Naïve Learning in Social Networks: Convergence, Influence and Wisdom of Crowds

We study learning and influence in a setting where agents communicate according to an arbitrary social network and naïvely update their beliefs by repeatedly taking weighted averages of their neighbors’ opinions. A focus is on conditions under which beliefs of all agents in large societies converge to the truth, despite their naïve updating. We show that this happens if and only if the influence of the most influential agent in the society is vanishing as the society grows. Using simple examples, we identify two main obstructions which can prevent this. By ruling out these obstructions, we provide general structural conditions on the social network that are sufficient for convergence to truth. In addition, we show how social influence changes when some agents redistribute their trust, and we provide a complete characterization of the social networks for which there is a convergence of beliefs. Finally, we survey some recent structural results on the speed of convergence and relate these to issues of segregation, polarization and propaganda.Social Networks, Learning, Diffusion, Bounded Rationality

Model-independent aspects of the reaction Kˉ+N→K+Ξ\bar{K} + N \to K + \Xi

Various model-independent aspects of the $\bar{K} N \to K \Xi$ reaction are investigated, starting from the determination of the most general structure of the reaction amplitude for $\Xi$ baryons with $J^P=\frac12^\pm$ and $\frac32^\pm$ and the observables that allow a complete determination of these amplitudes. Polarization observables are constructed in terms of spin-density matrix elements. Reflection symmetry about the reaction plane is exploited, in particular, to determine the parity of the produced $\Xi$ in a model-independent way. In addition, extending the work of Biagi $\mathrm{\textit{et al. } [Z. Phys.\ C \textbf{34}, 175 (1987)]}$, a way is presented of determining simultaneously the spin and parity of the ground state of $\Xi$ baryon as well as those of the excited $\Xi$ states

How Homophily Affects Learning and Diffusion in Networks

We examine how three different communication processes operating through social networks are affected by homophily - the tendency of individuals to associate with others similar to themselves. Homophily has no effect if messages are broadcast or sent via shortest paths; only connection density matters. In contrast, homophily substantially slows learning based on repeated averaging of neighbors' information and Markovian diffusion processes such as the Google random surfer model. Indeed, the latter processes are strongly affected by homophily but completely independent of connection density, provided this density exceeds a low threshold. We obtain these results by establishing new results on the spectra of large random graphs and relating the spectra to homophily. We conclude by checking the theoretical predictions using observed high school friendship networks from the Adolescent Health dataset.Networks, Learning, Diffusion, Homophily, Friendships, Social Networks, Random Graphs, Mixing Time, Convergence, Speed of Learning, Speed of Convergence

Isolation, characterisation and experimental evolution of phage that infect the horse chestnut tree pathogen, Pseudomonas syringae pv. aesculi

Bleeding canker of horse chestnut trees is a bacterial disease, caused by the bacterium Pseudomonas syringae pv. aesculi, estimated to be present in ~ 50% of UK horse chestnut trees. Currently, the disease has no cure and tree removal can be a common method of reducing inoculum and preventing spread. One potential method of control could be achieved using naturally occurring bacteriophages infective to the causative bacterium. Bacteriophages were isolated from symptomatic and asymptomatic horse chestnut trees in three locations in the South East of England. The phages were found to be belonging to both the Myoviridae and Podoviridae families by RAPD PCR and transmission electron microscopy. Experimental coevolution was carried out to understand the dynamics of bacterial resistance and phage infection and to determine whether new infective phage genotypes would emerge. The phages exhibited different coevolution patterns with their bacterial hosts across time. This approach could be used to generate novel phages for use in biocontrol cocktails in an effort to reduce the potential emergence of bacterial resistance

Genesis : the search for origins : the curation and contamination control of returned solar wind samples

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Materials Science and Engineering, 2005.Includes bibliographical references (leaves 61-62).The purpose of the studies carried out in this thesis was to aid in the curation of samples of solar wind returned to earth on the Genesis spacecraft. An experimental study was carried out to aid development of a set of protocols for the laser scribing and subdivision of the Genesis silicon collector array materials. Optimisation of the scribing speed and the positioning of the focal point of the laser were carried out. It was found that scribe width was independent of both factors. Slower scribing speeds were found to produce deeper scribes, while heating effects were minimised with faster speeds. Vertical movement of the stage of 5 pm/pass was found to optimise the focal point of the laser, and minimise heating effects. A procedure to measure the flexural modulus of samples was proposed to quantify the success of the optimisation of the scribing parameters. A theoretical study was carried out to develop a predictive kinetic model for the oxidation of the silicon collector arrays during flight. The mechanism proposed for the increase in oxide thickness over that present pre-flight was the formation of a less-dense suboxide at the SiO₂/Si interface. The driving force is the elevated temperature of the collectors during collection, in the vacuum of space.(cont.) A kinetic model was developed and growth rate expressions derived for two limiting kinetic cases. It was not possible to apply these expressions to the Genesis sample conditions, as the measurement of several experimental parameters was beyond the time limits of this study. A second model was developed alongside literature models to reduce further the number of unknown variables. Finally, the maximum possible thickness of oxide that could grow on the silicon surface was calculated. This was found to be 25 A, representing a 47 % increase over the original 17A of SiO₂ present pre-flight. It was noted that there was a non-linear increase of total oxide thickness with increase in suboxide thickness to due to density differences between Si and SiO, and SiO₂ and SiO.by Benjamin K. Jackson.S.M

