class: center, middle, inverse, title-slide # 4.4 — Products Liability ## ECON 315 • Economics of the Law • Spring 2021 ### Ryan Safner
Assistant Professor of Economics
<EMAIL_ADDRESS>ryansafner/lawS21
lawS21.classes.ryansafner.com
--- class: inverse # Outline ### [A Brief History of Products Liability Law](#6) ### [A Simple Economic Model of a Risky Product](#24) ### [Risky Products: Precaution Incentives](#39) ### [Workplace Accidents](#61) --- # Products Liability .pull-left[ - So far, we have been assuming accidents are entirely between strangers who have no personal or economic relationship - We now turn to .hi[products liability], an subfield of tort law where the injurer is a business and the victim is a consumer of their product - Parties have an economic relationship ] .pull-right[ .center[ ![](../images/productwarnings.png) ] ] --- # Products Liability .pull-left[ - Worth studying for three reasons - Used to be a minor type of tort case, now a major growing and specialized body of law - Source of growing dissatisfaction with our tort liability system - Accidents are between parties with a contractual relationship - Hybrid of contract and tort law ] .pull-right[ .center[ ![](../images/productwarnings.png) ] ] --- # The Growth of Products Liability Cases .pull-left[  ] .pull-right[  ] --- class: inverse, center, middle # A Brief History of Products Liability Law --- # A Brief History of Products Liability Law .pull-left[ - Three main phases of products liability law development - Corresponding to the main type of liability rule used 1. Pre-1916: No Liability 2. 1916-1960: Negligence 3. 1960—Present: Strict Liability ] .pull-right[ .center[ ![](../images/productwarnings.png) ] ] --- # A Brief History of Products Liability Law .pull-left[ - Since Industrial Revolution, focus on production and economic growth - Some suggest influence of Classical Economists (or at least, their ideas) on weak tort law against business - Some feared excessive liability for producers would threaten business - .hi[No Liability] for manufacturer - Essentially .hi-purple[*caveat emptor*] (buyer beware) ] .pull-right[ .center[ ![](../images/buyerbeware.jpg) ] ] --- # A Brief History of Products Liability Law .pull-left[ .smallest[ - Main law in operation was .hi[contract law] - Doctrine of .hi-purple[privity of contract]: only parties to a contract can sue one another - If `\(A\)` and `\(B\)` have a contract, party `\(C\)` cannot sue either `\(A\)` or `\(B\)`, unless `\(C\)` is part of the contract - i.e. consumer can only sue the immediate *retailer* from whom they purchased a product, not the product’s initial manufacturer - This effectively insulated manufacturers from liability ] ] .pull-right[ .center[ ![](../images/contract1.jpg) ] ] --- # A Brief History of Products Liability Law .pull-left[ - *MacPherson v. Buick* (217 N.Y. 382, 1916) - MacPherson had bought a Buick from a NY auto dealer, the car later lost a wheel and ejected him from the car - Buick says they have **no privity** with MacPherson, only with the dealership ] .pull-right[ .center[ ![](../images/wreckedbuick.jpg) ] ] --- # A Brief History of Products Liability Law .left-column[ .center[ ![:scale 70%](../images/cardozo.jpg) .smallest[ Benjamin N. Cardozo 1870—1938 Associate Justice of U.S. Supreme Court ] ] ] .right-column[ .smaller[ > “If the nature of a thing is such that it is reasonably certain to place life and limb in peril when negligently made, it is then a thing of danger. Its nature gives warning of the consequence to be expected. If to the element of danger there is added knowledge that the thing will be used by persons other than the purchaser, and used without new tests, then, irrespective of contract, the manufacturer of this thing of danger is under a duty to make it carefully. That is as far as we need to go for the decision of this case...If he is negligent, where danger is to be foreseen, a liability will follow.” ] .source[[Opinion of the Court](http://www.courts.state.ny.us/reporter/archives/macpherson_buick.htm)] ] --- # A Brief History of Products Liability Law .pull-left[ .smallest[ - *MacPherson v. Buick* (217 N.Y. 382, 1916) - Court rejected privity, arguing the manufacturer could have reasonably forseen the possibility of such injuries to car’s ultimate users (consumers), and not just the *immediate* purchaser (dealer) - Victim must prove negligence of the manufacturer - MacPherson successfully showed Buick negligent - Shift from 1916—1960 to a .hi[negligence] rule - Consumer must show manufacturer was negligent in its production ] ] .pull-right[ .center[ ![](../images/wreckedbuick.jpg) ] ] --- # A Brief History of Products Liability Law .pull-left[ - Final shift from negligence to strict liability occurred via two separate routes 1. Gradual increase in standard of due care owed by manufacturers 2. Increase in producer liability for breach of warranty ] .pull-right[ .center[ ![](../images/productwarnings.png) ] ] --- # A Brief History of Products Liability Law .pull-left[ - *Escola v. Coca-Cola Bottling Co.