cssi/SciSciGPT-SciSciNet huggingface dataset:
Parameter and State Estimation for a Diffusion Process

Publisher Summary This chapter presents a parameter and state estimation for a diffusion process. It focuses on diffusions governed by homogeneous autonomous linear parabolic partial differential equations. The chapter presents an inverse problem of determination of parameters in σ from certain observations of the evolving state x(·). The emphasis is primarily on problem formulations and the considerations relevant to possible approaches rather than on rigorous proof. The chapter also discusses the standard approaches to the identification of finite-dimensional, lumped parameter systems.
The intrinsically disordered C-terminal linker of FtsZ regulates protofilament dynamics and superstructure in vitro

The bacterial tubulin FtsZ polymerizes to form a discontinuous ring that drives bacterial cell division by directing local cell wall synthesis. FtsZ comprises a polymerizing GTPase domain, an intrinsically disordered C-terminal linker (CTL), and a C-terminal conserved peptide (CTC). FtsZ protofilaments align circumferentially in the cell, with the CTC mediating attachment to membrane-associated division proteins. The assembly of FtsZ protofilaments into dynamic clusters is critical for cell division, but the interactions between protofilaments and regulatory mechanisms that mediate cluster assembly and dynamics are unknown. Here, we describe a role for the CTL of Caulobacter crescentus FtsZ as an intrinsic regulator of lateral interactions between protofilaments in vitro. FtsZ lacking its CTL (ΔCTL) shows a dramatically increased propensity to form long multifilament bundles compared with wild type (WT). ΔCTL also displays a reduced GTP hydrolysis rate compared with WT, but this altered activity does not account for bundle formation, as reducing protofilament turnover in WT is not sufficient to induce bundling. Surprisingly, binding of the membrane-anchoring protein FzlC disrupts ΔCTL bundling in a CTC-dependent manner. Moreover, the CTL affects the ability of the FtsZ curving protein FzlA to promote formation of helical bundles. We conclude that the CTL of FtsZ influences polymer structure and dynamics both through intrinsic effects on lateral interactions and turnover and by influencing extrinsic regulation of FtsZ by binding partners. Our characterization of CTL function provides a biochemical handle for understanding the relationship between FtsZ-ring structure and function in bacterial cytokinesis.
T cell receptor sequencing of early-stage breast cancer tumors identifies altered clonal structure of the T cell repertoire.

Tumor-infiltrating T cells play an important role in many cancers, and can improve prognosis and yield therapeutic targets. We characterized T cells infiltrating both breast cancer tumors and the surrounding normal breast tissue to identify T cells specific to each, as well as their abundance in peripheral blood. Using immune profiling of the T cell beta-chain repertoire in 16 patients with early-stage breast cancer, we show that the clonal structure of the tumor is significantly different from adjacent breast tissue, with the tumor containing ∼2.5-fold greater density of T cells and higher clonality compared with normal breast. The clonal structure of T cells in blood and normal breast is more similar than between blood and tumor, and could be used to distinguish tumor from normal breast tissue in 14 of 16 patients. Many T cell sequences overlap between tissue and blood from the same patient, including ∼50% of T cells between tumor and normal breast. Both tumor and normal breast contain high-abundance “enriched” sequences that are absent or of low abundance in the other tissue. Many of these T cells are either not detected or detected with very low frequency in the blood, suggesting the existence of separate compartments of T cells in both tumor and normal breast. Enriched T cell sequences are typically unique to each patient, but a subset is shared between many different patients. We show that many of these are commonly generated sequences, and thus unlikely to play an important role in the tumor microenvironment.
Interlayer bond polarizability model for stacking-dependent low-frequency Raman scattering in layered materials

Two-dimensional (2D) layered materials have been extensively studied owing to their fascinating and technologically relevant properties. Their functionalities can be often tailored by the interlayer stacking pattern. Low-frequency (LF) Raman spectroscopy provides a quick, non-destructive and inexpensive optical technique for stacking characterization, since the intensities of LF interlayer vibrational modes are sensitive to the details of the stacking. A simple and generalized interlayer bond polarizability model is proposed here to explain and predict how the LF Raman intensities depend on complex stacking sequences for any thickness in a broad array of 2D materials, including graphene, MoS2, MoSe2, NbSe2, Bi2Se3, GaSe, h-BN, etc. Additionally, a general strategy is proposed to unify the stacking nomenclature for these 2D materials. Our model reveals the fundamental mechanism of LF Raman response to the stacking, and provides general rules for stacking identification.
Quadratic Polynomial Regression Using Serial Observation Processing: Implementation within DART

