cssi/SciSciGPT-SciSciNet huggingface dataset:
Evidence for a strain-tuned topological phase transition in ZrTe5

A phase transition between topologically distinct insulating phases involves closing and reopening the bandgap. Near the topological phase transition, the bulk energy spectrum is characterized by a massive Dirac dispersion, where the bandgap plays the role of mass. We report measurements of strain dependence of electrical transport properties of ZrTe5, which is known to host massive Dirac fermions in the bulk due to its proximity to a topological phase transition. We observe that the resistivity exhibits a pronounced minimum at a critical strain. We further find that the positive longitudinal magnetoconductance becomes maximal at the critical strain. This nonmonotonic strain dependence is consistent with the switching of sign of the Dirac mass and, hence, a strain-tuned topological phase transition in ZrTe5.
Effect of turbine nacelle and tower on the near wake of a utility-scale wind turbine

Abstract Super-large-scale particle image velocimetry using natural snowfall is used to investigate the influence of nacelle and tower generated flow structures on the near-wake of an operational 2.5 MW wind turbine. The measurement provides the velocity field over the entire rotor span in a plane centered behind the support tower, revealing a region of accelerated flow around the nacelle and a reduction in velocity behind the tower, causing asymmetry in the velocity deficit profile. The in-plane turbulent kinetic energy field shows increased turbulence in the regions of large shear behind the blade tips and nacelle, and a reduction in turbulence behind the tower. The nacelle wake meandering frequency is found to scale with the nacelle dimension rather than the rotor dimension, corresponding to the vortex shedding frequency of an Ahmed body. Persistent nacelle wake deflection is observed and shown to be connected with the turbine yaw error. Strong interaction between the tower- and blade-generated structures, quantified by the co-presence of two dominant frequencies, demonstrates the influence of the tower on blade tip vortex breakdown. This study highlights the influence of the tower and nacelle on the behavior of the near-wake, informing model development and elucidating the mechanisms that influence wake evolution.
Thompson Sampling with Information Relaxation Penalties

We consider a finite-horizon multi-armed bandit (MAB) problem in a Bayesian setting, for which we propose an information relaxation sampling framework. With this framework, we define an intuitive family of control policies that include Thompson sampling (TS) and the Bayesian optimal policy as endpoints. Analogous to TS, which, at each decision epoch pulls an arm that is best with respect to the randomly sampled parameters, our algorithms sample entire future reward realizations and take the corresponding best action. However, this is done in the presence of “penalties” that seek to compensate for the availability of future information. We develop several novel policies and performance bounds for MAB problems that vary in terms of improving performance and increasing computational complexity between the two endpoints. Our policies can be viewed as natural generalizations of TS that simultaneously incorporate knowledge of the time horizon and explicitly consider the exploration-exploitation trade-off. We prove associated structural results on performance bounds and suboptimality gaps. Numerical experiments suggest that this new class of policies perform well, in particular in settings where the finite time horizon introduces significant exploration-exploitation tension into the problem.
Food Insecurity and Child Health

OBJECTIVES: Food insecurity is an important public health problem facing children in the United States. Although a number of previous studies suggest that food insecurity has negative impacts on health, these studies have not dealt thoroughly with issues of selection bias. We use propensity scoring techniques to approximate the causal effects of food insecurity on children’s health and health care use outcomes. METHODS: We use nationally representative data from the 2013–2016 waves of the National Health Interview Study (N = 29 341). Using inverse probability of treatment weighting, a propensity scoring method, we examine a broad range of child health outcomes and account for a comprehensive set of controls, focusing on a sample of children 2 to 17 years old. RESULTS: Household food insecurity was related to significantly worse general health, some acute and chronic health problems, and worse health care access, including forgone care and heightened emergency department use, for children. Compared to rates had they not been food insecure, children in food-insecure household had rates of lifetime asthma diagnosis and depressive symptoms that were 19.1% and 27.9% higher, rates of foregone medical care that were 179.8% higher, and rates of emergency department use that were 25.9% higher. No significant differences emerged for most communicable diseases, such as ear infections or chicken pox, or conditions that may develop more gradually, including anemia and diabetes. CONCLUSIONS: Policies used to reduce household food insecurity among children may also reduce children’s chronic and acute health problems and health care needs.
Metric dimension reduction: A snapshot of the Ribe program

The purpose of this article is to survey some of the context, achievements, challenges and mysteries of the field of metric dimension reduction, including new perspectives on major older results as well as recent advances.
Upgraded molecular models of the human KCNQ1 potassium channel

