cssi/SciSciGPT-SciSciNet huggingface dataset:
Travelling spindles create necessary conditions for spike-timing-dependent plasticity in humans

Sleep spindles facilitate memory consolidation in the cortex during mammalian non-rapid eye movement sleep. In rodents, phase-locked firing during spindles may facilitate spike-timing-dependent plasticity by grouping pre-then-post-synaptic cell firing within ~25 ms. Currently, microphysiological evidence in humans for conditions conducive for spike-timing-dependent plasticity during spindles is absent. Here, we analyze field potentials and unit firing from middle/upper layers during spindles from 10 × 10 microelectrode arrays at 400 μm pitch in humans. We report strong tonic and phase-locked increases in firing and co-firing within 25 ms during spindles, especially those co-occurring with down-to-upstate transitions. Co-firing, spindle co-occurrence, and spindle coherence are greatest within ~2 mm, and high co-firing of units on different contacts depends on high spindle coherence between those contacts. Spindles propagate at ~0.28 m/s in distinct patterns, with correlated cell co-firing sequences. Spindles hence organize spatiotemporal patterns of neuronal co-firing in ways that may provide pre-conditions for plasticity during non-rapid eye movement sleep. Sleep spindles during non-rapid eye movement are important for memory consolidation and require specific neuronal firing conditions in non-human mammals. Here, the authors show these conditions are present in humans, potentially facilitating spike-timing-dependent plasticity.
Human-Robot Interaction in a Shared Augmented Reality Workspace

We design and develop a new shared Augmented Reality (AR) workspace for Human-Robot Interaction (HRI), which establishes a bi-directional communication between human agents and robots. In a prototype system, the shared AR workspace enables a shared perception, so that a physical robot not only perceives the virtual elements in its own view but also infers the utility of the human agent—the cost needed to perceive and interact in AR—by sensing the human agent’s gaze and pose. Such a new HRI design also affords a shared manipulation, wherein the physical robot can control and alter virtual objects in AR as an active agent; crucially, a robot can proactively interact with human agents, instead of purely passively executing received commands. In experiments, we design a resource collection game that qualitatively demonstrates how a robot perceives, processes, and manipulates in AR and quantitatively evaluates the efficacy of HRI using the shared AR workspace. We further discuss how the system can potentially benefit future HRI studies that are otherwise challenging.
The Role of Edge Offload for Hardware-Accelerated Mobile Devices

This position paper examines a spectrum of approaches to overcoming the limited computing power of mobile devices caused by their need to be small, lightweight and energy efficient. At one extreme is offloading of compute-intensive operations to a cloudlet nearby. At the other extreme is the use of fixed-function hardware accelerators on mobile devices. Between these endpoints lie various configurations of programmable hardware accelerators. We explore the strengths and weaknesses of these approaches and conclude that they are, in fact, complementary. Based on this insight, we advocate a software-hardware co-evolution path that combines their strengths.
Towards adversarial robustness with 01 loss neural networks

Motivated by the general robustness properties of the 01 loss we propose a single hidden layer 01 loss neural network trained with stochastic coordinate descent as a defense against adversarial attacks in machine learning. One measure of a model’s robustness is the minimum distortion required to make the input adversarial. This can be approximated with the Boundary Attack (Brendel et. al. 2018) and HopSkipJump (Chen et. al. 2019) methods. We compare the minimum distortion of the 01 loss network to the binarized neural network and the standard sigmoid activation network with cross-entropy loss all trained with and without Gaussian noise on the CIFAR10 benchmark binary classification between classes 0 and 1. Both with and without noise training we find our 01 loss network to have the largest adversarial distortion of the three models by non-trivial margins. To further validate these results we subject all models to substitute model black box attacks under different distortion thresholds and find that the 01 loss network is the hardest to attack across all distortions. At a distortion of 0.125 both sigmoid activated cross-entropy loss and binarized networks have almost 0% accuracy on adversarial examples whereas the 01 loss network is at 40%. Even though both 01 loss and the binarized network use sign activations their training algorithms are different which in turn give different solutions for robustness. Finally we compare our network to simple convolutional models under substitute model black box attacks and find their accuracies to be comparable. Our work shows that the 01 loss network has the potential to defend against black box adversarial attacks better than convex loss and binarized networks.
The p97-UBXN1 complex regulates aggresome formation

The recognition and disposal of misfolded proteins is essential for the maintenance of cellular homeostasis. Perturbations in the pathways that promote degradation of aberrant proteins contribute to a variety of protein aggregation disorders broadly termed proteinopathies. The AAA-ATPase p97 (also known as VCP), in combination with adaptor proteins, functions to identify ubiquitylated proteins and target them for degradation by the proteasome or through autophagy. Mutations in p97 cause multi-system proteinopathies; however, the precise defects underlying these disorders are unclear. Here, we systematically investigate the role of p97 and its adaptors in the process of formation of aggresomes, membrane-less structures containing ubiquitylated proteins that arise upon proteasome inhibition. We demonstrate that p97 mediates aggresome formation and clearance, and identify a novel role for the adaptor UBXN1 in the process of aggresome formation. UBXN1 is recruited to aggresomes, and UBXN1-knockout cells are unable to form aggresomes. Loss of p97-UBXN1 results in increased Huntingtin polyQ inclusion bodies both in mammalian cells and in a C. elegans model of Huntington's disease. Together, our results identify evolutionarily conserved roles for p97-UBXN1 in the disposal of protein aggregates.
A “China in the World” Paradigm for Scholarship

