cssi/SciSciGPT-SciSciNet huggingface dataset:
Smart Navigation for an In-pipe Robot Through Multi-phase Motion Control and Particle Filtering Method

In-pipe robots are promising solutions for condition assessment, leak detection, water quality monitoring in a variety of other tasks in pipeline networks. Smart navigation is an extremely challenging task for these robots as a result of highly uncertain and disturbing environment for operation. Wireless communication to control these robots during operation is not feasible if the pipe material is metal since the radio signals are destroyed in the pipe environment, and hence, this challenge is still unsolved. In this paper, we introduce a method for smart navigation for our previously designed in-pipe robot [1] based on particle filtering and a two-phase motion controller. The robot is given the map of the operation path with a novel approach and the particle filtering determines the straight and non-straight configurations of the pipeline. In the straight paths, the robot follows a linear quadratic regulator (LQR) and proportional- integral-derivative (PID) based controller that stabilizes the robot and tracks a desired velocity. In non-straight paths, the robot follows the trajectory that a motion trajectory generator block plans for the robot. The proposed method is a promising solution for smart navigation without the need for wireless communication and capable of inspecting long distances in water distribution systems.
Uniformization of Cantor sets with bounded geometry

In this note we provide a quasisymmetric taming of uniformly perfect and uniformly disconnected sets that generalizes a result of MacManus from 2 to higher dimensions. In particular, we show that a compact subset of $\mathbb{R}^n$ is uniformly perfect and uniformly disconnected if and only if it is ambiently quasiconformal to the standard Cantor set $\mathcal{C}$ in $\mathbb{R}^{n+1}$.
Social Experience Alters Oxytocinergic Modulation in the Nucleus Accumbens of Female Prairie Voles

Social relationships are dynamic and evolve with shared and personal experiences. Whether the functional role of social neuromodulators also evolves with experience to shape the trajectory of relationships is unknown. We utilized pair bonding in the socially monogamous prairie voles as an example of socio-sexual experience that dramatically alters behaviors displayed toward other individuals. We investigated oxytocin-dependent modulation of excitatory synaptic transmission in the nucleus accumbens as a function of pair bonding status. We found that an oxytocin receptor agonist decreases the amplitude of spontaneous Excitatory Postsynaptic Currents (EPSCs) in sexually naive virgin, but not pair-bonded, female voles, while it increases the amplitude of electrically evoked EPSCs in paired voles, but not in virgins. This oxytocin-dependent potentiation of synaptic transmission relies on the de novo coupling between oxytocin receptor signaling and endocannabinoid CB1 receptor signaling in pair bonded voles. Blocking CB1 receptors after pair bond formation increases the occurrence of a specific form of social rejection – defensive upright response – that is displayed towards the partner but not towards a novel individual. Altogether, our results demonstrate that oxytocin’s action in the nucleus accumbens is changed through social experience in a way that regulates the trajectory of social interactions as the relationship with the partner unfolds, potentially promoting the maintenance of a pair bond by inhibiting aggressive responses. These results provide a mechanism by which social experience and context shift oxytocinergic signaling to impact neural and behavioral responses to social cues.
Optomechanical Self-Stability of Freestanding Photonic Metasurfaces

We develop an analytical framework for self-stabilizing optical manipulation of freestanding metasurfaces in three dimensions. Our framework reveals that the challenging problem of stabilization against translational and rotational perturbations in three dimensions is reduced to a simpler scattering analysis of the metasurface unit cell in two dimensions. We derive universal analytical stiffness coefficients applicable to arbitrary three-dimensional radial metasurfaces and radial beam-intensity profiles. The analytical nature of our framework facilitates highly efficient discovery of optimal optomechanical metasurfaces. Such use of metasurfaces for mechanical stabilization enables macroscale and long-range control in collimated, but otherwise unfocused light beams, and could open up avenues for manipulation beyond traditional optical tweezing and transport.
Fairness Under Feature Exemptions: Counterfactual and Observational Measures

With the growing use of machine learning algorithms in highly consequential domains, the quantification and removal of disparity in decision making with respect to protected attributes, such as gender, race, etc., is becoming increasingly important. While quantifying disparity is essential, sometimes the needs of a business (e.g., hiring) may require the use of certain features that are critical in a way that any disparity that can be explained by them might need to be exempted. For instance, in hiring a software engineer for a safety-critical application, a coding-test score may be a critical feature that is weighed strongly in the decision even if it introduces disparity, whereas other features, such as name, zip code, or reference letters may be used to improve decision-making, but only to the extent that they do not add disparity. In this work, we propose a novel information-theoretic decomposition of the total disparity (a quantification inspired from counterfactual fairness) into two components: a non-exempt component which quantifies the part of the disparity that cannot be accounted for by the critical features, and an exempt component which quantifies the remaining disparity. This decomposition is important: it allows one to check if the disparity arose purely due to the critical features (inspired from the business necessity defense of disparate impact law) and also enables selective removal of the non-exempt component of disparity if desired. We arrive at this decomposition through canonical examples that lead to a set of desirable properties (axioms) that any measure of non-exempt disparity should satisfy. We then demonstrate that our proposed counterfactual measure of non-exempt disparity satisfies all of them. Our quantification bridges ideas of causality, Simpson’s paradox, and a body of work from information theory called Partial Information Decomposition (PID). We also obtain an impossibility result showing that no observational measure of non-exempt disparity can satisfy all of the desired properties, which leads us to relax our goals and examine alternative observational measures that satisfy only some of these properties. We perform case studies to show how one can audit existing models as well as train new models while reducing non-exempt disparity.
The Hippo pathway regulates density-dependent proliferation of iPSC-derived cardiac myocytes.