The voltage-gated potassium channel KCNQ1 (KV7.1) assembles with the KCNE1 accessory protein to generate the slow delayed rectifier current, IKS, which is critical for membrane repolarization as part of the cardiac action potential. Loss-of-function (LOF) mutations in KCNQ1 are the most common cause of congenital long QT syndrome (LQTS), type 1 LQTS, an inherited genetic predisposition to cardiac arrhythmia and sudden cardiac death. A detailed structural understanding of KCNQ1 is needed to elucidate the molecular basis for KCNQ1 LOF in disease and to enable structure-guided design of new anti-arrhythmic drugs. In this work, advanced structural models of human KCNQ1 in the resting/closed and activated/open states were developed by Rosetta homology modeling guided by newly available experimentally-based templates: X. leavis KCNQ1 and various resting voltage sensor structures. Using molecular dynamics (MD) simulations, the capacity of the models to describe experimentally established channel properties including state-dependent voltage sensor gating charge interactions and pore conformations, PIP2 binding sites, and voltage sensor-pore domain interactions were validated. Rosetta energy calculations were applied to assess the utility of each model in interpreting mutation-evoked KCNQ1 dysfunction by predicting the change in protein thermodynamic stability for 50 experimentally characterized KCNQ1 variants with mutations located in the voltage-sensing domain. Energetic destabilization was successfully predicted for folding-defective KCNQ1 LOF mutants whereas wild type-like mutants exhibited no significant energetic frustrations, which supports growing evidence that mutation-induced protein destabilization is an especially common cause of KCNQ1 dysfunction. The new KCNQ1 Rosetta models provide helpful tools in the study of the structural basis for KCNQ1 function and can be used to generate hypotheses to explain KCNQ1 dysfunction.
Connections between physics, mathematics, and deep learning.

Starting from Fermat’s principle of least action, which governs classical and quantum mechanics and from the theory of exterior differential forms, which governs the geometry of curved manifolds, we show how to derive the equations governing neural networks in an intrinsic, coordinate-invariant way, where the loss function plays the role of the Hamiltonian. To be covariant, these equations imply a layer metric which is instrumental in pretraining and explains the role of conjugation when using complex numbers. The differential formalism clarifies the relation of the gradient descent optimizer with Aristotelian and Newtonian mechanics. The Bayesian paradigm is then analyzed as a renormalizable theory yielding a new derivation of the Bayesian information criterion. We hope that this formal presentation of the differential geometry of neural networks will encourage some physicists to dive into deep learning and, reciprocally, that the specialists of deep learning will better appreciate the close interconnection of their subject with the foundations of classical and quantum field theory.
Expansion of a fly TBI model to four levels of injury severity reveals synergistic effects of repetitive injury for moderate injury conditions.

Several million traumatic brain injury (TBI) events are reported in the United States annually. However, mild TBI events often go unreported, and mild and repetitive mild TBI conditions are challen...
A Sub-mm 3 Ultrasonic Free-Floating Implant for Multi-Mote Neural Recording

A 0.8-mm3-wireless, ultrasonically powered, free-floating neural recording implant is presented. The device is comprised only of a 0.25-mm2 recording integrated circuit (IC) and a single piezoceramic resonator that are used for both power harvesting and data transmission. Uplink data transmission is performed by the analog amplitude modulation of the ultrasound echo. Using a 1.78-MHz main carrier, >35 kb/s/mote equivalent uplink data rate is achieved. A technique to linearize the echo amplitude modulation is introduced, resulting in $\mu \text{W}$ , while the neural recording front end consumes $4~\mu \text{W}$ and achieves a noise floor of 5.3 $\mu V_{\text {rms}}$ in a 5-kHz bandwidth. This work improves the sub-mm recording mote depth by >2.5 $\times $ , resulting in the highest measured depth/volume ratio by $\sim 3\times $ . Orthogonal subcarrier modulation enables simultaneous operation of multiple implants, using a single-element ultrasound external transducer. Dual-mote simultaneous power-up and data transmission are demonstrated at a rate of 7 kS/s at the depth of 50 mm.
Relational Symbolic Execution

Symbolic execution is a classical program analysis technique used to show that programs satisfy or violate given specifications. In this work we generalize symbolic execution to support program analysis for relational specifications in the form of relational properties - these are properties about two runs of two programs on related inputs, or about two executions of a single program on related inputs. Relational properties are useful to formalize notions in security and privacy, and to reason about program optimizations. We design a relational symbolic execution engine, named RelSym which supports interactive refutation, as well as proving of relational properties for programs written in a language with arrays and for-like loops.
Learning Semantic Maps from Natural Language Descriptions.