In this introduction to the special issue, we use the expression "China in the world" paradigm to define scholarship that purposefully migrates across the traditional borders of comparative politics and international relations in the study of China. We argue that such a paradigm represents a view that many issues of Chinese domestic politics are now issues of international politics; as a result, domestic politics in a globalized contemporary China often cannot be sufficiently understood without considering international consequences. More than this, the paradigm is about scholarly attentiveness to the fact that the politics in China that we study also shapes how the rest of the world views China. We describe the paradigm and its antecedents in the scholarly literature. We then illustrate, with reference to three momentous events that captured public attention around the world in 2020, the paradigm's usefulness as a perspective to scholars reaching out to engage intellectually on contemporary affairs in an environment in which global responses to China require nuanced knowledge as all parties seek to avoid dangerous pitfalls. We conclude by summarizing the five articles included in the special issue and the broader implications of the "China in the world" paradigm.
PARTITIONS INTO PRIME POWERS

For a subset $\mathcal A\subset \mathbb N$, let $p_{\mathcal A}(n)$ denote the restricted partition function which counts partitions of $n$ with all parts lying in $\mathcal A$. In this paper, we use a variation of the Hardy-Littlewood circle method to provide an asymptotic formula for $p_{\mathcal A}(n)$, where $\mathcal A$ is the set of $k$-th powers of primes (for fixed $k$). This combines Vaughan's work on partitions into primes with the author's previous result about partitions into $k$-th powers. This new asymptotic formula is an extension of a pattern indicated by several results about restricted partition functions over the past few years. Comparing these results side-by-side, we discuss a general strategy by which one could analyze $p_{\mathcal A}(n )$ for a given set $\mathcal A$.
Dimension reduction for thin films prestrained by shallow curvature

We are concerned with the dimension reduction analysis for thin three-dimensional elastic films, prestrained via Riemannian metrics with weak curvatures. For the prestrain inducing the incompatible...
X-ray Lite: A 1-credit pass/fail crystallography course.

Single-crystal x-ray structure determination is a critical component to research programs in many science and engineering fields. Yet students usually receive, at best, a cursory overview of the theory and usually zero practical experience with single-crystal x-ray diffraction (SCXRD) in the typical undergraduate and graduate curricula. This dearth of basic understanding among the “non-crystallographic” scientific community, unless addressed through education, can perpetuate to the faculty PI level, leading to misunderstandings and unintended mistakes at steps from sample preparation through publication. To address this, a new 1-credit pass/fail graduate-level crystallography course has been developed, informally dubbed X-ray Lite. This course has two main goals: (1) to reduce the black box nature for students who submit samples to the Virginia Tech X-ray Service Center and (2) to provide researchers with adequate “crystallographic literacy” to publish structures and feel comfortable reviewing and interpreting published structures. As a side benefit, students will hopefully gain new appreciation and a positive view of crystallography as a characterization tool.
Abstract 1026: Oncogenic alterations in FGFR3 and ERBB2 lead to ligand-independent activation of PPARG in bladder cancer

PPARG is genomically activated in muscle-invasive bladder cancer through focal PPARG gene amplification and hotspot mutations in its heterodimer partner, RXRA. However, more than half of the PPARG-activated bladder tumors and cell lines do not have identifiable somatic alterations in either PPARG or RXRA. Using a PPARG-driven reporter assay in RT112 bladder cancer cell line, we screened probe compounds to identify candidate drivers of ligand-independent PPARG activation. We found that pan-FGFR inhibitors and MEK1/2 inhibitors antagonized the PPARG-driven reporter assay with potency similar to reported values for their cognate targets (1-20 nM) and interestingly, RT112 cells carry an FGFR3-TACC3 oncogenic fusion. In addition to the expected effects of these inhibitors on phospho-MEK1/2 and phospho-ERK1/2, they also inhibited production of canonical PPARG targets, including FABP4. In a second subset of cell lines, a parallel story was also observed for the effects of ERBB2 inhibitors in ERBB2 hotspot mutant bladder cancer. Taken together, these data uncover additional mechanisms for functional activation of PPARG in bladder cancer, suggest potential resistance mechanisms for FGFR and ERBB2 inhibitors, and provide a rationale for therapeutic combinations of PPARG modulators. Citation Format: Jonathan T. Goldstein, Ashton C. Berger, Craig A. Strathdee, Matthew Meyerson. Oncogenic alterations in FGFR3 and ERBB2 lead to ligand-independent activation of PPARG in bladder cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1026.
Emergent Fractional Charge and Multiple Majoranas