Inducing cardiac myocytes to proliferate is considered a potential therapy to target heart disease, however, modulating cardiac myocyte proliferation has proven to be a technical challenge. The Hippo pathway is a kinase signaling cascade that regulates cell proliferation during the growth of the heart. Inhibition of the Hippo pathway increases the activation of the transcription factors YAP/TAZ, which translocate to the nucleus and upregulate transcription of pro-proliferative genes. The Hippo pathway regulates the proliferation of cancer cells, pluripotent stem cells, and epithelial cells through a cell–cell contact-dependent manner, however, it is unclear if cell density-dependent cell proliferation is a consistent feature in cardiac myocytes. Here, we used cultured human iPSC-derived cardiac myocytes (hiCMs) as a model system to investigate this concept. hiCMs have a comparable transcriptome to the immature cardiac myocytes that proliferate during heart development in vivo. Our data indicate that a dense syncytium of hiCMs can regain cell cycle activity and YAP expression and activity when plated sparsely or when density is reduced through wounding. We found that combining two small molecules, XMU-MP-1 and S1P, increased YAP activity and further enhanced proliferation of low-density hiCMs. Importantly, these compounds had no effect on hiCMs within a dense syncytium. These data add to a growing body of literature that link Hippo pathway regulation with cardiac myocyte proliferation and demonstrate that regulation is restricted to cells with reduced contact inhibition.
Passive, Adaptive and Active Tuned Vibration Absorbers—A Survey

An overview of the recent development of tuned vibration absorbers (TVAs) for vibration and noise suppression is presented. The paper summarizes some popular theory for analysis and optimal tuning of these devices, discusses various design configurations, and presents some contemporary applications of passive TVAs. Furthermore, the paper also presents a brief discussion on the recent progress of adaptive and semi-active TVAs along with their on-line tuning strategies, and active and hybrid fail-safe TVAs.
The growing impact of US monetary policy on emerging financial markets: Evidence from India

Abstract Much research has been devoted to studying the international spillover effects of US monetary policy. However, a lot of the focus has been on the recent unconventional monetary policies undertaken by the Federal Reserve. Combining high frequency financial market data with a time-varying parameter approach we show that US monetary policy decisions have had significant effects on the Indian stock markets well before the use of unconventional policy tools and that these effects have gotten stronger over time. In addition to the conventional channel of surprise changes in the policy rate, we find that US monetary shocks are also transmitted through an uncertainty channel, which is especially important for announcements about large scale asset purchases (quantitative easing). Using firm level stock prices, we also show that the higher sensitivity of the aggregate response is uniform across the stock market and is not driven by the increased exposure of any specific industry to US monetary policy. Instead, our results suggest that it is driven by the portfolio decisions of foreign institutional investors, the exchange rate becoming more sensitive to US monetary policy and the global financial cycle working through volatility and risk aversion.
Learning for Constrained Optimization: Identifying Optimal Active Constraint Sets

In many engineered systems, optimization is used for decision making at time scales ranging from real-time operation to long-term planning. This process often involves solving similar optimization ...
Quantification of Myxococcus xanthus Aggregation and Rippling Behaviors: Deep-Learning Transformation of Phase-Contrast into Fluorescence Microscopy Images

Myxococcus xanthus bacteria are a model system for understanding pattern formation and collective cell behaviors. When starving, cells aggregate into fruiting bodies to form metabolically inert spores. During predation, cells self-organize into traveling cell-density waves termed ripples. Both phase-contrast and fluorescence microscopy are used to observe these patterns but each has its limitations. Phase-contrast images have higher contrast, but the resulting image intensities lose their correlation with cell density. The intensities of fluorescence microscopy images, on the other hand, are well-correlated with cell density, enabling better segmentation of aggregates and better visualization of streaming patterns in between aggregates; however, fluorescence microscopy requires the engineering of cells to express fluorescent proteins and can be phototoxic to cells. To combine the advantages of both imaging methodologies, we develop a generative adversarial network that converts phase-contrast into synthesized fluorescent images. By including an additional histogram-equalized output to the state-of-the-art pix2pixHD algorithm, our model generates accurate images of aggregates and streams, enabling the estimation of aggregate positions and sizes, but with small shifts of their boundaries. Further training on ripple patterns enables accurate estimation of the rippling wavelength. Our methods are thus applicable for many other phenotypic behaviors and pattern formation studies.
OneLNK: One Link to Rule Them All: Web-based Wireless Experimentation for Multi-vendor Remotely Accessible Indoor/Outdoor Testbeds