This paper proposes an algorithm that enables robots to efficiently learn human-centric models of their environment from natural language descriptions. Typical semantic mapping approaches augment metric maps with higher-level properties of the robot’s surroundings (e.g., place type, object locations), but do not use this information to improve the metric map. The novelty of our algorithm lies in fusing high-level knowledge, conveyed by speech, with metric information from the robot’s low-level sensor streams. Our method jointly estimates a hybrid metric, topological, and semantic representation of the environment. This semantic graph provides a common framework in which we integrate concepts from natural language descriptions (e.g., labels and spatial relations) with metric observations from low-level sensors. Our algorithm efficiently maintains a factored distribution over semantic graphs based upon the stream of natural language and low-level sensor information. We evaluate the algorithm’s performance and demonstrate that the incorporation of information from natural language increases the metric, topological and semantic accuracy of the recovered environment model.
Change Point Analysis

This entry provides a brief introduction to the statistical methodology called change point analysis, which is oft used in risk assessment. The goal of change point analysis is to model changes in the characteristics of a response variable over a range of a predictor variable. The discussion below is framed by two simple examples from the risk assessment literature where the response characteristic of interest is the mean response. The examples illustrate two simple change point methods for detecting the location of the change point in terms of the predictor variable and the nature of the change in the mean response before and after the change point. More complicated change point methods are also discussed briefly. Keywords: change point; mean response; onset-of-trend model
Modifiable Areal Unit Problem ( MAUP )

Deciding on an appropriate research design, interpreting study results, or choosing a suitable public policy to mitigate environmental risk implies a decision, either implicit or explicit, about spatial scale and resolution. The modifiable areal unit problem (MAUP) is a formal statement of the dilemma facing researchers, risk assessors, and policy makers when they use geographical data—results of statistical analyses usually depend on subjective decisions about geographical units of analysis and the level of data aggregation within those units. Consideration of the MAUP is essential for the development of effective and efficient strategies for managing environmental health risks. Keywords: aggregation levels; environmental risk; geographical units; spatial resolution; spatial scale
Scaled and Dynamic Optimizations of Nudged Elastic Bands.

We present a modified nudged elastic band routine that can reduce the number of force calls by more than 50% for bands with nonuniform convergence. The method, which we call “dyNEB”, dynamically an...
Competition and Pay Inequality Within and Between Firms

How does market competition affect pay inequality between and within firms? Using division managers as a pool of similar workers and the Canada–U.S. Free Trade Agreement, we find that greater compe...
Faster, Hotter, and More Linked In: Managing Social Disapproval in the Social Media Era

Negative social evaluations and the strategies a firm employs to manage them have garnered significant attention among management scholars. However, past research has given less attention to the so...
Enhanced Resonant Faraday Rotation in Multilayer Magnetoplasmonic Nanohole Arrays and Their Sensing Application

The optical and magnetoplasmonic properties of Ag/Co and Ag/Co/Ag multilayer nanohole arrays have been investigated. By including a top- and bottom-Ag plasmonic layer onto a Co ferromagnetic perfor...
Infinite pretty-printing in eXene

We describe the design and implementation of a Standard ML of New Jersey library for the interactive pretty-printing of possibly infinite syntax trees. The library handles elision in a novel way, and is implemented using Concurrent ML and the eXene X Window System toolkit.
Fast, accurate, and transferable many-body interatomic potentials by symbolic regression

The length and time scales of atomistic simulations are limited by the computational cost of the methods used to predict material properties. In recent years there has been great progress in the use of machine-learning algorithms to develop fast and accurate interatomic potential models, but it remains a challenge to develop models that generalize well and are fast enough to be used at extreme time and length scales. To address this challenge, we have developed a machine-learning algorithm based on symbolic regression in the form of genetic programming that is capable of discovering accurate, computationally efficient many-body potential models. The key to our approach is to explore a hypothesis space of models based on fundamental physical principles and select models within this hypothesis space based on their accuracy, speed, and simplicity. The focus on simplicity reduces the risk of overfitting the training data and increases the chances of discovering a model that generalizes well. Our algorithm was validated by rediscovering an exact Lennard-Jones potential and a Sutton-Chen embedded-atom method potential from training data generated using these models. By using training data generated from density functional theory calculations, we found potential models for elemental copper that are simple, as fast as embedded-atom models, and capable of accurately predicting properties outside of their training set. Our approach requires relatively small sets of training data, making it possible to generate training data using highly accurate methods at a reasonable computational cost. We present our approach, the forms of the discovered models, and assessments of their transferability, accuracy and speed.
Dynamics of Dark Matter Cusps

Formation and disruption of dark-matter cusps are reviewed. Accumulation of baryons at the center of a halo can displace the dark matter, converting singular density cusps into low-density cores. The displaced mass can be of order ~ 10M ● with M ● the mass of the infailing population. If M ● is identified with the masses of the black holes currently observed at the centers of bright galaxies, predicted core radii are ~ a few х 102 pc. Other mechanisms, such as early mass outflow, may explain the large dark-matter cores in dwarf and low-surface-brightness galaxies. Predictions of dark matter annihilation radiation from the center of the Milky Way galaxy are shown to be strongly dependent on the galaxy’s merger history.
Towards Understanding Emotional Intelligence for Behavior Change Chatbots

