text:
using System;
using System.Collections.Generic;
using System.Reactive.Linq;
using System.Reactive.Threading.Tasks;
using MS.Core;

namespace Newtonsoft.Json.Schema
{
    public static class __JsonSchema
    {

        public static IObservable<Newtonsoft.Json.Schema.JsonSchema> Read(IObservable<Newtonsoft.Json.JsonReader> reader)
        {
            return Observable.Select(reader, (readerLambda) => Newtonsoft.Json.Schema.JsonSchema.Read(readerLambda));
        }

        public static IObservable<Newtonsoft.Json.Schema.JsonSchema> Read(IObservable<Newtonsoft.Json.JsonReader> reader, IObservable<Newtonsoft.Json.Schema.JsonSchemaResolver> resolver)
        {
            return Observable.Zip(reader, resolver, (readerLambda, resolverLambda) => Newtonsoft.Json.Schema.JsonSchema.Read(readerLambda, resolverLambda));
        }

        public static IObservable<Newtonsoft.Json.Schema.JsonSchema> Parse(IObservable<System.String> json)
        {
            return Observable.Select(json, (jsonLambda) => Newtonsoft.Json.Schema.JsonSchema.Parse(jsonLambda));
        }

        public static IObservable<Newtonsoft.Json.Schema.JsonSchema> Parse(IObservable<System.String> json, IObservable<Newtonsoft.Json.Schema.JsonSchemaResolver> resolver)
        {
            return Observable.Zip(json, resolver, (jsonLambda, resolverLambda) => Newtonsoft.Json.Schema.JsonSchema.Parse(jsonLambda, resolverLambda));
        }

        public static IObservable<System.Reactive.Unit> WriteTo(this IObservable<Newtonsoft.Json.Schema.JsonSchema> JsonSchemaValue, IObservable<Newtonsoft.Json.JsonWriter> writer)
        {
            return ObservableExt.ZipExecute(JsonSchemaValue, writer, (JsonSchemaValueLambda, writerLambda) => JsonSchemaValueLambda.WriteTo(writerLambda));
        }

        public static IObservable<System.Reactive.Unit> WriteTo(this IObservable<Newtonsoft.Json.Schema.JsonSchema> JsonSchemaValue, IObservable<Newtonsoft.Json.JsonWriter> writer, IObservable<Newtonsoft.Json.Schema.JsonSchemaResolver> resolver)
        {
            return ObservableExt.ZipExecute(JsonSchemaValue, writer, resolver, (JsonSchemaValueLambda, writerLambda, resolverLambda) => JsonSchemaValueLambda.WriteTo(writerLambda, resolverLambda));
        }

        public static IObservable<System.String> ToString(this IObservable<Newtonsoft.Json.Schema.JsonSchema> JsonSchemaValue)
        {
            return Observable.Select(JsonSchemaValue, (JsonSchemaValueLambda) => JsonSchemaValueLambda.ToString());
        }

        public static IObservable<System.String> get_Id(this IObservable<Newtonsoft.Json.Schema.JsonSchema> JsonSchemaValue)
        {
            return Observable.Select(JsonSchemaValue, (JsonSchemaValueLambda) => JsonSchemaValueLambda.Id);
        }

        public static IObservable<System.String> get_Title(this IObservable<Newtonsoft.Json.Schema.JsonSchema> JsonSchemaValue)
        {
            return Observable.Select(JsonSchemaValue, (JsonSchemaValueLambda) => JsonSchemaValueLambda.Title);
        }

        public static IObservable<System.Nullable<System.Boolean>> get_Required(this IObservable<Newtonsoft.Json.Schema.JsonSchema> JsonSchemaValue)
        {
            return Observable.Select(JsonSchemaValue, (JsonSchemaValueLambda) => JsonSchemaValueLambda.Required);
        }

        public static IObservable<System.Nullable<System.Boolean>> get_ReadOnly(this IObservable<Newtonsoft.Json.Schema.JsonSchema> JsonSchemaValue)
        {
            return Observable.Select(JsonSchemaValue, (JsonSchemaValueLambda) => JsonSchemaValueLambda.ReadOnly);
        }

        public static IObservable<System.Nullable<System.Boolean>> get_Hidden(this IObservable<Newtonsoft.Json.Schema.JsonSchema> JsonSchemaValue)
        {
            return Observable.Select(JsonSchemaValue, (JsonSchemaValueLambda) => JsonSchemaValueLambda.Hidden);
        }

        public static IObservable<System.Nullable<System.Boolean>> get_Transient(this IObservable<Newtonsoft.Json.Schema.JsonSchema> JsonSchemaValue)
        {
            return Observable.Select(JsonSchemaValue, (JsonSchemaValueLambda) => JsonSchemaValueLambda.Transient);
        }

        public static IObservable<System.String> get_Description(this IObservable<Newtonsoft.Json.Schema.JsonSchema> JsonSchemaValue)
        {
            return Observable.Select(JsonSchemaValue, (JsonSchemaValueLambda) => JsonSchemaValueLambda.Description);
        }

        public static IObservable<System.Nullable<Newtonsoft.Json.Schema.JsonSchemaType>> get_Type(this IObservable<Newtonsoft.Json.Schema.JsonSchema> JsonSchemaValue)
        {
            return Observable.Select(JsonSchemaValue, (JsonSchemaValueLambda) => JsonSchemaValueLambda.Type);
        }

        public static IObservable<System.String> get_Pattern(this IObservable<Newtonsoft.Json.Schema.JsonSchema> JsonSchemaValue)
        {
            return Observable.Select(JsonSchemaValue, (JsonSchemaValueLambda) => JsonSchemaValueLambda.Pattern);
        }

        public static IObservable<System.Nullable<System.Int32>> get_MinimumLength(this IObservable<Newtonsoft.Json.Schema.JsonSchema> JsonSchemaValue)
        {
            return Observable.Select(JsonSchemaValue, (JsonSchemaValueLambda) => JsonSchemaValueLambda.MinimumLength);
        }

        public static IObservable<System.Nullable<System.Int32>> get_MaximumLength(this IObservable<Newtonsoft.Json.Schema.JsonSchema> JsonSchemaValue)
        {
            return Observable.Select(JsonSchemaValue, (JsonSchemaValueLambda) => JsonSchemaValueLambda.MaximumLength);
        }

        public static IObservable<System.Nullable<System.Double>> get_DivisibleBy(this IObservable<Newtonsoft.Json.Schema.JsonSchema> JsonSchemaValue)
        {
            return Observable.Select(JsonSchemaValue, (JsonSchemaValueLambda) => JsonSchemaValueLambda.DivisibleBy);
        }

        public static IObservable<System.Nullable<System.Double>> get_Minimum(this IObservable<Newtonsoft.Json.Schema.JsonSchema> JsonSchemaValue)
        {
            return Observable.Select(JsonSchemaValue, (JsonSchemaValueLambda) => JsonSchemaValueLambda.Minimum);
        }

        public static IObservable<System.Nullable<System.Double>> get_Maximum(this IObservable<Newtonsoft.Json.Schema.JsonSchema> JsonSchemaValue)
        {
            return Observable.Select(JsonSchemaValue, (JsonSchemaValueLambda) => JsonSchemaValueLambda.Maximum);
        }

        public static IObservable<System.Nullable<System.Boolean>> get_ExclusiveMinimum(this IObservable<Newtonsoft.Json.Schema.JsonSchema> JsonSchemaValue)
        {
            return Observable.Select(JsonSchemaValue, (JsonSchemaValueLambda) => JsonSchemaValueLambda.ExclusiveMinimum);
        }

        public static IObservable<System.Nullable<System.Boolean>> get_ExclusiveMaximum(this IObservable<Newtonsoft.Json.Schema.JsonSchema> JsonSchemaValue)
        {
            return Observable.Select(JsonSchemaValue, (JsonSchemaValueLambda) => JsonSchemaValueLambda.ExclusiveMaximum);
        }

        public static IObservable<System.Nullable<System.Int32>> get_MinimumItems(this IObservable<Newtonsoft.Json.Schema.JsonSchema> JsonSchemaValue)
        {
            return Observable.Select(JsonSchemaValue, (JsonSchemaValueLambda) => JsonSchemaValueLambda.MinimumItems);
        }

        public static IObservable<System.Nullable<System.Int32>> get_MaximumItems(this IObservable<Newtonsoft.Json.Schema.JsonSchema> JsonSchemaValue)
        {
            return Observable.Select(JsonSchemaValue, (JsonSchemaValueLambda) => JsonSchemaValueLambda.MaximumItems);
        }

        public static IObservable<System.Collections.Generic.IList<Newtonsoft.Json.Schema.JsonSchema>> get_Items(this IObservable<Newtonsoft.Json.Schema.JsonSchema> JsonSchemaValue)
        {
            return Observable.Select(JsonSchemaValue, (JsonSchemaValueLambda) => JsonSchemaValueLambda.Items);
        }

        public static IObservable<System.Boolean> get_PositionalItemsValidation(this IObservable<Newtonsoft.Json.Schema.JsonSchema> JsonSchemaValue)
        {
            return Observable.Select(JsonSchemaValue, (JsonSchemaValueLambda) => JsonSchemaValueLambda.PositionalItemsValidation);
        }

        public static IObservable<Newtonsoft.Json.Schema.JsonSchema> get_AdditionalItems(this IObservable<Newtonsoft.Json.Schema.JsonSchema> JsonSchemaValue)
        {
            return Observable.Select(JsonSchemaValue, (JsonSchemaValueLambda) => JsonSchemaValueLambda.AdditionalItems);
        }

        public static IObservable<System.Boolean> get_AllowAdditionalItems(this IObservable<Newtonsoft.Json.Schema.JsonSchema> JsonSchemaValue)
        {
            return Observable.Select(JsonSchemaValue, (JsonSchemaValueLambda) => JsonSchemaValueLambda.AllowAdditionalItems);
        }

        public static IObservable<System.Boolean> get_UniqueItems(this IObservable<Newtonsoft.Json.Schema.JsonSchema> JsonSchemaValue)
        {
            return Observable.Select(JsonSchemaValue, (JsonSchemaValueLambda) => JsonSchemaValueLambda.UniqueItems);
        }

        public static IObservable<System.Collections.Generic.IDictionary<System.String, Newtonsoft.Json.Schema.JsonSchema>> get_Properties(this IObservable<Newtonsoft.Json.Schema.JsonSchema> JsonSchemaValue)
        {
            return Observable.Select(JsonSchemaValue, (JsonSchemaValueLambda) => JsonSchemaValueLambda.Properties);
        }

        public static IObservable<Newtonsoft.Json.Schema.JsonSchema> get_AdditionalProperties(this IObservable<Newtonsoft.Json.Schema.JsonSchema> JsonSchemaValue)
        {
            return Observable.Select(JsonSchemaValue, (JsonSchemaValueLambda) => JsonSchemaValueLambda.AdditionalProperties);
        }

        public static IObservable<System.Collections.Generic.IDictionary<System.String, Newtonsoft.Json.Schema.JsonSchema>> get_PatternProperties(this IObservable<Newtonsoft.Json.Schema.JsonSchema> JsonSchemaValue)
        {
            return Observable.Select(JsonSchemaValue, (JsonSchemaValueLambda) => JsonSchemaValueLambda.PatternProperties);
        }

