cssi/SciSciGPT-SciSciNet huggingface dataset:
A Modified Collagen Dressing Induces Transition of Inflammatory to Reparative Phenotype of Wound Macrophages

Collagen containing wound-care dressings are extensively used. However, the mechanism of action of these dressings remain unclear. Earlier studies utilizing a modified collagen gel (MCG) dressing demonstrated improved vascularization of ischemic wounds and better healing outcomes. Wound macrophages are pivotal in facilitating wound angiogenesis and timely healing. The current study was designed to investigate the effect of MCG on wound macrophage phenotype and function. MCG augmented recruitment of macrophage at the wound-site, attenuated pro-inflammatory and promoted anti-inflammatory macrophage polarization. Additionally, MCG increased anti-inflammatory IL-10, IL-4 and pro-angiogenic VEGF production, indicating a direct role of MCG in resolving wound inflammation and improving angiogenesis. At the wound-site, impairment in clearance of apoptotic cell bioburden enables chronic inflammation. Engulfment of apoptotic cells by macrophages (efferocytosis) resolves inflammation via a miR-21-PDCD4-IL-10 pathway. MCG-treated wound macrophages exhibited a significantly bolstered efferocytosis index. Such favorable outcome significantly induced miR-21 expression. MCG-mediated IL-10 production was dampened under conditions of miR-21 knockdown pointing towards miR-21 as a causative factor. Pharmacological inhibition of JNK attenuated IL-10 production by MCG, implicating miR-21-JNK pathway in MCG-mediated IL-10 production by macrophages. This work provides direct evidence demonstrating that a collagen-based wound-care dressing may influence wound macrophage function and therefore modify wound inflammation outcomes.
Analysis and Design of Robust Max Consensus for Wireless Sensor Networks

A novel distributed algorithm for estimating the maximum of the node initial state values in a network, in the presence of additive communication noise is proposed. Conventionally, the maximum is estimated locally at each node by updating the node state value with the largest received measurements in every iteration. However, due to the additive channel noise, the estimate of the maximum at each node drifts at each iteration and this results in nodes diverging from the true max value. Max-plus algebra is used as a tool to study this ergodic process. The subadditive ergodic theorem is invoked to establish a constant growth rate for the state values due to noise, which is studied by analyzing the max-plus Lyapunov exponent of the product of noise matrices in a max-plus semiring. The growth rate of the state values is upper bounded by a constant which depends on the spectral radius of the network and the noise variance. Upper and lower bounds are derived for both fixed and random graphs. Finally, a two-run algorithm robust to additive noise in the network is proposed and its variance is analyzed using concentration inequalities. Simulation results supporting the theory are also presented.
Stroke Prevents Exercise-induced Gains in Bone Microstructure But Not Composition in Mice.

Ischemic stroke induces rapid loss in bone mineral density up to 13 times greater than during normal aging, leading to markedly increased risk of fracture. Little is known about skeletal changes following stroke beyond density loss. In this study we use a mild-moderate middle cerebral artery occlusion model to determine the effects of ischemic stroke without bedrest on bone microstructure, dynamic bone formation, and tissue composition. Twenty-seven 12-week-old male C57Bl/6J mice received either a stroke or sham surgery and then either received daily treadmill exercise or remained sedentary for four weeks. All mice were ambulatory immediately following stroke, and limb coordination during treadmill exercise was unaffected by stroke, indicating similar mechanical loading across limbs for surgery groups. Stroke did not directly detriment microstructure, but exercise only stimulated adaptation in sham group, not stroke group, with increased bone volume fraction and trabecular thickness in the sham distal femoral metaphysis. Stroke differentially decreased cortical area in the affected limb relative to the unaffected limb of the distal femoral metaphysis, and endosteal bone formation rate in the affected tibial diaphysis. Although exercise failed to improve bone microstructure following stroke, exercise increased mineral-to-matrix content in stroke but not sham. Together, these results show that stroke inhibits exercise-induced changes to femoral microstructure but not tibial composition, even without changes to gait. Similarly, affected-unaffected limb differences in cortical bone structure and bone formation rate in ambulatory mice show that stroke affects bone health even without bedrest.
Distill-Net: Application-Specific Distillation of Deep Convolutional Neural Networks for Resource-Constrained IoT Platforms

Many Internet-of-Things (IoT) applications demand fast and accurate understanding of a few key events in their surrounding environment. Deep Convolutional Neural Networks (CNNs) have emerged as an effective approach to understand speech, images, and similar high-dimensional data types. Algorithmic performance of modern CNNs, however, fundamentally relies on learning class-agnostic hierarchical features that only exist in comprehensive training datasets with many classes. As a result, fast inference using CNNs trained on such datasets is prohibitive for most resource-constrained IoT platforms. To bridge this gap, we present a principled and practical methodology for distilling a complex modern CNN that is trained to effectively recognize many different classes of input data into an application-dependent essential core that not only recognizes the few classes of interest to the application accurately but also runs efficiently on platforms with limited resources. Experimental results confirm that our approach strikes a favorable balance between classification accuracy (application constraint), inference efficiency (platform constraint), and productive development of new applications (business constraint).
C99 selectively accumulates in vulnerable neurons in Alzheimer's disease