* (24 Cal.2d 453, 1944) - Waitress in restaurant served a Coca-cola bottle that spontaneously exploded, causing injuries - Plaintiff could not offer any evidence that Coca Cola was negligent in its production ] .pull-right[ .center[ ![](../images/coke-explode.png) ] ] --- # A Brief History of Products Liability Law .left-column[ .center[ ![:scale 100%](../images/court1.jpg) ] ] .right-column[ Majority opinion (Chief Justice Phil S. Gibson): .smallest[ > “Upon an examination of the record, the evidence appears sufficient to support a reasonable inference that the bottle here involved was not damaged by any extraneous force after delivery to the restaurant by defendant. It follows, therefore, that the bottle was in some manner defective at the time defendant relinquished control, because sound and properly prepared bottles of carbonated liquids do not ordinarily explode when carefully handled.” ] .source[[Opinion of the Court](https://law.justia.com/cases/california/supreme-court/2d/24/453.html)] ] --- # A Brief History of Products Liability Law .left-column[ .center[ ![:scale 100%](../images/court1.jpg) ] ] .right-column[ Concurring opinion (Justice Roger Traynor): .smallest[ > “[Public policy demands] responsibility be fixed wherever it will most effectively reduce the hazards to life and health inherent in defective products that reach the market...In leaving it to the jury to decide whether the inference has been dispelled, regardless of the evidence against it, the negligence rule approaches the rule of strict liability. It is needlessly circuitous to make negligence the basis of recovery and impose what is in reality liability without negligence. If public policy demands that a manufacturer of goods be responsible for their quality regardless of negligence there is no reason not to fix that responsibility openly.” ] .source[[Opinion of the Court](https://law.justia.com/cases/california/supreme-court/2d/24/453.html)] ] --- # A Brief History of Products Liability Law .pull-left[ .smallest[ - *Escola v. Coca-Cola Bottling Co.* (24 Cal.2d 453, 1944) - Court held Injurer liable under legal doctrine of .hi-purple[*res ipsa loquitor*] (“the thing speaks for itself”) - Evidence of the accident alone is sufficient to hold injurer liable - Only a defective Coke bottle would explode - As due care does not entirely eliminate the risk of accidents under this rule, effectively a rule of .hi[strict liability] for the Injurer - Modern production makes it too difficult for consumers to inspect or verify the product is safe; strict liability more practical ] ] .pull-right[ .center[ ![](../images/coke-explode.png) ] ] --- # A Brief History of Products Liability Law .pull-left[ - In contract law, sellers are strictly liable for damages caused by products that fail to operate as presented (regardless of negligence), a .hi-purple[breach of warranty] - However, privity of contract operates here — only those who are a party to the contract may sue (essentially for breach of contract) ] .pull-right[ .center[ ![](../images/contract1.jpg) ] ] --- # A Brief History of Products Liability Law .pull-left[ .smallest[ - *Henningsen v. Bloomfield Motors, Inc.* (32 N.J. 358, 1960) - Henningsen bought a Chrysler car from Bloomfield Motors (an auto dealer) - Steering mechanism in Henningsen’s Chrysler car failed, causing an accident - Sale contract between Henningsen’s and the manufacturer expressly limited Chrysler’s liability to the original purchaser (dealer), and only for certain types of damages (defective parts, etc.) - Contract stipulated no express or implied warranty to customer ] ] .pull-right[ .center[ ![](../images/henningsen.jpg) .source[[Source](https://www.youtube.com/watch?v=MSlaPJAlAew)] ] ] --- # A Brief History of Products Liability Law .left-column[ .center[ ![:scale 100%](../images/court1.jpg) ] ] .right-column[ .smaller[ - Court rejected this