AbstractIt is well known that the ensemble-based variants of the Kalman filter may be thought of as producing a state estimate that is consistent with linear regression. Here, it is shown how quadratic polynomial regression can be performed within a serial data assimilation framework. The addition of quadratic polynomial regression to the Data Assimilation Research Testbed (DART) is also discussed and its performance is illustrated using a hierarchy of models from simple scalar systems to a GCM.
Nonlinear structured illumination microscopy with surface plasmon resonance enhanced stimulated emission depletion

Nonlinear structured illumination microscopy (SIM) allows full-field imaging at resolutions <100 nm. Two nonlinear effects, excitation saturation (SSIM) and the photo-switching of protein had been applied to nonlinear SIM. We report a new SIM technique which utilizes the nonlinearity of STED effect. Resolution and signal noise ratio simulation shows that STED-SIM may serve as a better alternative to SSIM and SIM with photo-switchable protein. SIM requires a strong nonlinear effect in a large area. We use Surface Plasmon Resonant to enhance of evanescence field near a dielectric-metal-dielectric interface. An 8 times STED effect enhancement is achieved on an optimized glass-silver-glass-water planar structure. We further use the interference of two SPR-enhanced STED fields propagating at opposite direction to generate a 1D structured STED field. Combined with a uniform excitation field, the structure STED field allows full field total internal reflection imaging with an enhanced resolution along the structured dimension. Less than 50 nm resolution is demonstrated. A STED-SIM microscope with 2D structured STED field is under development. Future research will apply the microscope to superresolution imaging of membrane resident or near membrane structure at super-resolution in live cells.
Traffic and Crime

Abstract We study the link between crime and extreme traffic congestion to estimate the psychological costs of traffic. Our empirical analysis combines police incident reports with observations of local traffic data in Los Angeles from 2011 to 2015. This rich dataset allows us to link traffic with criminal activity at a fine spatial and temporal dimension. Our identification relies on deviations from normal traffic to isolate the impact of abnormally high traffic on crime. We find that extreme traffic increases the incidence of domestic violence, a crime shown to be affected by emotional cues, but not other crimes. The result is robust to a variety of specifications and falsification tests. The results represent a lower bound of the psychological costs of traffic congestion, since most drivers stuck in traffic do not commit domestic violence but still bear some emotional costs.
High specific power electrical machines: A system perspective

There has been a growing need for high specific power electrical machines for a wide range of applications. These include hybrid/electric traction applications, aerospace applications and Oil and Gas applications. A lot of work has been done to accomplish significantly higher specific power electrical machines especially for aerospace applications. Several machine topologies as well as thermal management schemes have been proposed. Even though there has been a few publications that provided an overview of high-speed and high specific power electrical machines [1-3], the goal of this paper is to provide a more comprehensive review of high specific power electrical machines with special focus on machines that have been built and tested and are considered the leading candidates defining the state-of-the art. Another key objective of this paper is to highlight the key “system-level” tradeoffs involved in pushing electrical machines to higher specific power. Focusing solely on the machine specific power can lead to a sub-optimal solution at the system-level.
Dynamic Architecture of DNA Repair Complexes and the Synaptonemal Complex at Sites of Meiotic Recombination

Meiotic double-strand breaks (DSBs) are generated and repaired in a highly regulated manner to ensure formation of crossovers (COs) while also enabling efficient non-CO repair to restore genome integrity. We use structured-illumination microscopy to investigate the dynamic architecture of DSB repair complexes at meiotic recombination sites in relationship to the synaptonemal complex (SC). DSBs resected at both ends are converted into inter-homolog repair intermediates harboring two populations of BLM helicase and RPA, flanking a single population of MutSγ. These intermediates accumulate until late pachytene, when repair proteins disappear from non-CO sites and CO-designated sites become enveloped by SC-central region proteins, acquire a second MutSγ population, and lose RPA. These and other data suggest that the SC may protect CO intermediates from being dismantled inappropriately and promote CO maturation by generating a transient CO-specific repair compartment, thereby enabling differential timing and outcome of repair at CO and non-CO sites.
Political economy of reform

This paper reviews the literature relevant to understanding political constraints to economic reforms. Reform refers to changes in government policies or institutional rules because status quo policies and institutions are not working well to achieve the goals of economic well-being and development. Further, reforms refer to the alternative policies and institutions that are available that would most likely perform better than the status quo. The main question examined in the political economy of reform literature has been why reforms are not undertaken when they are needed for the good of society. The succinct answer from the first generation of research is that conflict of interest between organized socio-political groups is responsible for some groups being able to stall reforms so that they can extract greater private rents from status quo policies. The next generation of research is tackling a more fundamental question: why does conflict of interest persist; or, why do some interest groups exert influence against reforms if there are indeed large gains to be had for society? These are questions about norms and preferences in society for public goods. The next step is to examine where norms and preferences for public goods come from, and which institutional arrangements are more conducive to solve the public goods problem of pursuing reforms. After reviewing the available and future directions for research, the paper concludes with what all of this means for policy makers who are interested in understanding the factors behind successful reforms.
Terrorist Incitement on the Internet