To mark the 111 th birthday of Eugene Wigner, we review topological excita- tions in diverse dimensions. Although this meeting commemorates the 111 birthday of Wigner, my talk draws on the work of Dirac. I do not apologize for this. Both men (brothers-in-law) were led by mathematics to descriptions of fundamental particles: Wigner by group theory, Dirac by his differential equation. It is Dirac's framework that recently resulted in further discoveries about quantum excitations. This is the topic that I shall describe. Let me begin with a bit of history about Dirac and his equation. Dirac was seeking a relativistic equation for electrons and eventually arrived at the following first-order matrix/differential equation.
A shuffling theorem for reflectively symmetric tilings

Abstract The author and Rohatgi recently proved a ‘shuffling theorem’ for doubly-dented hexagons. In particular, they showed that shuffling removed unit triangles along a horizontal axis in a hexagon changes the tiling number by only a simple multiplicative factor. In this paper, we consider a similar phenomenon for a symmetry class of tilings, namely, the reflectively symmetric tilings. We also prove several shuffling theorems for halved hexagons.
Optimal Priority-Based Allocation Mechanisms

This paper develops a tractable methodology for designing an optimal priority system for assigning agents to heterogeneous items while accounting for agents’ choice behavior. The space of mechanism...
The Promise of Hedge Fund Governance: How Incentive Compensation Can Enhance Institutional Investor Monitoring

Progressive legal scholars argue that institutional investors should play a greater role in disciplining corporate managers. These reformers seek to harness the talent and resources of mutual funds and public pension funds to increase managerial accountability to shareholder interests. Conservative scholars respond that empowering institutional investors would do little more than relocate the underlying agency costs. Although shirking by corporate managers might indeed be reduced, institutional investors suffer from their own set of agency problems and so would need their own monitor. Ultimately, someone must watch the watchers. This Article argues that neither corporate managers nor institutional investors are properly incentivized to serve shareholder interests. Therefore, neither is appropriately positioned to serve as the ultimate decision maker. A better model of governance is the incentive fee structure employed by hedge funds and other private equity funds. If institutional fund managers were permitted to adopt a similar compensation scheme, their interests and the interests of their investors would merge. As a result, they would be transformed into ideal servants of shareholder interests, capable of bringing much-needed discipline to corporate America. * Assistant Professor, University of Oregon School of Law. B.A. 1991, Williams College; J.D. 1996, Vanderbilt University. E-mail: rillig@uoregon.edu. I am grateful to my many wonderful friends and colleagues in the legal academy for their helpful thoughts and comments. More importantly, I benefited from the insights of my fund manager friends, most notably Jamie Hague of the Millburn Corp. family of hedge funds, Jay Namyet of the UO Foundation, and Rusty Olsen, formerly of Eastman
Stochastic Completeness of Graphs: Bounded Laplacians, Intrinsic Metrics, Volume Growth and Curvature

The goal of this article is to survey various results concerning stochastic completeness of graphs. In particular, we present a variety of formulations of stochastic completeness and discuss how a discrepancy between uniqueness class and volume growth criteria in the continuous and discrete settings was ultimately resolved via the use of intrinsic metrics. Along the way, we discuss some equivalent notions of boundedness in the sense of geometry and of analysis. We also discuss various curvature criteria for stochastic completeness and discuss how weakly spherically symmetric graphs establish the sharpness of results.
Risking Your Life without a Second Thought: Intuitive Decision-Making and Extreme Altruism

When faced with the chance to help someone in mortal danger, what is our first response? Do we leap into action, only later considering the risks to ourselves? Or must instinctive self-preservation be overcome by will-power in order to act? We investigate this question by examining the testimony of Carnegie Hero Medal Recipients (CHMRs), extreme altruists who risked their lives to save others. We collected published interviews with CHMRs where they described their decisions to help. We then had participants rate the intuitiveness versus deliberativeness of the decision-making process described in each CHMR statement. The statements were judged to be overwhelmingly dominated by intuition; to be significantly more intuitive than a set of control statements describing deliberative decision-making; and to not differ significantly from a set of intuitive control statements. This remained true when restricting to scenarios in which the CHMRs had sufficient time to reflect before acting if they had so chosen. Text-analysis software found similar results. These findings suggest that high-stakes extreme altruism may be largely motivated by automatic, intuitive processes.
Adaptive Subband Compression for Streaming of Continuous Point-on-Wave and PMU Data

A data compression system capable of providing real-time streaming of high-resolution continuous point-on-wave (CPOW) and phasor measurement unit (PMU) measurements is proposed. Referred to as adaptive subband compression (ASBC), the proposed technique partitions the signal space into subbands and adaptively compresses subband signals based on each subband's active bandwidth. The proposed technique conforms to existing industry phasor measurement standards, making it suitable for streaming high-resolution CPOW and PMU data either in continuous or burst on-demand/event-triggered modes. Experiments on synthetic and real data show that ASBC reduces the CPOW sampling rates by several orders of magnitude for real-time streaming while maintaining the precision required by industry standards.
Semiparametric analysis of clustered interval-censored survival data using soft Bayesian additive regression trees (SBART)