As evolving wireless network architectures become more diverse, complex, and interdependent, and equipment costs prohibit broad access to such networks, remotely accessible experimental testbeds are gaining interest in recent years in wireless communication and networking research. This interest has exacerbated in 2020 and became a vital need during the current global pandemic. However, providing end-users of various educational backgrounds access to radio devices from a heterogeneous set of vendors is challenging. This paper introduces OneLNK, a remotely accessible testbed consisting of radio devices from three different vendors and developed using open source cloud-native technologies. End-users can access the functionalities of OneLNK from a single webpage without any local installations. Using the web URL, users can operate radio devices, set experiment parameters, observe results in real-time, and save generated experiment data for all radio devices. The interactive web UI and its working mechanism for supporting radio equipment are covered with specific experiment capabilities. A diverse set of radio equipment (mmWave, sub-GHz SDR, and sub-6GHz SDR) are facilitated to explain these capabilities. Moreover, measurements of path loss, Received Signal Strength (RSS), and Signal-to-Noise Ratio (SNR) using devices from three different vendors operating on a vast spectrum (568 MHz, 5.8 GHz, and 60 GHz) are reported. The majority of the remotely accessible OneLNK platform was developed remotely during the pandemic by a team of experts from five U.S. states.
Crowded Trades and Tail Risk

Hedge fund positions are an important component of crowded trades. These vehicles are particularly active, take highly concentrated positions, and utilize leverage and short sales. Using a database of hedge fund holdings, we measure the degree of security-level crowdedness. The difference between the average returns on portfolios sorted by high versus low crowdedness portfolios is sizable and their variation is distinct from other traditional risk factors. Further, hedge fund exposures to crowdedness are often significant and they help to explain downside "tail risk", as funds with higher exposures experience relatively larger drawdowns during periods of industry distress.
On the Solutions of Three-Variable Frobenius-Related Problems Using Order Reduction Approach

This paper presents a new approach to determine the number of solutions of three-variable Frobenius-related problems and to find their solutions by using order reducing methods. Here, the order of a Frobenius-related problem means the number of variables appearing in the problem. We present two types of order reduction methods that can be applied to the problem of finding all nonnegative solutions of three-variable Frobenius-related problems. The first method is used to reduce the equation of order three from a three-variable Frobenius-related problem to be a system of equations with two fixed variables. The second method reduces the equation of order three into three equations of order two, for which an algorithm is designed with an interesting open problem on solutions left as a conjecture.
Property-optimized Gaussian basis sets for lanthanides.

Property-optimized Gaussian basis sets of split-valence, triple-zeta valence, and quadruple-zeta valence quality are developed for the lanthanides Ce–Lu for use with small-core relativistic effective core potentials. They are constructed in a systematic fashion by augmenting def2 orbital basis sets with diffuse basis functions and minimizing negative static isotropic polarizabilities of lanthanide atoms with respect to basis set exponents within the unrestricted Hartree–Fock method. The basis set quality is assessed using a test set of 70 molecules containing the lanthanides in their common oxidation states and f electron occupations. 5d orbital occupation turns out to be the determining factor for the basis set convergence of polarizabilities in lanthanide atoms and the molecular test set. Therefore, two series of property-optimized basis sets are defined. The augmented def2-SVPD, def2-TZVPPD, and def2-QZVPPD basis sets balance the accuracy of polarizabilities across lanthanide oxidation states. The relative errors in atomic and molecular polarizability calculations are ≤8% for augmented split-valence basis sets, ≤ 2.5% for augmented triple-zeta valence basis sets, and ≤1% for augmented quadruple-zeta valence basis sets. In addition, extended def2-TZVPPDD and def2-QZVPPDD are provided for accurate calculations of lanthanide atoms and neutral clusters. The property-optimized basis sets developed in this work are shown to accurately reproduce electronic absorption spectra of a series of LnCp3′− complexes (Cp′ = C5H4SiMe3, Ln = Ce–Nd, Sm) with time-dependent density functional theory.
A Dynamically Reconfigurable ECG Analog Front-End with a 2.5 Data-Dependent Power Reduction.

