cssi/SciSciGPT-SciSciNet huggingface dataset:
How should depiction be represented and reasoned about

Interpreting a scene requires understanding how its visual properties and context yield evidence about the spatial and conceptual properties of what it depicts. Depiction is intimately tied to spatial language, since describing a scene linguistically, or imagining a scene described in language, involves connecting linguistic and spatial knowledge. We focus here on scenes described via sketching.
New advances in controlling poultry disease.

In today’s global climate of consumer preferences or legislation for reduced antibiotic use in animal production, genetic approaches to health provide a natural and sustainable method to help ensure the wholesomeness of the food supply. The tools of molecular genetics have recently enabled the search for molecular genetic markers for health (Lamont, 1994, 1998). Although there are a few notable examples of health traits controlled by single genes, the vast majority of health traits are controlled by multiple genes.
Sinusitis in children.

UPPER respiratory tract infections are the most common clinical problems for primary practitioners who care for children. It has recently been estimated that approximately 5 to 10 percent of upper respiratory infections in early childhood are complicated by acute sinusitis.1 Since children average six to eight colds per year, sinusitis is a very common problem in clinical practice. Anatomy and Physiology A brief review of the anatomy and physiology of the paranasal sinuses will help clarify certain clinical features of sinus infection. Figure 1 shows a coronal and two sagittal views demonstrating the relation between the nose and the paranasal sinuses. The . . .
Convolutional neural network architectures for predicting DNA–protein binding

Motivation: Convolutional neural networks (CNN) have outperformed conventional methods in modeling the sequence specificity of DNA–protein binding. Yet inappropriate CNN architectures can yield poorer performance than simpler models. Thus an in-depth understanding of how to match CNN architecture to a given task is needed to fully harness the power of CNNs for computational biology applications. Results: We present a systematic exploration of CNN architectures for predicting DNA sequence binding using a large compendium of transcription factor datasets. We identify the best-performing architectures by varying CNN width, depth and pooling designs. We find that adding convolutional kernels to a network is important for motif-based tasks. We show the benefits of CNNs in learning rich higher-order sequence features, such as secondary motifs and local sequence context, by comparing network performance on multiple modeling tasks ranging in difficulty. We also demonstrate how careful construction of sequence benchmark datasets, using approaches that control potentially confounding effects like positional or motif strength bias, is critical in making fair comparisons between competing methods. We explore how to establish the sufficiency of training data for these learning tasks, and we have created a flexible cloud-based framework that permits the rapid exploration of alternative neural network architectures for problems in computational biology. Availability and Implementation: All the models analyzed are available at http://cnn.csail.mit.edu. Contact: ude.tim@droffig Supplementary information: Supplementary data are available at Bioinformatics online.
Repair of a mirror coating on a large optic for high laser damage applications using ion milling and over-coating methods

When an optical coating is damaged, deposited incorrectly, or is otherwise unsuitable, the conventional method to restore the optic often entails repolishing the optic surface, which can incur a large cost and long lead time. We propose three alternative options to repolishing, including (i) burying the unsuitable coating under another optical coating, (ii) using ion milling to etch the unsuitable coating completely from the optic surface and then recoating the optic, and (iii) using ion milling to etch through a number of unsuitable layers, leaving the rest of the coating intact, and then recoating the layers that were etched. Repairs were made on test optics with dielectric mirror coatings according to the above three options. The mirror coatings to be repaired were quarter wave stacks of HfO2 and SiO2 layers for high reflection at 1054 nm at 45 deg incidence in P-polarization. One of the coating layers was purposely deposited incorrectly as Hf metal instead of HfO2 to evaluate the ability of each repair method to restore the coating’s high laser-induced damage threshold (LIDT) of 64.0 J/cm2. The repaired coating with the highest resistance to laser-induced damage was achieved using repair method (ii) with an LIDT of 49.0 to 61.0 J/cm2.
STRIBECK ANALYSIS OF ARTICULAR CARTILAGE LUBRICATION: ROLES OF DIFFERENT INJECTABLE HYALURONIC ACIDS AND ENDOGENOUS LUBRICIN