Popular parametric and semiparametric hazards regression models for clustered survival data are inappropriate and inadequate when the unknown effects of different covariates and clustering are complex. This calls for a flexible modeling framework to yield efficient survival prediction. Moreover, for some survival studies involving time to occurrence of some asymptomatic events, survival times are typically interval censored between consecutive clinical inspections. In this article, we propose a robust semiparametric model for clustered interval-censored survival data under a paradigm of Bayesian ensemble learning, called soft Bayesian additive regression trees or SBART (Linero and Yang, 2018), which combines multiple sparse (soft) decision trees to attain excellent predictive accuracy. We develop a novel semiparametric hazards regression model by modeling the hazard function as a product of a parametric baseline hazard function and a nonparametric component that uses SBART to incorporate clustering, unknown functional forms of the main effects, and interaction effects of various covariates. In addition to being applicable for left-censored, right-censored, and interval-censored survival data, our methodology is implemented using a data augmentation scheme which allows for existing Bayesian backfitting algorithms to be used. We illustrate the practical implementation and advantages of our method via simulation studies and an analysis of a prostate cancer surgery study where dependence on the experience and skill level of the physicians leads to clustering of survival times. We conclude by discussing our method's applicability in studies involving high-dimensional data with complex underlying associations.
Integrative Factor Regression and Its Inference for Multimodal Data Analysis

Multimodal data, where different types of data are collected from the same subjects, are fast emerging in a large variety of scientific applications. Factor analysis is commonly used in integrative...
On the Light Ray Transform of Wave Equation Solutions

We study the light ray transform on Minkowski space-time and its small metric perturbations acting on scalar functions which are solutions to wave equations. We show that the light ray transform uniquely determines the function in a stable way. The problem is of particular interest because of its connection to inverse problems of the Sachs–Wolfe effect in cosmology.
BOOSTR: A Dataset for Accelerator Control Systems

The Booster Operation Optimization Sequential Time-series for Regression (BOOSTR) dataset was created to provide a cycle-by-cycle time series of readings and settings from instruments and controllable devices of the Booster, Fermilab’s Rapid-Cycling Synchrotron (RCS) operating at 15 Hz. BOOSTR provides a time series from 55 device readings and settings that pertain most directly to the high-precision regulation of the Booster’s gradient magnet power supply (GMPS). To our knowledge, this is one of the first well-documented datasets of accelerator device parameters made publicly available. We are releasing it in the hopes that it can be used to demonstrate aspects of artificial intelligence for advanced control systems, such as reinforcement learning and autonomous anomaly detection.
Multi-Muscle Deep Learning Segmentation to Automate the Quantification of Muscle Fat Infiltration in Cervical Spine Conditions

Muscle fat infiltration (MFI) is observed in participants with cervical spine disorders using magnetic resonance imaging. The quantification of MFI using magnetic resonance imaging (MRI) requires time-consuming and rater-dependent manual segmentation techniques. Convolutional neural network (CNN) models have demonstrated state-of-the-art performance in a single cervical spine muscle segmentation task in participants with whiplash injury following a motor vehicle collision (MVC). Here, we expanded this work and trained a CNN for multi-muscle segmentation and automatic MFI calculation of seven cervical spine muscle groups (left and right muscles segmented separately, 14 muscles total) from high-resolution fat-water imaging in participants following a MVC (34 training datasets, n = 17, 14 females, 3 males, age = 33.7 ± 11.4 years). First, we demonstrate high test reliability and accuracy of the CNN MFI measures compared to manual segmentation in an independent testing dataset (n = 18, 11 females, 7 males, age = 31.7 ± 9.6 years). Then in 84 participants imaged within two weeks following a MVC (61 females, 23 males, age = 34.2 ± 10.7 years), we examined MFI across the muscle groups and explored the relationship between MFI and sex, age, and body mass index (BMI). Averaging across all muscle groups, females had significantly higher MFI than males (p 0.300, p
A passive Stokes flow rectifier for Newtonian fluids.