This paper presents a reconfigurable electrocardiogram (ECG) analog front-end (AFE) exploiting bio-signals' inherent low activity and quasi-periodicity to reduce power consumption. This is realized by an agile, on-the-fly dynamic noise-power trade-off performed over specific cardiac cycle regions guided by a least mean squares (LMS)-based adaptive predictor leading to ~2.5 data-dependent power savings. Implemented in 65 nm CMOS, the AFE has tunable performance exhibiting an input-referred noise ranging from 1.90 2.91 VRMS while consuming 307 769 nW from a 0.8 V supply. A comprehensive system performance verification was performed using ECG records from standard databases to establish the feasibility of the proposed predictor-based approach for power savings without compromising the systems anomaly detection capability or ability to extract pristine ECG features.
Meshless discretization of the discrete-ordinates transport equation with integration based on Voronoi cells

Abstract The time-dependent, gray, linear radiation transport equation is discretized using the meshless local Petrov-Galerkin method with reproducing kernels. The integration is performed using a Voronoi tessellation, which creates a partition of unity that only depends on the position and extent of the kernels. The resolution of the integration automatically follows the particles and requires no manual adjustment. The discretization includes streamline-upwind Petrov-Galerkin stabilization to prevent oscillations and improve numerical conditioning. The angular quadrature is selectively refineable to increase angular resolution in chosen directions. The time discretization is done using backward Euler. The transport solve for each direction and the solve for the scattering source are both done using Krylov iterative methods. Results indicate first-order convergence in time and second-order convergence in space for linear reproducing kernels.
Precision Aggregated Local Models

Large scale Gaussian process (GP) regression is infeasible for larger data sets due to cubic scaling of flops and quadratic storage involved in working with covariance matrices. Remedies in recent literature focus on divide-and-conquer, e.g., partitioning into sub-problems and inducing functional (and thus computational) independence. Such approximations can be speedy, accurate, and sometimes even more flexible than an ordinary GPs. However, a big downside is loss of continuity at partition boundaries. Modern methods like local approximate GPs (LAGPs) imply effectively infinite partitioning and are thus pathologically good and bad in this regard. Model averaging, an alternative to divide-and-conquer, can maintain absolute continuity but often over-smooths, diminishing accuracy. Here we propose putting LAGP-like methods into a local experts-like framework, blending partition-based speed with model-averaging continuity, as a flagship example of what we call precision aggregated local models (PALM). Using $K$ LAGPs, each selecting $n$ from $N$ total data pairs, we illustrate a scheme that is at most cubic in $n$, quadratic in $K$, and linear in $N$, drastically reducing computational and storage demands. Extensive empirical illustration shows how PALM is at least as accurate as LAGP, can be much faster in terms of speed, and furnishes continuous predictive surfaces. Finally, we propose sequential updating scheme which greedily refines a PALM predictor up to a computational budget.
ALOHA: Aggregated local extrema splines for high-throughput dose-response analysis.

Abstract Computational methods for genomic dose-response integrate dose-response modeling with bioinformatics tools to evaluate changes in molecular and cellular functions related to pathogenic processes. These methods use parametric models to describe each gene’s dose-response, but such models may not adequately capture expression changes. Additionally, current approaches do not consider gene co-expression networks. When assessing co-expression networks, one typically does not consider the dose-response relationship, resulting in ‘co-regulated’ gene sets containing genes having different dose-response patterns. To avoid these limitations, we develop an analysis pipeline called Aggregated Local Extrema Splines for High-throughput Analysis (ALOHA), which computes individual genomic dose-response functions using a flexible class Bayesian shape constrained splines and clusters gene co-regulation based upon these fits. Using splines, we reduce information loss due to parametric lack-of-fit issues, and because we cluster on dose-response relationships, we better identify co-regulation clusters for genes that have co-expressed dose-response patterns from chemical exposure. The clustered pathways can then be used to estimate a dose associated with a pre-specified biological response, i.e., the benchmark dose (BMD), and approximate a point of departure dose corresponding to minimal adverse response in the whole tissue/organism. We compare our approach to current parametric methods and our biologically enriched gene sets to cluster on normalized expression data. Using this methodology, we can more effectively extract the underlying structure leading to more cohesive estimates of gene set potency.
Armor: A Benchmark for Meta-evaluation of Artificial Music

Objective evaluation (OE) is essential to artificial music, but it's often very hard to determine the quality of OEs. Hitherto, subjective evaluation (SE) remains reliable and prevailing but suffers inevitable disadvantages that OEs may overcome. Therefore, a meta-evaluation system is necessary for designers to test the effectiveness of OEs. In this paper, we present Armor, a complex and cross-domain benchmark dataset that serves this purpose. Since OEs should correlate with human judgment, we provide music as test cases for OEs and human judgment scores as touchstones. We also provide two meta-evaluation scenarios and their corresponding testing methods to assess the effectiveness of OEs. To the best of our knowledge, Armor is the first comprehensive and rigorous framework that future works could follow, take example by, and improve upon for the task of evaluating computer-generated music and the field of computational music as a whole. By analyzing different OE methods on our dataset, we observe that there is still a huge gap between SE and OE, meaning that hard-coded algorithms are far from catching human's judgment to the music.
MIDAS: Multi-agent Interaction-aware Decision-making with Adaptive Strategies for Urban Autonomous Navigation