Summary Both endogenous lubricin and injectable hyaluronic acid reduced cartilage friction coefficients, but by distinct mechanisms. Lubricin operated in boundary mode and hyaluronic acid shifted lubrication to mixed or hydrodynamic mode. Introduction Intra-articular injections of viscous agents and boundary lubricants have been presented as options to mitigate the progression of articular cartilage damage after the onset of osteoarthritis 1,2 . Mechanically, these injections are predicted to lower the friction coefficient within a load bearing joint and consequently slow the propagation of damage at the articular surface. Tribologically, boundary lubricants and viscous agents are hypothesised to be effective through different mechanisms affecting boundary-mode lubrication and transition to mixed-mode lubrication, respectively. By normalizing sliding speeds on a Stribeck curve, this study evaluated the efficacy of injectable hyaluronic acid (HA) supplements and endogenous lubricin to alter tribological properties. Methods Cartilage samples were extracted from the patellofemoral groove of neonatal bovine. A custom-built tribometer was used to measure friction coefficients of cartilage sliding against polished glass while in a lubricant solution. Cartilage samples were compressed to 20% strain and the normal load was allowed to reach a steady-state value before sliding at speeds from 0.1 to 10mm/s. For some samples, endogenous lubricin was removed from the surface as described previously 3 via incubation in 1.5M NaCl in PBS for 20 minutes followed by re-equilibration in PBS for 1hr. Samples were tested in bathing solutions of PBS (control), equine synovial fluid (ESF), 10mg/ml HA, and a hydrophobic HA derivative (HYADD). Results Friction coefficients as a function of sliding speed for some lubricants are presented. Comparisons show that lubricin removal from the tissue surface increases friction coefficients when PBS is used as a lubricant (p −6 m, suggesting that this lubricant may have enabled hydrodynamic lubrication, a phenomenon not noted previously in this system. Lubricin removal increased friction coefficient from 0.16 to 0.25, occurring at slow sliding speeds and Hersey numbers of 10 −12 m Conclusions Endogenous lubricin and injectable hyaluronan both effectively lower friction coefficients, but do so by distinct mechanism. At the same operating conditions (normal loads and sliding speed), lubricin lowers boundary mode friction coefficient, while hyaluronan shifts behavior to mixed mode (HA) or hydrodynamic mode (HYADD). The combination of the presence of lubricin on cartilage surface in the most viscous formulation of HA (HYADD) lowered the friction coefficient of articular cartilage from 0.26 to 0.05.
Segmental Spatiotemporal CNNs for Fine-Grained Action Segmentation

Joint segmentation and classification of fine-grained actions is important for applications of human-robot interaction, video surveillance, and human skill evaluation. However, despite substantial recent progress in large-scale action classification, the performance of state-of-the-art fine-grained action recognition approaches remains low. We propose a model for action segmentation which combines low-level spatiotemporal features with a high-level segmental classifier. Our spatiotemporal CNN is comprised of a spatial component that represents relationships between objects and a temporal component that uses large 1D convolutional filters to capture how object relationships change across time. These features are used in tandem with a semi-Markov model that captures transitions from one action to another. We introduce an efficient constrained segmental inference algorithm for this model that is orders of magnitude faster than the current approach. We highlight the effectiveness of our Segmental Spatiotemporal CNN on cooking and surgical action datasets for which we observe substantially improved performance relative to recent baseline methods.
A Minimum Principle for Potentials with Application to Chebyshev Constants

For “Riesz-like” kernels K(x,y) = f(|x−y|) on A×A, where A is a compact d-regular set \(A\subset \mathbb {R}^{p}\), we prove a minimum principle for potentials \(U_{K}^{\mu }=\int K(x,y)\textup {d}\mu (x)\), where μ is a Borel measure supported on A. Setting \(P_{K}(\mu )=\inf _{y\in A}U^{\mu }(y)\), the K-polarization of μ, the principle is used to show that if {νN} is a sequence of measures on A that converges in the weak-star sense to the measure ν, then PK(νN)→PK(ν) as \(N\to \infty \). The continuous Chebyshev (polarization) problem concerns maximizing PK(μ) over all probability measures μ supported on A, while the N-point discrete Chebyshev problem maximizes PK(μ) only over normalized counting measures for N-point multisets on A. We prove for such kernels and sets A, that if {νN} is a sequence of N-point measures solving the discrete problem, then every weak-star limit measure of νN as \(N \to \infty \) is a solution to the continuous problem.
Large-Scale Collaborative Imaging Genetics Studies of Risk Genetic Factors for Alzheimer’s Disease Across Multiple Institutions

Genome-wide association studies (GWAS) offer new opportunities to identify genetic risk factors for Alzheimer’s disease (AD). Recently, collaborative efforts across different institutions emerged that enhance the power of many existing techniques on individual institution data. However, a major barrier to collaborative studies of GWAS is that many institutions need to preserve individual data privacy. To address this challenge, we propose a novel distributed framework, termed Local Query Model (LQM) to detect risk SNPs for AD across multiple research institutions. To accelerate the learning process, we propose a Distributed Enhanced Dual Polytope Projection (D-EDPP) screening rule to identify irrelevant features and remove them from the optimization. To the best of our knowledge, this is the first successful run of the computationally intensive model selection procedure to learn a consistent model across different institutions without compromising their privacy while ranking the SNPs that may collectively affect AD. Empirical studies are conducted on 809 subjects with 5.9 million SNP features which are distributed across three individual institutions. D-EDPP achieved a 66-fold speed-up by effectively identifying irrelevant features.
Public Company Audits and City‐Specific Labor Characteristics