        public static IObservable<System.Boolean> get_AllowAdditionalProperties(this IObservable<Newtonsoft.Json.Schema.JsonSchema> JsonSchemaValue)
        {
            return Observable.Select(JsonSchemaValue, (JsonSchemaValueLambda) => JsonSchemaValueLambda.AllowAdditionalProperties);
        }

        public static IObservable<System.String> get_Requires(this IObservable<Newtonsoft.Json.Schema.JsonSchema> JsonSchemaValue)
        {
            return Observable.Select(JsonSchemaValue, (JsonSchemaValueLambda) => JsonSchemaValueLambda.Requires);
        }

        public static IObservable<System.Collections.Generic.IList<Newtonsoft.Json.Linq.JToken>> get_Enum(this IObservable<Newtonsoft.Json.Schema.JsonSchema> JsonSchemaValue)
        {
            return Observable.Select(JsonSchemaValue, (JsonSchemaValueLambda) => JsonSchemaValueLambda.Enum);
        }

        public static IObservable<System.Nullable<Newtonsoft.Json.Schema.JsonSchemaType>> get_Disallow(this IObservable<Newtonsoft.Json.Schema.JsonSchema> JsonSchemaValue)
        {
            return Observable.Select(JsonSchemaValue, (JsonSchemaValueLambda) => JsonSchemaValueLambda.Disallow);
        }

        public static IObservable<Newtonsoft.Json.Linq.JToken> get_Default(this IObservable<Newtonsoft.Json.Schema.JsonSchema> JsonSchemaValue)
        {
            return Observable.Select(JsonSchemaValue, (JsonSchemaValueLambda) => JsonSchemaValueLambda.Default);
        }

        public static IObservable<System.Collections.Generic.IList<Newtonsoft.Json.Schema.JsonSchema>> get_Extends(this IObservable<Newtonsoft.Json.Schema.JsonSchema> JsonSchemaValue)
        {
            return Observable.Select(JsonSchemaValue, (JsonSchemaValueLambda) => JsonSchemaValueLambda.Extends);
        }

        public static IObservable<System.String> get_Format(this IObservable<Newtonsoft.Json.Schema.JsonSchema> JsonSchemaValue)
        {
            return Observable.Select(JsonSchemaValue, (JsonSchemaValueLambda) => JsonSchemaValueLambda.Format);
        }

        public static IObservable<System.Reactive.Unit> set_Id(this IObservable<Newtonsoft.Json.Schema.JsonSchema> JsonSchemaValue, IObservable<System.String> value)
        {
            return ObservableExt.ZipExecute(JsonSchemaValue, value, (JsonSchemaValueLambda, valueLambda) => JsonSchemaValueLambda.Id = valueLambda);
        }

        public static IObservable<System.Reactive.Unit> set_Title(this IObservable<Newtonsoft.Json.Schema.JsonSchema> JsonSchemaValue, IObservable<System.String> value)
        {
            return ObservableExt.ZipExecute(JsonSchemaValue, value, (JsonSchemaValueLambda, valueLambda) => JsonSchemaValueLambda.Title = valueLambda);
        }

        public static IObservable<System.Reactive.Unit> set_Required(this IObservable<Newtonsoft.Json.Schema.JsonSchema> JsonSchemaValue, IObservable<System.Nullable<System.Boolean>> value)
        {
            return ObservableExt.ZipExecute(JsonSchemaValue, value, (JsonSchemaValueLambda, valueLambda) => JsonSchemaValueLambda.Required = valueLambda);
        }

        public static IObservable<System.Reactive.Unit> set_ReadOnly(this IObservable<Newtonsoft.Json.Schema.JsonSchema> JsonSchemaValue, IObservable<System.Nullable<System.Boolean>> value)
        {
            return ObservableExt.ZipExecute(JsonSchemaValue, value, (JsonSchemaValueLambda, valueLambda) => JsonSchemaValueLambda.ReadOnly = valueLambda);
        }

        public static IObservable<System.Reactive.Unit> set_Hidden(this IObservable<Newtonsoft.Json.Schema.JsonSchema> JsonSchemaValue, IObservable<System.Nullable<System.Boolean>> value)
        {
            return ObservableExt.ZipExecute(JsonSchemaValue, value, (JsonSchemaValueLambda, valueLambda) => JsonSchemaValueLambda.Hidden = valueLambda);
        }

        public static IObservable<System.Reactive.Unit> set_Transient(this IObservable<Newtonsoft.Json.Schema.JsonSchema> JsonSchemaValue, IObservable<System.Nullable<System.Boolean>> value)
        {
            return ObservableExt.ZipExecute(JsonSchemaValue, value, (JsonSchemaValueLambda, valueLambda) => JsonSchemaValueLambda.Transient = valueLambda);
        }

        public static IObservable<System.Reactive.Unit> set_Description(this IObservable<Newtonsoft.Json.Schema.JsonSchema> JsonSchemaValue, IObservable<System.String> value)
        {
            return ObservableExt.ZipExecute(JsonSchemaValue, value, (JsonSchemaValueLambda, valueLambda) => JsonSchemaValueLambda.Description = valueLambda);
        }

        public static IObservable<System.Reactive.Unit> set_Type(this IObservable<Newtonsoft.Json.Schema.JsonSchema> JsonSchemaValue, IObservable<System.Nullable<Newtonsoft.Json.Schema.JsonSchemaType>> value)
        {
            return ObservableExt.ZipExecute(JsonSchemaValue, value, (JsonSchemaValueLambda, valueLambda) => JsonSchemaValueLambda.Type = valueLambda);
        }

        public static IObservable<System.Reactive.Unit> set_Pattern(this IObservable<Newtonsoft.Json.Schema.JsonSchema> JsonSchemaValue, IObservable<System.String> value)
        {
            return ObservableExt.ZipExecute(JsonSchemaValue, value, (JsonSchemaValueLambda, valueLambda) => JsonSchemaValueLambda.Pattern = valueLambda);
        }

        public static IObservable<System.Reactive.Unit> set_MinimumLength(this IObservable<Newtonsoft.Json.Schema.JsonSchema> JsonSchemaValue, IObservable<System.Nullable<System.Int32>> value)
        {
            return ObservableExt.ZipExecute(JsonSchemaValue, value, (JsonSchemaValueLambda, valueLambda) => JsonSchemaValueLambda.MinimumLength = valueLambda);
        }

        public static IObservable<System.Reactive.Unit> set_MaximumLength(this IObservable<Newtonsoft.Json.Schema.JsonSchema> JsonSchemaValue, IObservable<System.Nullable<System.Int32>> value)
        {
            return ObservableExt.ZipExecute(JsonSchemaValue, value, (JsonSchemaValueLambda, valueLambda) => JsonSchemaValueLambda.MaximumLength = valueLambda);
        }

        public static IObservable<System.Reactive.Unit> set_DivisibleBy(this IObservable<Newtonsoft.Json.Schema.JsonSchema> JsonSchemaValue, IObservable<System.Nullable<System.Double>> value)
        {
            return ObservableExt.ZipExecute(JsonSchemaValue, value, (JsonSchemaValueLambda, valueLambda) => JsonSchemaValueLambda.DivisibleBy = valueLambda);
        }

        public static IObservable<System.Reactive.Unit> set_Minimum(this IObservable<Newtonsoft.Json.Schema.JsonSchema> JsonSchemaValue, IObservable<System.Nullable<System.Double>> value)
        {
            return ObservableExt.ZipExecute(JsonSchemaValue, value, (JsonSchemaValueLambda, valueLambda) => JsonSchemaValueLambda.Minimum = valueLambda);
        }

        public static IObservable<System.Reactive.Unit> set_Maximum(this IObservable<Newtonsoft.Json.Schema.JsonSchema> JsonSchemaValue, IObservable<System.Nullable<System.Double>> value)
        {
            return ObservableExt.ZipExecute(JsonSchemaValue, value, (JsonSchemaValueLambda, valueLambda) => JsonSchemaValueLambda.Maximum = valueLambda);
        }

        public static IObservable<System.Reactive.Unit> set_ExclusiveMinimum(this IObservable<Newtonsoft.Json.Schema.JsonSchema> JsonSchemaValue, IObservable<System.Nullable<System.Boolean>> value)
        {
            return ObservableExt.ZipExecute(JsonSchemaValue, value, (JsonSchemaValueLambda, valueLambda) => JsonSchemaValueLambda.ExclusiveMinimum = valueLambda);
        }

        public static IObservable<System.Reactive.Unit> set_ExclusiveMaximum(this IObservable<Newtonsoft.Json.Schema.JsonSchema> JsonSchemaValue, IObservable<System.Nullable<System.Boolean>> value)
        {
            return ObservableExt.ZipExecute(JsonSchemaValue, value, (JsonSchemaValueLambda, valueLambda) => JsonSchemaValueLambda.ExclusiveMaximum = valueLambda);
        }

        public static IObservable<System.Reactive.Unit> set_MinimumItems(this IObservable<Newtonsoft.Json.Schema.JsonSchema> JsonSchemaValue, IObservable<System.Nullable<System.Int32>> value)
        {
            return ObservableExt.ZipExecute(JsonSchemaValue, value, (JsonSchemaValueLambda, valueLambda) => JsonSchemaValueLambda.MinimumItems = valueLambda);
        }

        public static IObservable<System.Reactive.Unit> set_MaximumItems(this IObservable<Newtonsoft.Json.Schema.JsonSchema> JsonSchemaValue, IObservable<System.Nullable<System.Int32>> value)
        {
            return ObservableExt.ZipExecute(JsonSchemaValue, value, (JsonSchemaValueLambda, valueLambda) => JsonSchemaValueLambda.MaximumItems = valueLambda);
        }

        public static IObservable<System.Reactive.Unit> set_Items(this IObservable<Newtonsoft.Json.Schema.JsonSchema> JsonSchemaValue, IObservable<System.Collections.Generic.IList<Newtonsoft.Json.Schema.JsonSchema>> value)
        {
            return ObservableExt.ZipExecute(JsonSchemaValue, value, (JsonSchemaValueLambda, valueLambda) => JsonSchemaValueLambda.Items = valueLambda);
        }

        public static IObservable<System.Reactive.Unit> set_PositionalItemsValidation(this IObservable<Newtonsoft.Json.Schema.JsonSchema> JsonSchemaValue, IObservable<System.Boolean> value)
        {
            return ObservableExt.ZipExecute(JsonSchemaValue, value, (JsonSchemaValueLambda, valueLambda) => JsonSchemaValueLambda.PositionalItemsValidation = valueLambda);
        }

        public static IObservable<System.Reactive.Unit> set_AdditionalItems(this IObservable<Newtonsoft.Json.Schema.JsonSchema> JsonSchemaValue, IObservable<Newtonsoft.Json.Schema.JsonSchema> value)
        {
            return ObservableExt.ZipExecute(JsonSchemaValue, value, (JsonSchemaValueLambda, valueLambda) => JsonSchemaValueLambda.AdditionalItems = valueLambda);
        }

        public static IObservable<System.Reactive.Unit> set_AllowAdditionalItems(this IObservable<Newtonsoft.Json.Schema.JsonSchema> JsonSchemaValue, IObservable<System.Boolean> value)
        {
            return ObservableExt.ZipExecute(JsonSchemaValue, value, (JsonSchemaValueLambda, valueLambda) => JsonSchemaValueLambda.AllowAdditionalItems = valueLambda);
        }