The internet provides a platform for speech across national borders and allows users to communicate destructive information quickly, both on open forums such as YouTube and Facebook as well as on “darknets.” This symposium foreword examines government regulation of terrorist speech on the internet and suggests that understanding terrorist speech through law, culture, and contemporary media can help governments better prevent the spread of hate speech on the internet. First Amendment protection of free speech permits the government to censor speech that explicitly or implicitly threatens others and this foreword considers whether governments – local, state, and federal – should also engage in the censorship of internet speech that indoctrinates and incites people to engage in future terrorist operations. The symposium assessed the limits of legitimate government regulation of terrorist communication on the internet and this foreword introduces diverse topics such as applying the imminent harm test to terrorist speech, critical race theory and white terrorism, and the role of the material-support statute in holding private companies responsible for terrorist messages disseminated through their social media platforms.
Cross-Domain Self-Supervised Multi-task Feature Learning Using Synthetic Imagery

In human learning, it is common to use multiple sources of information jointly. However, most existing feature learning approaches learn from only a single task. In this paper, we propose a novel multi-task deep network to learn generalizable high-level visual representations. Since multitask learning requires annotations for multiple properties of the same training instance, we look to synthetic images to train our network. To overcome the domain difference between real and synthetic data, we employ an unsupervised feature space domain adaptation method based on adversarial learning. Given an input synthetic RGB image, our network simultaneously predicts its surface normal, depth, and instance contour, while also minimizing the feature space domain differences between real and synthetic data. Through extensive experiments, we demonstrate that our network learns more transferable representations compared to single-task baselines. Our learned representation produces state-of-the-art transfer learning results on PASCAL VOC 2007 classification and 2012 detection.
Outcomes of Grazing Impacts between Sub-Neptunes in Kepler Multis

Studies of high-multiplicity, tightly-packed planetary systems suggest that dynamical instabilities are common and affect both the orbits and planet structures, where the compact orbits and typically low densities make physical collisions likely outcomes. Since the structure of many of these planets is such that the mass is dominated by a rocky core, but the volume is dominated by a tenuous gas envelope, the sticky-sphere approximation, used in dynamical integrators, may be a poor model for these collisions. We perform five sets of collision calculations, including detailed hydrodynamics, sampling mass ratios and core mass fractions typical in Kepler Multis. In our primary set of calculations, we use Kepler-36 as a nominal remnant system, as the two planets have a small dynamical separation and an extreme density ratio. We use an N-body code, Mercury 6.2, to integrate initially unstable systems and study the resultant collisions in detail. We use these collisions, focusing on grazing collisions, in combination with realistic planet models created using gas profiles from Modules for Experiments in Stellar Astrophysics and core profiles using equations of state from Seager et al. (2007), to perform hydrodynamic calculations, finding scatterings, mergers, and even a potential planet-planet binary. We dynamically integrate the remnant systems, examine the stability, and estimate the final densities, finding the remnant densities are sensitive to the core masses, and collisions result in generally more stable systems. We provide prescriptions for predicting the outcomes and modeling the changes in mass and orbits following collisions for general use in dynamical integrators.
Do trade flows respond to nudges? Evidence from the WTO’s Trade Policy Review Mechanism

Relatively little is known about the economic effects of WTO members’ communications outside of official negotiations and dispute proceedings. This paper considers whether interactions between members through the Trade Policy Review Mechanism, the WTO’s prime transparency institution, lead to subsequent changes in bilateral trade flows. Trade policy concern submissions are more likely to lead to positive trade responses when (i) the receiving country is less concerned about terms‐of‐trade losses, (ii) the submitter is more willing to engage in WTO disputes with the reviewed member to challenge controversial trade policies, and (iii) the submitting country challenges trade policies in the nonchemical manufacturing sector. Nudges through the TPR process are not successful in raising agricultural trade.
Filling the Niche: The Role of the Parents of Immigrants in the United States

Care-providing parents of immigrants fill a labor market niche in the receiving economy. My research examines the intergenerational support for working women in immigrant families in the United States. Using panel data derived from the Current Population Survey (CPS), I find that having a coresiding parent increases the labor force participation probability of foreign-born women with children by about 7.4 percent, and that the effects differ by birth region and educational level. I use a difference-in-differences approach to reinforce the finding that coresiding parents significantly alleviate the short-term labor force participation decline of foreign-born females after their transition to motherhood.
Rationale and design of the Clinical and Histologic Analysis of Mesenchymal Stromal Cells in AmPutations (CHAMP) trial investigating the therapeutic mechanism of mesenchymal stromal cells in the treatment of critical limb ischemia