Prior research emphasizes the centrality of audit offices in understanding auditing practices, and documents significant interoffice variation in audit outcomes based on industry expertise and office size. Our study examines how two city-specific labor characteristics also affect audit offices and local audit markets: the city's average educational attainment, and the number of accountants in a city, which proxy for a city's human capital. Our argument draws on the urban economics literature and predicts that the level of human capital in a city is positively associated with an audit office's ability to conduct high-quality audits. As expected, there is a positive association between audit quality (quality of audited earnings and accuracy of going-concern reports) and average education level in the city in which the lead engagement office is located. This association is generally significant for both Big 4 and non-Big 4 offices, but is relatively stronger for non-Big 4 firms that are more tied to local labor markets. A company is also more likely to choose a non-Big 4 auditor in cities with higher educational levels and relatively more accountants, and there is evidence of higher non-Big 4 audit fees as a city's education level increases. Collectively, these results suggest that local labor characteristics affect audit offices, audit quality, and the ability of non-Big 4 auditors to compete with Big 4 auditors in the audits of public companies. This article is protected by copyright. All rights reserved.
Science, AskScience, and BadScience: On the Coexistence of Highly Related Communities

When large social-media platforms allow users to easily form and self-organize into interest groups, highly related communities can arise. For example, the Reddit site hosts not just a group called food, but also HealthyFood, foodhacks, foodporn, and cooking, among others. Are these highly related communities created for similar classes of reasons (e.g., true to distinguish one as a better community and advice to focus on helping fellow members)? How do users allocate attention between such close alternatives when they are available or emerge over time? Are there different types of relations between close alternatives such as sharing many users vs. a new community drawing away members of an older one vs. a splinter group failing to cohere into a viable separate community? We investigate the interactions between highly related communities using data from reddit.com consisting of 975M posts and comments spanning an 8-year period. We identify a set of typical affixes that users adopt to create highly related communities and build a taxonomy of affixes. One interesting finding regarding users’ behavior is: after a newer community is created, for several types of highly-related community pairs, users that engage in a newer community tend to be more active in their original community than users that do not explore, even when controlling for previous level of engagement.
Hypophosphatemia in Users of Cannabis

As cannabis use has legalized for medical and recreational use in several states, the medical community has become more aware of the drug's potential toxicities. First described in 2004, cannabinoid hyperemesis syndrome is increasingly recognized as a cause of hospitalization among drug users. However, little information is available in the medical literature regarding electrolyte abnormalities in this syndrome. Between 2011 and 2014, six men were treated for cannabinoid hyperemesis syndrome at the Veterans Affairs Medical Center in San Diego, CA, and found to have significant hypophosphatemia (phosphate range,
Nanocrystalline Al-Mg with extreme strength due to grain boundary doping

Abstract Nanocrystalline Al-Mg alloys are used to isolate the effect of grain boundary doping on the strength of nanostructured metals. Mg is added during mechanical milling, followed by low homologous temperature annealing treatments to induce segregation without grain growth. Nanocrystalline Al -7 at% Mg that is annealed for 1 h at 200 °C is the strongest alloy fabricated, with a hardness of 4.56 GPa or approximately three times that of pure nanocrystalline Al. Micropillar compression experiments indicate a yield strength of 865 MPa and a specific strength of 329 kN m/kg, making this one of the strongest lightweight metals reported to date.
Differences in Empathic Concern and Perspective Taking Across 63 Countries

Cultural practices socialize people to relate to others in different ways. One critical way in which these interpersonal bonds are formed and maintained is via empathy, our emotional reactivity toward others’ experiences. However, the extent to which individuals from different cultures vary in their dispositional empathy, and the correlates of these differences, are relatively unknown. Thus, the current study explored cultural variation in empathy, and how this variation is related to psychological characteristics and prosocial behavior across cultures. Evidence from an original sample of 104,365 adults across 63 countries reveals that higher empathy countries also have higher levels of collectivism, agreeableness, conscientiousness, self-esteem, emotionality, subjective well-being, and prosocial behavior. These findings reveal that empathy is situated within a broader nomological network of other psychological characteristics, emotional expression and experiences, and prosocial behavior across cultures. ...
Managing (fiscally) resource price volatility : exploring policy options for the Democratic Republic of Congo

This chapter discusses the ‘spend today’ and ‘save now-spend tomorrow’ approach of revenue allocation in the Democratic Republic of Congo (DRC), a very natural resource-rich country. The approach uses a three-sector model in which public infrastructure investment has tangible benefits for private production and investment, while it is also subject to absorption constraints. The chapter calibrates the optimal allocation rule between spending today and asset accumulation by minimizing a social loss function defined in terms of household welfare (measured by consumption volatility) and macroeconomic volatility (measured in terms of fiscal volatility). Sensitivity analysis is also conducted with respect to various key parameters, including the efficiency of public investment. The results indicate that, if properly managed, sovereign fund could contribute significantly to macroeconomic stability in the DRC.
Spectacle and Subversive Laughter in Ngũgĩ wa Thiong’o’s Wizard of the Crow