Abstract Introduction The levels and distribution of amyloid deposits in the brain does not correlate well with Alzheimer's disease (AD) progression. Therefore, it is likely that amyloid precursor protein and its proteolytic fragments other than amyloid β (Aβ) contribute to the onset of AD. Methods We developed a sensitive assay adapted to the detection of C99, the direct precursor of β-amyloid. Three postmortem groups were studied: control with normal and stable cognition; patients with moderate AD, and individuals with severe AD. The amount of C99 and Aβ was quantified and correlated with the severity of AD. Results C99 accumulates in vulnerable neurons, and its levels correlate with the degree of cognitive impairment in patients suffering from AD. In contrast, Aβ levels are increased in both vulnerable and resistant brain areas. Discussion These results raise the possibility that C99, rather than Aβ plaques, is responsible for the death of nerve cells in AD.
Min–max formulas for nonlocal elliptic operators

In this work, we give a characterization of Lipschitz operators on spaces of $$C^2(M)$$ functions (also $$C^{1,1}$$, $$C^{1,\gamma }$$, $$C^1$$, $$C^\gamma $$) that obey the global comparison property—i.e. those that preserve the global ordering of input functions at any points where their graphs may touch, often called “elliptic” operators. Here M is a complete Riemannian manifold. In particular, we show that all such operators can be written as a min–max over linear operators that are a combination of drift–diffusion and integro-differential parts. In the linear (and nonlocal) case, these operators had been characterized in the 1960s, and in the local, but nonlinear case—e.g. local Hamilton–Jacobi–Bellman operators—this characterization has also been known and used since approximately since 1960s or 1970s. Our main theorem contains both of these results as special cases. It also shows any nonlinear scalar elliptic equation can be represented as an Isaacs equation for an appropriate differential game. Our approach is to “project” the operator to one acting on functions on large finite graphs that approximate the manifold, use non-smooth analysis to derive a min–max formula on this finite dimensional level, and then pass to the limit in order to lift the formula to the original operator.
Goodwill Amortization and the Usefulness of Earnings

This study provides evidence of the effect of goodwill amortization on the usefulness of earnings data as an indicator of share value for a large sample of publicly traded companies over the 1993–98 period. This issue is of special interest because the Financial Accounting Standards Board recently adopted new accounting standards that eliminate the systematic amortization of goodwill in favor of a requirement to review goodwill for impairment when circumstances warrant. We found that earnings before goodwill amortization explain significantly more of the observed distribution of share prices than earnings after goodwill amortization and that when share valuations are based on earnings alone, goodwill amortization simply adds noise to the measure. These results suggest that eliminating goodwill amortization from the computation of net income will not reduce its usefulness to investors and analysts as a summary indicator of share value.
Named Entity Recognition with Partially Annotated Training Data.

Supervised machine learning assumes the availability of fully-labeled data, but in many cases, such as low-resource languages, the only data available is partially annotated. We study the problem of Named Entity Recognition (NER) with partially annotated training data in which a fraction of the named entities are labeled, and all other tokens, entities or otherwise, are labeled as non-entity by default. In order to train on this noisy dataset, we need to distinguish between the true and false negatives. To this end, we introduce a constraint-driven iterative algorithm that learns to detect false negatives in the noisy set and downweigh them, resulting in a weighted training set. With this set, we train a weighted NER model. We evaluate our algorithm with weighted variants of neural and non-neural NER models on data in 8 languages from several language and script families, showing strong ability to learn from partial data. Finally, to show real-world efficacy, we evaluate on a Bengali NER corpus annotated by non-speakers, outperforming the prior state-of-the-art by over 5 points F1.
Music Performance Analysis: A Survey

Music Information Retrieval (MIR) tends to focus on the analysis of audio signals. Often, a single music recording is used as representative of a "song" even though different performances of the same song may reveal different properties. A performance is distinct in many ways from a (arguably more abstract) representation of a "song," "piece," or musical score. The characteristics of the (recorded) performance -- as opposed to the score or musical idea -- can have a major impact on how a listener perceives music. The analysis of music performance, however, has been traditionally only a peripheral topic for the MIR research community. This paper surveys the field of Music Performance Analysis (MPA) from various perspectives, discusses its significance to the field of MIR, and points out opportunities for future research in this field.
The character field theory and homology of character varieties