The multifunctional NS1 protein of influenza A viruses

The non-structural (NS1) protein of influenza A viruses is a non-essential virulence factor that has multiple accessory functions during viral infection. In recent years, the major role ascribed to NS1 has been its inhibition of host immune responses, especially the limitation of both interferon (IFN) production and the antiviral effects of IFN-induced proteins, such as dsRNA-dependent protein kinase R (PKR) and 2'5'-oligoadenylate synthetase (OAS)/RNase L. However, it is clear that NS1 also acts directly to modulate other important aspects of the virus replication cycle, including viral RNA replication, viral protein synthesis, and general host-cell physiology. Here, we review the current literature on this remarkably multifunctional viral protein. In the first part of this article, we summarize the basic biochemistry of NS1, in particular its synthesis, structure, and intracellular localization. We then discuss the various roles NS1 has in regulating viral replication mechanisms, host innate/adaptive immune responses, and cellular signalling pathways. We focus on the NS1-RNA and NS1-protein interactions that are fundamental to these processes, and highlight apparent strain-specific ways in which different NS1 proteins may act. In this regard, the contributions of certain NS1 functions to the pathogenicity of human and animal influenza A viruses are also discussed. Finally, we outline practical applications that future studies on NS1 may lead to, including the rational design and manufacture of influenza vaccines, the development of novel antiviral drugs, and the use of oncolytic influenza A viruses as potential anti-cancer agents.Publisher PDFPeer reviewe

Muscle Synergies Facilitate Computational Prediction of Subject-Specific Walking Motions.

Researchers have explored a variety of neurorehabilitation approaches to restore normal walking function following a stroke. However, there is currently no objective means for prescribing and implementing treatments that are likely to maximize recovery of walking function for any particular patient. As a first step toward optimizing neurorehabilitation effectiveness, this study develops and evaluates a patient-specific synergy-controlled neuromusculoskeletal simulation framework that can predict walking motions for an individual post-stroke. The main question we addressed was whether driving a subject-specific neuromusculoskeletal model with muscle synergy controls (5 per leg) facilitates generation of accurate walking predictions compared to a model driven by muscle activation controls (35 per leg) or joint torque controls (5 per leg). To explore this question, we developed a subject-specific neuromusculoskeletal model of a single high-functioning hemiparetic subject using instrumented treadmill walking data collected at the subject's self-selected speed of 0.5 m/s. The model included subject-specific representations of lower-body kinematic structure, foot-ground contact behavior, electromyography-driven muscle force generation, and neural control limitations and remaining capabilities. Using direct collocation optimal control and the subject-specific model, we evaluated the ability of the three control approaches to predict the subject's walking kinematics and kinetics at two speeds (0.5 and 0.8 m/s) for which experimental data were available from the subject. We also evaluated whether synergy controls could predict a physically realistic gait period at one speed (1.1 m/s) for which no experimental data were available. All three control approaches predicted the subject's walking kinematics and kinetics (including ground reaction forces) well for the model calibration speed of 0.5 m/s. However, only activation and synergy controls could predict the subject's walking kinematics and kinetics well for the faster non-calibration speed of 0.8 m/s, with synergy controls predicting the new gait period the most accurately. When used to predict how the subject would walk at 1.1 m/s, synergy controls predicted a gait period close to that estimated from the linear relationship between gait speed and stride length. These findings suggest that our neuromusculoskeletal simulation framework may be able to bridge the gap between patient-specific muscle synergy information and resulting functional capabilities and limitations

Inferring the probability of the derived <i>vs</i>. the ancestral allelic state at a polymorphic site