"this paper examines the resistant spectator in Wizard of the Crow who refuses to be awed by state power and who, by laughing at the spectacular excess, inscribes a different meaning to the spectacles. The spectator essentially contests the state’s monopoly of meaning and its performance. It will be the argument in this paper that by answering back to the monologue of the spectacle through unscripted performances including silence where they are supposed to applaud, the average citizen subjects of the fictional state of Aburiria engage in a dialogic exchange with the repressive state, contesting what Simon Gikandi has described elsewhere as the postcolonial state’s “monopoly of meanings, performances and discourses” (Ngugi wa Thiong’o 38). Spectator laughter is a central component to this contestation."
The Reverse Cuthill-McKee Algorithm in Distributed-Memory

Ordering vertices of a graph is key to minimize fill-in and data structure size in sparse direct solvers, maximize locality in iterative solvers, and improve performance in graph algorithms. Except for naturally parallelizable ordering methods such as nested dissection, many important ordering methods have not been efficiently mapped to distributed-memory architectures. In this paper, we present the first-ever distributed-memory implementation of the reverse Cuthill-McKee (RCM) algorithm for reducing the profile of a sparse matrix. Our parallelization uses a two-dimensional sparse matrix decomposition. We achieve high performance by decomposing the problem into a small number of primitives and utilizing optimized implementations of these primitives. Our implementation attains up to 38x speedup on matrices from various applications on 1024 cores of a Cray XC30 supercomputer and shows strong scaling up to 4096 cores for larger matrices.
High-energy femtosecond fiber lasers based on dissipative solitons

Virtually all practical femtosecond-pulse generation has been based on soliton formation, which is the compensation of a self-focusing nonlinear phase by anomalous dispersion. Although it has many advantageous properties, soliton - like pulse-shaping also limits the stable pulse energy, and this limit is severe in fiber lasers. Recent research has shown that it is possible to generate ultrashort pulses by a completely different mechanism: in a cavity with only normal-dispersion components, a stable highly-chirped pulse can be produced by the balance of spectral broadening and spectral filtering. Such a pulse balances amplitude modulations (gain and loss) as well as the phase modulations, and is referred to as a dissipative soliton. The pulse can be dechirped to the Fourier transform limit outside t he cavity. This approach allows the generation of ultrashort pulses from fiber lasers with much higher energies than was possible previously. In particular, lasers based on ordinary single-mode fiber generate 100-fs and 30-nJ pulses, for average powers w ell above 1 W. These are the first fiber lasers to compete directly with the performance of solid-state lasers. Elimination of segments or components with anomalous dispersion produces simple and practical designs. Dissipative-soliton lasers can also be designed to generate high-energy pulses chirped to ∼1000 times the transform-limited duration, which should be valuable for chirped-pulse amplifiers. In addition to their potential for applications, normal-dispersion lasers provide a convenient setting for the study of dissipative solitons, which are of much current interest in the nonlinear -waves community. Theoretical and experimental results will be reviewed.
The Origin of Coupled Chloride and Proton Transport in a Cl–/H+ Antiporter

The ClC family of transmembrane proteins functions throughout nature to control the transport of Cl– ions across biological membranes. ClC-ec1 from Escherichia coli is an antiporter, coupling the transport of Cl– and H+ ions in opposite directions and driven by the concentration gradients of the ions. Despite keen interest in this protein, the molecular mechanism of the Cl–/H+ coupling has not been fully elucidated. Here, we have used multiscale simulation to help identify the essential mechanism of the Cl–/H+ coupling. We find that the highest barrier for proton transport (PT) from the intra- to extracellular solution is attributable to a chemical reaction, the deprotonation of glutamic acid 148 (E148). This barrier is significantly reduced by the binding of Cl– in the “central” site (Cl–cen), which displaces E148 and thereby facilitates its deprotonation. Conversely, in the absence of Cl–cen E148 favors the “down” conformation, which results in a much higher cumulative rotation and deprotonation barrier...
Trend in aggregate idiosyncratic volatility

Abstract We suggest that price interaction among stocks is an important determinant of idiosyncratic volatility. We demonstrate that as more (less) stocks are listed in the markets, price interaction among stocks increases (decreases), and hence stocks, on average, become more (less) volatile. Our results show that price interaction has a significant positive effect of idiosyncratic volatility. The results of various robustness checks indicate that the effect of price interaction is still significant to the presence of liquidity, newly listed firms, cash flow variables, business cycle variables, and market volatility. Once the price interaction effect is taken into account, no trend remains in idiosyncratic volatility. We conclude that there is no trend, but a reflection of the positive effect of price interaction on idiosyncratic volatility.
The Impact of Ozone-Depleting Substances on Tropical Upwelling, as Revealed by the Absence of Lower-Stratospheric Cooling since the Late 1990s