Autonomous navigation in crowded, complex urban environments requires interacting with other agents on the road. A common solution to this problem is to use a prediction model to guess the likely future actions of other agents. While this is reasonable, it leads to overly conservative plans because it does not explicitly model the mutual influence of the actions of interacting agents. This paper builds a reinforcement learning-based method named MIDAS where an Ego agent learns to affect the control actions of other cars in urban driving scenarios. MIDAS uses an attention mechanism to handle an arbitrary number of other agents and includes a "driver-type" parameter to learn a single policy that works across different planning objectives. We build a simulation environment that enables diverse interaction experiments with a large number of agents and develop methods for quantitatively studying the safety, efficiency, and interaction among vehicles. MIDAS is validated using extensive experiments and we show that it (i) can work across different road geometries, (ii) results in an adaptive Ego policy that can be tuned easily to satisfy different performance criteria, such as aggressive or cautious driving, (iii) is robust to changes in the driving policies of external agents, and (iv) is safer and more efficient than existing approaches to interaction-aware decision-making. Code available here.
Delays in public investment projects

Abstract The returns from public investment, especially during periods of scaling up, are often lower than expected. To understand the mechanisms behind this regularity we exploit original information on investment projects obtained from World Bank project reports to document the extent and the drivers of time delays in project implementation. We find that almost 60 percent of investment projects are delayed by at least one year. Time overruns are common across sectors and countries. A sound planning and preparation matter for the timing of project execution. Country characteristics also play a role, as projects undertaken in countries with weaker institutions and in periods of public investment scaling up are completed with longer delays.
City-Wide Modeling of Vehicle-to-Grid Economics to Understand Effects of Battery Performance

Vehicle-to-grid (V2G) is a promising approach to solve the problem of grid-level intermittent supply and demand mismatch, caused due to renewable energy resources, because it uses the existing resource of electric vehicle (EV) batteries as the energy storage medium. EV battery design together with an impetus on profitability for participating EV owners is pivotal for V2G success. To better understand what battery device parameters are most important for V2G adoption, we model the economics of V2G process under realistic conditions. Most previous studies that perform V2G economic analysis, assume ideal driving conditions, use linear battery degradation models, or only consider V2G for ancillary services. Our model accounts realistic battery degradation, empirical charging efficiencies, for randomness in commute behavior, and historic hourly electricity prices in six cities in the United States. We model user behavior with Bayesian optimization to provide a best-case scenario for V2G. Across all cities, we find that charging rate and efficiency are the most important factors that determine EV users' profits. Surprisingly, EV battery cost and thus degradation due to cycling has little effect. These findings should help focus research on figures of merit that better reflect real usage of batteries in a V2G economy.
Relative Maximum Likelihood updating of ambiguous beliefs

Abstract This paper proposes and axiomatizes a new updating rule: Relative Maximum Likelihood (RML) updating for ambiguous beliefs represented by a set of priors ( C ). This rule takes the form of applying Bayes’ rule to a subset of C . This subset is a linear contraction of C towards its subset ascribing the maximal probability to the observed event. The degree of contraction captures the extent of willingness to discard priors based on likelihood when updating. Two well-known updating rules of multiple priors, full Bayesian (FB) and Maximum Likelihood (ML), are included as special cases of RML. An axiomatic characterization of conditional preferences generated by RML updating is provided when the preferences admit Maxmin Expected Utility representations. The axiomatization relies on weakening the axioms characterizing FB and ML. The axiom characterizing ML is identified for the first time in this paper, addressing a long-standing open question in the literature.
ALGAMES: a fast augmented Lagrangian solver for constrained dynamic games

Dynamic games are an effective paradigm for dealing with the control of multiple interacting actors. This paper introduces augmented Lagrangian GAME-theoretic solver (ALGAMES), a solver that handles trajectory-optimization problems with multiple actors and general nonlinear state and input constraints. Its novelty resides in satisfying the first-order optimality conditions with a quasi-Newton root-finding algorithm and rigorously enforcing constraints using an augmented Lagrangian method. We evaluate our solver in the context of autonomous driving on scenarios with a strong level of interactions between the vehicles. We assess the robustness of the solver using Monte Carlo simulations. It is able to reliably solve complex problems like ramp merging with three vehicles three times faster than a state-of-the-art DDP-based approach. A model-predictive control (MPC) implementation of the algorithm, running at more than 60 Hz, demonstrates ALGAMES’ ability to mitigate the “frozen robot” problem on complex autonomous driving scenarios like merging onto a crowded highway.
Heavy Ball Momentum for Conditional Gradient