A natural conversational interface that allows longitudinal symptom tracking would be extremely valuable in health/wellness applications. However, the task of designing emotionally-aware agents for behavior change is still poorly understood. In this paper, we present the design and evaluation of an emotion-aware chatbot that conducts experience sampling in an empathetic manner. We evaluate it through a human-subject experiment with N=39 participants over the course of a week. Our results show that extraverts preferred the emotion-aware chatbot significantly more than introverts. Also, participants reported a higher percentage of positive mood reports when interacting with the empathetic bot. Finally, we provide guidelines for the design of emotion-aware chatbots for potential use in mHealth contexts.
Parametrized spectra, multiplicative Thom spectra and the twisted Umkehr map

We introduce a general theory of parametrized objects in the setting of infinity categories. Although spaces and spectra parametrized over spaces are the most familiar examples, we establish our theory in the generality of objects of a presentable infinity category parametrized over objects of an infinity topos. We obtain a coherent functor formalism describing the relationship of the various adjoint functors associated to base-change and symmetric monoidal structures. Our main applications are to the study of generalized Thom spectra. We obtain fiberwise constructions of twisted Umkehr maps for twisted generalized cohomology theories using a geometric fiberwise construction of Atiyah duality. In order to characterize the algebraic structures on generalized Thom spectra and twisted (co)homology, we characterize the generalized Thom spectrum as a categorification of the well-known adjunction between units and group rings.
Stock performance subsequent to combinations in quarterly revenue surprise, earnings surprise, guidance, valuation, and report time

Finance literature highlights various reasons for stock performance subsequent to earnings announcements. However, other moving parts in these scenarios must also be simultaneously specified. While both revenue and earnings surprises are important for determining stock performance, forward-looking guidance and firm valuation prior to earnings should also be considered. Additionally, analyses that solely consider market-level data miss important subtleties evident in a sector-specific study, as “normal” growth and valuation metrics across sectors widely differ. We differentiate between firms that announce earnings during the evening hours (after the close) and firms that announce earnings during the morning hours (prior to the open).
Interruptible Exact Sampling in the Passive Case

We establish, for various scenarios, whether or not interruptible exact stationary sampling is possible when a finite-state Markov chain can only be viewed passively. In particular, we prove that such sampling is not possible using a single copy of the chain. Such sampling is possible when enough copies of the chain are available, and we provide an algorithm that terminates with probability one.
A Covariant Poisson Deformation Quantization with Separation of Variables up to the Third Order

We give a simple formula for the operator C 3 of the standard deformation quantization with separation of variables on a Kahler manifold M. Unlike C 1 and C 2, this operator cannot be expressed in terms of the Kahler–Poisson tensor on M. We modify C 3 to obtain a covariant deformation quantization with separation of variables up to the third order which is expressed in terms of the Poisson tensor on M and can thus be defined on an arbitrary complex manifold endowed with a Poisson bivector field of type (1,1).
Amorphous intergranular films mitigate radiation damage in nanocrystalline Cu-Zr

Abstract Nanocrystalline metals are promising radiation tolerant materials due to their large interfacial volume fraction, but irradiation-induced grain growth can eventually degrade any improvement in radiation tolerance. Therefore, methods to limit grain growth and simultaneously improve the radiation tolerance of nanocrystalline metals are needed. Amorphous intergranular films are unique grain boundary structures that are predicted to have improved sink efficiencies due to their increased thickness and amorphous structure, while also improving grain size stability. In this study, ball milled nanocrystalline Cu-Zr alloys are heat treated to either have only ordered grain boundaries or to contain amorphous intergranular films distributed within the grain boundary network, and are then subjected to in situ transmission electron microscopy irradiation and ex situ irradiation. Differences in defect density and grain growth due to grain boundary complexion type are then investigated. When amorphous intergranular films are incorporated within the material, fewer and smaller defect clusters are observed while grain growth is also limited, leading to nanocrystalline alloys with improved radiation tolerance.
Reactive intermediates in the interconversions of C3H4 hydrocarbons

Structure electronique et geometrie de vinylmethylenes, cyclopropylidenes et allenes plans; relations mecanistiques de chaque espece dans les interconversions des C 3 H 4 . Isomerisations thermiques et photochimiques d'allenes, cyclopropenes et propynes. Chimie des intermediaires: vinylmethylenes, carbenacyclopropane et allenes plans
Measuring transboundary water cooperation within the framework of Agenda 2030: a proposal for a revision of SDG Indicator 6.5.2

Indicator 6.5.2 of the Sustainable Development Goals is defined as the ‘proportion of transboundary basin area with an operational arrangement for water cooperation’. While the establishment of suc...
How to store a random walk