AbstractThe impact of ozone-depleting substances on global lower-stratospheric temperature trends is widely recognized. In the tropics, however, understanding lower-stratospheric temperature trends has proven more challenging. While the tropical lower-stratospheric cooling observed from 1979 to 1997 has been linked to tropical ozone decreases, those ozone trends cannot be of chemical origin, as active chlorine is not abundant in the tropical lower stratosphere. The 1979–97 tropical ozone trends are believed to originate from enhanced upwelling, which, it is often stated, would be driven by increasing concentrations of well-mixed greenhouse gases. This study, using simple arguments based on observational evidence after 1997, combined with model integrations with incrementally added single forcings, argues that trends in ozone-depleting substances, not well-mixed greenhouse gases, have been the primary driver of temperature and ozone trends in the tropical lower stratosphere until 1997, and this has occurre...
A DC-to-Three-Phase Boost–Buck Inverter With Stored Energy Modulation and a Tiny DC-Link Capacitor

Three-phase voltage-source inverters typically feature a significant amount of dc energy storage to maintain a stiff dc bus. In these cases, the dc-link capacitor is sized to store enough energy to maintain several tens of cycles of ac output at the rated power. In this paper, we propose to reduce the size of the dc-link capacitor dramatically to store enough energy to provide just one high-frequency switching cycle of the ac output power. The dc bus is no longer stiff, and hence, classical sinusoidal pulse-width modulation cannot be used. However, the stored energy modulation (SEM) proposed herein synthesizes high-quality sinusoidal output voltage waveforms, even with such tiny dc-link capacitors. In SEM, the switching intervals of the interconnecting switches are carefully determined nonlinear functions of various operating parameters such as reactive component sizing, switching frequency, load levels, etc. This paper presents the analytical and detailed design development of the SEM approach along with circuit simulation, experimental results, and comparative evaluation of a dc-to-three-phase system example.
Provable learning of noisy-OR networks

Many machine learning applications use latent variable models to explain structure in data, whereby visible variables (= coordinates of the given datapoint) are explained as a probabilistic function of some hidden variables. Learning the model ---that is, the mapping from hidden variables to visible ones and vice versa---is NP-hard even in very simple settings. In recent years, provably efficient algorithms were nevertheless developed for models with linear structure: topic models, mixture models, hidden markov models, etc. These algorithms use matrix or tensor decomposition, and make some reasonable assumptions about the parameters of the underlying model. But matrix or tensor decomposition seems of little use when the latent variable model has nonlinearities. The current paper shows how to make progress: tensor decomposition is applied for learning the single-layer noisy-OR network, which is a textbook example of a bayes net, and used for example in the classic QMR-DT software for diagnosing which disease(s) a patient may have by observing the symptoms he/she exhibits. The technical novelty here, which should be useful in other settings in future, is analysis of tensor decomposition in presence of systematic error (i.e., where the noise/error is correlated with the signal, and doesn't decrease as number of samples goes to infinity). This requires rethinking all steps of tensor decomposition methods from the ground up. For simplicity our analysis is stated assuming that the network parameters were chosen from a probability distribution but the method seems more generally applicable.
In the Shadows of Sunlight: The Effects of Transparency on State Political Campaigns

Abstract In recent years, the courts have invalidated a variety of campaign finance laws while simultaneously upholding disclosure requirements. Courts view disclosure as a less-restrictive means to root out corruption while critics claim that disclosure chills speech and deters political participation. Using individual-level contribution data from state elections between 2000 and 2008, we find that the speech-chilling effects of disclosure are negligible. On average, less than one donor per candidate is likely to stop contributing when the public visibility of campaign contributions increases. Moreover, we do not observe heterogeneous effects for small donors or ideological outliers despite an assumption in First Amendment jurisprudence that these donors are disproportionately affected by campaign finance regulations. In short, the argument that disclosure chills speech is not strongly supported by the data.
Grouped Data Estimation and Testing in Simple Labor Supply Models

Abstract Inappropriate identifying assumptions and measurement error in hourly wage data may account for the poor performance of some empirical labor-supply models. This paper discusses an efficient generalization of Wald's method of fitting straigth lines that is robust to measurement error, imposes mild identifying assumptions, and is useful for the estimation of labor-supply models with panel data. A Two-Stage Least-Squares (TSLS) equivalent of the Efficient Wald estimator is presented and a TSLS overidentification test statistic is shown to be a test statistic for the equality of alternative Wald estimates of the same parameter. In an emperical example, estimated labor-supply elasticities range from 0.6 to 0.8. A test for measurement error based on the difference between Efficient Wald and Analysis-of-Covariance estimators is also proposed. Application of the test indicates that measurement error may be responsible for low or negative estimates of labor-supply elasticities.
Millimeter-Wave Phase-Locked Loops for Terahertz transceiver using sub-harmonic injection locking

We propose a 100 GHz sub-harmonic injection locked oscillator (ILO) based Phase-Locked Loop (PLL) in CMOS for use in low power Millimeter-Wave (mm-Wave) and sub-Terahertz (THz) phased-array systems. PLL parameters for an imaging system are derived. Mixed-mode simulation to enhance simulation speed has been done with custom Verilog-A models for the PFD/CP/divider and circuit schematic of the ILO. PLL with a 2nd sub-harmonic ILO at 101 GHz driving a 50 Ω load is shown. Simulated using 1.1 V supply, the PLL phase noise is -76.5 dBc/Hz @ 1 MHz offset, frequency tuning range of ILO is 7 GHz, output power is -9.1 dBm at the load, and power consumption is 14.4 mW. The circuits are implemented in standard digital 65 nm CMOS, enabling high level of on-chip integration.
GNSS Integrity in The Arctic