Conditional gradient, aka Frank Wolfe (FW) algorithms, have well-documented merits in machine learning and signal processing applications. Unlike projection-based methods, momentum cannot improve the convergence rate of FW, in general. This limitation motivates the present work, which deals with heavy ball momentum, and its impact to FW. Specifically, it is established that heavy ball offers a unifying perspective on the primal-dual (PD) convergence, and enjoys a tighter per iteration PD error rate, for multiple choices of step sizes, where PD error can serve as the stopping criterion in practice. In addition, it is asserted that restart, a scheme typically employed jointly with Nesterov's momentum, can further tighten this PD error bound. Numerical results demonstrate the usefulness of heavy ball momentum in FW iterations.
Bayesian nonparametric tests for multivariate locations

Abstract In this paper, we propose novel, fully Bayesian non-parametric tests for one-sample and two-sample multivariate location problems. We model the underlying distribution using a Dirichlet process prior, and develop a testing procedure based on the posterior credible region for the spatial median functional of the distribution. For the one-sample problem, we fail to reject the null hypothesis if the credible set contains the null value. For the two-sample problem, we form a credible set for the difference of the spatial medians of the two samples and we fail to reject the null hypothesis of equality if the credible set contains zero. We derive the local asymptotic power of the tests under shrinking alternatives, and also present a simulation study to compare the finite-sample performance of our testing procedures with existing parametric and non-parametric tests.
Grooming at the Cusp: All-Orders Predictions for the Transition Region of Jet Groomers

Jet grooming has emerged as a necessary and vital tool for mitigating contamination radiation in jets. The additional restrictions on emissions imposed by the groomer can result in non-smooth behavior of resulting fixed-order distributions of observables measured on groomed jets. As a concrete example, we study the cusp in the hemisphere mass distribution of e+e− → hadrons events groomed with soft drop. We identify the leading emissions that contribute in the region about the cusp and formulate an all-orders factorization theorem that describes how the cusp is resolved through arbitrary strongly-ordered soft and collinear emissions. The factorization theorem exhibits numerous novel features such as contributions from collinear modes that can cross hemisphere boundaries as well as requiring explicit subtraction of the limit in which resolved emissions become collinear to the hard core. We present resummation of the cusp region through next-to-leading logarithmic accuracy and describe how it can be matched with established factorization theorems that describe other groomed phase space regions.
Unsupervised Extraction of Diagnosis Codes from EMRs Using Knowledge-Based and Extractive Text Summarization Techniques

Diagnosis codes are extracted from medical records for billing and reimbursement and for secondary uses such as quality control and cohort identification. In the US, these codes come from the standard terminology ICD-9-CM derived from the international classification of diseases (ICD). ICD-9 codes are generally extracted by trained human coders by reading all artifacts available in a patient’s medical record following specific coding guidelines. To assist coders in this manual process, this paper proposes an unsupervised ensemble approach to automatically extract ICD-9 diagnosis codes from textual narratives included in electronic medical records (EMRs). Earlier attempts on automatic extraction focused on individual documents such as radiology reports and discharge summaries. Here we use a more realistic dataset and extract ICD-9 codes from EMRs of 1000 inpatient visits at the University of Kentucky Medical Center. Using named entity recognition (NER), graph-based concept-mapping of medical concepts, and extractive text summarization techniques, we achieve an example based average recall of 0.42 with average precision 0.47; compared with a baseline of using only NER, we notice a 12% improvement in recall with the graph-based approach and a 7% improvement in precision using the extractive text summarization approach. Although diagnosis codes are complex concepts often expressed in text with significant long range non-local dependencies, our present work shows the potential of unsupervised methods in extracting a portion of codes. As such, our findings are especially relevant for code extraction tasks where obtaining large amounts of training data is difficult.
A neural circuit for circadian regulation of arousal

An unknown aspect of behavioral state regulation is how the circadian oscillator of the suprachiasmatic nucleus (SCN) regulates sleep and waking. In this report, we describe the necessary elements for a circuit that provides circadian regulation of arousal. Trans-synaptic retrograde tracing revealed a prominent indirect projection from the SCN to the noradrenergic nucleus locus coeruleus (LC), a brain arousal system. Double-labeling experiments revealed several possible links between the SCN and the LC, including the dorsomedial (DMH) and paraventricular hypothalamic nuclei (PVN), as well as medial and ventrolateral pre-optic areas. Lesion studies confirmed that the DMH is a substantial relay in this circuit. Next, neurophysiology experiments revealed circadian variations in LC impulse activity. Lesions of the DMH eliminated these circadian changes in LC activity, confirming the functionality of the SCN–DMH–LC circuit. These results reveal mechanisms for regulation of circadian and sleep–waking functions.
Impact of velocity and bundle holding time on bundle dropping event in Vehicular Delay tolerant network