        public static IObservable<System.Reactive.Unit> set_UniqueItems(this IObservable<Newtonsoft.Json.Schema.JsonSchema> JsonSchemaValue, IObservable<System.Boolean> value)
        {
            return ObservableExt.ZipExecute(JsonSchemaValue, value, (JsonSchemaValueLambda, valueLambda) => JsonSchemaValueLambda.UniqueItems = valueLambda);
        }

        public static IObservable<System.Reactive.Unit> set_Properties(this IObservable<Newtonsoft.Json.Schema.JsonSchema> JsonSchemaValue, IObservable<System.Collections.Generic.IDictionary<System.String, Newtonsoft.Json.Schema.JsonSchema>> value)
        {
            return ObservableExt.ZipExecute(JsonSchemaValue, value, (JsonSchemaValueLambda, valueLambda) => JsonSchemaValueLambda.Properties = valueLambda);
        }

        public static IObservable<System.Reactive.Unit> set_AdditionalProperties(this IObservable<Newtonsoft.Json.Schema.JsonSchema> JsonSchemaValue, IObservable<Newtonsoft.Json.Schema.JsonSchema> value)
        {
            return ObservableExt.ZipExecute(JsonSchemaValue, value, (JsonSchemaValueLambda, valueLambda) => JsonSchemaValueLambda.AdditionalProperties = valueLambda);
        }

        public static IObservable<System.Reactive.Unit> set_PatternProperties(this IObservable<Newtonsoft.Json.Schema.JsonSchema> JsonSchemaValue, IObservable<System.Collections.Generic.IDictionary<System.String, Newtonsoft.Json.Schema.JsonSchema>> value)
        {
            return ObservableExt.ZipExecute(JsonSchemaValue, value, (JsonSchemaValueLambda, valueLambda) => JsonSchemaValueLambda.PatternProperties = valueLambda);
        }

        public static IObservable<System.Reactive.Unit> set_AllowAdditionalProperties(this IObservable<Newtonsoft.Json.Schema.JsonSchema> JsonSchemaValue, IObservable<System.Boolean> value)
        {
            return ObservableExt.ZipExecute(JsonSchemaValue, value, (JsonSchemaValueLambda, valueLambda) => JsonSchemaValueLambda.AllowAdditionalProperties = valueLambda);
        }

        public static IObservable<System.Reactive.Unit> set_Requires(this IObservable<Newtonsoft.Json.Schema.JsonSchema> JsonSchemaValue, IObservable<System.String> value)
        {
            return ObservableExt.ZipExecute(JsonSchemaValue, value, (JsonSchemaValueLambda, valueLambda) => JsonSchemaValueLambda.Requires = valueLambda);
        }

        public static IObservable<System.Reactive.Unit> set_Enum(this IObservable<Newtonsoft.Json.Schema.JsonSchema> JsonSchemaValue, IObservable<System.Collections.Generic.IList<Newtonsoft.Json.Linq.JToken>> value)
        {
            return ObservableExt.ZipExecute(JsonSchemaValue, value, (JsonSchemaValueLambda, valueLambda) => JsonSchemaValueLambda.Enum = valueLambda);
        }

        public static IObservable<System.Reactive.Unit> set_Disallow(this IObservable<Newtonsoft.Json.Schema.JsonSchema> JsonSchemaValue, IObservable<System.Nullable<Newtonsoft.Json.Schema.JsonSchemaType>> value)
        {
            return ObservableExt.ZipExecute(JsonSchemaValue, value, (JsonSchemaValueLambda, valueLambda) => JsonSchemaValueLambda.Disallow = valueLambda);
        }

        public static IObservable<System.Reactive.Unit> set_Default(this IObservable<Newtonsoft.Json.Schema.JsonSchema> JsonSchemaValue, IObservable<Newtonsoft.Json.Linq.JToken> value)
        {
            return ObservableExt.ZipExecute(JsonSchemaValue, value, (JsonSchemaValueLambda, valueLambda) => JsonSchemaValueLambda.Default = valueLambda);
        }

        public static IObservable<System.Reactive.Unit> set_Extends(this IObservable<Newtonsoft.Json.Schema.JsonSchema> JsonSchemaValue, IObservable<System.Collections.Generic.IList<Newtonsoft.Json.Schema.JsonSchema>> value)
        {
            return ObservableExt.ZipExecute(JsonSchemaValue, value, (JsonSchemaValueLambda, valueLambda) => JsonSchemaValueLambda.Extends = valueLambda);
        }

        public static IObservable<System.Reactive.Unit> set_Format(this IObservable<Newtonsoft.Json.Schema.JsonSchema> JsonSchemaValue, IObservable<System.String> value)
        {
            return ObservableExt.ZipExecute(JsonSchemaValue, value, (JsonSchemaValueLambda, valueLambda) => JsonSchemaValueLambda.Format = valueLambda);
        }

    }
}


text:
Home College Sports College Football
Scouting Combine: Wide Receivers
by Chris Boughman
in College Football, NFL, NFL Draft, Philadelphia Eagles
Welcome back to the next installment of the series highlighting the 2019 Scouting combine. Last time we looked at running backs, this edition will focus on Wide Receivers. A few of the top-ranked receivers were unable to participate due to injury, but there's plenty of talent available in this year's crop of players at the wide receiver position. Let's dive into some players that stood out in Indianapolis at the combine.
Parris Campbell
The former High school track star flew into Indianapolis and left with the fastest 40 yard dash time among wide receivers. The 4.31 time tied for the best time of the day. Parris Campbell, 1st team all-Big 10 in 2018, performed very well at the combine ranking among the top 5 in nearly every category. Campbell was a late bloomer to the wide receiver position at Ohio State. He spent time behind Michael Thomas and Curtis Samuel during his freshman and sophomore year. He played primarily as a kick returner during that time. Campbell introduced himself in 2017 leading the team in yards per catch.
2018 was the year for Campbell. He played out of his mind! He broke school records in receptions and touchdowns while compiling 1,263 yards receiving in his senior season. Campbell's limited experience at the wide receiver position could hinder his immediate production at the pro level. He'll need to improve his route running and work on his hands. However, Parris Campbell is an athlete above all else and can be a solid contributor to any NFL team. Campbell has dangerous speed and does a tremendous job with change of direction in open space. Campbell will be a package specific player but could be utilized anywhere in an offensive formation. Expect to hear the name Parris Campbell early on draft day!
N'Keal Harry
TEMPE, AZ – SEPTEMBER 09: Wide receiver N'Keal Harry #1 of the Arizona State Sun Devils catches a five-yard touchdown pass against cornerback Ron Smith #17 of the San Diego State Aztecs during the first half of the college football game at Sun Devil Stadium on September 9, 2017, in Tempe, Arizona. (Photo by Christian Petersen/Getty Images)
The 6'2, 228lb wideout from Arizona State University opened some eyes at the combine. The strength was expected to be there, and it showed by benching 27 times at 225lbs, 1st among wide receivers. However, the 40 yard dash time is what caught the attention of coaches and scouts. He ran the 40-yard dash in a time of 4.53 seconds. Although he didn't finish near the top of the rankings, the quickness displayed at his size was impressive. N'Keal Harry, a 1st team All Pac 12 this year, brings to the draft a good work ethic and displays excellent character. He's coming off back to back 1,000-yard seasons at ASU. There's concern among scouts that he'll have trouble creating separation from defenders. However, what he lacks in speed is made up in physicality. N'Keal Harry could hear his name called in the first few rounds of the 2019 draft.
DK Metcalf
Holy Batman! This kid showed up and made an impact. It all started with a photo pre-combine displaying his freakish physique and culminated on the field during his performance at Lucas Oil Stadium. DK (DeKaylin Zecharius) Metcalf, the redshirt Sophomore from Ole Miss, wowed the scouts with his on-field performance in several categories. Metcalf measured in at 6'3, 228lbs, and only 1.6% body fat! The biggest eye-popping moment of the day came early during the 40-yard dash. He finished with a time of 4.33 seconds! The only competitors finishing above him were Parris Campbell, Andy Isabella, and MeCole Hardman.
The difference between him and other competitors is their stature. Metcalf weighs at least 30 lbs more than his peers. He became the fastest runner at 225lbs+ ever at the combine. While all the boxes check off for Metcalf as a successful pro, there are a few concerns. He does have limited experience playing in college. He suffered injuries in 2 seasons at Ole Miss, a broken foot in 2016, and suffered a neck injury this season. In limited action, he recorded 26 receptions for 569 yards and 5 TDs. Most expect DK Metcalf to be the first wide receiver off the board in April. The Metcalf's have a strong NFL pedigree as his father, grandfather, and uncle have all logged time in the NFL. If all goes well, Metcalf will follow in his family's footsteps as a successful player at the NFL level.
Miles Boykin
The 2018 Citrus Bowl MVP continued his hot streak in Indianapolis at the combine. Miles Boykin is the only wide receiver to rank in the top ten across every category. The vertical jump reached 43.5 inches, which is the 2nd highest in combine history. He also placed 1st in the 3 cone drill (6.77 seconds), recorded the 2nd best Broad jump (140), and the 2nd fastest 20-yard shuttle (4.07). Boykin finished his senior season at Notre Dame with 59 receptions for 879 yards and 8 touchdowns. His limited production at the collegiate level could affect his immediate impact in the NFL. Boykin definitely needs to work on his route running. His freakish athleticism could make up for his lack of production. The impressive performance at the combine is rising his stock and could see his name called on day 2 of the NFL Draft.
Andy Isabella
ATHENS, GA – NOVEMBER 17: Andy Isabella #5 of the Massachusetts Minutemen makes a catch for a fourth-quarter touchdown against Mark Webb #23 of the Georgia Bulldogs on November 17, 2018, at Sanford Stadium in Athens, Georgia. (Photo by Scott Cunningham/Getty Images)
Andy Isabella, a 2018 Biletnikoff award finalist, took his quickness to the combine, or did he? The first 40 yard dash time registered at 4.56 seconds, or did it? The lasers on the field recorded him at 4.56. However, scouts using stopwatches recorded him at a much better time, 4.27 seconds! So, which was the correct time? After speculation, they realized the lasers on the field weren't working, and he recorded a time of 4.31 seconds. This time tied him for 1st place among receivers, with Parris Campbell of Ohio State.
The 2nd team AP All-American from UMass exploded on the scene in his senior season. He finished with 102 receptions for 1,698 yards and 13 touchdowns! He led the FBS with 141 yards per game. Obviously, he didn't play with the best of the best considering the conference in which he played. However, Isabella had a huge game against nationally ranked Georgia Bulldogs. He recorded 15 receptions for 219 yards and 2 touchdowns. Isabella may not bring size with him to the wide receiver position, but his quickness could very well make up for it. Isabella does lack some consistency in pass catching, letting the ball come into his body occasionally. He's been working with Randy Moss recently, and hopefully, that'll catapult his way up the draft boards and onto an NFL team at the draft!
Emmanuel Hall
The 6'2, 201lb senior from Missouri left an impression with the scouts in Indianapolis. Emmanuel Hall recorded the 2nd best broad jump score in combine history, best among wide receivers. He also tied for 1st place in the vertical jump. Hall blazed down the sideline with a 4.39-second 40-yard dash.
He recorded excellent scores in each category, all while dealing with a lingering groin injury. Hall was unable to perform any on-field activities, but he left an imprint in the minds of the scouts and coaches in attendance. The speed Hall displays on the field are enough to take the top off a defense. However, his inconsistencies start when the ball reaches his hands. He has a tendency to bobble the football, resulting in some ugly catches. Despite the knock on his hands, he recorded 828 yards with 8 touchdowns this year in only 8 games. Furthermore, if Emmanuel Hall can work out the inconsistencies with his hands, he'll be an excellent deep threat on any NFL team.
The wide receivers in the draft class of 2019 is a talented group of football players. They all display extraordinary skills that'll help a team at the next level. Obviously, some are better than others. If a team misses out on the top tier of talents such as DK Metcalf, AJ Brown, Marquise Brown, or N'Keal Harry, they can still draft excellent players deep into this draft. Players such as Miles Boykin and Andy Isabella could be valuable assets to an NFL team in the future.
Tags: 2019 NFL DraftCollege FootballNFLNFL Draft Wide Receiversphiladelphia eagles
Wildcats Face Tough Foe, Hard Questions, As Sweet Sixteen Looms
Vikings Draft Prospect: Edge Jordan Brailford
Chris Boughman