Growing activity in the Arctic calls for high integrity navigation in this region. This can be achieved using Global Navigation Satellite Systems (GNSS) in conjunction with Satellite Based Augmentation Systems (SBAS) or Advanced Receiver Autonomous Integrity Monitoring (ARAIM). Single frequency GPS-only SBAS is in service in some regions today and is reliant on ground and space infrastructure. ARAIM will be more autonomous and will rely on the multitude of signals and core constellations coming in the future. Here, we examine both SBAS and ARAIM in the context of aviation and maritime requirements in the Arctic. Results demonstrate that the introduction of multi-frequency and multi-constellation to these systems enables navigation safety in the Arctic. SBAS brings aircraft precision approach as well as precise maritime operations such as mapping. ARAIM also supports precision approach in addition to autonomous ice navigation at sea but falls short of precision maritime requirements.
Reverse engineering of irreducible polynomials in GF(2 m ) arithmetic

Current techniques for formally verifying circuits implemented in Galois field (GF) arithmetic are limited to those with a known irreducible polynomial P(x). This paper presents a computer algebra based technique that extracts the irreducible polynomial P(x) used in the implementation of a multiplier in GF(2m). The method is based on first extracting a unique polynomial in Galois field of each output bit independently. P(x) is then obtained by analyzing the algebraic expression in GF(2m) of each output bit. We demonstrate that this method is able to reverse engineer the irreducible polynomial of an n-bit GF multiplier in n threads. Experiments were performed on Mastrovito and Montgomery multipliers with different P(x), including NIST-recommended polynomials and optimal polynomials for different microprocessor architectures.
Specific cutting energy variations under different rice stem cultivars and blade parameters

espanolLa energia especifica de corte necesaria para segar el tallo de arroz es un requisito fundamental al momento de disenar u optimizar las propiedades mecanicas de una maquina cosechadora de granos. Las propiedades fisicas y mecanicas del tallo asi como la influencia de los parametros geometricos y cinematicas del par cortante (cuchilla/sufridera) son componentes efectivos para determinar esta energia. Especificar estos parametros y su impacto en la energia de corte es fundamental para evaluar la eficiencia de cada mecanismo de corte y la energia total utilizada. En este estudio, se utilizo una prueba de pendulo con desplazamiento de los componentes de corte para calcular la energia especifica de corte de un tallo de arroz. El diseno experimental fue factorial completamente al azar con tres repeticiones con el objetivo de evaluar los efectos de cultivares de arroz (en cuatro niveles: Hashemi, Ali Kazemi, Fajr, y Khazar), angulo de corte (en tres niveles: 25, 30 y 35 grados), el angulo de bisel de la hoja (en cuatro niveles: 25, 30, 35, y 40 grados), y velocidad de la hoja (en tres niveles: 1,5; 2,0 y 2,5 m s-1) en la energia especifica de corte para tallo del arroz. Los resultados revelaron que cultivar de arroz y la velocidad de la cuchilla tuvieron efectos significativos (P EnglishCutting energy requirement for rice stem is a momentous touchstone in design or optimization of cutting mechanism on harvesting machines. Various parameters such as physical and mechanical properties of a plant stem and blade shearing components are effective on the cutting energy requirement. Specifying these parameters and their impacts on the cutting energy would be especially important in the assessment of each cutting mechanism efficiency and total energy utilization. In this study, using a test-rig pendulum displacement cutting apparatus, specific cutting energy for single stem cutting of rice stem was identified. The experiments were analyzed in a factorial arrangement laid out in a completely randomized design (CRD) with three replications in order to examine the effects of rice cultivars (at four levels: Hashemi, Ali Kazemi, Fajr, and Khazar), cutting angle (at three levels: 25, 30, and 35 degrees), blade bevel angle (at four levels: 25, 30, 35, and 40 degrees), and blade speed (at three levels: 1.5, 2.0, and 2.5 m s-1) on the specific cutting energy for rice stem. The results revealed that rice cultivar and blade velocity had significant effects (P
Products of Compartmental Models in Epidemiology

We show that many structured epidemic models may be described using a straightforward product structure in this paper. Such products, derived from products of directed graphs, may represent useful refinements including geographic and demographic structure, age structure, gender, risk groups, or immunity status. Extension to multistrain dynamics, that is, pathogen heterogeneity, is also shown to be feasible in this framework. Systematic use of such products may aid in model development and exploration, can yield insight, and could form the basis of a systematic approach to numerical structural sensitivity analysis.
Trends in Adsorption Characteristics of Organic Molecules on Transition Metal Surfaces: Role of Surface Chemistry and van der Waals Interactions