Vehicular Delay tolerant network (DTN) is mainly characterized by intermittent connectivity, long and variable delay, asymmetric data rates and high error rates. Nodes are operated by using store-carry-and-forward mechanism to forward the packets. DTN nodes are mobile in nature and they have limited time to hold a packet to forward it to the other nodes. Therefore, after specified amount of time bundle dropping event happens. Velocity and bundle holding time are the two major parameters which cause bundle dropping. In this paper, we studied and analyze the impact of velocity and bundle holding time on the bundle dropping event. More the velocity of a DTN node and the bundle holding time, more the probability a DTN node can contact with other DTN nodes which increases the bundle delivery.
The Environmental Monitoring of “Emergent Properties” as Related to Specimen Banking

A stated objective of this workshop is to develop a plan for implementing a pilot network for environmental specimen banking and for monitoring as related to banking. The purpose of the network would be to provide a basis for dealing with environmental problems brought about by man’s activities. Because the human environment is a global environment, the network must be giobal too. This paper identifies certain properties of the living environment that may be most indicative of our environment’s well being and upon which we may be most dependent; properties we have not previously considered as candidates for specimen selection. The purpose of this paper is to offer a theoretical basis for considering these properties and two specific examples to illustrate their usefulness.
Occupational hazards, living conditions, and physical assault of sugar cane workers in KwaZulu-Natal, South Africa

OBJECTIVES: To characterise the occupational hazards and living conditions of sugar cane workers in KwaZulu-Natal. DESIGN: Based on information provided by shop stewards, a survey instrument (questionnaire) was constructed for administration to union members. SETTING: Seven sugar cane farms and estates owned by one large corporation in late 1993. SUBJECTS: Members of the South African Farm and Allied Workers Union (SAFAWU). RESULTS: Of the 632 participants, 50% were permanent workers, 22.3% were seasonal workers and 27.7% were casual workers. Mean daily pay ranged from R5 to R35 per worker. The majority of participants reported substandard housing both during the growing season and during the off-season. Percentages reporting health problems in the last 12 months believed by the respondent to be caused or made worse by work included 79% with eye problems, 78% with upper respiratory problems, 88% with lower respiratory problems, 93% with musculoskeletal problems, and 81% with an acute traumatic injury. More than half the participants reported fainting, collapsing or illness from working on hot or sunny days. Fourteen per cent reported being struck with the fist or hand, or being pushed, shoved or kicked by a farm owner, member of the owner's family, manager or supervisor; 9% reported being struck with an object, whipped, or attacked or threatened with a knife or gun by one of these same individuals. CONCLUSIONS: Sugar cane workers employed by a large corporation in KwaZulu-Natal appear to face severe threats to their physical and psychological well-being including: (i) inadequate pay to meet basic living needs; (ii) substandard living conditions; (iii) significant occupational hazards resulting in high reported levels of occupational illness and injury; and (iv) physical and psychological abuse and intimidation by farm owners and their agents. Language: en
Association of face-to-face handoffs and outcomes of hospitalized internal medicine patients.

BACKGROUND Failures in communication at the time of patient handoff have been implicated as contributing factors to preventable adverse events. OBJECTIVE Examine the relationship between face-to-face handoffs and the rate of patient outcomes, including adverse events. DESIGN Retrospective cohort. SETTING A 1157-bed academic tertiary referral hospital. PATIENTS There were 805 adult patients admitted to general internal medicine services. INTERVENTION Retrospective comparison of clinical outcomes, including the rate of adverse events, of patients whose care was transitioned with and without face-to-face handoffs. MEASUREMENTS Rapid response team calls, code team calls, transfers to a higher level of care, death in hospital, 30-day readmission rate, length of stay, and adverse events (as identified using the Global Trigger Tool). RESULTS There was no significant difference with respect to the frequency of rapid response team calls, code team calls, transfers to a higher level of care, deaths in hospital, length of stay, 30-day readmission rate, or adverse events between patients whose care was transitioned with or without a face-to-face handoff. CONCLUSIONS Face-to-face handoff of patients admitted to general medical services at a large academic tertiary referral hospital was not associated with a significant difference in measured patient outcomes, including the rate of adverse events, compared to a non–face-to-face handoff. Additional study is needed to determine the qualities of patient handoff that optimize efficiency and safety. Journal of Hospital Medicine 2015;10:137–141. © 2015 Society of Hospital Medicine
Electromagnetic interaction of the Bargmann-Wigner field with spin 1/2

It is shown that the Bargmann-Wigner multispinor field for particles of spin \textonehalf{} gives the usual result for the magnetic moment, provided it is ensured that the auxiliary field appearing in the Lagrangian formulation remains vanishing in the presence of electromagnetic interaction.
Sympathetic Inhibition of Papaverine Induced Erection