Abstract It is known that the allele ancestral to the variation at a polymorphic site cannot be assigned with certainty, and that the most frequently used method to assign the ancestral state—maximum parsimony—is prone to misinference. Estimates of counts of sites that have a certain number of copies of the derived allele in a sample (the unfolded site frequency spectrum, uSFS) made by parsimony are therefore also biased. We previously developed a maximum likelihood method to estimate the uSFS for a focal species using information from two outgroups while assuming simple models of nucleotide substitution. Here, we extend this approach to allow multiple outgroups (implemented for three outgroups), potentially any phylogenetic tree topology, and more complex models of nucleotide substitution. We find, however, that two outgroups and the Kimura two-parameter model are adequate for uSFS inference in most cases. We show that using parsimony to infer the ancestral state at a specific site seriously breaks down in two situations. The first is where the outgroups provide no information about the ancestral state of variation in the focal species. In this case, nucleotide variation will be underestimated if such sites are excluded. The second is where the minor allele in the focal species agrees with the allelic state of the outgroups. In this situation, parsimony tends to overestimate the probability of the major allele being derived, because it fails to account for the fact that sites with a high frequency of the derived allele tend to be rare. We present a method that corrects this deficiency and is capable of providing nearly unbiased estimates of ancestral state probabilities on a site-by-site basis and the uSFS.</jats:p

Detection Thresholds of Falling Snow from Satellite-Borne Active and Passive Sensors

Precipitation, including rain and snow, is a critical part of the Earth's energy and hydrology cycles. Precipitation impacts latent heating profiles locally while global circulation patterns distribute precipitation and energy from the equator to the poles. For the hydrological cycle, falling snow is a primary contributor in northern latitudes during the winter seasons. Falling snow is the source of snow pack accumulations that provide fresh water resources for many communities in the world. Furthermore, falling snow impacts society by causing transportation disruptions during severe snow events. In order to collect information on the complete global precipitation cycle, both liquid and frozen precipitation must be collected. The challenges of estimating falling snow from space still exist though progress is being made. These challenges include weak falling snow signatures with respect to background (surface, water vapor) signatures for passive sensors over land surfaces, unknowns about the spherical and non-spherical shapes of the snowflakes, their particle size distributions (PSDs) and how the assumptions about the unknowns impact observed brightness temperatures or radar reflectivities, differences in near surface snowfall and total column snow amounts, and limited ground truth to validate against. While these challenges remain, knowledge of their impact on expected retrieval results is an important key for understanding falling snow retrieval estimations. Since falling snow from space is the next precipitation measurement challenge from space, information must be determined in order to guide retrieval algorithm development for these current and future missions. This information includes thresholds of detection for various sensor channel configurations, snow event system characteristics, snowflake particle assumptions, and surface types. For example, can a lake effect snow system with low (approx 2.5 km) cloud tops having an ice water content (IWC) at the surface of 0.25 g / cubic m and dendrite snowflakes be detected? If this information is known, we can focus retrieval efforts on detectable storms and concentrate advances on achievable results. Here, the focus is to determine thresholds of detection for falling snow for various snow conditions over land and lake surfaces. The results rely on simulated Weather Research Forecasting (WRF) simulations of falling snow cases since simulations provide all the information to determine the measurements from space and the ground truth. Sensitivity analyses were performed to better ascertain the relationships between multifrequency microwave and millimeter-wave sensor observations and the falling snow/underlying field of view. In addition, thresholds of detection for various sensor channel configurations, snow event system characteristics, snowflake particle assumptions, and surface types were studied. Results will be presented for active radar at Ku, Ka, and W-band and for passive radiometer channels from 10 to 183 GHz

Surface and Atmospheric Contributions to Passive Microwave Brightness Temperatures

Physically-based passive microwave precipitation retrieval algorithms require a set of relationships between satellite observed brightness temperatures (TB) and the physical state of the underlying atmosphere and surface. These relationships are typically non-linear, such that inversions are ill-posed especially over variable land surfaces. In order to better understand these relationships, this work presents a theoretical analysis using brightness temperature weighting functions to quantify the percentage of the TB resulting from absorption/emission/reflection from the surface, absorption/emission/scattering by liquid and frozen hydrometeors in the cloud, the emission from atmospheric water vapor, and other contributors. The results are presented for frequencies from 10 to 874 GHz and for several individual precipitation profiles as well as for three cloud resolving model simulations of falling snow. As expected, low frequency channels (<89 GHz) respond to liquid hydrometeors and the surface, while the higher frequency channels become increasingly sensitive to ice hydrometeors and the water vapor sounding channels react to water vapor in the atmosphere. Low emissivity surfaces (water and snow-covered land) permit energy downwelling from clouds to be reflected at the surface thereby increasing the percentage of the TB resulting from the hydrometeors. The slant path at a 53deg viewing angle increases the hydrometeor contributions relative to nadir viewing channels and show sensitivity to surface polarization effects. The TB percentage information presented in this paper answers questions about the relative contributions to the brightness temperatures and provides a key piece of information required to develop and improve precipitation retrievals over land surfaces