Abstract Objective Currently, there are no accepted nonsurgical therapies that improve the delivery of blood-derived nutrients to patients with critical limb ischemia. Here, we describe the ongoing phase 1/2 Clinical and Histologic Analysis of Mesenchymal Stromal Cells in AmPutations (CHAMP) trial, which will provide crucial evidence of the safety profile of mesenchymal stromal cells (MSCs) and explore their therapeutic mechanisms in the setting of critical limb ischemia requiring below-knee amputation (BKA). Methods In the CHAMP and the parallel marrowCHAMP trials (hereafter grouped together as CHAMP), a total of 32 extremities with rest pain or tissue loss requiring BKA will be enrolled to receive intramuscular injections of allogeneic MSCs (CHAMP; n = 16) or autogenous concentrated bone marrow aspirate (marrowCHAMP; n = 16) along the distribution of the BKA myocutaneous flap and proximal tibialis anterior. After treatment, subjects are randomized to BKA at four time points after injection (days 3, 7, 14, and 21). At the time of amputation, skeletal muscle is collected at 2-cm increments from the tibialis injection site and used to determine proangiogenic cytokine description, MSC retention, quantification of proangiogenic hematopoietic progenitor cells, and histologic description. Clinical limb perfusion before and after treatment will be quantified using transcutaneous oximetry, toe-brachial index, ankle-brachial index, and indocyanine angiography. Additional clinical end points include all-cause mortality, need for amputation revision, and gangrene incidence during the 6-month post-treatment follow-up. Results Enrollment is under way, with 10 patients treated per protocol thus far. We anticipate full conclusion of follow-up within the next 24 months. Conclusions CHAMP will be pivotal in characterizing the safety, efficacy, and, most important, therapeutic mechanism of allogeneic MSCs and autogenous concentrated bone marrow aspirate in ischemic skeletal muscle.
Satiation state-dependent dopaminergic control of foraging in Drosophila

Hunger evokes stereotypic behaviors that favor the discovery of nutrients. The neural pathways that coordinate internal and external cues to motivate foraging behaviors are only partly known. Drosophila that are food deprived increase locomotor activity, are more efficient in locating a discrete source of nutrition, and are willing to overcome adversity to obtain food. We developed a simple open field assay that allows flies to freely perform multiple steps of the foraging sequence, and we show that two distinct dopaminergic neural circuits regulate measures of foraging behaviors. One group, the PAM neurons, functions in food deprived flies while the other functions in well fed flies, and both promote foraging. These satiation state-dependent circuits converge on dopamine D1 receptor-expressing Kenyon cells of the mushroom body, where neural activity promotes foraging independent of satiation state. These findings provide evidence for active foraging in well-fed flies that is separable from hunger-driven foraging.
Multi-instance Domain Adaptation for Vaccine Adverse Event Detection

Detection of vaccine adverse events is crucial to the discovery and improvement of problematic vaccines. To achieve it, traditionally formal reporting systems like VAERS support accurate but delayed surveillance, while recently social media have been mined for timely but noisy observations. Utilizing the complementary strengths of these two domains to boost the detection performance looks good but cannot be effectively achieved by existing methods due to significant differences between their data characteristics, including: 1) formal language v.s. informal language, 2) single-message per user v.s. multi-messages per user, and 3) one class v.s. binary class. In this paper, we propose a novel generic framework named Multi-instance Domain Adaptation (MIDA) to maximize the synergy between these two domains in the vaccine adverse event detection task for social media users. Specifically, we propose a generalized Maximum Mean Discrepancy (MMD) criterion to measure the semantic distances between the heterogeneous messages from these two domains in their shared latent semantic space. Then these message-level generalized MMD distances are synthesized by newly proposed mixed instance kernels to user-level distances. We finally minimize the distances between the samples of the partially-matched classes from these two domains. In order to solve the non-convex optimization problem, an efficient Alternating Direction Method of Multipliers (ADMM) based algorithm combined with the Convex-Concave Procedure (CCP) is developed to optimize parameters accurately. Extensive experiments demonstrated that our model outperformed the baselines by a large margin under six metrics. Case studies showed that formal reports and extracted adverse-relevant tweets by MIDA shared a similarity of keyword and description patterns.
On the Round Complexity of OT Extension

We show that any OT extension protocol based on one-way functions (or more generally any symmetric-key primitive) either requires an additional round compared to the base OTs or must make a non-black-box use of one-way functions. This result also holds in the semi-honest setting or in the case of certain setup models such as the common random string model. This implies that OT extension in any secure computation protocol must come at the price of an additional round of communication or the non-black-box use of symmetric key primitives. Moreover, we observe that our result is tight in the sense that positive results can indeed be obtained using non-black-box techniques or at the cost of one additional round of communication.
pytc: open source python software for global analyses of isothermal titration calorimetry data

Here we describe pytc, an open-source Python package for global fits of thermodynamic models to multiple isothermal titration calorimetry experiments. Key features include simplicity, the ability to implement new thermodynamic models, a robust maximum likelihood fitter, a fast Bayesian Markov-Chain Monte Carlo sampler, rigorous implementation, extensive documentation, and full cross-platform compatibility. pytc fitting can be done using an application program interface or via a graphical user interface. It is available for download at https://github.com/harmslab/pytc.
Widowhood, leisure activity engagement, and cognitive function among older adults.