redpajama_set_name: RedPajamaCommonCrawl


AppleMacRepairDubai.com, providing support and service to the Apple iMac A1419 Core i5 3.3 27-Inch users. Whether you are a user, a professional, small business or large corporation, we have a service package to suit your needs.
At Apple Mac Repair Dubai, our aim is to provide our customers with the best possible services at lowest price.
Our Mac experts always help whenever you needed, Whether you need to repair a Macbook pro / MacBook Air or setting up a new MacBook Pro / MacBook Air, upgrading your MacBook Memory, SSD, OS or replacement of screen, Body. Hinges, Track pad, keyboard etc. AppleMacRepairDubai.com is a perfect place for Macbook Repair Dubai, iMac Repair Dubai & iPhone Repair Dubai, iMac A1419 Core i5 3.3 27 inche Dubai UAE.
Do you need to replace / repair the Apple iMac A1419 Core i5 3.3 27-Inch Body?
Do you need to replace / repair the Apple iMac A1419 Core i5 3.3 27-Inch Screen?
Do you need to replace / repair the Apple iMac A1419 Core i5 3.3 27-Inch Speaker?
Do you need to replace / repair the Apple iMac A1418 Core i7 3.3 21.5-Inch Logic Board?
Do you need to upgrade / replace the Apple iMac A1418 Core i7 3.3 21.5-Inch SSD?
Do you need to replace / repair the Apple iMac A1418 Core i7 3.3 21.5-Inch USB?
Do you need to replace / repair the Apple iMac A1418 Core i7 3.3 21.5-Inch Power Button?
Do you need to replace / repair the Apple iMac A1418 Core i7 3.3 21.5-Inch HDMI?
Do you need to replace / repair the Apple iMac A1418 Core i7 3.3 21.5-Inch Display Port?
Do you need to replace / repair the Apple iMac A1418 Core i7 3.3 21.5-Inch Reader?
Do you need to replace / repair the Apple iMac A1418 Core i7 3.3 21.5-Inch audio Jack?
Do you need to replace / repair the Apple iMac A1418 Core i7 3.3 21.5-Inch Power port?
Do you need to replace / repair the Apple iMac A1418 Core i7 3.3 21.5-Inch Ethernet Port?
Do you need to replace / repair the Apple iMac A1418 Core i7 3.3 21.5-Inch DVD?
Do you need to replace / repair the Apple iMac A1418 Core i7 3.3 21.5-Inch Camera?
Do you need to replace / repair the Apple iMac A1418 Core i7 3.3 21.5-Inch Graphic Card?
Do you need to replace / repair the Apple iMac A1418 Core i7 3.3 21.5-Inch Sound Card?
Do you need to replace / repair the Apple iMac A1418 Core i7 3.3 21.5-Inch Chip, FET, Capacitors?
Do you need to replace / repair the Apple iMac A1418 Core i7 3.3 21.5-Inch backlit?
Do you need to fix the Apple iMac A1418 Core i7 3.3 21.5-Inch over heating problem?
Do you need to replace / repair the Apple iMac A1418 Core i7 3.3 21.5-Inch Fan?
Do you need to replace / repair the Apple iMac A1418 Core i7 3.3 21.5-Inch Wi-Fi Card?
Do you need to upgrade / fix the Apple iMac A1418 Core i7 3.3 21.5-Inch OS?
Dubai Maritime City, Dubai Techno Park, Dubai Waterfront, Dubai Silicon Oasis, Jumeirah Village Circle (JVC), Jumeirah Village, New Dubai, Jumeirah Islands, Jumeirah Park, Jumeirah Lakes Towers (JLT), Umm Suqeim, Discovery Gardens, Satwa , Emirates Hills, Nad Al Sheba (Nadd Al Sheba), Palm Deira, Palm Jebel Ali, Al Mamzar, Al Jafiliya, Al Barsha, Al Bastakia, Al Qusais, Al Khail, Al Khalidiya, , Al Muhaisnah, Al Safia, Al Twar, Bur Dubai, Business Bay, Downtown Dubai , Dubai Marina - Marsa Dubai, Dubailand, Jumeirah, Jumeirah Golf Estates, Jumeirah Heights, Emirates Living, Al Khawaneej, Al Quoz, Deira, Dubai Airport North, Tecom, Meadows, The Springs, The Greens, Dubai Airport South, Dubai Industrial City, Arabian Ranches, Motor City, Palm Jumeirah, Abu Shagarah, Sharjah and Ajman, iMac A1419 Core i5 3.3 27-Inch Repair in Dubai UAE.