limitation, arguing implied warranty of fitness prevailed regardless of any expressed contractual terms to the contrary - Additionally, struck down the privity doctrine - Although the victim was not the original purchaser, she: ] .quitesmall[ > “in the reasonable contemplation of the parties to the warranty, might be expected to become a user of the automobile. Accordingly, her lack of privity does not stand in the way of prosecution of the injury suit against the defendant Chrysler.” ] .smaller[ - So now both tort and contract law converge to a strict liability standard for manufacturers ] ] --- # A Brief History of Products Liability Law .quitesmall[ - *The Restatement (Second) of Torts 1965* `\(\S\)` 402A: > (1) One who sells any product in a defective condition unreasonably dangerous to the user or consumer or to his property is subject to liability for physical harm thereby caused to the ultimate user or consumer, or to his property, if > (a) the seller is engaged in the business of selling such a product, and > (b) it is expected to and does reach the user or consumer without substantial change in the condition in which it is sold. > (2) The rule stated in Subsection (1) applies although > (a) the seller has exercised all possible care in the preparation and sale of the product, and > (b) the user or consumer has not bought the product from or entered into any contractual relation with the seller. ] - Note that part (2)(a) excludes consideration of producer care (hence, liability is strict), part (2)(b) eliminates privity --- # Strict Liability for Manufacturers? .pull-left[ - Somewhat misleading to label the rule as “strict liability” - In addition to harm & causation, Plaintiffs must show product is .hi[defective] in design or manufacture - Or, if it is inherently dangerous, the manufacturer .hi[failed to warn] consumers of danger - e.g. cigarettes, dynamite, lithium ion batteries ] .pull-right[ .center[ ![](../images/productwarnings.png) ] ] --- # Strict Liability for Manufacturers? .pull-left[ - Element of .hi[negligence] here: manufacturers can avoid liability by meeting the design standard or the duty to warn - Though courts are still becoming more strict in determining whether or not Defendants meet these standards ] .pull-right[ .center[ ![](../images/productwarnings.png) ] ] --- class: inverse, center, middle # A Simple Economic Model of a Risky Product --- # A Simple Economic Model of a Risky Product .pull-left[ .smallest[ - Consider a typical .red[supply] and .blue[demand] model, first for a .hi-purple[safe product] - .blue[Demand]: `\(\color{blue}{p=a-bq}\)` - marginal benefit to consumer - `\(a\)`: choke price (intercept) - `\(b\)`: slope - .red[Supply]: `\(\color{red}{p=c}\)` - `\(c\)` is marginal cost - Equilibrium `\((q^\star,p^\star)\)` `$$q^{\star}=\frac{c+b}{a}; p^{\star}=c$$` ] ] -- .pull-right[  ] --- # A Simple Economic Model of a Risky Product .pull-left[ - Now suppose the product comes with some .hi-turquoise[risk of an accident] - `\(p\)`: probability of accident (per unit) - `\(D\)`: damages from accident - `\(pD\)`: expected damages (per unit) - `\(pDq\)`: total expected damages - Accidents are determined by both parties’: - care levels (e.g. `\(x\)` and `\(y\)` from before) - activity levels ] .pull-right[  ] --- # A Simple Economic Model of a Risky Product .pull-left[ - We need to consider the .hi-purple[liability] rule in place - .hi-purple[s]: .red[manufacturer’s] share of liability - .hi-purple[1-s]: .blue[consumer’s] share of liability - Two main alternatives: - .hi-purple[s=1: strict liability (SL)] - .hi-purple[s=0: no liability (NL)] ] .pull-right[  ] --- # A Simple Economic Model of a Risky Product .pull-left[ .smallest[ - Now adjust .blue[Demand] and .red[Supply] to account for accident risk, determined by liability: - .blue[Demand] with risk: `$$\color{blue}{p=a-bq-(1-s)pD}$$` - `\(a-bq\)`: marginal benefit (WTP) ignoring risk - `\((1-s)pD\)`: discounting by consumer’s expected liability ] ] .pull-right[  ] --- # A Simple Economic Model of a Risky Product .pull-left[ .smallest[ - .blue[Demand] with risk: `$$\color{blue}{p=a-bq-(1-s)pD}$$` - If .hi-purple[s=1 (SL)], no change in .blue[demand], consumer behaves as if product was perfectly safe - `\(\color{blue}{p=a-bq}\)` - Fully insured against any risk (manufacturer is strictly liable) - If .hi-purple[s=0 (NL)], reduce spending exactly by expected damages per unit - Consumer full bears the risk of accident ] ] .pull-right[  ] --- # A Simple Economic Model of a Risky Product .pull-left[ .smallest[ - Now adjust .blue[Demand] and .red[Supply] to account for accident risk, determined by liability: - .red[Supply] with risk `$$\color{red}{p=c+spD}$$` - `\(c\)`: marginal cost ignoring risk - `\(spD\)`: premium for seller’s expected liability ] ] .pull-right[  ] --- # A Simple Economic Model of a Risky Product .pull-left[ .smallest[ - Now adjust .blue[Demand] and .red[Supply] to account for accident risk, determined by liability: - .red[Supply] with risk `$$\color{red}{p=c+spD}$$` - If .hi-purple[s=0 (NL)], no change in .red[supply], producer behaves as if product was perfectly safe - Fully insured against any risk (caveat emptor, consumer incurs full cost) - If .hi-purple[s=1 (SL)], raise asking price exactly by expected damages per unit - Seller fully bears the risk of accident ] ] .pull-right[  ] --- # A Simple Economic Model of a Risky Product .pull-left[ .smallest[ - Regardless of the rule `\((s)\)`, equilibrium quantity is always `\(q^{\star \star}\)`! `$$\begin{align*} a-b(q)-(1-s)pD &= c+spD \\ \color{green}{a-b(q)} &= \color{orange}{c +pD}\\ \end{align*}$$` - `\(q^{\star \star}\)` efficient level where .green[marginal social benefit] = .orange[*full* marginal social cost] (including expected accident costs) - Application of the .hi-purple[Coase Theorem]: resource allocated efficiently `\((MSB = MSC)\)` regardless of the assignment of liability! - Main reason is liability is shifted via price (see next) ] ] .pull-right[  ] --- # A Simple Economic Model of a Risky Product .pull-left[ .smallest[ - The price of the product does depend on rule .purple[s]: - When .hi-purple[s=0 (No Liability)], `\(p=p_{NL}\)` - When .hi-purple[s=1 (Strict Liability)], `\(p=p_{SL}\)` - .green[Area B] = `\(cq^{\star \star} = p_{NL}q^{\star \star}\)` - cost & revenue to seller - .orange[Area A] = `\(pDq\)` - total expected damages, an additional cost - Area .green[B] + .orange[A] = total social costs to produce (under Strict Liability `\(s=1)\)` ] ] .pull-right[  ] --- # A Simple Economic Model of a Risky Product .pull-left[ - Producer will always directly bear area .green[B] as cost (MC of production) - Liability rule .purple[s] will determine who will bear cost of area .orange[A] - No liability .purple[(s=0)]: consumer bears full cost of .orange[A] - Strict liability .purple[(s=1)]: producer bears full cost of .orange[A] - raises the price to `\(p_{SL}\)` to collect cost upfront ] .pull-right[  ] --- # A Simple Economic Model of a Risky Product .pull-left[ - Under a rule of .hi-purple[no liability (s=0)] (caveat emptor), consumer .hi-purple[self-insures] - decides how much to allocate extra savings of (not paying) area .orange[A] (lower price `\(p_{NL})\)` - save money, take out insurance, buy a different product, etc. to prepare for expected damages `\(pD\)` ] .pull-right[  ] --- # A Simple Economic Model of a Risky Product .pull-left[ - Under .hi-purple[strict liability (s=1)], producer pays production costs .green[B] + expected liability costs .orange[A] - sells product bundled with an “insurance policy” - `\((p_{SL}-p_{NL})\)`: “insurance premium” - law incentivizes producers to insure consumers against risk ] .pull-right[  ] --- # A Simple Economic Model of a Risky Product .pull-left[ .smallest[ - The main question is, .hi-turquoise[who is the better insurer?] - Self-insurance at price `\(p_{NL}\)` - Manufacturer bundles good and insurance at price `\(p_{SL}\)` - If accident happens with first unit - Consumer has not built up enough savings, far better to purchase insurance in market rather than self-insure - Same issue with (small) firms, usually also purchase insurance in market to cover expected tort liability under SL ] ] .pull-right[  ] --- class: inverse, center, middle # Risky Products: Precaution Incentives --- # Risky Products: Precaution Incentives .pull-left[ .quitesmall[ - Will the .hi-purple[level of precaution] be optimal regardless of the liability rule? (like output `\(q^{\star \star})\)`? - Coase Theorem implies yes - If .hi-purple[s=0 (no liability)] - In model of accidents between strangers (lesson 3.2), we saw .blue[victims] take efficient care `\(y=y^{\star}\)` and .red[injurers] take no care `\(x=0\)` - In today's model, suppose .red[manufacturer] & .blue[consumer] bargain: .red[manufacturer] agrees to produce safe product in return for higher price `\(p_{SL}\)` - .red[Manufacturer] will invest in efficient safety level: where MB of reduction in accidents = MC ] ] .pull-right[ .center[ ![](../images/caution.jpg) ]] --- # Risky Products: Precaution Incentives .pull-left[ .quitesmall[ - Will the .hi-purple[level of precaution] be optimal regardless of the liability rule? (like output `\(q^{\star \star})\)`? - Coase Theorem implies yes - If .hi-purple[s=1 (strict liability)] - In model of accidents between strangers (lesson 3.2), we saw .red[injurers] take efficient care `\(x=x^{\star}\)` and .blue[victims] take no care `\(y=0\)` - In today's model, suppose .red[manufacturer] & .blue[consumer] bargain: ,blue[consumer] promises to use product carefully in exchange for a price reduction by .red[manufacturer] `\((p_{NL})\)` - .blue[Consumer] will use product safely in an efficient way: where MB of reduction in accidents = MC - Both lead to safer products, **assuming low transaction costs** ] ] .pull-right[ .center[ ![](../images/caution.jpg) ]] --- # Risky Products: Precaution Incentives .pull-left[ .smallest[ - How likely are these to work in practice? - First case (no liability), .blue[consumer] pays higher price if she can see product is safer - sometimes .blue[consumers] *can't* inspect & determine safety at purchase (e.g. drugs) - Second case (strict liability) is virtually impossible - .blue[consumer] has to live up to their long-term promise to use product safely - .red[Manufacturer] can't monitor .blue[consumer]'s behavior, hence, unwilling to make this bargain ] ] .pull-right[ .center[ ![](../images/caution.jpg) ]] --- # Risky Products: Precaution Incentives .pull-left[ .smallest[ - Coase theorem fails with these high transaction costs - liability rule `\(s\)` now very relevant - back to a world of strangers in accidents with other strangers - We saw a strict liability rule is unlikely to achieve efficient level of victim (consumer) care - Further complicates matters if (as they often do), **consumers mispercieve risks** - Empirical evidence that people overestimate low probability risks and underestimate high probabiliy risks - Product accidents primarily are the first case ] ] .pull-right[ .center[ ![](../images/caution.jpg) ]] --- # Consumer Misperceptions of Risk .pull-left[ - Return to our model, let's first look at equilibrium output - .red[Producers] correctly perceive probability of accident as `\(p\)` - Have better knowledge about its risk - Suppose .blue[consumers] misperceive the probability of an accident as `\(\alpha p\)` - `\(\alpha = 1\)`: true probability ] .pull-right[ .center[ ![](../images/risk_management.png) ] ] --- # Consumer Misperceptions of Risk .pull-left[ - Return to our model, let's first look at equilibrium output - .red[Producers] correctly perceive probability of accident as `\(p\)` - Have better knowledge about its risk - Suppose .blue[consumers] misperceive the probability of an accident as `\(\alpha p\)` - `\(\alpha = 1\)`: true probability - `\(\alpha > 1\)`: overestimate probability - `\(\alpha < 1\)`: underestimate probability ] .pull-right[ .center[ ![](../images/risk_management.png) ] ] --- # Consumer Misperceptions of Risk - Now .blue[Demand] under any legal rule: `$$\color{blue}{a-b(q)-(1-s)\alpha pD}$$` -- - .red[Supply] under any legal rule remains: `$$\color{red}{c+spD}$$` -- - Setting these equal, equilibrium output is: `$$a-b(q)-(1-s)\alpha pD=c+spD$$` - If `\(\alpha=1\)`, then output is `\(q^{\star \star}\)` -- - Recall if .purple[s=1], then equilibrium is `\(q^{\star \star}\)` where `\(a-b(q)=c+pD\)` -- - If `\(\alpha \neq 1\)`, then for any liability rule *other* than strict liability `\((s<1)\)`, output will **not** be the efficient level `\(q^{\star \star}\)`! --- # Consumer Misperceptions of Risk .pull-left[ - As an example, conside .purple[no liability (s=0)] - Equilibrium output where: `$$\color{blue}{a-b(q)-\alpha pD}=\color{red}{c}$$` - If `\(\alpha>1\)`, consumers overestimate risk, demand too little output ] .pull-right[  ] --- # Consumer Misperceptions of Risk .pull-left[ - As an example, conside .purple[no liability (s=0)] - Equilibrium output where: `$$\color{blue}{a-b(q)-\alpha pD}=\color{red}{c}$$` - If `\(\alpha>1\)`, consumers overestimate risk, demand too little output - If `\(\alpha<1\)`, consumers underestimate risk, demand too much output ] .pull-right[  ] --- # Consumer Misperceptions of Risk .pull-left[ - Different under .hi-purple[strict liability (s=1)], we saw equilibrium output is still efficient `\(q^{\star \star}\)` - Efficient for any `\(\alpha\)` (i.e. regardless