We construct an extended oriented $(2+\epsilon)$-dimensional topological field theory, the character field theory $X_G$ attached to a affine algebraic group in characteristic zero, which calculates the homology of character varieties of surfaces. It is a model for a dimensional reduction of Kapustin-Witten theory ($N=4$ $d=4$ super-Yang-Mills in the GL twist), and a universal version of the unipotent character field theory introduced in arXiv:0904.1247. Boundary conditions in $X_G$ are given by quantum Hamiltonian $G$-spaces, as captured by de Rham (or strong) $G$-categories, i.e., module categories for the monoidal dg category $D(G)$ of $D$-modules on $G$. We show that the circle integral $X_G(S^1)$ (the center and trace of $D(G)$) is identified with the category $D(G/G)$ of "class $D$-modules", while for an oriented surface $S$ (with arbitrary decorations at punctures) we show that $X_G(S)\simeq{\rm H}_*^{BM}(Loc_G(S))$ is the Borel-Moore homology of the corresponding character stack. We also describe the "Hodge filtration" on the character theory, a one parameter degeneration to a TFT whose boundary conditions are given by classical Hamiltonian $G$-spaces, and which encodes a variant of the Hodge filtration on character varieties.
Reconstructing a Bounded-Degree Directed Tree Using Path Queries

We present a randomized algorithm for reconstructing directed rooted trees of n nodes and node degree at most d, by asking at most $\mathcal{O}(dn \log^{2}\,n) {path queries}$. Each path query takes as input an origin node and a target node, and answers whether there is a directed path from the origin to the target. Regarding lower bounds, we show that any randomized algorithm requires at least $\Omega (n \log n)$ queries, while any deterministic algorithm requires at least $\Omega (dn)$ queries. Additionally, we present a $\mathcal{O}(dn \log^{3}\,n)$ randomized algorithm for noisy queries, and a $\mathcal{O}(dn \log^{2}\,n)$ randomized algorithm for additive queries on weighted trees.
Dimensions of Monomial Varieties

The dimensions of certain varieties defined by monomials are computed using only high school algebra.
Regulation of defeat-induced social avoidance by medial amygdala DRD1 in male and female prairie voles

Social interaction with unfamiliar individuals is necessary for species-preserving behaviors such as finding mates and establishing social groups. However, social conflict is a potential negative outcome to interaction with a stranger that can be distressing enough to cause an individual to later avoid interactions with other unfamiliar conspecifics. Unfortunately, stress research using a prominent model of social conflict, social defeat stress, has largely omitted female subjects. This has left a void in the literature regarding social strain on female stress biology and adequate comparison of the effect of sex in stress pathways. The prairie vole (Microtus ochrogaster) exhibits aggressive behavior in both sexes, making voles an attractive candidate to model social defeat in both sexes. This study sought to establish a model of social defeat stress in both male and female prairie voles, characterize behavioral changes in response to this stressor, and investigate the role of dopamine signaling in the response to social defeat stress. Defeated male and female prairie voles displayed social avoidance as well as an increase in the level of dopamine receptor D1 (DRD1) in the medial amygdala (MeA). Pharmacological manipulation of DRD1 signaling in the MeA revealed that increased DRD1 signaling is sufficient to induce a social avoidant state, and could be a necessary component in the defeat-induced social avoidance response. These findings provide the prairie vole as a model of social defeat in both sexes, and implicate the MeA in avoidance of unfamiliar conspecifics after a distressing social encounter.
Dilated FCN: Listening Longer to Hear Better

Deep neural network solutions have emerged as a new and powerful paradigm for speech enhancement (SE). The capabilities to capture long context and extract multi-scale patterns are crucial to design effective SE networks. Such capabilities, however, are often in conflict with the goal of maintaining compact networks to ensure good system generalization.In this paper, we explore dilation operations and apply them to fully convolutional networks (FCNs) to address this issue. Dilations equip the networks with greatly expanded receptive fields, without increasing the number of parameters. Different strategies to fuse multi-scale dilations, as well as to install the dilation modules are explored in this work. Using Noisy VCTK and AzBio sentences datasets, we demonstrate that the proposed dilation models significantly improve over the baseline FCN and outperform the state-of-the-art SE solutions.
Multi-Fidelity Multi-Objective Bayesian Optimization: An Output Space Entropy Search Approach

We study the novel problem of blackbox optimization of multiple objectives via multi-fidelity function evaluations that vary in the amount of resources consumed and their accuracy. The overall goal is to appromixate the true Pareto set of solutions by minimizing the resources consumed for function evaluations. For example, in power system design optimization, we need to find designs that trade-off cost, size, efficiency, and thermal tolerance using multi-fidelity simulators for design evaluations. In this paper, we propose a novel approach referred as Multi-Fidelity Output Space Entropy Search for Multi-objective Optimization (MF-OSEMO) to solve this problem. The key idea is to select the sequence of candidate input and fidelity-vector pairs that maximize the information gained about the true Pareto front per unit resource cost. Our experiments on several synthetic and real-world benchmark problems show that MF-OSEMO, with both approximations, significantly improves over the state-of-the-art single-fidelity algorithms for multi-objective optimization. Please note: A corrigendum was submitted for this paper on 24 September 2020.
Axiomatisability of the class of monolithic groups in a variety of nilpotent groups

