by brief intermittent bursts. However, this alternative picture has not been characterized or tested on empirical spike-train data. Here we combine mathematical analysis, computational model simulation and experimental data analysis to test empirically theses two classes of models and show that the trial-to-trial variability of empirical spike trains is not consistent with burst coding. These findings provide constraints for theoretical models of working memory.
Coupling of IGA and peridynamics for air-blast fluid-structure interaction using an immersed approach

Abstract We present a novel formulation based on an immersed coupling of Isogeometric Analysis (IGA) and Peridynamics (PD) for the simulation of fluid-structure interaction (FSI) phenomena for air blast. We aim to develop a practical computational framework that is capable of capturing the mechanics of air blast coupled to solids and structures that undergo large, inelastic deformations with extreme damage and fragmentation. An immersed technique is used, which involves an a priori monolithic FSI formulation with the implicit detection of the fluid-structure interface and without limitations on the solid domain motion. The coupled weak forms of the fluid and structural mechanics equations are solved on the background mesh. Correspondence-based PD is used to model the meshfree solid in the foreground domain. We employ the Non-Uniform Rational B-Splines (NURBS) IGA functions in the background and the Reproducing Kernel Particle Method (RKPM) functions for the PD solid in the foreground. We feel that the combination of these numerical tools is particularly attractive for the problem class of interest due to the higher-order accuracy and smoothness of IGA and RKPM, the benefits of using immersed methodology in handling the fluid-structure coupling, and the capabilities of PD in simulating fracture and fragmentation scenarios. Numerical examples are provided to illustrate the performance of the proposed air-blast FSI framework.
A von Neumann type inequality for an annulus

Abstract Let A r = { r | z | 1 } be an annulus. We consider the class of operators F r : = { T ∈ B ( H ) : r 2 T − 1 ( T − 1 ) ⁎ + T T ⁎ ≤ r 2 + 1 , σ ( T ) ⊂ A r } and show that for every bounded holomorphic function ϕ on A r : sup T ∈ F r ⁡ | | ϕ ( T ) | | ≤ 2 | | ϕ | | ∞ , where the constant 2 is the best possible. We do this by characterizing the calcular norm induced on H ∞ ( A r ) by F r as the multiplier norm of a suitable holomorphic function space on A r .
Stability of twisted states on lattices of Kuramoto oscillators.

Real world systems comprised of coupled oscillators have the ability to exhibit spontaneous synchronization and other complex behaviors. The interplay between the underlying network topology and the emergent dynamics remains a rich area of investigation for both theory and experiment. In this work, we study lattices of coupled Kuramoto oscillators with non-local interactions. Our focus is on the stability of twisted states. These are equilibrium solutions with constant phase shifts between oscillators resulting in spatially linear profiles. Linear stability analysis follows from studying the quadratic form associated with the Jacobian matrix. Novel estimates on both stable and unstable regimes of twisted states are obtained in several cases. Moreover, exploiting the “almost circulant” nature of the Jacobian obtains a surprisingly accurate numerical test for stability. While our focus is on 2D square lattices, we show how our results can be extended to higher dimensions.
Educators, Solicitors, Flamers, Motivators, Sympathizers: Characterizing Roles in Online Extremist Movements

Social media provides the means by which extremist social movements, such as white supremacy and anti-LGBTQ, thrive online. Yet, we know little about the roles played by the participants of such movements. In this paper, we investigate these participants to characterize their roles, their role dynamics, and their influence in spreading online extremism. Our participants-online extremist accounts-are 4,876 public Facebook pages or groups that have shared information from the websites of 289 Southern Poverty LawCenter (SPLC) designated extremist groups. Guided by theories of participatory activism, we map the information sharing features of these extremists accounts. By clustering the quantitative features followed by qualitative expert validation, we identify five roles surrounding extremist activism-educators, solicitors, flamers, motivators, sympathizers. For example, solicitors use links from extremist websites to attract donations and participation in extremist issues, whereas flamers share inflammatory extremist content inciting anger. We further investigate role dynamics such as, how stable these roles are over time and how likely will extremist accounts transition from one role into another. We find that roles core to the movement-educators and solicitors-are more stable, while flamers and motivators can transition to sympathizers with high probability. Finally, using a Hawkes process model, we test which roles are more influential in spreading various types of information. We find that educators and solicitors exert the most influence in triggering extremist link posts, whereas flamers are influential in triggering the spread of information from fake news sources. Our results help in situating various roles on the trajectory of deeper engagement into the extremist movements and understanding the potential effect of various counter-extremism interventions. Our findings have implications for understanding how online extremist movements flourish through participatory activism and how they gain a spectrum of allies for mobilizing extremism online.
A Hybrid Position/Force Controller for Joint Robots

In this paper, we present a hybrid position/force controller for operating joint robots. The hybrid controller has two goals—motion tracking and force regulating. As long as these two goals are not mutually exclusive, they can be decoupled in some way. In this work, we make use of the smooth and invertible mapping from the joint space to the task space to decouple the two control goals and design controllers separately. The traditional motion controller in the task space is used for motion control, while the force controller is designed through manipulating the desired trajectory to regulate the force indirectly. Two case studies—contour tracking/polishing surfaces and grabbing boxes with two robotic arms—are presented to show the efficacy of the hybrid controller, and simulations with physics engines are carried out to validate the efficacy of the proposed method.