of .blue[consumer’s] mistaken beliefs) - .blue[Consumer] misperceptions of risk have no effect on output - .red[Manufacturer] fully internalizes the expected damages via the higher market price `\(p_{SL}\)`, accurately reflecting the risk ] .pull-right[  ] --- # Consumer Misperceptions of Risk .pull-left[ - In other words, .hi-purple[when consumers misperceive risk, the liability rule matters for efficiency] - Only under strict liability will misperceptions of risk not affect efficient outcome - .hi-purple[In general, the party who more accurately perceives the risk should bear the liability] - Supports general historical trend of more strict liability for products ] .pull-right[  ] --- # Consumer Precaution - What about the level of precaution? -- | Rule | .red[Injurer] Precaution | .blue[Victim] Precaution |------|---------|--------| | No liability | Zero | .green[Efficient] | | Strict liability | .green[Efficient] | Zero | -- - We saw before, under strict liability, .blue[Victim] has no incentive to take precautions `\((y=0)\)` - High cost of enforcing contract conditioned on consumer appropriately using product --- # Consumer Precaution - What about the level of precaution? -- | Rule | .red[Injurer] Precaution | .blue[Victim] Precaution |------|---------|--------| | No liability | Zero | .green[Efficient] | | Strict liability | .green[Efficient] | Zero | | Strict Liability w/Contributory Negligence | .green[Efficient] | .green[Efficient] | -- .pull-left[ ] .pull-left[ .smallest[ - We also saw before that introducing a defense of contributory negligence restores incentive for .blue[Victim] to take due care to avoid liability - However, most cases do not accept this as a defense! ] ] .pull-right[  ] --- # Conclusions .pull-left[ .quitesmall[ - Contract law is not an adequate remedy for most product related accidents between .red[Producer] and .blue[Consumer] - In theory, .hi-purple[Coase Theorem] suggests the parties could bargain to fully internalize the accident risk (between `\(p_{SL}\)` and `\(p_{NL})\)` - But high transaction costs prevent this: - .blue[Consumer] misperceptions of product risk - Inability of .red[producers] to monitor .blue[consumers] carefully using product - High cost of contracting in the presence of these remote risks disproportionate to the benefits of a negotiated level of safety - Cheaper to use the .hi[default rule] of having tort law assign safety ] ] .pull-right[ .center[ ![](../images/productwarnings.png) ] ] --- class: inverse, center, middle # Workplace Accidents --- # Workplace Accidents .pull-left[ - What about accidents where workers are injured on the job? - unsafe working conditions - negligence by coworkers - Or accidents between workers and strangers/customers in course of employment? ] .pull-right[ .center[ ![](../images/workplaceaccident.jpg) ] ] --- # Workplace Accidents .pull-left[ .smallest[ - Workers injured on the job are similar to products liability - An economic relationship between the parties - Wage adjusts to reflect legal assignment of liability between parties - Contract law governs these relationships, but may fail in the presence of market failures like above (misperceptions of risk, etc.) - Accidents between worker and stranger are like accidents between strangers ] ] .pull-right[ .center[ ![](../images/workplaceaccident.jpg) ] ] --- # Vicarious Liability and Employment .pull-left[ .smaller[ - .hi[Vicarious liability]: one party is held liable for the harm caused by another - Parents may be liable for harms their children cause - Employers may be liable for harms their employees cause - .hi[*Respondeat superior*]: “let the master answer” - Employer is held liable for employee’s torts **if employee was acting within the scope of employment** ] ] .pull-right[ .center[ ![](../images/vicariousliability.jpg) ] ] --- # Vicarious Liability and Employment: Example .center[ ![](../images/dominoslawsuit.png) ] .source[Sources: [Ranker (2019)](https://www.ranker.com/list/dominos-30-minutes-or-less-lawsuit/genevieve-carlton), [New York Times (1993)](https://www.nytimes.com/1993/12/22/business/domino-s-ends-fast-pizza-pledge-after-big-award-to-crash-victim.html)] --- # Vicarious Liability and Employment .pull-left[ .smallest[ - .hi[*Respondeat superior*] gives employers incentives to: - be more careful **who they hire** - be more careful **what they assign employees to do** - **monitor** employees more carefully - Employers are better able to make