AbstractWe studied the effect of neurostimulation of the lumbar sympathetic trunks on papaverine-Induced penile erection in dogs and monkeys. The mean of 124cm. H20 intracavernous pressure after papaverine injection was reduced under sympathetic trunk stimulation to 40.6cm. H20 within a mean of 39sec. In addition the flow of the internal pudendal artery was reduced indicating a decrease of the Inflow to the penis. After the sympathetic stimulation was terminated, the Intracavernous pressure increased again to the same level as before. This pressure recovery time was delayed after several sympathetic stimulations from 134sec. (5min. after papaverine injection) to at least 362sec. (45minutes after papaverine injection). Stimulation of the Inferior hypogastric plexus had no effect on the intracavernous pressure.The neurostimulation range (3–4V; 20Hz) was most effective in reducing the papaverine action on penile smooth muscles.The effect of sympathetic trunk stimulation seems to antagonize the relaxing actio...
3D modeling and tracking of human lip motions

We address the problem of tracking and reconstructing 3D human lip motions from a 2D view. This problem is challenging due both to the complex nature of lip motions and the minimal data available from a raw video stream of the face. We counter both of these difficulties with statistical approaches. We first build a physically-based 3D model of lips and train it to cover only the subspace of lip motions. We then track this model in video by finding the shape within the subspace that maximizes the posterior probability of the model given the observed features. In this study, the features are the likelihoods of the lip and non-lip color classes: we iteratively derive forces from these values to apply to the physical model and converge to the final solution. Because of the full 3D nature of the model, this framework allows us to track the lips from any head pose. In addition, because of the constraints imposed by the learned subspace of the model, we are able to accurately estimate the full 3D lip shape from the 2D view.
Abnormal Situation Management in petrochemical plants: can a Pilot's Associate crack crude?

Abnormal Situations comprise a range of process disruptions in which petrochemical plant personnel must intervene to correct problems with which the control systems can not cope. Preventable losses from abnormal situations cost the U.S. economy at least $20B annually. The Abnormal Situation Management (ASM) Joint Research and Development Consortium (Honeywell, the seven largest U.S. petrochemical companies, and two software companies) was formed to develop the technologies needed to allow plant personnel to control and prevent abnormal situations. The Consortium is working on a NIST-funded, 3.5-year, $16.6 million program to demonstrate the technical feasibility of a collaborative decision support system (called AEGIS) for helping operations personnel deal with abnormal situations. Many of the issues faced in the development of AEGIS have also been faced in the research and development of associate systems for military aviation domains, especially the U.S. Air Force's Pilot's Associate (PA) and the U.S. Army's Rotorcraft Pilot's Associate (RPA). Honeywell intends to apply associate technologies as vigorously as possible to the ASM problem. The two domains have a number of features in common, which we hope will permit significant technology transfer in both directions. This paper describes the similarities of and differences between the technical and organizational domains in which Abnormal Situation Management and the PA and RPA systems must operate, and assesses the issues thus raised. Finally, we describe our approach to resolving these issues and assuring successful demonstration of the feasibility of associate technology in this new domain.
SIM configuration trades

The Space Interferometry Mission (SIM) will perform astrometry to a resolution of a few micro arc seconds. The development of this mission is being led by the California Institute of Technology, JPL for the National Aeronautics and Space Administration. A recent trade study was performed to compare two significantly different architectures. This paper will describe the two configurations and contrast some of their differences and similarities.
Nine Years Of Euv Bright Points

We discuss early results derived from an algorithm that automates the detection, cataloging, and analysis of extreme-ultraviolet (EUV) “bright points” (BP) from 9 years of data acquired by the Extreme-ultraviolet Imaging Telescope (EIT) on the Solar and Heliospheric Observatory (SOHO). The algorithm relies upon the computation of a map of “intensity significance”; this then contains the location of the EUV BPs. By mapping the location of BPs in each image and linking them through long sequences of EIT images we can describe the temporal and spatial variations of the 1.3× 108 EUV BPs observed by SOHO to date. We suggest that there is a considerable amount of physical information about the solar coronal plasma that can be readily gleamed from the BP detection database. In this paper we discuss only a small portion of the possible correlations, but we point to the possibility of BP lifetime distributions that are well described by modified power-laws; the components of which vary with the different temperature filters and with time over the present solar cycle.
Is quantum gravity deterministic and/or time symmetric?

S. W. Hawking suggests that quantum gravity introduces a new level of uncertainty into physics by turning pure states into mixed states. Although the evidence for this information loss is based upon a semiclassical approximation and hence is not conclusive, it is interesting to examine the implications. As originally formulated, Hawking's proposal violates CPT invariance by singling out one direction of time in which pure states turn into mixed states. An alternate hypothesis is suggested whereby the theory could be time symmetric and yet allow a loss of information. In this model the universe as a whole would be an open system, and even its density matrix would not have a deterministic evolution. The question remains of how much uncertainty there actually is in quantum gravity.