Objectives: Maintaining cognitive function is an essential aspect of successful aging. Widowhood is a salient life transition that can affect older adults’ cognitive function. Leisure engagement ha...
Lay public's use of a support group for general dental problems.

OBJECTIVE To categorize and tabulate layperson inquiries made to an Internet dental health support site to identify oral conditions and associated behaviors of concern. METHODS A retrospective tabulation of wall postings was made from an established dental health support website hosted by WebMD over a 2-year period (April 2013-April 2015). A mixed method approach of content and thematic analysis was used. Content analysis identified content of oral health concerns, while thematic analysis using grounded theory identified themes and beliefs concerning associated behaviors. RESULTS The presence of non-descript growths on the tongue and swelling and/or discoloration of the lips elicited the most inquires. These were often self-diagnosed as oral candidiasis, a STD or a carcinoma, provoking high levels of self-concern. Unprotected sexual activity, excessive ingestion of soft and hard beverages, smoking and overexposure to the sun were the most frequently self-reported behaviors associated with their concerns. Many questions focused upon "normal" healing, including the amount of edema and discomfort that could occur following oral surgery or other dental procedures. Information concerning alternative treatments was often solicited, and many alluded that they had not been provided sufficient information to make informed decisions prior to dental treatment. CONCLUSIONS Laypersons have many oral health concerns particularly in relationship to the development of undiagnosed pathologies in conjunction with a history of volitional behaviors. The dental community should monitor these sites to provide advice and links to information about common oral health concerns and their possible association with detrimental behaviors.
Topological dynamics on moduli spaces, I

Let M be an orientable genus g > 0 surface with boundary O3M. Let 17 be the mapping class group of M fixing OM. The group 17 acts on Mc = Homc(7r1(M),SU(2))/SU(2), the space of SU(2)-gauge equivalence classes of flat SU(2)-connections on M with fixed holonomy on O3M. We study the topological dynamics of the 1-action and give conditions for the individual F-orbits to be dense in Mc.
Financial Reforms and Corruption: Which Dimensions Matter?

This paper investigates the effects of reforms in different dimensions of the financial sector on corruption in a panel of 82 countries. It finds that several, but not all, of the policies targeted toward liberalizing financial sector reduce corruption. Specifically, entry barriers, directed credit, securities market development, and the extent of banking supervision are significantly negatively associated with corruption. The effects of reforms in different dimensions of the financial sector also depend on the quality of the governance (bad versus good governance) and whether the country is an advanced or a non‐advanced economy. Finally, a stronger democracy and better law and order are found to be associated with lower corruption.
Optimal shape and motion planning for dynamic planar manipulation

This paper presents a framework for optimizing both the shape and the motion of a planar rigid end-effector to satisfy a desired manipulation task. Both shape and motion play key roles in determining contact interaction, and while both are commonly seen as design/control freedoms, their synergies are rarely formally explored. In this paper we study quasistatic problems like cam design, or dynamic problems like ball throwing, where both the shape and motion of the surfaces at contact are relevant. We frame this design problem as a nonlinear optimization program, where shape and motion are decision variables represented as splines. The task is represented as a series of constraints, along with a fitness cost, which force the solution to be compatible with the dynamics of frictional hard contact while satisfying the task. We illustrate the approach with the example problem of moving a disk along a desired path or trajectory, and we verify it by applying it to three classical design problems: the rolling brachistochrone, the design of teeth of involute gears, and the pitch curve of rolling cams. We conclude with a case study involving the optimization and real implementation of the shape and motion of a dynamic throwing arm.
Integrating variable renewable energy in the Bangladesh power system : a planning analysis

Integration of large-scale variable renewable energy (VRE) generation resources -- wind and solar -- into national grids has been gaining importance as costs of these technologies, especially that of solar, continues to fall rapidly. However, there continues to be a lack of a framework to systematically analyze the role played by large-scale VRE integration for most developing countries. This study develops such a framework and applies it to analyze VRE policies in Bangladesh. The study uses a least-cost planning approach to assess the volume of solar and wind that can technically and economically be integrated in the power system, accounting for spinning reserve generation capacity requirements and adequacy of transmission capacity. The study shows that solar and wind can provide a significant share of the 13 to 21 GW of new capacity needed by 2025 to meet rapidly growing electricity demand, although most of it does not pass the cost/benefit test in the near term till 2022. Efforts are also required to cope with what otherwise would be a large and costly increase in on-demand ("spinning") reserve capacity. The analysis demonstrates how an investment strategy to cover peak demand, and prudent changes in system operational practices, allow for the system to provide the needed reserve capacity without a prohibitive increase in system costs. In addition, the study examines the adequacy of transmission capacity for the first large-scale solar and wind project in Bangladesh.
Long-term forecasts of military technologies for a 20–30 year horizon: An empirical assessment of accuracy