these decisions (and bear the risks) than employees - Employees are also likely .hi-purple[“judgement proof”]: unable to pay the full costs of harms - Employers have .hi-turquoise[“deeper pockets”] ] ] .pull-right[ .center[ ![](../images/workplacewarnings.jpg) ] ] --- # Vicarious Liability and Employment .pull-left[ .smallest[ - Can implement vicarious liability via: - .hi-purple[Strict liability]: employer liable for *any* harm caused by employee (within scope of employment) - .hi-purple[Negligence]: employer liable only if employer was *negligent in supervising* the employee - Which rule is better? Depends: - If proving negligent supervision is too difficult, strict liability better - But negligence gives employer incentives to report employee harms rather than keep quiet (under strict liability) ] ] .pull-right[ .center[ ![](../images/supervision.jpg) ] ] --- # When Victim is Employee .pull-left[ .smallest[ - Historically, employer liability for when employee is the *victim* was very limited - Common law duty imposed on employers to maintain a safe workplace and warn of dangers - Employer could avoid liability by demonstrating contributory negligence on part of injured employee - As we saw before, this leads both parties to take efficient precaution `\((x^\star, y^\star)\)` - Wage will adjust to reflect who bears the cost of accident ] ] .pull-right[ .center[ ![](../images/workplaceaccident.jpg) ] ] --- # When Victim is Employee .pull-left[ - Historically, .hi-purple[“fellow servant rule”] shielded employers from liability if the cause of the accident to an employee was the action of *another* employee - Gives incentive for employees to monitor each other - Perhaps works well for small workplaces where coworkers all frequently interact, but not so well for large businesses ] .pull-right[ .center[ ![](../images/workplaceaccident.jpg) ] ] --- # Workers Compensation Laws .pull-left[ .smaller[ - In 20th Century, dissatisfaction with common law of workplace accidents led to legislation in all States: .hi[workers compensation] - Created a form of .hi[strict liability] for employer - Employees no longer needed to prove employer negligence to recover damages - Contributory negligence and fellow servant rules no longer valid defenses ] ] .pull-right[ .center[ ![:scale 100%](../images/workerscomp.jpg) ] ] --- # Workers Compensation Laws .pull-left[ .smaller[ - However, amount of compensation for workers was fixed by a schedule of damages by injury, and administered by government agencies rather than courts (& lawsuits) - Often 2/3 of wages - Employees waive their right to sue employer - .hi-turquoise[“The compensation tradeoff”]: employee waives right to sue, but is essentially guaranteed a limited of insurance from injury ] ] .pull-right[ .center[ ![:scale 100%](../images/workerscomp.jpg) ] ] --- # Workers Compensation Laws .pull-left[ .smallest[ - Benefit to employers: lowers risk of bankruptcy from high damage awards in tort cases - Potential issue in reducing employees incentives for precaution (no defense of contributory negligence), but: - employers can contract with workers to pay higher wage for greater worker care - compensation is limited, so reduces employee moral hazard ] ] .pull-right[ .center[ ![:scale 100%](../images/workerscomp.jpg) ] ] --- # Workers Compensation Laws .pull-left[ - Another issue: employee must prove the harm was job-related - Easy for accidents causing physical injury - Harder for exposure to chemicals that may *contribute* to cancer decades later... ] .pull-right[ .center[ ![:scale 100%](../images/workerscomp.jpg) ] ] --- # Example: Asbestos .pull-left[ .smallest[ - .hi-turquoise[Asbestos] is a fibrous material that is an excellent electrical insulator and highly heat resistant, used for decades as a building material - Probably saved countless lives from preventing fires - But, inhalation of asbestos fibers can lead to serious conditions such as lung cancer, mesothelioma, heart disease - But this takes 20-30 years to develop - Believed that about 100,000 people die a year from asbestos exposure-related diseases - Since public health developments in 1970s, asbestos is banned as a building material due to safety hazards ] ] .pull-right[ .center[ ![:scale 100%](../images/asbestos.jpg) ] ] --- # Example: Asbestos .pull-left[ .smallest[ - Like any occupational hazard, risk can be properly internalized via two mechanisms: 1) Compensating differentials in wages - Wages are higher for employees