Non-linear effects of the Navier–Stokes equations disappear under the Stokes regime of Newtonian fluid flows disallowing a flow rectification behavior. Here we show that passive flow rectification of Newtonian fluids is obtainable under the Stokes regime of both compressible and incompressible flows by introducing nonlinearity into the otherwise linear Stokes equations. Asymmetric flow resistances arise in shallow nozzle/diffuser microchannels with deformable ceiling, in which the fluid flow is governed by a non-linear coupled fluid–solid mechanics equation. The proposed model captures the unequal deflection profile of the deformable ceiling depending on the flow direction under the identical applied pressure, permitting a larger flow rate in the nozzle configuration. Ultra-low aspect ratio microchannels sealed by a flexible membrane have been fabricated to demonstrate passive flow rectification for low-Reynolds-number flows (0.001 < Re < 10) of common Newtonian fluids such as water, methanol, and isopropyl alcohol. The proposed rectification mechanism is also extended to compressible flows, leading to the first demonstration of rectifying equilibrium gas flows under the Stokes flow regime. While the maximum rectification ratio experimentally obtained in this work is limited to 1.41, a higher value up to 1.76 can be achieved by optimizing the width profile of the asymmetric microchannels.
The geography of parasite local adaptation to host communities

Fitness responses to environment can shape species distributions, though opposing eco-evolutionary processes can obscure environmental effects. For example, host specificity influences parasite dynamics, but is unclear how adaptation of parasites to local host communities may scale up to continental distributions. Here, we develop a macroecological framework to determine how host community structure affects the distribution of specialist and generalist populations of Striga hermonthica, an African parasitic plant of cereal crops. Combining data from global crop production and parasite experimental trials, we find that parasites perform best on the host species that is most common in their location of origin. Moreover, niche model contrasts predict parasite specialization on two hosts that evolved alongside Striga during domestication (pearl millet and sorghum), indicating that specialist parasites may be most likely to occur where host niches differ most in multivariate environmental space. Our study demonstrates that patterns of parasite local adaptation to host communities can emerge at continental scales and that differential environmental tolerances of hosts indirectly shape the distribution of specialist and generalist parasites. By predicting spatial dynamics of parasite specialization versus generalization directly from environmental data, our approach may help inform current and future management of pests and disease.
A (slightly) improved approximation algorithm for metric TSP

For some $\epsilon > 10^{-36}$ we give a $3/2-\epsilon$ approximation algorithm for metric TSP.
How emoji and word embedding helps to unveil emotional transitions during online messaging

During online chats, body-language and vocal characteristics are not part of the communication mechanism making it challenging to facilitate an accurate interpretation of feelings, emotions, and attitudes. The use of emojis to express emotional feeling is an alternative approach in these types of communication. In this project, we focus on modeling a customer’s emotion in an online messaging session with a chatbot. We use Affect Control Theory (ACT) to predict emotional change during the interaction. To let the customer use emojis, we also extend the affective dictionaries used by ACT. For this purpose, we mapped Emoji2vec embedding to the affective space. Our framework can find emotional change during messaging and how a customer’s reaction is changed accordingly.
Structural insights into the dynamic process of G‐protein– coupled receptor activation

Research in my lab is directed at understanding the structural basis for the functional properties of G protein coupled receptors (GPCRs), which constitute the largest family of membrane proteins in the human genome. GPCRs conduct the majority of transmembrane responses to hormones and neurotransmitters, and mediate the senses of sight, smell and taste. The β2AR adrenoceptor (β2AR) is a prototypical Family A GPCR that mediates physiologic responses to adrenaline and noradrenaline. It regulates the activity of several distinct signaling pathways through both G protein dependent and G protein independent mechanisms. Like many GPCRs that respond to hormones and neurotransmitters, the β2AR exhibits modest basal activity in the absence of an agonist. This activity can be modulated by a spectrum of synthetic ligands ranging from inverse agonists, which suppress basal activity, to full agonists. We have obtained three-dimensional structures of the β2AR in inactive and active conformations, as well as a structure of the β2AR in complex with the G protein Gs. We have also used fluorescence, EPR and NMR spectroscopy to study the dynamic properties of the receptor, and to map ligand-specific conformational changes. I will discuss what we these studies have taught us about the structural basis of β2AR function.
Deep learning and self-consistent field theory: A path towards accelerating polymer phase discovery

Abstract A new framework that leverages data obtained from self-consistent field theory (SCFT) simulations with deep learning to accelerate the exploration of parameter space for block copolymers is presented. Deep neural networks are adapted and trained in Sobolev space to better capture the saddle point nature of the SCFT approximation. The proposed approach consists of two main problems: 1) the learning of an approximation to the effective Hamiltonian as a function of the average monomer density fields and the relevant physical parameters and 2) the prediction of saddle density fields given the polymer parameters. There is an additional challenge: the effective Hamiltonian has to be invariant under shifts (and rotations in 2D and 3D). A data-enhancing approach and an appropriate regularization are introduced to effectively achieve said invariance. In this first study, the focus is on one-dimensional (in physical space) systems to allow for a thorough exploration and development of the proposed methodology.
More than Meets the Tie: Examining the Role of Interpersonal Relationships in Social Networks.

Topics in conversations depend in part on the type of interpersonal relationship between speakers, such as friendship, kinship, or romance. Identifying these relationships can provide a rich description of how individuals communicate and reveal how relationships influence the way people share information. Using a dataset of more than 9.6M dyads of Twitter users, we show how relationship types influence language use, topic diversity, communication frequencies, and diurnal patterns of conversations. These differences can be used to predict the relationship between two users, with the best predictive model achieving a macro F1 score of 0.70. We also demonstrate how relationship types influence communication dynamics through the task of predicting future retweets. Adding relationships as a feature to a strong baseline model increases the F1 and recall by 1% and 2%. The results of this study suggest relationship types have the potential to provide new insights into how communication and information diffusion occur in social networks.
Wobble-spin technique for spacecraft inversion and earth photography.