Abstract During the 1990s, while exploring the impact of the collapse of the Soviet Union on developments in future warfare, a number of authors offered forecasts of military technology appearing by the year 2020. The forecasts covered diverse systems ranging from unmanned systems to guns to missiles to electronic warfare. This paper offers a quantitative assessment of the accuracy of this group of forecasts. The overall accuracy — by several measures — was assessed as quite high. In particular, when measured by the expert-assessed accuracy of the forecast statements, the average accuracy is 0.76. This points to the potential value of such forecasts in managing investments in long-term research and development. Major differences in accuracy, with strong statistical significance, were found between forecasts pertaining primarily to information acquisition and processing technologies, as opposed to technologies that aim primarily at physical effects. This paper also proposes several recommendations regarding methodological aspects of forecast accuracy assessments. Although the assessments were restricted to information available in open literature, the expert assessors did not find this constraint a significant detriment to the assessment process.
Coupling integrin dynamics to cellular adhesion behaviors.

ABSTRACT Visualizing fluorescent proteins is essential for understanding cellular function. While advances in microscopy can now resolve individual molecules, determining whether the labeled molecules report native behaviors and how the measured behaviors can be coupled to cellular outputs remains challenging. Here, we used integrin alpha-beta heterodimers – which connect extracellular matrix (ECM) and the cytoskeleton – to quantify the mobility and conformation of labeled integrins. We found that while unlabeled and labeled integrins all localized to adhesions and support anchorage-dependent cell function, integrin mobility decreased when the beta rather than the alpha subunit was labeled. In contrast to unlabeled and alpha labeled subunits, beta labeled subunits changed cellular behavior; decreasing protrusive activity and increasing adhesion size and the extent of cell spreading. Labeling the beta subunit changed the integrin conformation, extending the molecule and exposing an epitope that is revealed by activation with Mn2+ treatment. Our findings indicate labeling induced changes in dynamic integrin behavior alter molecular conformation as well as cellular adhesion-dependent function to demonstrate a coupling between molecular inputs and distinct cellular outputs. This article has an associated First Person interview with the first author of the paper.
Structured Adversarial Attack: Towards General Implementation and Better Interpretability

When generating adversarial examples to attack deep neural networks (DNNs), Lp norm of the added perturbation is usually used to measure the similarity between original image and adversarial example. However, such adversarial attacks perturbing the raw input spaces may fail to capture structural information hidden in the input. This work develops a more general attack model, i.e., the structured attack (StrAttack), which explores group sparsity in adversarial perturbations by sliding a mask through images aiming for extracting key spatial structures. An ADMM (alternating direction method of multipliers)-based framework is proposed that can split the original problem into a sequence of analytically solvable subproblems and can be generalized to implement other attacking methods. Strong group sparsity is achieved in adversarial perturbations even with the same level of Lp norm distortion as the state-of-the-art attacks. We demonstrate the effectiveness of StrAttack by extensive experimental results onMNIST, CIFAR-10, and ImageNet. We also show that StrAttack provides better interpretability (i.e., better correspondence with discriminative image regions)through adversarial saliency map (Papernot et al., 2016b) and class activation map(Zhou et al., 2016).
Contingent Convertibles with Stock Price Triggers: The Case of Perpetuities

Initial proposals for contingent convertibles (CoCos) envisioned that these bonds would convert to equity when the issuing bank’s stock price declined to a prespecified trigger. Subsequent research has claimed that doing so causes the stock price to have multiple equilibria or no equilibrium. We show that when CoCos are perpetuities, which characterizes most actual CoCos, a unique stock price equilibrium exists, except under unrealistic conditions. Unique equilibria occur when conversion favors or disfavors CoCo investors, when CoCos convert to equity or are written down, and when CoCos are callable. We also analyze a bank’s risk choices before and after conversion. Received November 29, 2016; editorial decision July 15, 2018 by Editor Itay Goldstein.
Heavy-traffic Delay Optimality in Pull-based Load Balancing Systems: Necessary and Sufficient Conditions