Motivated by storage applications, we study the following data structure problem: An encoder wishes to store a collection of jointly-distributed files [MATH HERE] which are correlated [MATH HERE], using as little (expected) memory as possible, such that each individual file Xi can be recovered quickly with few (ideally constant) memory accesses. In the case of independent random files, a dramatic result by Pǎtrascu (FOCS'08) and subsequently by Dodis, Pǎtrascu and Thorup (STOC'10) shows that it is possible to store [MATH HERE] using just a constant number of extra bits beyond the information-theoretic minimum space, while at the same time decoding each Xi in constant time. However, in the (realistic) case where the files are correlated, much weaker results are known, requiring at least Ω(n/poly lg n) extra bits for constant decoding time, even for "simple" joint distributions μ. We focus on the natural case of compressing Markov chains, i.e., storing a length-n random walk on any (possibly directed) graph G. Denoting by K(G, n) the number of length-n walks on G, we show that there is a succinct data structure storing a random walk using lg2 K(G, n) + O(lg n) bits of space, such that any vertex along the walk can be decoded in O(1) time on a word-RAM. If the graph is strongly connected (e.g., undirected), the space can be improved to only lg2 K(G, n) + 5 extra bits. For the harder task of matching the point-wise optimal space of the walk, i.e., the empirical entropy [MATH HERE], we present a data structure with O(1) extra bits at the price of O(lg n) decoding time, and show that any improvement on this would lead to an improved solution on the long-standing Dictionary problem. All of our data structures support the online version of the problem with constant update and query time.
Evaluation of the Visibility and Artifacts of 11 Common Fiducial Markers for Image Guided Stereotactic Body Radiation Therapy in the Abdomen

Abstract Purpose The purpose of this study was to quantitatively evaluate the visibility and artifacts of commercially available fiducial markers to optimize their selection for image guided stereotactic body radiation therapy. Methods and Materials From 6 different vendors, we selected 11 fiducials commonly used in image guided radiation therapy. The fiducials varied in material composition (e.g., gold, platinum, carbon), shape (e.g., cylindrical, notched/linear, coiled, ball-like, step), and size measured in terms of diameter (0.28-1.0 mm) and length (3.0-20.0 mm). Each fiducial was centered in 4-mm bolus within a 13-cm-thick water-equivalent phantom. Fiducials were imaged with the use of a simulation computed tomography (CT) scanner, a CT-on-rails system, and an onboard cone beam CT system. Acquisition parameters were set according to clinical protocols. Visibility was assessed in terms of contrast (Δ Hounsfield unit [HU]) and the Michelson visibility metric. Artifacts were quantified in terms of relative standard deviation and relative streak artifacts level (rSAL). Twelve radiation oncologists ranked each fiducial in terms of clinical usefulness. Results Contrast and artifacts increased with fiducial size. For CT imaging, maximum contrast (2722 HU) and artifacts (rSAL = 2.69) occurred for the largest-diameter (0.75 mm) platinum fiducial. Minimum contrast (551 HU) and reduced artifacts (rSAL = 0.65) were observed for the smallest-diameter (0.28 mm) gold fiducial. Carbon produced the least severe artifacts (rSAL = 0.29). The survey indicated that physicians preferred gold fiducials with a 0.35- to 0.43-mm diameter, 5- to 10-mm length, and coiled or cylindrical shape that balanced contrast and artifacts. Conclusions We evaluated 11 different fiducials in terms of visibility and artifacts. The results of this study may assist radiation oncologists who seek to maximize contrast, minimize artifacts, or balance contrast versus artifacts by fiducial selection.
Towards Refinement and Generalization of Reliability Models Based on Component States

Complex system design often proceeds in an iterative fashion, starting from a high-level model and adding detail as the design matures. This process can be assisted by metamod-eling techniques that automate some model manipulations and check for or eliminate modeling mistakes. Our work focuses on metamodeling reliability models: we describe generalization and refinement operations for these models. Generalization relaxes constraints that may be infeasible or costly to evaluate; refinement adds further detail to produce a model that more closely describes the desired system. We define these operations in terms of operations on system constraints. To illustrate the proposed method, we relate these constraints to a common Markov chain-based reliability modeling formalism.
Sim-to-Real Transfer for Biped Locomotion

We present a new approach for transfer of dynamic robot control policies such as biped locomotion from simulation to real hardware. Key to our approach is to perform system identification of the model parameters $\mu$ of the hardware (e.g. friction, center-of-mass) in two distinct stages, before policy learning (pre-sysID) and after policy learning (post-sysID). Pre-sysID begins by collecting trajectories from the physical hardware based on a set of generic motion sequences. Because the trajectories may not be related to the task of interest, pre-sysID does not attempt to accurately identify the true value of $\mu$, but only to approximate the range of $\mu$ to guide the policy learning. Next, a Projected Universal Policy (PUP) is created by simultaneously training a network that projects $\mu$ to a low-dimensional latent variable $\eta$ and a family of policies that are conditioned on $\eta$. The second round of system identification (post-sysID) is then carried out by deploying the PUP on the robot hardware using task-relevant trajectories. We use Bayesian Optimization to determine the values for $\eta$ that optimize the performance of PUP on the real hardware. We have used this approach to create three successful biped locomotion controllers (walk forward, walk backwards, walk sideways) on the Darwin OP2 robot.
Outliagnostics: Visualizing Temporal Discrepancy in Outlying Signatures of Data Entries