If certain prescribed spacecraft moment-of-inertia relationships are maintained, a small cylindrical flywheel can be used to tilt a spin-stabilized satellite with respect to its fixed angular momentum vector without inducing nutation and without changing the basic spin rate. A camera rigidly attached to such a tilting vehicle will have the same view as a pivoting camera in a satellite whose spin axis is fixed. This technique can be extended, if desired, to turn the satellite upside down—a very useful capability for spin-scan camera satellites using radiation-cooled instruments. The control flywheel, which can be located anyplace in the satellite, must have its axis of rotation perpendicular to the satellite spin axis. The total vehicle must have a cylindrical moment of inertia configuration, with its axis of symmetry also perpendicular to the vehicle spin axis, and parallel to the flywheel's axis of rotation.
Lighting Enhancement Aids Reconstruction of Colonoscopic Surfaces

High screening coverage during colonoscopy is crucial to effectively prevent colon cancer. Previous work has allowed alerting the doctor to unsurveyed regions by reconstructing the 3D colonoscopic surface from colonoscopy videos in real-time. However, the lighting inconsistency of colonoscopy videos can cause a key component of the colonoscopic reconstruction system, the SLAM optimization, to fail. In this work we focus on the lighting problem in colonoscopy videos. To successfully improve the lighting consistency of colonoscopy videos, we have found necessary a lighting correction that adapts to the intensity distribution of recent video frames. To achieve this in real-time, we have designed and trained an RNN network. This network adapts the gamma value in a gamma-correction process. Applied in the colonoscopic surface reconstruction system, our light-weight model significantly boosts the reconstruction success rate, making a larger proportion of colonoscopy video segments reconstructable and improving the reconstruction quality of the already reconstructed segments.
Planning Multimodal Exploratory Actions for Online Robot Attribute Learning

Robots frequently need to perceive object attributes, such as “red,” “heavy,” and “empty,” using multimodal exploratory actions, such as “look,” “lift,” and “shake.” Robot attribute learning algorithms aim to learn an observation model for each perceivable attribute given an exploratory action. Once the attribute models are learned, they can be used to identify attributes of new objects, answering questions, such as “Is this object red and empty?” Attribute learning and identification are being treated as two separate problems in the literature. In this paper, we first define a new problem called online robot attribute learning (On-RAL), where the robot works on attribute learning and attribute identification simultaneously. Then we develop an algorithm called information-theoretic reward shaping (ITRS) that actively addresses the trade-off between exploration and exploitation in On-RAL problems. ITRS was compared with competitive robot attribute learning baselines, and experimental results demonstrate ITRS’ superiority in learning efficiency and identification accuracy.
The Disciplinary Effect of Social Media: Evidence from Firms' Responses to Glassdoor Reviews

We examine how firms respond to the increased workplace transparency due to the coverage on Glassdoor.com, which collects and disseminates reviews on employee satisfaction. Leveraging the staggered timing of first-time reviews on Glassdoor, we use a difference-in-differences design and find that after being reviewed on Glassdoor, firms improve their workplace practices, measured by corporate social responsibility scores on employee relations and diversity. Consistent with firms improving their workplace practices to remain competitive in the labor market, we find that such improvement concentrates in firms with negative initial reviews and with high labor intensity. We also find firms increase disclosures about workplace practices after being reviewed and the increase concentrates in firms with high institutional ownership, consistent with firms providing more disclosures to appease investors. Overall, our findings suggest that the increased workplace transparency through social media has a disciplinary effect on corporate policies.
Automatic Electrocardiogram Detection of Suspected Hypertrophic Cardiomyopathy: Application to Wearable Heart Monitors

In this letter, an automatic detection algorithm for hypertrophic cardiomyopathy (HCM) is presented. Of particular interest is the algorithm's ability to differentiate HCM subjects and healthy volunteers from a single lead ECG dataset. Suspected HCM subjects are identified by the primary clinical abnormality associated with HCM: left ventricular hypertrophy (LVH). In total, n = 43 human subjects ECG datasets are investigated: n = 21 healthy volunteers and n = 22 LVH patients. Significant differences of p -value 0.01 and 0.04 were found for the respective ECG parameters, i.e., S-wave amplitude and ST-segment, when differentiating between the LVH patients and healthy human volunteers.
Better Compression With Deep Pre-Editing