The class of all subdirectly irreducible groups belonging to a variety generated by a finite nilpotent group can be axiomatised by a finite set of elementary sentences.
Super-Resolution PET Imaging Using Convolutional Neural Networks

Positron emission tomography (PET) suffers from severe resolution limitations which reduce its quantitative accuracy. In this article, we present a super-resolution (SR) imaging technique for PET based on convolutional neural networks (CNNs). To facilitate the resolution recovery process, we incorporate high-resolution (HR) anatomical information based on magnetic resonance (MR) imaging. We introduce the spatial location information of the input image patches as additional CNN inputs to accommodate the spatially-variant nature of the blur kernels in PET. We compared the performance of shallow (3-layer) and very deep (20-layer) CNNs with various combinations of the following inputs: low-resolution (LR) PET, radial locations, axial locations, and HR MR. To validate the CNN architectures, we performed both realistic simulation studies using the BrainWeb digital phantom and clinical studies using neuroimaging datasets. For both simulation and clinical studies, the LR PET images were based on the Siemens HR+ scanner. Two different scenarios were examined in simulation: one where the target HR image is the ground-truth phantom image and another where the target HR image is based on the Siemens HRRT scanner — a high-resolution dedicated brain PET scanner. The latter scenario was also examined using clinical neuroimaging datasets. A number of factors affected relative performance of the different CNN designs examined, including network depth, target image quality, and the resemblance between the target and anatomical images. In general, however, all deep CNNs outperformed classical penalized deconvolution and partial volume correction techniques by large margins both qualitatively (e.g., edge and contrast recovery) and quantitatively (as indicated by three metrics: peak signal-to-noise-ratio, structural similarity index, and contrast-to-noise ratio).
Establishing Human-Robot Trust through Music-Driven Robotic Emotion Prosody and Gesture

As human-robot collaboration opportunities continue to expand, trust becomes ever more important for full engagement and utilization of robots. Affective trust, built on emotional relationship and interpersonal bonds is particularly critical as it is more resilient to mistakes and increases the willingness to collaborate. In this paper we present a novel model built on music-driven emotional prosody and gestures that encourages the perception of a robotic identity, designed to avoid uncanny valley. Symbolic musical phrases were generated and tagged with emotional information by human musicians. These phrases controlled a synthesis engine playing back pre-rendered audio samples generated through interpolation of phonemes and electronic instruments. Gestures were also driven by the symbolic phrases, encoding the emotion from the musical phrase to low degree-of-freedom movements. Through a user study we showed that our system was able to accurately portray a range of emotions to the user. We also showed with a significant result that our non-linguistic audio generation achieved an 8% higher mean of average trust than using a state-of-the-art text-to-speech system.
Integrative analysis reveals RNA G-quadruplexes in UTRs are selectively constrained and enriched for functional associations

G-quadruplex (G4) sequences are abundant in untranslated regions (UTRs) of human messenger RNAs, but their functional importance remains unclear. By integrating multiple sources of genetic and genomic data, we show that putative G-quadruplex forming sequences (pG4) in 5' and 3' UTRs are selectively constrained, and enriched for cis-eQTLs and RNA-binding protein (RBP) interactions. Using over 15,000 whole-genome sequences, we find that negative selection acting on central guanines of UTR pG4s is comparable to that of missense variation in protein-coding sequences. At multiple GWAS-implicated SNPs within pG4 UTR sequences, we find robust allelic imbalance in gene expression across diverse tissue contexts in GTEx, suggesting that variants affecting G-quadruplex formation within UTRs may also contribute to phenotypic variation. Our results establish UTR G4s as important cis-regulatory elements and point to a link between disruption of UTR pG4 and disease.
The Blackbird Dataset: A Large-Scale Dataset for UAV Perception in Aggressive Flight

The Blackbird unmanned aerial vehicle (UAV) dataset is a large-scale, aggressive indoor flight dataset collected using a custom-built quadrotor platform for use in evaluation of agile perception. Inspired by the potential of future high-speed fully-autonomous drone racing, the Blackbird dataset contains over 10 h of flight data from 168 flights over 17 flight trajectories and 5 environments at velocities up to 7.0 m \(\mathrm{s}^{-1}\). Each flight includes sensor data from 120 Hz stereo and downward-facing photorealistic virtual cameras, 100 Hz IMU, \(\sim \)190 Hz motor speed sensors, and 360 Hz millimeter-accurate motion capture ground truth. Camera images for each flight were photorealistically rendered using FlightGoggles [1] across a variety of environments to facilitate easy experimentation of high performance perception algorithms. The dataset is available for download at http://blackbird-dataset.mit.edu/.
Source shot noise mitigation in focused ion beam microscopy by time-resolved measurement.