The accurate description of interface characteristics between organic molecules and metal surfaces has long been debated in theoretical studies. A well-founded description of interface geometry and adsorption energy is highly desirable for these hybrid inorganic/organic interfaces. Using first principles calculations with the inclusion of five van der Waals functionals (vdW-DF family), benzene (C6H6) adsorption on seven transition metal surfaces is studied to explore the performance of these vdW functionals under varying surface chemistry. Our results reveal that vdW interactions are crucial for an accurate description of bonding on transition metal substrates. We find that vdW interactions increase adsorption energy on coinage metal surfaces (Au, Ag, Cu) by about 0.7 eV, while they lead to even larger increases in the adsorption energies on the reactive transition metal surfaces (Pd, Pt, Rh, Ni). Our calculations also reveal that changes in adsorption energies stemming from vdW functionals show significa...
Attacking Beacon-Enabled 802.15.4 Networks

The IEEE 802.15.4 standard has attracted time-critical applications in wireless sensor networks (WSNs) because of its beacon-enabled mode and guaranteed time slots (GTSs). However, the GTS management scheme’s built-in security mechanisms still leave the 802.15.4 MAC vulnerable to attacks. Further, the existing techniques in the literature for securing 802.15.4 either focus on non beacon-enabled mode 802.15.4 or cannot defend against insider attacks for beacon-enabled mode 802.15.4. In this paper we illustrate this by demonstrating attacks on the availability and integrity of a beacon-enabled 802.15.4 sensor network. To confirm the validity of the attacks, we implement the attacks using Tmote Sky Motes for sensor nodes, where the malicious node is deployed as an inside attacker. We show that the malicious node can easily exploit information retrieved from the beacon frames to compromise the integrity and availability of the network. We also discuss possible defense mechanisms against these attacks.
Alternative Ways of Involving Stakeholders: The Rise of Entrepreneurism in Higher Education and the Case of a Learning Enterprise

The education field has not traditionally stressed entrepreneurship; however, in light of current difficult economic scenarios and shrinking global job markets, a myriad of Educational Technology entrepreneurs is emerging across the world. Edupreneurs are here. They are self-motivated members of an enterprise who can recognize opportunities and take action on complex educational problems while aiming to create social value, financial value, and/or social benefits. Stakeholders hold a critical role when relating with edupreneurs. Stakeholders are the various individuals and groups who are directly invested in and may be affected (positively or negatively) by the entrepreneurial activities. The purpose of this chapter is to discuss the rise of entrepreneurism in higher education and explore the case of a learning enterprise that supports the preparation of educational technologists as up-and-coming entrepreneurs. The development of such a learning enterprise is connected to the concept of a civic-minded professional who is someone interested in using his or her knowledge and skills for the public good. The learning enterprise enhances its members’ civic-minded agency, meaning the group’s purposeful and reflective acts that aim to address community members’ educational needs. Stakeholders are deeply involved in addressing needs and issues in collaboration with educational technologists.
Planarian Epidermal Stem Cells Respond to Positional Cues to Promote Cell-Type Diversity

Successful regeneration requires that progenitors of different lineages form the appropriate missing cell types. However, simply generating lineages is not enough. Cells produced by a particular lineage often have distinct functions depending on their position within the organism. How this occurs in regeneration is largely unexplored. In planarian regeneration, new cells arise from a proliferative cell population (neoblasts). We used the planarian epidermal lineage to study how the location of adult progenitor cells results in their acquisition of distinct functional identities. Single-cell RNA sequencing of epidermal progenitors revealed the emergence of distinct spatial identities as early in the lineage as the epidermal neoblasts, with further pre-patterning occurring in their post-mitotic migratory progeny. Establishment of dorsal-ventral epidermal identities and functions, in response to BMP signaling, required neoblasts. Our work identified positional signals that activate regionalized transcriptional programs in the stem cell population and subsequently promote cell-type diversity in the epidermis.
Robust Polynomial Reconstruction via Chinese Remainder Theorem in the Presence of Small Degree Residue Errors

Based on unique decoding of the polynomial residue code with non-pairwise coprime moduli, a polynomial with degree less than that of the least common multiple of all the moduli can be accurately reconstructed when the number of residue errors is less than half the minimum distance of the code. However, once the number of residue errors is beyond half the minimum distance of the code, the unique decoding may fail and lead to a large reconstruction error. In this brief, assuming that all the residues are allowed to have errors with small degrees, we consider how to reconstruct the polynomial as accurately as possible in the sense that a reconstructed polynomial is obtained with only the last ${\tau }$ number of coefficients being possibly erroneous, when the residues are affected by errors with degrees upper bounded by ${\tau }$ . In this regard, we first propose a multi-level robust Chinese remainder theorem for polynomials, namely, a tradeoff between the dynamic range of the degree of the polynomial to be reconstructed and the residue error bound ${\tau }$ is formulated. Furthermore, a simple closed-form reconstruction algorithm is also proposed.
RobustFill: neural program learning under noisy I/O