In this paper, we consider a load balancing system under a general pull-based policy. In particular, each arrival is randomly dispatched to any server whose queue length is below a threshold; if no such server exists, then the arrival is randomly assigned to any server. We are interested in the fundamental relationship between the threshold and the delay performance of the system in heavy traffic. To this end, we first establish the following necessary condition to guarantee heavy-traffic delay optimality: the threshold needs to grow to infinity as the exogenous arrival rate approaches the boundary of the capacity region (i.e., the load intensity approaches one) but the growth rate should be slower than a polynomial function of the mean number of tasks in the system. As a special case of this result, we directly show that the delay performance of the popular pull-based policy Join-Idle-Queue (JIQ) is not heavy traffic optimal, but performs strictly better than random routing. We further show that a sufficient condition for heavy-traffic delay optimality is that the threshold grows logarithmically with the mean number of tasks in the system. This result directly resolves a generalized version of the conjecture by Kelly and Laws.
Low-Temperature Energy Efficiency of Lithium-Ion Batteries

In this study, the low-temperature energy efficiency of lithium-ion batteries (LIBs) with different chemistries and nominal capacities at various charge and discharge rates is studied through multi-physics modeling and computer simulation. The model is based on the irreversible heat generation in the battery, leading to the charge/discharge efficiency in LIBs with graphite/LiFePO4, graphite/LiMn2O4, and graphite/LiCoO2 electrode materials in which the effects of the battery nominal capacity at various charge and discharge rates are studied. Using characterized sources of the heat generation in the LIB leads to providing a battery efficiency plot at different operation condition for each LIBs. The results of this study assist the battery engineers to have much more accurate prediction over the efficiency of the LIBs at low temperatures.
Why do the Elderly Save? The Role of Medical Expenses

This paper constructs a model of saving for retired single people that includes heterogeneity in medical expenses and life expectancies, and bequest motives. We estimate the model using Assets and Health Dynamics of the Oldest Old data and the method of simulated moments. Out‐of‐pocket medical expenses rise quickly with age and permanent income. The risk of living long and requiring expensive medical care is a key driver of saving for many higher‐income elderly. Social insurance programs such as Medicaid rationalize the low asset holdings of the poorest but also benefit the rich by insuring them against high medical expenses at the ends of their lives.
A Re-Ranker Scheme For Integrating Large Scale NLU Models

Large scale Natural Language Understanding (NLU) systems are typically trained on large quantities of data, requiring a fast and scalable training strategy. A typical design for NLU systems consists of domain-level NLU modules (domain classifier, intent classifier and named entity recognizer). Hypotheses (NLU interpretations consisting of various intent+slot combinations) from these domain specific modules are typically aggregated with another downstream component. The re-ranker integrates outputs from domain-level recognizers, returning a scored list of cross domain hypotheses. An ideal re-ranker will exhibit the following two properties: (a) it should prefer the most relevant hypothesis for the given input as the top hypothesis and, (b) the interpretation scores corresponding to each hypothesis produced by the re-ranker should be calibrated. Calibration allows the final NLU interpretation score to be comparable across domains. We propose a novel re-ranker strategy that addresses these aspects, while also maintaining domain specific modularity. We design optimization loss functions for such a modularized re-ranker and present results on decreasing the top hypothesis error rate as well as maintaining the model calibration. We also experiment with an extension involving training the domain specific re-rankers on datasets curated independently by each domain to allow further asynchronization.
From Distance Correlation to Multiscale Graph Correlation

Understanding and developing a correlation measure that can detect general dependencies is not only imperative to statistics and machine learning, but also crucial to general scientific discovery i...
Joint nonparametric precision matrix estimation with confounding

We consider the problem of precision matrix estimation where, due to extraneous confounding of the underlying precision matrix, the data are independent but not identically distributed. While such confounding occurs in many scientific problems, our approach is inspired by recent neuroscientific research suggesting that brain function, as measured using functional magnetic resonance imagine (fMRI), is susceptible to confounding by physiological noise such as breathing and subject motion. Following the scientific motivation, we propose a graphical model, which in turn motivates a joint nonparametric estimator. We provide theoretical guarantees for the consistency and the convergence rate of the proposed estimator. In addition, we demonstrate that the optimization of the proposed estimator can be transformed into a series of linear programming problems, and thus be efficiently solved in parallel. Empirical results are presented using simulated and real brain imaging data, which suggest that our approach improves precision matrix estimation, as compared to baselines, when confounding is present.
Dynamic Transmission Control Based on All-Dielectric Huygens Metasurfaces

Tunable metasurfaces open new doors for achieving dynamic wavefront manipulation in an ultracompact footprint. Dielectric metasurfaces are particularly attractive for this application due to their low-loss modes. However, their volumetric modes make them difficult to dynamically tune compared to plasmonic variants with strong field confinement. We overcome this challenge by combining dielectric resonators with an epsilon-near-zero (ENZ) mode in a thin film. By tuning the coupling between modes in the resonators and the ENZ thin film, active control over the transmittance amplitude is achieved. Operating at the wavelength of the Huygens mode, we demonstrate transmittance modulation with an on-state transmittance of 70% and a modulation depth of 31%. In addition, we create a tunable diffraction grating and demonstrate its potential use in beam steering applications. This approach provides a new avenue for high speed and low-power modulation of dielectric metasurfaces.
Learning Rules-First Classifiers