text:
AI Governance Fundamentals - Curriculum and Application
by Mauricio23 min read30th Nov 2021No comments
AI GovernanceEducationAI
The very short version: If you are interested in an introduction to long-term AI governance, apply here to be a participant or here to be a (compensated) facilitator by December 15th.
Summary & Information About Applying
Collaborators, advisors, and acknowledgements:
I encourage you to apply if:
Each week's curriculum contains:
Some high-level approaches that informed the syllabus design:
Topics for each week:
Some limitations of this program and suggested mindsets:
Abbreviated curriculum (only key ideas and core readings)
Week 0 (Recommended Background): AI, Machine Learning, and the Importance of Their Long-Term Impacts
Week 1: Introduction to Governing World-Transforming AI
Week 2: Deep Dive - The Alignment Problem
Week 3: Potential Sources of AI Existential Risk
Week 4: Avoiding a Race to the Bottom
Week 5: Corporate Actors & Levers
Week 6: (Inter)Governmental Actors & Levers, With Historical Case Studies
Week 7: Career Advice & Opportunities for People Who Are Interested in Helping
Week 8: AI Governance Projects
Click here for the full version of the curriculum, which contains additional readings, notes/context on each core reading, and project ideas, and will include exercises and discussion prompts.
Why is this valuable if there's a mentorship bottleneck in AI governance?
What should I do if I already did the AGI safety fundamentals program and am interested in this program?
What if I want to do both the technical alignment and the governance tracks?
I didn't receive a confirmation email—did you receive my application?
What if I already applied to the upcoming AGI Safety Fundamentals program and indicated an interest in the governance track?
What if I already did an AI governance reading group?
What if I have feedback on the syllabus, or ideas for exercises/discussion prompts/etc?
(Crossposted from the EA Forum.)
While many people are interested in long-term AI governance, there is currently no scalable introduction to the field that offers substantial breadth, depth, context, accountability, and information about relevant career opportunities. Anecdotally, finding even a few of those things can be tough. Aiming to improve this state of affairs, I'm excited to introduce the AI Governance Fundamentals program. This program seeks to efficiently bring people up to speed on long-term issues in AI governance through an 11-week virtual course. It consists of 8 weeks of readings, facilitated group discussions, speaker sessions, and a 4-week capstone project, for a total of ~2-4 hours per week.
In creating the curriculum, I drew gratefully and extensively from eight older AI governance reading lists,[1] collaboration with Richard Ngo (a former machine learning research engineer at DeepMind, now working on the policy team at OpenAI), generously detailed feedback from Ben Garfinkel (The Centre for the Governance of AI's Acting Director) and Luke Muehlhauser (Open Philanthropy's AI Governance and Policy Program Officer), and additional useful advice from Jenny Xiao (Columbia University), Sam Clarke (CSER), a researcher from CSET/FHI, and two affiliates of Concordia Consulting.[2] (This does not constitute endorsements from any of these advisors or their organizations.)
Professionals with relevant experience in the Future of Humanity Institute, the UK Office for AI, the Center for Security and Emerging Technology, the Centre for the Study of Existential Risk, the Center for Human-Compatible Artificial Intelligence, and the Stanford Institute for Human-Centered Artificial Intelligence have already expressed interest in facilitating course discussions, as have some students with relevant context.[3]
Logistically, the course will be run as another track alongside the technical AI alignment track of the AGI Safety Fundamentals Programme (after which this program and post are modeled, if the name didn't give it away), in collaboration with Dewi Erwan and Jamie Bernardi, with additional support from Vince Huang and Will Aldred.
If you're interested in joining the next version of the course (taking place January - March 2022) apply here to be a participant or here to be a (compensated) facilitator. Applications are open to anyone and close December 15th. Note that:
This is the same application form as the one used for the technical AI alignment track of the AGI Safety Fundamentals programme; you can select to apply for the governance track or the technical track. (The AGI Safety Fundamentals Programme is now what we're currently calling the umbrella program that is hosting both tracks.)
If you've already applied to the AGI Safety Fundamentals programme and selected the governance track, then you've already applied for this program—no need to do anything else.
We will offer honoraria of £800 (~$1,070) to facilitators for their time, through Cambridge EA CIC.
The curriculum is intended to be accessible to people with a wide range of backgrounds—backgrounds in computer science, AI, or social sciences are not necessary.
You are potentially interested in eventually doing work aimed at improving the trajectory of AI through AI governance research/policy (or full-time work that could indirectly help a lot, like technical AI safety research or recruiting people to work on pressing problems).
And you'd like to learn more about whether to pursue a career in this field, and/or you'd like to get background context that would be a useful step toward contributing.
And the syllabus seems interesting to you, including its focus on long-term, global risks.
This post contains an overview of the course and an abbreviated version of the curriculum; the full version (which also contains optional readings and notes/context on each core reading, and future discussion prompts and project ideas) can be found here. Comments and feedback are very welcome, either on this post or in the full curriculum document; suggestions of new exercises, prompts, or readings would be particularly helpful. I'll continue to make updates until shortly before the program starts.
See the final section of this post for an FAQ.
Participants are divided into groups of 4-6 people, matched based on their prior knowledge of long-term AI risks and governance. From weeks 1 to 7, each group and their discussion facilitator will meet for 1.5 hours to discuss the readings and exercises. The course consists of 8 weeks of readings, plus a final project. Broadly speaking, the first half of the course explores potential risks, while the second half focuses on strategic considerations and possibilities for good governance. After Week 7, participants will have several weeks to work on projects of their choice, to present at the final session. Each week (apart from week 0) each group and their discussion facilitator will meet for 1.5 hours to discuss the readings and exercises.
Key ideas for that week
Core readings
Optional readings
Two exercises (participants should pick one to do each week)
Further notes on the readings
Discussion prompts for the weekly session
Week 0 replaces the small group discussions with a lecture plus live group exercises, since it's aimed at getting people with little machine learning knowledge up to speed quickly.
Long-term: A focus on long-term risks that future AI systems may pose
Problem-first: An emphasis on understanding relevant problems and risk scenarios, for better generating and prioritizing among paths to impact
Pluralistic: (Attempted) Inclusion of a range of the (very different) views that are prominent in the long-term AI governance community
Foundational: An emphasis on relevant concepts, context, and big-picture ideas, to help prepare participants to themselves discover new strategic considerations and promising policy options
This syllabus is structured into three parts:
Before the main readings, there is a recommended week of background context:
Part 1 dives into the risks, with the motivating idea that thoroughly understanding problems is very helpful for both identifying potential solutions and prioritizing among them.
Part 2 dives into the questions of how governance decisions can help address AI risks.
In general, long-term AI governance remains at least partly pre-paradigmatic, and explanation of existing ideas has been limited. Somewhat more concretely:
Many ideas in long-term AI strategy and governance have never been properly written up and published.[4]
Viewpoints on important questions (e.g. timelines) often change pretty rapidly.
Many arguments and ideas have yet to be proposed and scrutinized in much depth.
There is much disagreement and uncertainty about:
What abstractions are useful[5]
What topics are useful to think about
How large different risks are
Other critical object-level questions
In other words, borrowing from Olah and Carter's essay on "Research Debt," the long-term AI governance field currently has substantial limitations from limited exposition of important ideas, undigested ideas, and (debatably) bad abstractions.
Some long-term AI governance research focuses on preparing for advances in AI, and some strategies involve drawing on advances in AI safety technical research. However, we don't know precisely what any of these advances will be, or in precisely what (e.g. political) context they will take place. So some strategic clarity and precision might only come with time. More generally though, big-picture strategic research has much worse feedback loops than some other fields.
While the above state of affairs need not be a reason to despair (the field is still very young and has arguably made significant progress), it means that it can be useful to keep these things in mind (much more so than in an average course):
Unfortunately, public materials often:
Give impressions of views that are outdated by a few years.
Include little of the thinking of researchers who write and publish less often.
Include little material on some important topics.
Facilitators/mentors (and organizers) are still in the process of learning, especially if they're new to the area.
Overall, this content is not a settled map of the AI governance landscape.
There is very little "established wisdom" or informed consensus.
Skeptical and questioning mindsets are especially valuable.
This is (an abbreviated version of) the current version of the curriculum, as of writing. It will be further edited leading up to the start of the program.
This week aims to provide background context on AI and machine learning, as well as on why this course focuses on their long-term impacts.
We'll look at: what is modern AI like? How are machine learning models structured and trained? Learning about these things will hopefully help you form your own informed views on relevant questions, be able to reason about AI's impacts with more concrete examples and a higher awareness of current trends, and be better able to sound like you know what you are talking about.
This course also makes the somewhat unconventional choice of focusing on the long-term impacts of AI. That choice requires some context.
Core Readings:
Why governing AI is our opportunity to shape the long-term future (Leung, 2020)
A short introduction to machine learning (Ngo, 2021)
But what is a neural network? (3Blue1Brown, 2017)
More perspectives on focusing on especially long-term and high-stakes impacts:
What We Owe the Future (MacAskill, 2020)
Sections "Longtermism isn't necessary…" (8:21-11:16) and "How X-Risk Reduction Compares…" (34:34-50:22) of this interview (Wiblin and Harris, 2021)
This week, we'll see an overview of AI governance research. Additionally, we'll examine several ideas that, at least for some researchers, are core motivations for focusing on AI governance: a mix of historical, economic, and other empirical arguments which suggest that AI will drastically transform the world, likely starting this century. (That is a strong, controversial claim—worth scrutinizing.)
AI Strategy, Policy, and Governance (Dafoe, 2019)
On just how world-transforming AI could be:
The "Most Important Century" Blog Post Series Summary (Karnofsky, 2021)
The Impact of Artificial Intelligence: A Historical Perspective (Garfinkel, 2021)
A few shorter pieces aiming to better understand and forecast advanced AI:
AI Timelines: Where the Arguments, and the "Experts," Stand (Karnofsky, 2021)
AGI safety from first principles: Superintelligence (Ngo, 2020)
Four background claims (Soares, 2015)
Last week, we saw arguments for the view that AI will drastically transform the world, very possibly this century. We should arguably expect such transformative impacts to come with many long-term risks and opportunities. This week, we will examine one potential source of risk that some researchers see as especially pressing: the AI alignment problem.
We'll ask, "Why, precisely, might it be hard to align AI systems' objectives with humans' values, and how can people help solve this problem?" The purpose of this is to help us build a solid foundation for thinking clearly about (a) what risks, if any, the alignment problem poses, and (b) what these risks mean for AI governance. We take this focus because (as we will see next week) the alignment problem is at the center of many (though far from all) worries about how AI may cause long-lasting harms, and existing publications on these risks are relatively detailed.
Arguably, being able to reason independently about the alignment problem is especially important because just how much risk this problem poses is one of the (many) open questions in AI strategy and governance. To paraphrase, even among leading researchers focused on long-term AI risks, views range from "the alignment problem is the main source of AI existential risk, and it is very likely to cause existential catastrophe," to "the alignment problem is a minor source of AI existential risk, and it is very unlikely to cause existential catastrophe." Many researchers also have intermediate shades of worry.
Why AI alignment could be hard with modern deep learning - Cotra (2021)
Specification gaming: the flip side of AI ingenuity (Krakovna et al., 2020)
"Inner Alignment: Explain like I'm 12 Edition" (Harth, 2020)
AI alignment landscape (Christiano, 2020)
Researchers have proposed several paths by which advances in AI may cause especially long-lasting harms—harms that are especially worrying from the perspective that the long-term impacts of our actions are very important. This week, we'll consider: What are some of the best arguments for and against various claims about how AI poses existential risk?
A 2020 survey of AI safety and governance researchers (Clarke et al., 2021) found high levels of uncertainty and disagreement about what the most plausible AI existential risk scenarios. These imply that many researchers must be wrong about important questions, which arguably makes skeptical and questioning mindsets (with skepticism toward both your own view and others') especially valuable.
What Failure Looks Like (Christiano, 2019)
Is power-seeking AI an existential risk? (Carlsmith, 2021) (Excerpt)
AI Governance: Opportunity and Theory of Impact (Dafoe, 2020)
Sharing the World with Digital Minds (Shulman and Bostrom, 2020) (Excerpt)
This week, we shift gears from assessing potential AI risks to considering potential ways to mitigate these risks, while seeking to better understand strategic considerations that can help us identify new opportunities for steering toward better outcomes.
While many questions in AI governance remain unclear, researchers often agree that an AI-related "race to the bottom"—a situation in which intense, AI-related competition pushes AI developers to neglect important values (such as safety) or be out-competed—would be bad. Some researchers see the main risk here as under-investment in AI alignment research, while other researchers have more general concerns about such competition. Additionally, some researchers have argued that inadequately constrained competition poses other threats. This week, we'll examine several (though far from all) strategic considerations that may help us find ways to avoid such risky dynamics. Many of the issues covered this week have potential implications for both government and corporate policies—areas we will examine individually in the two weeks that follow.
Cooperation, conflict and transformative AI: Sections 1 & 2 (Clifton, 2019)
Cooperative AI: machines must learn to find common ground (Dafoe et al., 2021)
AI, the space race, and prestige (Barnhart, 2021) (Excerpts)
On AI hardware supply chains:
"The Semiconductor Supply Chain" (Khan, 2021) (Excerpt)
"Maintaining China's Dependence on Democracies for Advanced Computer Chips" (Khan and Flynn, 2020) (Excerpt)
Corporations are the leading developers of AI, and there is some historical precedent of early corporate standards influencing later government regulation (Leung, 2018), so corporate AI developers' governance choices may be of great significance for the future of AI. How can, and how should, corporate AI developers shape the trajectory of AI?
Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims (Brundage et al., 2020) (Excerpt)
The Offense-Defense Balance of Scientific Knowledge: Does Publishing AI Research Reduce Misuse? (Shevlane and Dafoe, 2020)
Various short readings:
OpenAI Charter (2018) and OpenAI LP announcement (2019)
DeepMind reportedly lost a yearslong bid to win more independence from Google (Vincent, 2021)
How do a corporation's shareholders influence its Board of Directors? (Ross, 2021)
Information security careers for GCR reduction (Zabel and Muehlhauser, 2019) (Excerpt)
Governments have unique authorities to engage in acts like international coordination and commercial regulation, and there is historical precedent (Leung, 2019) of governments taking major roles in the governance of strategic general-purpose technologies. For these reasons, governments may be highly influential actors in shaping the future of AI. How can, and how should, governments and intergovernmental organizations shape the trajectory of AI?
This week emphasizes historical case studies, since these seem useful for informing intuitions about at least some of the future impacts of (and political/organizational reactions to) AI.
China's Current Capabilities, Policies, and Industrial Ecosystem in AI - Ding (2019)
Compilation: 7+ historical case studies of technology governance and international agreements (various authors)
"Policy Making for the Long Term in Advanced Democracies" (Jacobs, 2016) (Excerpt)
Global AI Talent Tracker (MacroPolo, c. 2019)
Twitter thread summary of the Evans et al. paper "Truthful AI" (Evans, 2021)
Twitter thread summary of Clark and Whittlestone paper "Why and How Governments Should Monitor AI Development" (Clark, 2021)
What is the EU AI Act and why should you care about it? (2021)
There is a growing ecosystem of professionals and organizations aiming to positively shape the long-term impacts of AI. This week, we'll dive into the question: What is happening in this ecosystem, and—if you want to join the efforts to positively shape AI's trajectory—how can you do so?
Like AI governance research, AI governance career opportunities are rapidly changing, so current opportunities and advice are likely far from all that will ever be available.
A personal take on longtermist AI governance (Muehlhauser, 2021)
AGI Safety Fundamentals - Further Resources
Description of some organizations in or adjacent to long-term AI governance (non-exhaustive) (2021)
Read about whichever of these AI governance career paths you're interested in:
AI governance research:
Unfortunately, I'm not aware of detailed, published career guidance for entering AI governance research. Here is some information about some specific opportunities: [...]
US policy work aimed at improving AI governance:
"How to pursue a career in US AI public policy," section from 80,000 Hours' article on US AI policy
Pick and read at least one of the US policy readings from the "Additional Recommendations" section.
Read these bullet points about some specific opportunities: [...]
China-based work aimed at improving AI governance
China-related AI safety and governance paths, 80,000 Hours career review (2021)
The "China and AI" section of the "Further Readings" section lists relevant reading lists. Pick and read at least one reading from one of these, for some additional context on AI governance in China.
Europe/UK-based work aimed at improving AI governance:
AI policy careers in the EU (Langosco, 2019)
AI Governance Career Paths for Europeans (2020)
Some AI governance research ideas (Anderljung and Carlier, 2021)
The final part of this course will be projects where you get to dig into something related to the course. The project is a chance for you to explore your_ _interests, so try to find something you're excited about! The goal of this project is to help you practice taking an intellectually productive stance toward AI governance - to go beyond just reading and discussing existing ideas, and take a tangible step towards contributing to the field yourself. This is particularly valuable because it's such a new field, with lots of room to explore.
This project might be especially valuable for learning more about how much you like AI governance research, identifying research areas you're excited about, and in some cases to produce directly useful work or a useful writing sample. (This is partly because people in the field often take such projects more seriously than, say, academics often do.) Still, a useful mindset for research in the field is often, "I'm trying to figure out what I should believe and explain it clearly"—that will often produce better (and, ironically, more impressive) work than if you were directly aiming for looking impressive.
We've allocated four weeks between the last week of the curriculum content and the sessions where people present their projects. As a rough guide, spending 5-10 hours on the project during that time seems reasonable, but we're happy for participants to be flexible about spending more or less time on it. You may find it useful to write up a rough project proposal in the first week of working on your project and to send it to your cohort for feedback.
The format of the project is very flexible. The default project will probably be a piece of writing, roughly the length and scope of a typical blog post; we'd encourage participants to put these online after finishing them (although this is entirely optional). Projects in the form of presentations are also possible, but we slightly discourage them; we'd prefer if you spend more time creating some piece of writing, then just casually talk through it with your cohort, rather than spending time on trying to make a polished presentation. We expect most projects to be individual ones; but feel free to do a collaborative project if you'd like to.
To find additional materials about topics you're interested in (or to find interesting topics), you may find this compilation of AI governance readings by topic useful.
This is the current version of the curriculum, as of writing. It will be further edited leading up to the start of the program.
A view that seems common in the long-term AI governance field is that the research field's growth is bottlenecked by capacity to train/mentor junior researchers. We might worry that, because of this, introducing more people to the field is of little value (or maybe even harmful, if it means more people will be frustrated by the difficulty of breaking into the field). I think there are several reasons why this program is valuable anyway:
There is a good chance the mentorship bottleneck will soon be eased.
From talking with a few people at relevant organizations, there seems to be significant interest in easing the mentorship bottleneck, as well as some promising, scalable ideas for doing so.
Mentorship is not a bottleneck in all important areas of the field.
My sense is that mentorship (more specifically, mentorship from members of this community) is not as severe of a bottleneck for ladder-climbing in government, which also seems important.
Even under mentorship bottlenecks, efficient introductions to the field are useful.
It's convenient for people to be efficiently introduced to the field, both for saving their time and for lowering mentorship costs.
As an "outside view" consideration, relevant professionals have generally been encouraging.
If you're interested after checking out this program's syllabus, we'd encourage you to apply! The governance track is sufficiently different in focus that you're still likely to learn a lot of new content, and the foundational ideas within AGI safety are complex and confusing enough to warrant more discussion and evaluation.
Since this would mean a roughly doubled time commitment, we mainly just recommend this to people who have a lot of free time or are at a particularly pivotal point in their career (such that learning more about these fields soon would inform time-sensitive decisions). If you'd like to apply to do both tracks simultaneously, there's an option to indicate that preference in the "choose a track" question of the application form.
We likely did—our version of the software used to process applications doesn't send confirmation emails.
Then you're all set! No need to submit an additional application here.
Take a look at the syllabus and decide from there! If you previously participated in one of the smaller AI governance reading groups I helped run, I think you'll find lots of new content in this syllabus.
I'd love to hear your ideas! Please let me know by commenting below or on the linked syllabus, messaging me through the forum, or emailing stanfordeateam [at] gmail [dot] com (although that is checked less frequently).
I'll be editing the syllabus and adding exercises and discussion prompts until the program starts in January.
These, as well as an incomplete private list: [1] [2] [3] [4] [5] [6] [7] ↩
For context on my own background, among other hats, I currently run the Stanford Existential Risks Initiative's AI governance and US policy programming. I've only been learning about this field for about a year, so I'm especially thankful for the 20+ years of collective expertise our advisors brought to this curriculum. ↩
If you are listed here and would prefer a different description/additional caveats/etc., please let me know! ↩
Some specific content gaps in current public materials, as flagged by one reviewer:
A good taxonomy of plausible sources of existential risk from AI
More thorough analysis of some potential AI-related existential risks to which misalignment is non-central
A good overview of different views on "theories of victory" and cruxes between people who disagree on them
(I don't have a very precise sense of what this reviewer has in mind with this term.)
(There are probably many more things.)
↩
As an example of researchers having different views on which abstractions to use, researchers have proposed and used a slew of different terms and concepts for thinking about future AI capabilities: AGI, human-level AI, advanced AI, strong AI, superintelligence (collective, speed, quality), transformative AI, oracle AI, genie AI, sovereign AI, tool AI, agentic AI, optimizer, mesa-optimizer, comprehensive AI services, and probably others. As another example, definitions/framings of "outer alignment," "inner alignment," and related terms are contested (you can find relevant in-the-weeds discussion e.g. here, here, here, and here) (thanks to Sam Clarke for flagging these to me). ↩
AI Governance1Education1AI3