The problem of automatically generating a computer program from some specification has been studied since the early days of AI. Recently, two competing approaches for automatic program learning have received significant attention: (1) neural program synthesis, where a neural network is conditioned on input/output (I/O) examples and learns to generate a program, and (2) neural program induction, where a neural network generates new outputs directly using a latent program representation. Here, for the first time, we directly compare both approaches on a large-scale, real-world learning task and we additionally contrast to rule-based program synthesis, which uses hand-crafted semantics to guide the program generation. Our neural models use a modified attention RNN to allow encoding of variable-sized sets of I/O pairs, which achieve 92% accuracy on a real-world test set, compared to the 34% accuracy of the previous best neural synthesis approach. The synthesis model also outperforms a comparable induction model on this task, but we more importantly demonstrate that the strength of each approach is highly dependent on the evaluation metric and end-user application. Finally, we show that we can train our neural models to remain very robust to the type of noise expected in real-world data (e.g., typos), while a highly-engineered rule-based system fails entirely.
An Analysis of Big 4 Pricing and Firm Size: The Differential Impact of Demand and Supply Shocks on Large and Small Clients:

We investigate Big 4 pricing over the period of 2000 to 2010. We classify the data into five periods: 2000-2001 as the pre-Sarbanes–Oxley Act (SOX) period, 2002-2003 as the SOX period, 2004-2006 as...
Discovering scholarly orphans using ORCID

Archival efforts such as (C)LOCKSS and Portico are in place to ensure the longevity of traditional scholarly resources like journal articles. At the same time, researchers are depositing a broad variety of other scholarly artifacts into emerging online portals that are designed to support web based scholarship. These web-native scholarly objects are largely neglected by current archival practices and hence they become scholarly orphans. We therefore argue for a novel paradigm that is tailored towards archiving these scholarly orphans. We are investigating the feasibility of using Open Researcher and Contributor ID (ORCID) as a supporting infrastructure for the process of discovery of web identities and scholarly orphans for active researchers. We analyze ORCID in terms of coverage of researchers, subjects, and location and assess the richness of its profiles in terms of web identities and scholarly artifacts. We find that ORCID currently lacks in all considered aspects and hence can only be considered in conjunction with other discovery sources. However, ORCID is growing fast so there is potential that it could achieve a satisfactory level of coverage and richness in the near future.
Team Care: The Preferred Approach to Diabetes Treatment

Diabetes is a lifestyle disease that requires the person living with the disease to make many daily decisions about diet, activity level, and medications, as well as adequate support to manage the disease successfully. Research has shown that a team-based approach to diabetes care is an effective way to help people with diabetes manage the disease, prevent and treat complications, provide behavior-change strategies, and cope with the emotional challenges this chronic disease brings [1]. Although the team may look different for each patient, it usually represents a variety of disciplines and may include a physician, registered dietitian, diabetes educator, nurse, pharmacist, psychologist, podiatrist, dentist, other professionals and resources within the community.
Carlson's theorem for different measures

Abstract We use an observation of Bohr connecting Dirichlet series in the right half plane C + to power series on the polydisk to interpret Carlson's theorem about integrals in the mean as a special case of the ergodic theorem by considering any vertical line in the half plane as an ergodic flow on the polytorus. Of particular interest is the imaginary axis because Carlson's theorem for Lebesgue measure does not hold there. In this note, we construct measures for which Carlson's theorem does hold on the imaginary axis for functions in the Dirichlet series analog of the disk algebra A ( C + ) .
Collision Avoidance Based on Line-of-Sight Angle

This paper focuses on the problem of collision avoidance for Unmanned Aerial Vehicles (UAVs). The dynamics of the UAV are modeled as a Dubins vehicle flying at constant altitude. The angular velocity is used as control input in order to avert a possible collision with a single obstacle, while the speed is left as an extra degree of freedom to achieve some temporal requirements. The proposed control algorithm uses only the line-of-sight angle as feedback: in this sense, the main contribution of this paper is providing a solution to the collision avoidance problem that can be used in situations where it is not possible to measure data such as position and velocity of the obstacle. A theoretical analysis of the result is provided, followed by simulation results that validate the efficacy of the control strategy.
A Single-Shot Approach to Lossy Source Coding Under Logarithmic Loss

This paper considers the problem of lossy source coding with a specific distortion measure: logarithmic loss. The focus of this paper is on the single-shot approach, which exposes crisply the connection between lossless source coding with list decoding and lossy source coding with log-loss. Fixed-length and variable-length bounds are presented. Fixed-length bounds include the single-shot fundamental limit for average as well as excess distortion. Variable-length bounds include the single-shot fundamental limit for average as well as excess length. Two multi-terminal problems are addressed: coding with side information (Wyner–Ziv) and multiple descriptions coding. In both the cases, the application of the Shannon–McMillan theorem to the single-shot bounds yields the rate-distortion function and the rate distortion-region for stationary ergodic sources.