Complex classifiers may exhibit ``embarassing'' failures in cases where humans can easily provide a justified classification. Avoiding such failures is obviously of key importance. In this work, we focus on one such setting, where a label is perfectly predictable if the input contains certain features, or rules, and otherwise it is predictable by a linear classifier. We define a hypothesis class that captures this notion and determine its sample complexity. We also give evidence that efficient algorithms cannot achieve this sample complexity. We then derive a simple and efficient algorithm and show that its sample complexity is close to optimal, among efficient algorithms. Experiments on synthetic and sentiment analysis data demonstrate the efficacy of the method, both in terms of accuracy and interpretability.
Nanocircuits in loop structures: Continuous waves preclude gauge invariant wavelengths

Tunnel junctions for quantum computing require discrete spectra from continuous waves on a doubly connected coordinate or loop. For an electron on a metal ring discrete spectra follow from discontinuous Bloch waves. Can both propositions be true? We find using a gauge function originating in the Lagrangian that continuity on a ring or loop violates gauge invariance of the de Broglie wavelength. This same gauge function shows that Lagrangians for the electron on a ring and the charge on a junction are mutual transforms. Thus persistent current on a metal ring and the Coulomb blockade on a tunnel junction seem to be the same dynamical theory based on discontinuous Bloch waves on the compact perimeter of a circle.
A Context-Aware Framework for Detecting Sensor-Based Threats on Smart Devices

Sensors (e.g., light, gyroscope, and accelerometer) and sensing-enabled applications on a smart device make the applications more user-friendly and efficient. However, the current permission-based sensor management systems of smart devices only focus on certain sensors and any App can get access to other sensors by just accessing the generic sensor Application Programming Interface (API). In this way, attackers can exploit these sensors in numerous ways: they can extract or leak users’ sensitive information, transfer malware, or record or steal sensitive information from other nearby devices. In this paper, we propose 6thSense, a context-aware intrusion detection system which enhances the security of smart devices by observing changes in sensor data for different tasks of users and creating a contextual model to distinguish benign and malicious behavior of sensors. 6thSense utilizes three different Machine Learning-based detection mechanisms (i.e., Markov Chain, Naive Bayes, and LMT). We implemented 6thSense on several sensor-rich Android-based smart devices (i.e., smart watch and smartphone) and collected data from typical daily activities of 100 real users. Furthermore, we evaluated the performance of 6thSense against three sensor-based threats: (1) a malicious App that can be triggered via a sensor, (2) a malicious App that can leak information via a sensor, and (3) a malicious App that can steal data using sensors. Our extensive evaluations show that the 6thSense framework is an effective and practical approach to defeat growing sensor-based threats with an accuracy above 96 percent without compromising the normal functionality of the device. Moreover, our framework reveals minimal overhead.
Poly-use of cannabis and other substances among juvenile-justice involved youth: variations in psychological and substance-related problems by typology.

ABSTRACTBackground: Adolescent cannabis use is associated with increased risk for psychological problems, with evidence for more severe problems among youth who use cannabis in combination with oth...
Can Regulation Promote Financial Inclusion

Despite the commitments of the development community toward broader access to finance, financial inclusion rates worldwide are rather unsatisfactory. To date, around two billion adults do not have access to basic financial services such as savings and checking accounts. Attempting to bridge such gap between policy objectives and outcomes, several economists have probed the determinants of financial inclusion. This paper contributes to the debate by investigating the role played by financial regulation. First, the paper proposes a broad index of regulatory quality for financial inclusion, emphasizing the role of nontraditional delivery models, for example, branchless banking, and actors, for example, nonbank lending institutions. Second, the paper tests the relationship between regulatory quality and financial inclusion outcomes. The analysis finds that in countries where regulatory quality is within the top quartile, individuals are 12.4 percent more likely to have an account at a financial institution with respect to bottom quartile countries.
Random Digit Dialing Sampling for Case–Control Studies †

Random digit dialing (RDD) is a useful tool in epidemiological research, particularly for selecting controls in population-based case–control studies. The paper discusses the two principal methods currently used for sample selection in RDD, and indicates why other, possibly simple methods, are unsatisfactory. The principal considerations that should be taken into account in determining the details of a sample selection method (e.g. geographical boundaries of cases and controls, whether to choose more than one control per household, response rates, etc.) are described. Issues relating to data collection, once the sample has been selected, are also described including how these follow-up activities could influence the choice of sample design and could complicate the methods of analysis of the study results. Keywords: random digit dialing; telephone sampling; case–control studies; selecting controls