Abstract Focused ion beam microscopy suffers from source shot noise – random variation in the number of incident ions in any fixed dwell time – along with random variation in the number of detected secondary electrons per incident ion. This multiplicity of sources of randomness increases the variance of the measurements and thus worsens the trade-off between incident ion dose and image accuracy. Repeated measurement with low dwell time, without changing the total ion dose, is a way to introduce time resolution to this form of microscopy. Through theoretical analyses and Monte Carlo simulations, we show that three ways to process time-resolved measurements result in mean-squared error (MSE) improvements compared to the conventional method of having no time resolution. In particular, maximum likelihood estimation provides reduction in MSE or reduction in required dose by a multiplicative factor approximately equal to the secondary electron yield. This improvement factor is similar to complete mitigation of source shot noise. Experiments with a helium ion microscope are consistent with the analyses and suggest accuracy improvement for a fixed source dose by a factor of about 4.
Stably expressed genes in single-cell RNA sequencing.

Motivation In single-cell RNA-sequencing (scRNA-seq) experiments, RNA transcripts are extracted and measured from isolated cells to understand gene expression at the cellular level. Measurements from this technology are affected by many technical artifacts, including batch effects. In analogous bulk gene expression experiments, external references, e.g. synthetic gene spike-ins often from the External RNA Controls Consortium (ERCC), may be incorporated to the experimental protocol for use in adjusting measurements for technical artifacts. In scRNA-seq experiments, the use of external spike-ins is controversial due to dissimilarities with endogenous genes and uncertainty about sufficient precision of their introduction. Instead, endogenous genes with highly stable expression could be used as references within scRNA-seq to help normalize the data. First, however, a specific notion of stable expression at the single-cell level needs to be formulated; genes could be stable in absolute expression, in proportion to cell volume, or in proportion to total gene expression. Different types of stable genes will be useful for different normalizations and will need different methods for discovery. Results We compile gene sets whose products are associated with cellular structures and record these gene sets for future reuse and analysis. We find that genes whose final products are associated with the cytosolic ribosome have expressions that are highly stable with respect to the total RNA content. Notably, these genes appear to be stable in bulk measurements as well. Supplementary information Supplementary data are available through GitHub (johanngb/sc-stable).
An infinite-dimensional phenomenon in finite-dimensional metric topology

We show that there are homotopy equivalences $h:N\to M$ between closed manifolds which are induced by cell-like maps $p:N\to X$ and $q:M\to X$ but which are not homotopic to homeomorphisms. The phenomenon is based on construction of cell-like maps that kill certain $\mathbb L$-classes. The image space in these constructions is necessarily infinite-dimensional. In dimension $>6$ we classify all such homotopy equivalences. As an application, we show that such homotopy equivalences are realized by deformations of Riemannian manifolds in Gromov-Hausdorff space preserving a contractibility function.
On the Euler-alignment system with weakly singular communication weights

We study the pressureless Euler equations with nonlocal alignment interactions, which arises as a macroscopic representation of the Cucker–Smale model on animal flocks. For the Euler-alignment system with bounded interactions, a critical threshold phenomenon is proved in Tadmor and Tan (2014 Phil. Trans. R. Soc. A 372 20130401), where global regularity depends on initial data. With strongly singular interactions, global regularity is obtained in Do et al (2018 Arch. Ration. Mech. Anal. 228 1–37), for all initial data. We consider the remaining case when the interaction is weakly singular. We show a critical threshold, similar to the system with bounded interaction. However, different global behaviors may happen for critical initial data, which reveals the unique structure of the weakly singular alignment operator.
Kindlin Assists Talin to Promote Integrin Activation

Integrin αIIbβ3 is a predominant type of integrin abundantly expressed on the surface of platelets and its activation regulates the process of thrombosis. Talin and kindlin are cytoplasmic proteins that bind to integrin and modulate its affinity for extracellular ligands. Although the molecular details of talin-mediated integrin activation are known, the mechanism of kindlin involvement in this process remains elusive. Here, we demonstrate that the interplay between talin and kindlin promotes integrin activation. Our all-atomic molecular dynamics simulations on complete transmembrane and cytoplasmic domains of integrin αIIbβ3, talin1 F2/F3 subdomains, and the kindlin2 FERM domain in an explicit lipid-water environment over a microsecond timescale unraveled the role of kindlin as an enhancer of the talin interaction with the membrane proximal region of β-integrin. The cooperation of kindlin with talin results in a complete disruption of salt bridges between R995 on αIIb and D723/E726 on β3. Furthermore, kindlin modifies the molecular mechanisms of inside-out activation by decreasing the crossing angle between transmembrane helices of integrin αIIbβ3, which eventually results in parallelization of integrin dimer. In addition, our control simulation featuring integrin in complex with kindlin reveals that kindlin binding is not sufficient for unclasping the inner-membrane and outer-membrane interactions of integrin dimer, thus ruling out the possibility of solitary action of kindlin in integrin activation.
Acceleration in First Order Quasi-strongly Convex Optimization by ODE Discretization