Hulu has struck a new licensing agreement with Disney-ABC Television Group for unique subscription VOD rights that makes the complete libraries of Boy Meets World and House Improvement accessible on a streaming service for the first time. Together with the perk of presumably showing on an episode , individuals wanted to go to Home Enchancment tapings because Allen was known to use some off-shade language and improvise. Wonderful Article. Also, it is important to have your website arrange properly to take advantage of any free organic traffic you are seemingly missing out on. To do so your web site or blog have to be free of errors in the code and content so that you rank larger and people can find you when conducting a google search. Try to be using a free crawler analytic device repeatedly wish to determine and proper points and keep an optimized website architecture.
We love being able to make a night time natural infusion that helps the body to de-stress, loosen up and go to sleep. This particular mix is a combination I put collectively for my very own children. We undergo waves of consistently having a cup of this tea before bed. It is enjoyable to hear the children ask for this tea once I fall out of doing this nightly routine. Not only do they love the tea however they love the consistency of such a routine and love being given one thing particular earlier than they go to bed. It is especially cozy to drink a tea like this earlier than mattress in the course of the colder months. This mild-tasting natural tea tastes great and is soothing to all techniques within the physique.
Dan Aykroyd makes a guest appearance as a blues-loving minister in the episode "Losing My Faith" during the seventh season, the character he was at present taking part in on the time in the sequence Soul Man , a collection that was also created by the creators of Residence Improvement that lasted two seasons. Aykroyd helps Tim perceive Tim's son Randy's determination to not go to church after he starts volunteering at a hospice. The Building Division's certified workers can be found to help you throughout your house enchancment undertaking and supply the next instruments that will help you with the process.
Admission rumors make me snicker sometimes, but not today. At present, I heard that a number of college students at a selected highschool told their counselors that UVA would put them in a "particular class" in the event that they were capable of get 9 AP programs on their transcripts. Early Installment Weirdness : In the first a number of episodes, Jill's cooking is definitely good. Tim even remarks on how good the chili is in For Whom the Belch Tolls. It wasn't until later that Jill's reputation as a Lethal Chef grew to become a operating gag.
Overly Slender Superlative : During a special episode of Tool Time, Tim thanked the viewers for making it "Michigan's fourth-highest-rated cable instrument show". We are able to add that publishing a put up or article must be a promotional instrument for each enterprise, not simply to get extra site visitors, also to get more prospects for any business. The Device Time theme music, an early Sixties-type saxophone-dominated instrumental rock tune, was sometimes used because the closing theme music for Residence Enchancment, especially when behind the credit were operating the blooper scenes that occurred in the course of the taping of a Software Time segment.
⟵See The Solid Then And Now!


text:
When would you use brackets in your writing?
Brackets (parentheses) are punctuation marks used within a sentence to include information that is not essential to the main point. Information within parentheses is usually supplementary; were it removed, the meaning of the sentence would remain unchanged.
How do you use brackets in a sentence?
Square brackets (also called brackets, especially in American English) are mainly used to enclose words added by someone other than the original writer or speaker, typically in order to clarify the situation: He [the police officer] can't prove they did it.
Why are square brackets used in writing?
Square bracketsalso called crotchets or simply brackets (US)are often used to insert explanatory material or to mark where a [word or] passage was omitted from an original material by someone other than the original author, or to mark modifications in quotations.
What does it mean when a word is in brackets?
Brackets, sometimes called square brackets, are most often used to show that words have been added to a direct quotation. Sometimes, when quoting a person or document, adding a word or two is necessary to provide enough context for the quote to make sense.
How do you know when to use parentheses or brackets?
Use a bracket (sometimes called a square bracket) to indicate that the endpoint is included in the interval, a parenthesis (sometimes called a round bracket) to indicate that it is not. parentheses are like strict inequalities. (3,7) includes 3.1 and 3.007 and 3., but it does not include 3 .
What is the difference between bracket and parenthesis?
Parentheses are used to enclose numbers, words, phrases, sentences, letters, symbols, and other items while brackets are used to enclose information that is inserted into a quote as well as parenthetical materials within the parentheses.
Which brackets are inclusive?
The largest number in the interval is written second, following a comma. Parentheses, ( or ), are used to signify that an endpoint value is not included, called exclusive. Brackets, [ or ], are used to indicate that an endpoint value is included, called inclusive.
Can you have two brackets next to each other?
Parentheses should not be used in immediate proximity to each other or within another set of parentheses; in the latter case, use brackets instead (or commas or dashes). Avoid situating a complete sentence in parentheses within another sentence.
Do periods go inside brackets?
The period is a strong punctuation mark—think of it as controlling the action in the sentence, which occurs outside the parentheses. 2. When a whole sentence falls inside parentheses, the period goes inside. Correct: (Several other courses were offered, but they were not as popular.)
What are pointed brackets?
{ }: brace or curly bracket. : pointy bracket or inequality sign. One more paired punctuation, the angle bracket (〈 〉), is used mainly in mathematics; however, most computer keyboards do not have these symbols and writers tend to use the pointy brackets in its place.
How many types of brackets are there?
Types of brackets include: parentheses or "round brackets" ( ) "square brackets" or "box brackets" [ ] braces or "curly brackets" { }
What is the difference between () and [] in math?
Hope it helps! [], are used for "commands", whereas parenthesis, (), are used for functions. In other way, the most common use of () parenthesis in programming is strictly the mathematical use of all the symbols. The other symbols are reserved for other purposes.
What do [] mean in math?
A square bracket at one end of an interval indicates that the interval is closed at that end (i.e., the number adjacent to the opening or closing square bracket is included in the interval). …
What does U mean in math?
more The set made by combining the elements of two sets. So the union of sets A and B is the set of elements in A, or B, or both. The symbol is a special "U" like this: ∪
What do three equal lines mean?
A symbol with three horizontal line segments ( ) resembling the equals sign is used to denote both equality by definition (e.g., means is defined to be equal to ) and congruence (e.g., means 13 divided by 12 leaves a remainder of 1–a fact known to all readers of analog clocks).
What is math symbol called?
^ The carat. In math, particularly when typing on a keyboard, this symbol can be used to indicate raised to a power. For example, 4^3 = 4 to the 3rd power = 4 x 4 x 4 = 64. & This symbol is called an ampersand. It almost always means "and," both in and outside of mathematics.
What are all the math words?
The Basic OperationsSymbolWords Used+Addition, Add, Sum, Plus, Increase, Total−Subtraction, Subtract, Minus, Less, Difference, Decrease, Take Away, Deduct×Multiplication, Multiply, Product, By, Times, Lots Of÷Division, Divide, Quotient, Goes Into, How Many Times
What does F mean in math?
more A special relationship where each input has a single output. It is often written as "f(x)" where x is the input value. Example: f(x) = x/2 ("f of x equals x divided by 2")
Previous: How many pages is a 400 word essay?
Next: What is red Blue Green called in MTG?