This paper presents an approach to analyzing two-dimensional temporal datasets focusing on identifying observations that are significant in calculating the outliers of a scatterplot. We also propose a prototype, called Outliagnostics, to guide users when interactively exploring abnormalities in large time series. Instead of focusing on detecting outliers at each time point, we monitor and display the discrepant temporal signatures of each data entry concerning the overall distributions. Our prototype is designed to handle these tasks in parallel to improve performance. To highlight the benefits and performance of our approach, we illustrate and validate the use of Outliagnostics on real-world datasets of various sizes in different parallelism configurations. This work also discusses how to extend these ideas to handle time series with a higher number of dimensions and provides a prototype for this type of datasets.
Transglutaminase-2 facilitates extracellular vesicle-mediated establishment of the metastatic niche.

The ability of breast cancer cells to interconvert between epithelial and mesenchymal states contributes to their metastatic potential. As opposed to cell autonomous effects, the impact of epithelial–mesenchymal plasticity (EMP) on primary and metastatic tumor microenvironments remains poorly characterized. Herein we utilize global gene expression analyses to characterize a metastatic model of EMP as compared to their non-metastatic counterparts. Using this approach, we demonstrate that upregulation of the extracellular matrix crosslinking enzyme tissue transglutaminase-2 (TG2) is part of a novel gene signature that only emerges in metastatic cells that have undergone induction and reversion of epithelial–mesenchymal transition (EMT). Consistent with our model system, patient survival is diminished when primary tumors demonstrate enhanced levels of TG2 in conjunction with its substrate, fibronectin. Targeted depletion of TG2 inhibits metastasis, while overexpression of TG2 is sufficient to enhance this process. In addition to being present within cells, we demonstrate a robust increase in the amount of TG2 and crosslinked fibronectin present within extracellular vesicle (EV) fractions derived from metastatic breast cancer cells. Confocal microscopy of these EVs suggests that FN undergoes fibrillogenesis on their surface via a TG2 and Tensin1-dependent process. Upon in vivo administration, the ability of tumor-derived EVs to induce metastatic niche formation and enhance subsequent pulmonary tumor growth requires the presence and activity of TG2. Finally, we develop a novel 3D model of the metastatic niche to demonstrate that conditioning of pulmonary fibroblasts via pretreatment with tumor-derived EVs promotes subsequent growth of breast cancer cells in a TG2-dependent fashion. Overall, our studies illustrate a novel mechanism through which EMP contributes to metastatic niche development and distant metastasis via tumor-derived EVs containing aberrant levels of TG2 and fibrillar FN.
Getting the (Gender-Disaggregated) Lay of the Land : Impact of Survey Respondent Selection on Measuring Land Ownership and Rights

Foundational to the monitoring of international goals on land ownership and rights are the household survey respondents who provide the required individual-disaggregated data. Leveraging two national surveys in Malawi that differed in their approach to respondent selection, this study shows that, compared with the international best practice of privately interviewing adults about their personal asset ownership and rights, the business-as-usual approach of interviewing the most knowledgeable household member(s) on adult household members' ownership of and rights to assets leads to (i) higher rates of exclusive reported and economic ownership of agricultural land among men, and (ii) lower rates of joint reported and economic ownership among women. Further, substantial agreement exists on agricultural landowners and rights holders, as reported by the privately-interviewed spouses. When discrepancies emerge, proxies for greater household status for women are positively associated with the scenarios where women attribute at least some land ownership to themselves.
Ecological Correlates Contributing to Reporting of School Delinquency among Caribbean Adolescents.

Many studies have examined significant factors associated with school delinquency among adolescents, including relationship violence, property damage, and other serious threats to students’ safety....
Business cycle fluctuations in Mirrlees economies: The case of i.i.d. shocks

Abstract I consider a real business cycle model in which agents have private information about the i.i.d. realizations of their value of leisure. For the case of logarithmic preferences I provide an analytical characterization of the solution to the associated mechanism design problem. Moreover, I show a striking irrelevance result: that the stationary behavior of all aggregate variables are exactly the same in the private information economy as in the full information case. Numerical simulations indicate that the irrelevance result approximately holds for more general preferences.
An Attack-Resilient Pulse-Based Synchronization Strategy for General Connected Topologies

Synchronization of pulse-coupled oscillators (PCOs) has gained significant attention recently due to increased applications in sensor networks and wireless communications. Given the distributed and unattended nature of wireless sensor networks, it is imperative to enhance the resilience of pulse-based synchronization against malicious attacks. However, most existing results on resilient PCO synchronization are obtained for all-to-all networks. We propose a new pulse-based synchronization mechanism to improve the resilience of PCO synchronization that is applicable under general connected topologies. Under the proposed synchronization mechanism, we rigorously characterize the condition for stealthy Byzantine attacks and prove that perfect synchronization of legitimate oscillators can be guaranteed in the presence of multiple stealthy Byzantine attackers, irrespective of whether the attackers collude with each other or not. The new mechanism can guarantee resilient synchronization even when the initial phases of legitimate oscillators are widely distributed in a half circle, which is in distinct difference from most existing attack-resilient synchronization algorithms (including the seminal paper from Lamport and Melliar-Smith 1985) that require a priori (almost) synchronization among legitimate oscillators. Numerical simulation results are given to confirm the theoretical results.
Headwinds, tailwinds and preferences for redistributive healthcare