We study gradient-based optimization methods obtained by direct Runge-Kutta discretization of the ordinary differential equation (ODE) describing the movement of a heavy-ball under constant friction coefficient. When the function is high-order smooth and strongly convex, we show that directly simulating the ODE with known numerical integrators achieve acceleration in a nontrivial neighborhood of the optimal solution. In particular, the neighborhood may grow larger as the condition number of the function increases. Furthermore, our results also hold for nonconvex but quasi-strongly convex objectives. We provide numerical experiments that verify the theoretical rates predicted by our results.
Adaptive Kalman Tracking for Indoor Visible Light Positioning

Visible light communication (VLC) utilizes light-emitting diodes (LEDs) to transmit wireless data. A VLC network can also be used to localize mobile users in indoor environments, where the global positioning system (GPS) signals are weak. However, the line-of-sight (LOS) links of mobile VLC devices can be blocked easily, which decreases the accuracy of localization. In this paper, we study tracking a VLC user when the availability of VLC access point (AP) link changes over the user's route. We propose a localization method for a single available AP and use known estimation methods when a larger number of APs are available. Tracking mobile users with Kalman filter can increase the accuracy of the positioning, but the generic Kalman filter does not consider instant changes in the measurement method. In order to include this information in the position estimation, we implement an adaptive Kalman filter by modifying the filter parameters based on the availability of APs to the user. Simulation results show that the implemented method decreases the root-mean-square error (RMSE) of the localization down to 30% −50% of the original estimation.
Explicit Agent-Level Optimal Cooperative Controllers for Dynamically Decoupled Systems with Output Feedback

We consider a dynamically decoupled network of agents each with a local output-feedback controller. We assume each agent is a node in a directed acyclic graph and the controllers share information along the edges in order to cooperatively optimize a global objective. We develop explicit state-space formulations for the jointly optimal networked controllers that highlight the role of the graph structure. Specifically, we provide generically minimal agent-level implementations of the local controllers along with intuitive interpretations of their states and the information that should be transmitted between controllers.
Shock tube/laser absorption measurements of the pyrolysis of JP-10 fuel

Abstract This work presents a novel application of multi-wavelength, multi-species laser absorption methods in the pyrolysis of JP-10 fuel. Two different multi wavelength approaches are used to measure the species from JP-10 pyrolysis in a shock tube at pressures of 2.5–3 atm and temperatures of 1166–1522 K. A five wavelength (5λ) analysis combination produces species time histories for ethylene, cyclopentadiene, and methane formation, as well as estimates of fuel decay. These results are extended further using a seven wavelength (7λ) combination of methods to measure ethylene, cyclopentadiene, propene, methane, allene, 1,3 butadiene, benzene, toluene, and fuel. Supplementary material includes details on the cyclopentadiene cross sections for 10.532 µm, 10.958 µm, and 11.345 µm. Simulations using two recent models are compared to the experimental data and the discrepancies are examined.
Extraperitoneal Uterosacral Ligament Hysteropexy: A Novel Treatment for Apical Compartment Prolapse.

Abstract Objective To treat mild-to-moderate apical compartment prolapse while preserving the uterus, our extraperitoneal uterosacral ligament hysteropexy repair technique was developed. Hysterectomy for prolapse accounted for 13% of all benign hysterectomies, which is the third most common reason. 1 Although concomitant hysterectomy risks at time of prolapse surgery are not well defined, risks associated with hysterectomy have been well described in the literature. Additionally, it is well known that anterior compartment prolapse often has an apical component. 2 Benefits of hysteropexy in prolapse surgery consistently cited in the literature include reduced surgical time, blood loss, and morbidity. 3 A study by Frick et al, reported more than 60% of women would decline a concomitant hysterectomy if offered an equally efficacious surgical option. 4 Our primary aim was to achieve anatomical success as defined as apical POP-Q less than or equal to Stage 1, resolution of subjective bulge and no reoperation for recurrent prolapse. Methods We performed a retrospective case series of extraperitoneal uterosacral ligament hysteropexy (EPUSLH) procedures from 2017 to 2019. Results In our case series, the objective cure rate (POP-Q apical prolapse stage ≤1) and the subjective cure rate at 5 months follow up was 100%, refer to Table 1. The mean length lost in total vaginal length compared to baseline was 0.5 cm, refer to Table 2. Mean surgical time for EPUSLH with combined procedures was 110 minutes. The mean EBL was 101 mL. Conclusion EPUSLH demonstrated good short-term success with a low rate of complications in this small cohort. Being a tertiary referral center with many patients traveling long distances or internationally has limited follow-up. Although larger numbers and long-term follow-up are needed to better understand the success of this procedure, the early results are encouraging that this technique could be a simple alternative hysteropexy method.
Lorentz Violation and Riemann-Finsler Geometry

The general charge-conserving effective scalar field theory incorporating violations of Lorentz symmetry is presented. The dispersion relation is used to infer the effects of spin-independent Lorentz violation on point-particle motion. A large class of associated Finsler spaces is derived, and the properties of these spaces is explored.
EXTENSIONS OF AUTOCORRELATION INEQUALITIES WITH APPLICATIONS TO ADDITIVE COMBINATORICS