Could we compress images via standard codecs while avoiding visible artifacts? The answer is obvious – this is doable as long as the bit budget is generous enough. What if the allocated bit-rate for compression is insufficient? Then unfortunately, artifacts are a fact of life. Many attempts were made over the years to fight this phenomenon, with various degrees of success. In this work we aim to break the unholy connection between bit-rate and image quality, and propose a way to circumvent compression artifacts by pre-editing the incoming image and modifying its content to fit the given bits. We design this editing operation as a learned convolutional neural network, and formulate an optimization problem for its training. Our loss takes into account a proximity between the original image and the edited one, a bit-budget penalty over the proposed image, and a no-reference image quality measure for forcing the outcome to be visually pleasing. The proposed approach is demonstrated on the popular JPEG compression, showing savings in bits and/or improvements in visual quality, obtained with intricate editing effects.
SAMPLING IN SPACES OF BANDLIMITED FUNCTIONS ON COMMUTATIVE SPACES

A homogeneous space \(\mathbf{X} = G/K\) is called commutative if G is a locally compact group, K is a compact subgroup, and the Banach ∗ -algebra \({L}^{1}{(\mathbf{X})}^{K}\) of K-invariant integrable functions on \(\mathbf{X}\) is commutative. In this chapter we introduce the space \({L}_{\Omega }^{2}(\mathbf{X})\) of \(\Omega \)-bandlimited function on \(\mathbf{X}\) by using the spectral decomposition of \({L}^{2}(\mathbf{X})\). We show that those spaces are reproducing kernel Hilbert spaces and determine the reproducing kernel. We then prove sampling results for those spaces using the smoothness of the elements in \({L}_{\Omega }^{2}(\mathbf{X})\). At the end we discuss the example of \({\mathbb{R}}^{d}\), the spheres S d , compact symmetric spaces, and the Heisenberg group realized as the commutative space \(\mathrm{U}(n) \ltimes {\mathbb{H}}_{n}/\mathrm{U}(n)\).
The norm residue symbol for higher local fields

Abstract In this paper we investigate the Kummer pairing associated to an arbitrary (one-dimensional) formal group. In particular, we obtain formulae describing the values of the pairing in terms of multidimensional p-adic differentiation, the logarithm of the formal group, the generalized trace and the norm on Milnor K-groups. The results are a generalization to higher-dimensional local fields of Kolyvagin's explicit reciprocity laws. In particular, they constitute a generalization of the reciprocity laws of Artin-Hasse, Iwasawa and Wiles.
Integrating Solvency and Liquidity Stress Tests: The Use of Markov Regime-Switching Models

The paper presents a framework to integrate liquidity and solvency stress tests. An empirical study based on European bond trading data finds that asset sales haircuts depend on the total amount of assets sold and general liquidity conditions in the market. To account for variations in market liquidity, the study uses Markov regime-switching models and links haircuts with market volatility and the amount of securities sold by banks. The framework is accompanied by a Matlab program and an Excel-based tool, which allow the calculations to be replicated for any type of traded security and to be used for liquidity and solvency stress testing.
Emergent geometry through quantum entanglement in Matrix theories

In the setting of the Berenstein-Maldacena-Nastase Matrix theory, dual to light-cone M-theory in a PP-wave background, we compute the Von Neumann entanglement entropy between a probe giant graviton and a source. We demonstrate that this entanglement entropy is directly and generally related to the local tidal acceleration experienced by the probe. This establishes a new map between local spacetime geometry and quantum entanglement, suggesting a mechanism through which geometry emerges from Matrix quantum mechanics. We extend this setting to light-cone M-theory in flat space, or the Banks-Fischler-Shenker-Susskind Matrix model, and we conjecture a new general relation between a certain measure of entanglement in Matrix theories and local spacetime geometry. The relation involves a ‘c-tensor’ that measures the evolution of local transverse area and relates to the local energy-momentum tensor measured by a probe.
ArGoT: A Glossary of Terms extracted from the arXiv

We introduce ArGoT, a data set of mathematical terms extracted from the articles hosted on the arXiv website. A term is any mathematical concept defined in an article. Using labels in the article's source code and examples from other popular math websites, we mine all the terms in the arXiv data and compile a comprehensive vocabulary of mathematical terms. Each term can be then organized in a dependency graph by using the term's definitions and the arXiv's metadata. Using both hyperbolic and standard word embeddings, we demonstrate how this structure is reflected in the text's vector representation and how they capture relations of entailment in mathematical concepts. This data set is part of an ongoing effort to align natural mathematical text with existing Interactive Theorem Prover Libraries (ITPs) of formally verified statements.
Identifying Acoustic Wave Sources on the Sun. I. Two-dimensional Waves in a Simulated Photosphere