Using different versions of a vignette, this study examines how preferences for redistributive healthcare change as luck plays a progressively stronger role in contributing to wealth and poverty. P...
Stackelberg Equilibria for Two-Player Network Routing Games on Parallel Networks

We consider a two-player zero-sum network routing game in which a router wants to maximize the amount of legitimate traffic that flows from a given source node to a destination node and an attacker wants to block as much legitimate traffic as possible by flooding the network with malicious traffic. We address scenarios with asymmetric information, in which the router must reveal its policy before the attacker decides how to distribute the malicious traffic among the network links, which is naturally modeled by the notion of Stackelberg equilibria. The paper focuses on parallel networks, and includes three main contributions: we show that computing the optimal attack policy against a given routing policy is an NP-hard problem; we establish conditions under which the Stackelberg equilibria lead to no regret; and we provide a metric that can be used to quantify how uncertainty about the attacker's capabilities limits the router's performance.
Simultaneous Object Detection and Semantic Segmentation.

Both object detection in and semantic segmentation of camera images are important tasks for automated vehicles. Object detection is necessary so that the planning and behavior modules can reason about other road users. Semantic segmentation provides for example free space information and information about static and dynamic parts of the environment. There has been a lot of research to solve both tasks using Convolutional Neural Networks. These approaches give good results but are computationally demanding. In practice, a compromise has to be found between detection performance, detection quality and the number of tasks. Otherwise it is not possible to meet the real-time requirements of automated vehicles. In this work, we propose a neural network architecture to solve both tasks simultaneously. This architecture was designed to run with around 10 Hz on 1 MP images on current hardware. Our approach achieves a mean IoU of 61.2% for the semantic segmentation task on the challenging Cityscapes benchmark. It also achieves an average precision of 69.3% for cars and 67.7% on the moderate difficulty level of the KITTI benchmark.
Resilient Leader-Follower Consensus with Time-Varying Leaders in Discrete-Time Systems

The problem of consensus in the presence of adversarially behaving agents has been studied extensively in the literature. The proposed algorithms typically guarantee that the consensus value lies within the convex hull of the initial states of the normally-behaving agents. In leader-follower consensus problems however, the objective for normally behaving agents is to track a time-varying reference state that may take on values outside of this convex hull. In this paper we present a method for agents with discrete-time dynamics to resiliently track a set of leaders’ common time-varying reference state despite a bounded subset of the leaders and followers behaving adversarially. The efficacy of our results are demonstrated through simulations.
Strategic Inference with a Single Private Sample

Motivated by applications in cyber security, we develop a simple game model for describing how a learning agent’s private information influences an observing agent’s inference process. The model describes a situation in which one of the agents (attacker) is deciding which of two targets to attack, one with a known reward and another with uncertain reward. The attacker receives a single private sample from the uncertain target’s distribution and updates its belief of the target quality. The other agent (defender) knows the true rewards, but does not see the sample that the attacker has received. This leads to agents possessing asymmetric information: the attacker is uncertain over the parameter of the distribution, whereas the defender is uncertain about the observed sample. After the attacker updates its belief, both the attacker and the defender play a simultaneous move game based on their respective beliefs. We offer a characterization of the pure strategy equilibria of the game and explain how the players’ decisions are influenced by their prior knowledge and the payoffs/costs.
On the Discrepancy of Random Matrices with Many Columns

Motivated by the Komlos conjecture in combinatorial discrepancy, we study the discrepancy of random matrices with $m$ rows and $n$ independent columns drawn from a bounded lattice random variable. It is known that for $n$ tending to infinity and $m$ fixed, with high probability the $\ell_\infty$-discrepancy is at most twice the $\ell_\infty$-covering radius of the integer span of the support of the random variable. However, the easy argument for the above fact gives no concrete bounds on the failure probability in terms of $n$. We prove that the failure probability is inverse polynomial in $m, n$ and some well-motivated parameters of the random variable. We also obtain the analogous bounds for the discrepancy in arbitrary norms. We apply these results to two random models of interest. For random $t$-sparse matrices, i.e. uniformly random matrices with $t$ ones and $m-t$ zeroes in each column, we show that the $\ell_\infty$-discrepancy is at most $2$ with probability $1 - O(\sqrt{ \log n/n})$ for $n = \Omega(m^3 \log^2 m)$. This improves on a bound proved by Ezra and Lovett (Ezra and Lovett, Approx+Random, 2016) showing that the same is true for $n$ at least $m^t$. For matrices with random unit vector columns, we show that the $\ell_\infty$-discrepancy is $O(\exp(\sqrt{ n/m^3}))$ with probability $1 - O(\sqrt{ \log n/n})$ for $n = \Omega(m^3 \log^2 m).$ Our approach, in the spirit of Kuperberg, Lovett and Peled (G. Kuperberg, S. Lovett and R. Peled, STOC 2012), uses Fourier analysis to prove that for $m \times n$ matrices $M$ with i.i.d. columns, and $m$ sufficiently large, the distribution of $My$ for random $y \in \{-1,1\}^n$ obeys a local limit theorem.
Applications of the microphysiology systems database for experimental ADME-Tox and disease models