text:
You are currently viewing all posts tagged with getting the prices right.
Sustainability accounting, carbon taxes and flying blind
Posted on February 25, 2013 by Gil Friend in Uncategorized with No Comments
There's a rising tide of interest in the intersection of sustainability and money—and specifically accounting and finance. The Sustainability Accounting Standards Board (I serve on its board of advisors), which was formed to develop industry-specific sustainability accounting standards, has gained ANSI accreditation and reported out from its first Industry Working Group—Health Care. Some 29 global financial institutions (though still no US banks) signed on to the Natural Capital Declaration in Rio last year, while a comparable number of global companies pledged to follow Puma's lead in development of "environmental profit and loss statements" and balance sheets.
The management challenge is stark. CFOs are still relatively rare players in the sustainability game, yet their active participation is essential. Sustainability is still seen as a cost by so many people who should know better. And most companies are still flying blind. As my colleague R. Paul Herman of HIP Investor, observes, most executives would acknowledge that people—what we lately call "human capital"—are any company's most valuable asset, but if they're to be found on the balance sheet at all they're listed as a liability—and as an expense on the income statement! "How can you manage what you don't measure?" Herman asks, and the same question holds for the many "externalities"—both costs and benefits—that are still absent from the world of finance but are inescapably present in the physical and biological world on which all wealth and well-being depends.
Meanwhile, China has announced plans to institute a carbon tax (and still plans to reduce carbon intensity 40% by 2025). While the impact remains to be seen, it's a bold move in contrast to US avoidance of the issue, and is another data point suggesting a growing shift in global leadership.
How would you connect the dots between these recent data points?
We've lost 80% of Arctic ice volume in the last 30 years.
The US General Accounting Office (GAO) has added climate to its finance "high risk" list.
Paying the true cost of environmental impacts would cut global profits by 40-50%.
NYC Mayor Michael Bloomberg has challenged the private sector to match the city by cutting emissions 30% within 10 years.
MIT's Sloan School and Boston Consulting Group report that 37% of companies say they have profited from sustainability initiatives, and "nearly 50% of companies have changed their business models as a result of sustainability opportunities."
Here's how I connect them:
The challenges we face are far more serious than most anyone admits;
The opportunities for innovation, leadership and profit are much larger than most anyone suspected; and
It's time for companies—for everyone—to be playing a much bigger game.
Just enter your email right here to subscribe to my blog.
Need a compelling speaker?
Book me to speak at your next conference or corporate event!
Click, Follow & Link!
• Follow me on Twitter!
• Link with me on LinkedIn!
News & Thoughts: The Certainty of Uncertainty, and The Problem with Purpose
Is it VUCA yet? Changing At The Speed of Now.
While Covid grips our minds…the climate crisis looms not far behind
"The only way out is through"— Reflections on covid, climate, crisis, contingency, and cultivation
Walking the Talk: What the CEO "Purpose of Business" Declaration Is — and What It Isn't
Just added: Nora Bateson & Gil Friend: Warm Data at the Edge of Now
Gil Friend and Nora Bateson: A conversation at the edge of now
To CalPERS Board. Re: Sustainable investment & Divestment Overview
News from Natural Logic: Welcome back! New offers. Get the sizzle. And join the party!
Business Risk & Strategic Duty—Moving beyond quarterly capitalism in a carbon-constrained world
How business can learn to navigate—and thrive in—the Anthropocene
Counting What Counts: The Evolution of New Metrics
Sustainability Leaders' Perspectives on 2016, 2017 & 2030
A Few Choice Holidays Wishes for You from Natural Logic
Deal of the century: buyout the US coal industry for $50bn
Mindsets and Money: Breaking the Grip of Distorted Economics
Smart Cities & Sustainable Development—Masdar Engage
Mandela. A reflection.
Big changes. Full circle.
Moving beyond benefit-cost analysis of climate change
Amory Lovins applied Materials Bill McDonough Bob Willard Bob Willard. Gil Friend bookstores Buckminster Fuller Bucky Fuller business case Communitelligence ecosystem services flourishing getting the prices right GMOs Grist haiku Interface International Society of Sustainability Professionals ISSP Joel Makower John Ehrenfeld Karl-Henrik Robert keynote leadership librarians libraries Marc Gunther Marsha Willard Michael Braungart News Pinnacle Award poetry Prop 37 Ray Anderson regeneration Seth Godin Stacey Mitchell sustainability Sustainability Hall of Fame Sustainability Index sustainable consumption systems thinking The Upcycle WalMart World Game Workshop
Mitra Ardron on "The only way out is through"— Reflections on covid, climate, crisis, contingency, and cultivation
Liza Loop on "The only way out is through"— Reflections on covid, climate, crisis, contingency, and cultivation
administrator on Power. Politics.
Taina Peller on Power. Politics.
Steve Brant on CSR? None for me, thanks!
administrator on Brand Slogan or Brand Promise?
administrator on 3 tools I absolutely can't do without
singapore business branding consultancy on Brand Slogan or Brand Promise?
Militsa on 3 tools I absolutely can't do without
motivational speaker singapore on Brand Slogan or Brand Promise?
Archives Select Month January 2021 (1) June 2020 (1) April 2020 (1) March 2020 (1) September 2019 (1) March 2019 (3) February 2019 (1) June 2018 (2) December 2017 (1) January 2017 (1) December 2014 (1) March 2014 (2) January 2014 (1) December 2013 (3) November 2013 (5) October 2013 (3) September 2013 (4) August 2013 (5) July 2013 (2) June 2013 (3) May 2013 (3) April 2013 (6) March 2013 (6) February 2013 (3) January 2013 (5) December 2012 (3) November 2012 (2) September 2012 (1) August 2012 (1) July 2012 (1) June 2012 (1) May 2012 (2) April 2012 (3) March 2012 (1) February 2012 (4) January 2012 (2) December 2011 (3) November 2011 (2) October 2011 (2) September 2011 (3) August 2011 (1) July 2011 (3) June 2011 (7) May 2011 (5) March 2011 (3) February 2011 (6) January 2011 (4) December 2010 (4) October 2010 (5) September 2010 (3) July 2010 (1) May 2010 (2) April 2010 (3) March 2010 (3) February 2010 (2) January 2010 (2) December 2009 (1) November 2009 (1) October 2009 (1) September 2009 (6) August 2009 (2) July 2009 (1) June 2009 (6) May 2009 (21) April 2009 (3) March 2009 (2) February 2009 (5) January 2009 (5) December 2008 (4) November 2008 (10) October 2008 (11) September 2008 (6) August 2008 (11) July 2008 (5) May 2008 (1) April 2008 (7) March 2008 (6) February 2008 (15) January 2008 (5) December 2007 (11) November 2007 (8) October 2007 (1) September 2007 (6) August 2007 (2) July 2007 (1) June 2007 (1) May 2007 (5) April 2007 (6) March 2007 (6) February 2007 (26) January 2007 (6) December 2006 (6) November 2006 (4) October 2006 (14) September 2006 (8) August 2006 (8) July 2006 (4) June 2006 (12) May 2006 (10) April 2006 (5) March 2006 (16) February 2006 (23) January 2006 (19) December 2005 (6) November 2005 (10) October 2005 (7) September 2005 (14) August 2005 (17) July 2005 (29) June 2005 (13) May 2005 (21) April 2005 (20) March 2005 (22) February 2005 (46) January 2005 (16) December 2004 (29) November 2004 (8) October 2004 (12) September 2004 (9) August 2004 (49) July 2004 (8) June 2004 (19) May 2004 (18) April 2004 (7) March 2004 (9) February 2004 (4) January 2004 (29) December 2003 (37) November 2003 (3) October 2003 (8) September 2003 (5) August 2003 (56) July 2003 (23) June 2003 (29) May 2003 (52) April 2003 (31) March 2003 (1) February 2003 (43) January 2003 (97) December 2002 (69) November 2002 (29) October 2002 (25) September 2002 (6) August 2002 (72) June 2002 (3)


text:
Willy Rampf announces 'big update' at Italian Grand Prix
Written by David on June 25th, 2008 at 4:25 pmLast Update: June 25th, 2008 at 4:25 pm
In a note appeared on BMW-Sauber's official website, the team's Technical Director Willy Rampf has promised a "big update" at the Italian Grand Prix later in September at the "Autodromo di Monza". "We don't have any major modifications planned, but will continue to concentrate on optimising details", said Rampf. "Of course, there will be new turning vanes, or new wings for the engine cover, but certainly no redesigned car."
"There will certainly be a big update, which will also be visible, for Monza," continued Rampf. "That is a circuit with low downforce."
Australian GP, Qualifiers: Excellent performance from Robert Kubica
2009 Turkish GP, race: BMW Sauber quotes
BMW Sauber: 2009 Singapore Grand Prix preview
2009 Italian GP: Post Qualifying press conference
Fernando Alonso impressed with BMW Sauber performance
BMW to quit F1 at the end of the 2009 season
2009 Italian GP, qualifying: McLaren quotes
2009 European GP, race: BMW Sauber quotes
BMW Sauber: 2009 British Grand Prix preview
2009 Italian GP, race: Brawn GP quotes


Alexandru Mihai Grumezescu » Testing antifungal activity of some essential oils using flow cytometry.
Crina Saviuc, Alina Maria Holban, Alexandru Mihai Grumezescu, Coralia Bleotu, Otilia Banu, Veronica Lazar, Dan Eduard Mihaiescu, Mariana Carmen Chifiriuc: Testing antifungal activity of some essential oils using flow cytometry. Letters in Applied Nanobioscience, 1(3), 67-71, 2012.
The use of natural antifungal compounds has become a viable alternative for fighting fungal infections since high rates of resistance to synthetic antifungal compounds has emerged. Classical techniques aimed to routinely investigate fungal susceptibility are often limited when using natural essential oils, because of their instability and great volatility that may lead to false results. In this study, we report the results obtained by classical antimicrobial susceptibility testing techniques and flow cytometry regarding the effect of some volatile oils on different Candida clinical isolates. The obtained results revealed that flow cytometry is a very useful and precise technique in investigating the influence of essential oils on the fungal cells, surpassing the disadvantage of their volatility and thus reducing false results often obtained by using the classical methods.