Barnard and Steinerberger show that for $f\in L^1(\mathbf{R})$, the following autocorrelation inequality holds: $$\min_{0 \leq t \leq 1} \int_\R f(x) f(x+t)\ \mathrm{d}x \ \leq\ 0.411 ||f||_{L^1}^2,$$ where the constant $0.411$ cannot be replaced by $0.37$. In addition to being interesting and important in their own right, inequalities such as these have applications in additive combinatorics where some problems, such as those of minimal difference basis, can be encapsulated by a convolution inequality similar to the above integral. Barnard and Steinerberger suggest that future research may focus on the existence of functions extremizing the above inequality (which is itself related to Brascamp-Lieb type inequalities). We show that for $f$ to be extremal under the above, we must have $$\max_{x_1 \in \R }\min_{0 \leq t \leq 1} \left[ f(x_1-t)+f(x_1+t) \right] \ \leq\ \min_{x_2 \in \R } \max_{0 \leq t \leq 1} \left[ f(x_2-t)+f(x_2+t) \right] .$$ Our central technique for deriving this result is local perturbation of $f$ to increase the value of the autocorrelation, while leaving $||f||_{L^1}$ unchanged. These perturbation methods can be extended to examine a more general notion of autocorrelation. Let $d,n \in \mathbb{Z}^+$, $f \in L^1$, $A$ be a $d \times n$ matrix with real entries and columns $a_i$ for $1 \leq i \leq n$, and $C$ be a constant. For a broad class of matrices $A$, we prove necessary conditions for $f$ to extremize autocorrelation inequalities of the form $$\min_{ \mathbf{t} \in [0,1]^d } \int_{\R} \prod_{i=1}^n\ f(x+ \mathbf{t} \cdot a_i)\ \mathrm{d}x\ \leq\ C ||f||_{L^1}^n.$$
Clinical characteristics associated with COVID-19 severity in California

Given the rapidly progressing coronavirus disease 2019 (COVID-19) pandemic, this report on a US cohort of 54 COVID-19 patients from Stanford Hospital and data regarding risk factors for severe disease obtained at initial clinical presentation is highly important and immediately clinically relevant. We identified low presenting oxygen saturation as predictive of severe disease outcomes, such as diagnosis of pneumonia, acute respiratory distress syndrome, and admission to the intensive care unit, and also replicated data from China suggesting an association between hypertension and disease severity. Clinicians will benefit by tools to rapidly risk stratify patients at presentation by likelihood of progression to severe disease.
Robust Testing Procedures

Robust testing procedures are hypothesis testing techniques that are relatively insensitive to violations of assumptions such as normality and homoscedasticity. Major advances on how to study and develop robust testing techniques have appeared during the last forty years. The result has been a deeper understanding of why conventional ANOVA and regression methods can be unsatisfactory, plus a collection of new methods that have practical value. Keywords: robust methods; heteroscedasticity; skewness; outliers
Semistructured data and XML

The distinguishing feature of semistructured data is that the schema is embedded with the data. The main challenge is to cope with the additional flexibility without sacrificing efficiency. We introduce semistructured data by presenting a syntax and describing the datamodel. We discuss some query languages designed for semistructured data and address some systems issues, such as storage and XML compression.
12 – Integrated accounts

Publisher Summary This chapter discusses integrated accounts. Cost book-keeping is essentially the bringing of the cost accounts into the double entry system. Just as in financial accounting, every entry made in the cost ledger must have another entry in some other part of the cost ledger. The cost accounts are kept in a cost ledger that consists of a number of subsidiary ledgers. When the product or service is completed, based on the documentation that authorizes the movement of finished products out of the production area, the work-in-progress ledger is credited and the finished goods ledger is debited. Interlocking accounts is a system in which the cost accounts are distinct from the financial accounts and the two sets of accounts are kept continuously in agreement by the use of control accounts or made readily reconcilable by other means. Fully integrated accounting systems is a set of accounting records that provides financial and cost accounts using a common input of data for all accounting purposes.
New Item Types and Scoring

The advent of the computer has broadened the concept of testing and assessment. Previously, tests were mainly restricted to a paper-and-pencil medium and a multiple-choice format; however, the computer coupled with recent advancements in its technological sophistication has afforded test developers much more freedom in the types of tests they create. Much research in this area has been devoted to the development of innovative item types, which utilize the computer's capabilities to improve assessment or assess characteristics that traditional items cannot. Innovative items allow for a more extensive assessment of individual differences; however, the benefits of these new items do not come without a cost. Scoring innovative items is usually not as simplistic as employing a multiple-choice key, and, often, the items require extremely complicated scoring algorithms. In this chapter, we provide a review of innovative item types and their accompanying scoring issues. Owing to limited space and a rapidly changing field, it is impossible to provide an exhaustive review; therefore, we focus on the most recent and influential research in the field. Keywords: computerized assessment; innovative item types; computerized scoring
Verlinde formula from entanglement