To accelerate the development and application of Microphysiological Systems (MPS) in biomedical research and drug discovery/development, a centralized resource is required to provide the detailed design, application, and performance data that enables industry and research scientists to select, optimize, and/or develop new MPS solutions, as well as to harness data from MPS models. We have previously implemented an open source Microphysiology Systems Database (MPS-Db), with a simple icon driven interface, as a resource for MPS researchers and drug discovery/development scientists (https://mps.csb.pitt.edu). The MPS-Db captures and aggregates data from MPS, ranging from static microplate models to integrated, multi-organ microfluidic models, and associates those data with reference data from chemical, biochemical, pre-clinical, clinical and post-marketing sources to support the design, development, validation, application and interpretation of the models. The MPS-Db enables users to manage their multifactor, multichip studies, then upload, analyze, review, computationally model and share data. Here we discuss how the sharing of MPS study data in the MS-Db is under user control and can be kept private to the individual user, shared with a select group of collaborators, or be made accessible to the general scientific community. We also present a test case using our liver acinus MPS model (LAMPS) as an example and discuss the use of the MPS-Db in managing, designing, and analyzing MPS study data, assessing the reproducibility of MPS models, and evaluating the concordance of MPS model results with clinical findings. We introduce the Disease Portal module with links to resources for the design of MPS disease models and studies and discuss the integration of computational models for the prediction of PK/PD and disease pathways using data generated from MPS models.
Monopole charge density wave states in Weyl semimetals

This work proposes a topological class of density wave order that cannot be described by spherical harmonic symmetry but is characterized by monopole harmonics. This order, termed monopole density wave order, is demonstrated in a Weyl semimetal model where electron and hole Fermi surfaces with different Chern numbers are nested so that the density wave order acquires a nontrivial pair Berry phase.
Better and simpler error analysis of the Sinkhorn–Knopp algorithm for matrix scaling

Given a non-negative $$n \times m$$ real matrix A, the matrix scaling problem is to determine if it is possible to scale the rows and columns so that each row and each column sums to a specified positive target values. The Sinkhorn–Knopp algorithm is a simple and classic procedure which alternately scales all rows and all columns to meet these targets. The focus of this paper is the worst-case theoretical analysis of this algorithm. We present an elementary convergence analysis for this algorithm that improves upon the previous best bound. In a nutshell, our approach is to show (i) a simple bound on the number of iterations needed so that the KL-divergence between the current row-sums and the target row-sums drops below a specified threshold $$\delta $$ , and (ii) then show that for a suitable choice of $$\delta $$ , whenever KL-divergence is below $$\delta $$ , then the $$\ell _1$$ -error or the $$\ell _2$$ -error is below $$\varepsilon $$ . The well-known Pinsker’s inequality immediately allows us to translate a bound on the KL divergence to a bound on $$\ell _1$$ -error. To bound the $$\ell _2$$ -error in terms of the KL-divergence, we establish a new inequality, referred to as (KL vs $$\ell _1/\ell _2$$ ). This inequality is a strengthening of Pinsker’s inequality and may be of independent interest.
Deterministic Factorization of Sparse Polynomials with Bounded Individual Degree

In this article, we study the problem of deterministic factorization of sparse polynomials. We show that if f ∈ F[x1,x2,… ,xn] is a polynomial with s monomials, with individual degrees of its variables bounded by d, then f can be deterministically factored in time spoly(d)log n. Prior to our work, the only efficient factoring algorithms known for this class of polynomials were randomized, and other than for the cases of d=1 and d=2, only exponential time-deterministic factoring algorithms were known.A crucial ingredient in our proof is a quasi-polynomial sparsity bound for factors of sparse polynomials of bounded individual degree. In particular, we show that if f is an s-sparse polynomial in n variables, with individual degrees of its variables bounded by d, then the sparsity of each factor of f is bounded by s(9 d2 log n). This is the first non-trivial bound on factor sparsity for d> 2. Our sparsity bound uses techniques from convex geometry, such as the theory of Newton polytopes and an approximate version of the classical Caratheodory’s Theorem.Our work addresses and partially answers a question of von zur Gathen and Kaltofen [1985] who asked whether a quasi-polynomial bound holds for the sparsity of factors of sparse polynomials.