The solar acoustic oscillations are likely stochastically excited by convective dynamics in the solar photosphere, though few direct observations of individual source events have been made and their detailed characteristics are still unknown. Wave source identification requires measurements that can reliably discriminate the local wave signal from the background convective motions and resonant modal power. This is quite challenging as these 'noise' contributions have amplitudes several orders of magnitude greater than the sources and the propagating wave fields they induce. In this paper, we employ a high-temporal-frequency filter to identify sites of acoustic emission in a radiative magnetohydrodynamic simulation. The properties of the filter were determined from a convolutional neural network trained to identify the two-dimensional acoustic Green's function response of the atmosphere, but once defined, it can be directly applied to an image time series to extract the signal of local wave excitation, bypassing the need for the original neural network. Using the filter developed, we have uncovered previously unknown properties of the acoustic emission process. In the simulation, acoustic events are found to be clustered at mesogranular scales, with peak emission quite deep, about 500 km below the photosphere, and sites of very strong emission can result from the interaction of two supersonic downflows that merge at that depth. We suggest that the method developed, when applied to high-resolution high-cadence observations, such as those forthcoming with Daniel K. Inouye Solar Telescope (DKIST), will have important applications in chromospheric wave-studies and may lead to new investigations in high-resolution local-helioseismology.
Characterization of gut microbiome and metabolome in Helicobacter pylori patients in an underprivileged community in the United States

Characterization of gut microbiome and metabolome in Helicobacter pylori patients in an underprivileged community in the United States
The backbone network of dynamic functional connectivity

1. Abstract Temporal networks have become increasingly pervasive in many real-world applications, including the functional connectivity analysis of spatially separated regions of the brain. A major challenge in analysis of such networks is the identification of noise confounds, which introduce temporal ties that are non-essential, or links that are formed by chance due to local properties of the nodes. Several approaches have been suggested in the past for static networks or temporal networks with binary weights for extracting significant ties whose likelihood cannot be reduced to the local properties of the nodes. In this work, we propose a data-driven procedure to reveal the irreducible ties in dynamic functional connectivity of resting state fRMI data with continuous weights. This framework includes a null model that estimates the latent characteristics of the distributions of temporal links through optimization, followed by a statistical test to filter the links whose formation can be reduced to the activities and local properties of their interacting nodes. We demonstrate the benefits of this approach by applying it to a resting state fMRI dataset, and provide further discussion on various aspects and advantages of it.
Fuzzy logic approaches to multi-objective decision-making in aerospace applications

Fuzzy logic allows for the quantitative representation of multi-objective decision-making problems which have vague or fuzzy objectives and parameters. As such, fuzzy logic approaches are well-suited to situations where alternatives must be assessed by using criteria that are subjective and of unequal importance. This paper presents an overview of fuzzy logic and provides sample applications from the aerospace industry. Applications include an evaluation of vendor proposals, an analysis of future space vehicle options, and the selection of a future space propulsion system. On the basis of the results provided in this study, fuzzy logic provides a unique perspective on the decision-making process, allowing the evaluator to assess the degree to which each option meets the evaluation criteria. Future decision-making should take full advantage of fuzzy logic methods to complement existing approaches in the selection of alternatives.
Increased Asymmetry of Pit-Over-Peak Statistics with Landscape Smoothing

The local extremes (i.e., peaks and pits) of the landscape-elevation field play a critical role in energy, water, and nutrient distribution over the region, but the statistical distributions of these points in relation to landscape advancing towards maturity have received limited research attention. In this work, we first explain how the spatial correlation structure of the elevation field affects the counts and frequency distributions of local extremes. We then analyze local extremes statistics for 8 mountainous landscapes worldwide with diverse hydroclimatic forcings and geologic histories using 24 Digital Elevation Models (DEMs) and compare them with complex terrain of the Erythraeum Chaos region on Mars. The results reveal that the spherical covariance structure captures the observed spatial correlation in these cases with the peak frequency distribution agreeing well with the elevation frequency distribution. The ratio of the pit-over-peak (POP) count is linked to the degree to which the pit and peak frequency distributions match, and carries the mark of landscape aging. The relationship between the geomorphic development stage (quantified by the reduced fatness of the slope-distribution tail) and the deviation of POP values from unity in old mountainous landscapes confirms that the evolution towards smoother topographies is atypically accompanied by reduced pit counts typical of organized valley and ridge patterns.
Uniaxial Extension and Compression in Stress-Strain Relations of Rubber

Abstract A comprehensive literature survey shows the general applicability of the generalized normalized Martin, Roth and Stiehler equation to uniaxial stress-strain data in extension and compression on rubber vulcanizates. The equation can be expressed as F/M=(L−1−L−2) expA (L−L−1) where F is the stress on the undeformed section and L the ratio of stressed to unstressed length. The equation contains two constants—M, Young's Modulus, the slope of the stress-strain curve at L=1, and A an empirical constant. The conformity of stress-strain data to the equation can readily be determined by a plot of logF/(L−1−L−2) against (L−L−1). In almost every case a straight line is obtained, from the slope and intercept of which both the constants can be determined. The range of validity of the equation usually begins near L=0.5 (in the compression region) and continuing through the region of low deformations often extends to the region of rupture in extension. If uniaxial compression data are available the modulus can ...
Theory of Ergodic Quantum Processes

A new universality theorem for how a large class of quantum systems behaves over long periods of time paves the way for improved quantum devices and fundamental insight into the evolution of generic quantum systems.