We derive the Verlinde formula from a recently advocated set of axioms about entanglement entropy [Shi, Kato, and Kim, Ann. Phys., 168164 (2020)10.1016/j.aop.2020.168164]. For any state that obeys these axioms, we can define a quantity that can be identified as the topological S matrix of an abstract anyon theory. We show that the S matrix is unitary and that it recovers the fusion multiplicities of the underlying anyon theory through the Verlinde formula. Importantly, we rigorously prove the modularity of the theory, which further implies that the mutual braiding statistics of anyons are nontrivial. The key to the proof is a generalized quantum state merging technique, which generates a topology beyond that of any subsystem of the original physical system.
Development of Pavement Performance Models by Combining Experimental and Field Data

The objective of this paper is to demonstrate the development of pavement performance models by combining experimental and field data. A two step approach was used. In the first step a riding quality model based on serviceability consideration is developed. The data set of the American Association of State Highways Officials (AASHO) Road Test is used to this effect. Due to the experimental nature of the AASHO Road Test data set, some of the estimated parameters of the model may be biased when the model is to be applied to predict performance in the field. In the second step, the original model parameters are reestimated by applying joint estimation with the incorporation of field data set. This data set was collected through the Minnesota Road Research Project (MnRoad). The final model is referred to as the joint model, and it can be used to predict the performance of in-service pavement sections. Joint estimation allowed for the full potential of both data sources to be exploited. First, the effect of variables not available in the first data source were identified and quantified. Further, the parameter estimates had lower variance because multiple data sources were pooled, and biases in the parameters of the experimental model were corrected. Finally, different measurements of the same property were incorporated by using a measurement error model. Thus, the methodology proposed in this paper makes optimum use of available data and yields models of improved statistical properties compared with techniques such as ordinary least squares.
Empire-builders and shirkers: Investment, firm performance, and managerial incentives

Abstract We consider the equilibrium relationships between incentives from compensation, investment, and firm performance. In an optimal contracting model, we show that the relationship between firm performance and managerial incentives, in isolation, is insufficient to identify whether managers have private benefits of investment, as in theories of managerial entrenchment. We estimate the joint relationships between incentives and firm performance and between incentives and investment. We provide new results showing that investment is increasing in incentives. Further, in contrast to previous studies, we find that firm performance is increasing in incentives at all levels of incentives. Taken together, these results are inconsistent with theories of overinvestment based on managers having private benefits of investment. These results are consistent with managers having private costs of investment and, more generally, models of underinvestment.
Spatial Models for Categorical Data

After reviewing characteristics of spatially correlated counts, rates, and proportions that compromise traditional methods of inference, we briefly survey general approaches for the analysis of such spatially referenced categorical data. The survey focuses on auto-models, generalized linear models, and generalized linear mixed models, emphasizing structural and conceptual aspects rather than computational details. Keywords: auto-models; auto-Gaussian; auto-logistic; auto-Poisson; pseudo-likelihood; quasi-likelihood; generalized linear mixed models; random effects; spatial autocorrelation
Tuning the stability of electrochemical interfaces by electron transfer reactions

The morphology of interfaces is known to play a fundamental role in the efficiency of energy-related applications, such as light harvesting or ion intercalation. Altering the morphology on demand, however, is a very difficult task. Here, we show ways the morphology of interfaces can be tuned by driven electron transfer reactions. By using non-equilibrium thermodynamic stability theory, we uncover the operating conditions that alter the interfacial morphology. We apply the theory to ion intercalation and surface growth where electrochemical reactions are described using Butler-Volmer or coupled ion-electron transfer kinetics. The latter connects microscopic/quantum mechanical concepts with the morphology of electrochemical interfaces. Finally, we construct non-equilibrium phase diagrams in terms of the applied driving force (current/voltage) and discuss the importance of engineering the density of states of the electron donor in applications related to energy harvesting and storage, electrocatalysis, and photocatalysis.
An $\ell_{\infty}$ Eigenvector Perturbation Bound and Its Application to Robust Covariance Estimation

In statistics and machine learning, we are interested in the eigenvectors (or singular vectors) of certain matrices (e.g. covariance matrices, data matrices, etc). However, those matrices are usually perturbed by noises or statistical errors, either from random sampling or structural patterns. The Davis-Kahan sin θ theorem is often used to bound the difference between the eigenvectors of a matrix A and those of a perturbed matrix A˜=A+E , in terms of l2 norm. In this paper, we prove that when A is a low-rank and incoherent matrix, the l∞ norm perturbation bound of singular vectors (or eigenvectors in the symmetric case) is smaller by a factor of d1 or d2 for left and right vectors, where d1 and d2 are the matrix dimensions. The power of this new perturbation result is shown in robust covariance estimation, particularly when random variables have heavy tails. There, we propose new robust covariance estimators and establish their asymptotic properties using the newly developed perturbation bound. Our theoretical results are verified through extensive numerical experiments.