Acute methamphetamine administration in rats has been used as an animal model of mania . The candidate genes identified in that study through a convergent functional genomics approach, as discussed in the section on mood disorders, may also represent players involved in stimulant addiction and provide a mechanistic basis for comorbidity between bipolar disorders and stimulant abuse and addiction.
Cocaine enhances dopamine-mediated neurotransmission by blocking dopamine re-uptake at axon terminals. The striatum is one such site of action. Chronic exposure to cocaine up-regulates several transcription factors that alter gene expression and which could mediate the long-term neural and behavioural changes induced by the drug. One such transcription factor is DeltaFosB, a protein that persists in striatum long after the end of cocaine exposure. Using DNA microarray analysis of striatal tissue from both indu-cible transgenic mice engineered to overexpress DeltaFosB and mice treated with cocaine, cyclin-dependent kinase 5 (Cdk5) was identified as a downstream target gene of DeltaFosB . Overexpression of DeltaFosB, or chronic cocaine administration, raised levels of Cdk5 messenger RNA, protein and activity in the striatum. Interestingly, injection of Cdk5 inhibitors into the striatum potentiated behavioural effects of repeated cocaine administration. This elegant study implicates the neuronal protein Cdk5 as a regulator of the effects of chronic exposure to cocaine, and identifies a novel cellular pathway as a potential target for pharmaceutical drug development.
Changes in brain gene expression are thought to be responsible for the tolerance, dependence and neurotoxicity produced by chronic alcohol abuse. DNA microarrays have been used recently with some success in studies of alcoholism . RNA was extracted from post-mortem samples of superior frontal cortex of alcoholics and non-alcoholics. Relative levels of RNA were determined by array techniques. Expression levels were determined for over 4000 genes, and 163 of these were found to differ by 40% or more between alcoholics and non-alcoholics. Analysis of these changes revealed a selective reprogramming of gene expression in this brain region, particularly for myelin-related genes, which were down-regulated in the alcoholic samples. In addition, cell cycle genes and several neuronal genes were changed in expression. The investigators conclude that the observed gene expression changes suggest a mechanism for the loss of cerebral white matter in alcoholics as well as alterations that may lead to the neurotoxic actions of ethanol.
A recent study comprehensively catalogued gene expression changes in rat brains following acute and chronic exposure to S-9-tetrahydrocannabinol (THC), the active ingredient in marijuana, using microarray technology profilinga total of24456 cDNAs . Of these, only 49 different genes showed specific changes in expression compared to control animals, including some signal transduction molecules (prostaglandin D synthase, calmodulin), and structural molecules [neural cell adhesion molecule (NCAM), myelinbasic protein].
The sequencing of the human and other mammalian genomes is a watershed event that will help us to understand the biology of addiction by enabling us to identify genes that contribute to individual risk for addiction and those through which drugs cause addiction. A preliminary search of a draft sequence of the human genome for genes related to desensitization of receptors that mediate the actions of drugs of abuse on the nervous system yielded multiple potential candidates and illustrates the impact this methodology can make in speeding up the discovery process .


Delray Beach resident S. Henry Dixon met Anita Hill at Brooklyn College.
I watched the recent United States Senate Judiciary Committee's dramatic hearing regarding the alleged sexual assault accusation against U.S. Supreme Court nominee Judge Brett Kavanaugh. This hearing reminded me of the Clarence Thomas hearing that was held some 27 years ago before the same committee. It seemed like history was repeating itself, except that the actors were different. Let us look at some of the striking similarities of both hearings, and their impact on American society.
Both Supreme Court candidates were conservative men who were nominated by a Republican president. It was only after their nomination was nearing a full senate vote that the allegation of a woman accuser was made public, thereby putting their final confirmation at risk. At first both women wanted to remain incognito, but then came forward publicly after changing their minds.
Each accuser's allegation pertained to sexual misconduct. In the 1991 hearing, a former female government employee, professor Anita Hill, accused her boss Thomas of sexual harassment in the workplace. Fast forward to today, another female professor, Dr. Christine Blasey Ford, accused Kavanaugh of attempted sexual assault when she was in high school some 36 years ago. Either by design or pure coincidence, both women wore blue dresses when they testified. Whereas in 1991 the Senate Judiciary Committee was composed exclusively of Caucasian men, this time around it was diversified racially and gender-wise.
When the committee did hear from the accusers, many viewers concluded that both women appeared to be credible. In response, both men categorically denied the accusation. As in the past with Thomas, Kavanaugh's supporters have claimed that the reputation of a good man was being smeared. To date, there has been no corroborating evidence for Dr. Ford's allegation. The allegation against Kavanaugh was investigated by the FBI as was the one against Thomas.
The showdown before the senate then and now demonstrated the deep partisan divide in Congress as well as throughout the country, and both publicly televised hearings took on an appearance of a circus. To many people, the emotionally charged hearings and the surrounding incivility they aroused were an unsettling spectacle. The hearings in 1991 encouraged more women to run for office. Many did run and did win. Today, more women hold various high political offices. This hearing will certainly generate more women activism. Since 1991, Anita Hill has been active in social issues. When I met her some years ago at a Brooklyn College event, where she spoke, among other things, about socio-economic issues occurring at that time, she came across as a very warm person who was passionate about the social issues she cared about.
Anita Hill has had an enormous impact on American culture, particularly in heightening the awareness of sexual harassment in the workplace. I am sure that Dr. Ford will likewise have a profound impact on society in the future. Regardless of the impact, my hope is that the right to fair treatment under the law does not suffer irreparable harm and that civility will prevail.
S. Henry Dixon is a Delray Beach resident and a retired New York public servant.


Hyatt Mountain Lodge is an old-world alpine chalet with emphasis on modern luxury and sophistication. Whether your visit brings you to Avon for adventure or relaxing, the resort is perfectly equipped to handle your needs with plenty of top-notch amenities, such as a relaxing pool and whirlpool spa to enjoy after a long day, concierge service for assistance with making the most of your stay and resort-wide Wi-Fi access to keep you connected around the clock and on the go.
Lavish studio, 1 and 3 bedroom residence with mountain, pool or village views include master suite with king-size bed, adjoining bath, walk-in shower and spa tub. Two guest suites with two twin beds and full baths. Full kitchen, balcony, fireplace, washer and dryer, sofa bed, stereo, DVD, and Internet.
Hyatt Mountain Lodge has a great location bordering Beaver Creek Village, Creekside Park, and the Vilar Center for the Arts. A skiway gives ski-out access to the Elkhorn Lift and all the slopes of Beaver Creek Mountain. Virtually every Beaver Creek shop, restaurant, and recreational amenity is within an easy walk from the Mountain Lodge. There are activities for all seasons - skiing, skating, hiking, fishing, golf, and much more.
summer hiking treks, whitewater rafting, horseback riding, mountain biking, fly-fishing, tennis and much more. Ask your concierge for details. Beaver Creek's enticing dining and nightlife runs the gamut from luxurious to laid-back.


text:
Mussel Strength: How Mussels Cling to Surfaces
(Image: © margouillat photo | Shutterstock)
When mussels dangle from marine surfaces, they hold on by a cluster of fine threads. These filaments may appear flimsy, but they can actually withstand powerful impacts from currents or crashing waves. Now, researchers are unraveling the secret of these thin, bungeelike cords in order to develop more effective glues and other synthetic biomedical materials.
Unlike barnacles, which fasten themselves tightly to rocks or piers, mussels use silky fibers, called byssus threads, to loosely attach to a surface while still being able to drift and absorb nutrients in the water. So, how do these seemingly delicate threads help mussels stay put?
From laboratory tests and computer models, scientists at the Massachusetts Institute of Technology (MIT) discovered that roughly 80 percent of the length of byssus threads — the same parts of the threads that connect the mussel to a hard surface on one end — is composed of stiff material, whereas the remaining 20 percent, at the end that is affixed to the mussel itself, is soft and stretchy. The combination of these different material properties likely helps the mussels adhere to surfaces, and enables them to survive the impact of various forces. [Photos: 101 Animal Shots You'll Go Wild Over]
"It turns out that the … 20 percent of softer, more extensible material is critical for mussel adhesion," Zhao Qin, a research scientist at MIT, said in a statement.
Researchers have studied byssus threads before, but Qin and his colleagues wanted to observe how these threads, and all their connecting parts, operate in simulated wave conditions.
"We figured there must be something else going on," Markus Buehler, head of MIT's department of civil and environmental engineering, said in a statement. "The adhesive is strong, but it's not sufficient."
The researchers placed an underwater cage in Boston Harbor for three weeks to see how mussels attached themselves to glass, ceramic, wood and clay surfaces. In the lab, the scientists used a tensile machine to test the strength of byssus threads as they were pulled and deformed.
Even though byssus threads have both stiff and stretchy properties, the filaments are made of a protein closely related to collagen, the researchers said. From their experiments, the scientists discovered that the distribution of stiffness along the threads is critical to their effectiveness.
Understanding how byssus threads work could help scientists design synthetic materials with similarly flexible properties, such as surgical stitches that connect tissues together. The findings could also help in the development of new building materials, sensors for underwater vehicles and other equipment that could be subjected to extreme conditions, the researchers said.
The detailed findings of the study were published online today (July 23) in the journal Nature Communications.


text:
SBP is a library that makes Objective C UI more maintainable by utilizing Storyboards and code together.

SBP currently allows you to do two things quickly:
- Produce static animations with autolayout in a maintainable way
- Switch from storyboard views ment for all screen sizes to specalized views for screen sizes and back again

## How To Get Started

### Installation with CocoaPods

[CocoaPods](https://cocoapods.org/) is a dependency manager for Objective-C, which automates and simplifies the process of using 3rd-party libraries like AFNetworking in your projects. See the ["Getting Started" guide for more information](https://github.com/AFNetworking/AFNetworking/wiki/Getting-Started-with-AFNetworking).

#### Podfile

```ruby
platform :ios, '5.0'
pod "SBP"
```

### Tutorial Youtube Videos can be found here
- https://www.youtube.com/watch?v=vc0KjFlMPeQ
- https://www.youtube.com/watch?v=8KVKXlh6sKI

### Example github project
https://github.com/awaran/SBPExample

## More Details

#### Include before use
```ruby
#import "UIViewController+SBP.h" //A category that adds on functionality to viewcontrollers
```

#### Swap two views layouts with animation
```ruby
- (void)switchViewConst:(UIView*)firstView secondView:(UIView*)secondView durationInSeconds:(double)durationInSeconds
```

#### Split universial screen size Storyboard path to multiple screen size paths

* Postfix the segue identifier strings with these
- @"_3_5"  //for the 3.5 inch screens
- @"_4"  //for the 4 inch screens
- @"_4_7" //for the 4.7 inch screens
- @"_5_5" //for the 5.5 inch screens

* Pick a single prefix segue identifier.  If you choose @"hello_world" as your prefix, your segue identifiers should look like this
- @"hello_world_3_5"
- @"hello_world_4"
- @"hello_world_4_7"
- @"hello_world_5_5"

* Use this method to split the single Storyboard path into multiple screen paths
```ruby
- (void)segueScreenSizeSplit:(NSString*)baseSegueName
```

* If you had chosen @"hello_world" as your prefix and you were in the viewcontroller that needed to split the path, the code should look something like this
```ruby
[self segueScreenSizeSplit:@"hello_world"];
```

This is my first open source project and would like to colaborate with others.  Please email me if you have any comments or questions at waran.arjay@gmail.com.

redpajama_set_name: RedPajamaGithub


"Yet Obama on Monday was blaming Russia and China for the mess Washington has created, saying, 'We see some major powers assert themselves in ways that contravene international law.' Obama cited Russia's 'annexation' of Crimea and 'further aggression' in Eastern Ukraine.
"At heart is either Obama's willful ignorance of Ukraine, a clumsy attempt at disinformation, or as Vladimir Putin suggested in his U.N. speech a half hour later, a big measure of self-deception.
"Obama said Ukrainians favor the West. That may be true of most western Ukrainians but not the whole country. Then, he said the U.S. has 'few economic interests' in Ukraine. That's woefully ignorant or a blatant lie. Monsanto has a big interest. Then there's Joe Biden's son, Hunter Biden, and a John Kerry family friend joining the board of Burisma Holdings, Ukraine's largest private gas producer, just after the coup.
A nationwide consortium, the Institute for Public Accuracy (IPA) represents an unprecedented effort to bring other voices to the mass-media table often dominated by a few major think tanks. IPA works to broaden public discourse in mainstream media, while building communication with alternative media outlets and grassroots activists.


•♥• JDM •♥•. . Wallpaper and background images in the Jeffrey Dean morgan club tagged: jeffrey dean morgan supernatural john winchester.
This Jeffrey Dean morgan photo might contain traje de passeio and terno de negócio.