in dangerous occupations (and give employers an incentive to invest in safety to push down wage premiums) - **Problem**: this only works when employees **know** there are dangers present! ] ] .pull-right[ .center[ ![:scale 100%](../images/asbestos.jpg) ] ] --- # Example: Asbestos .pull-left[ .smallest[ - Like any occupational hazard, risk can be properly internalized via two mechanisms: 2) Workers compensation laws - Even if employees (and employers) didn’t know about risks (and gives employers incentive to invest in safety to push down insurance premiums) - **Problem**: have to show harm was caused by on-job activity! - Statute of limitations is too short, harm doesn’t develop for 30 years! ] ] .pull-right[ .center[ ![:scale 100%](../images/asbestos.jpg) ] ] --- # Borel v. Fibreboard .pull-left[ - *Borel v. Fibreboard Paper Products Corporation* (1973) - Borel filed personal injury lawsuit against Fibreboard and 9 other asbestos-insulation manufacturers after being diagnosed with mesothelioma - Argued manufacturers should be liable because it asbestos did not come with warning labels - By then, strict liability for manufacturers in products liability ] .pull-right[ .center[ ![](../images/asbestosdanger.jpg) ] ] --- # Borel v. Fibreboard .pull-left[ - *Borel v. Fibreboard Paper Products Corporation* (1973) - Sadly, Borel died before the lawsuit was over - Court found these companies were strictly liable, even though Borel was not an employee of Fibreboard - Now, consumers can sue manufacturers directly, without an economic relationship (like employment) — products liability ] .pull-right[ .center[ ![](../images/asbestosdanger.jpg) ] ] --- # Joint and Several Liability .pull-left[ .quitesmall[ - Suppose a .blue[victim] was hurt ($1,000 worth of damages) in an accident caused by multiple .red[injurers] - .hi[Joint liability]: .blue[victim] can sue all .red[injurers] combined (as co-defendants) for $1,000 - Married couple defaulting - .hi[Several liability]: .blue[victim] can sue each .red[injurer] separately, but each is only liable for the amount they contributed to the accident - .hi[Joint and several liability]: law treats all .red[injurers] as jointly liable, and .blue[victim] can sue any of them for the full damages - .red[Injurers]' jointly are responsible for sorting out their respective contributions ] ] .pull-right[ .center[ ![](../images/fault.jpg) ] ] --- # Joint and Several Liability in Tort Law .pull-left[ .smallest[ - .hi[Joint and several liability] applies when: - .red[Defendants] acted together to cause the harm - Or harm was indivisible (impossible to tell who was at fault) - Good for the .red[Victim] - No need to prove exactly who caused the harm, or to what degree - Greater chance of recovering full damages - Instead of suing .red[Defendant] most responsible, sue the one with the greatest ability to pay (“deep pockets”) ] ] .pull-right[ .center[ ![](../images/fault.jpg) ] ] --- # Joint & Several Liability: Asbestos - With joint & several liability for asbestos, the claims are increasing faster than the actual injuries - From [Wikipedia](https://en.wikipedia.org/wiki/Asbestos_and_the_law_(United_States)): > “Asbestos lawsuits in the U.S. have included the following as defendants: > manufacturers of machinery that are alleged to have utilized asbestos-containing parts; > owners of premises at which asbestos-containing products were installed; > retailers of asbestos-containing products, including hardware, home improvement and automotive parts stores; > corporations that allegedly conspired with asbestos manufacturers to deliberately conceal the dangers of asbestos; > manufacturers of tools that were used to cut or shape asbestos-containing parts; and > manufacturers of respiratory protective equipment that allegedly failed to protect workers from asbestos exposure.” --- # Joint & Several Liability: Asbestos .pull-left[ .smaller[ - The problem is, you’re not suing the least-cost avoider of the harm! - The lawsuits are not generating incentives for optimal safety or deterrence - An excessive amount of rent-seeking and frivolous claims - Creates stress on legal system, reducing ability to actually compensate for real harms ] ] .pull-right[ .center[ ![](../images/ambulencechaser.png) ] ] --- # Joint & Several Liability: Asbestos .pull-left[ .center[ ![](../images/asbestoscases.png) ] ] -- .pull-right[ .center[ ![:scale 40%](../images/mesoad2.jpg) ![:scale 40%](../images/mesoad.jpg) ![](../images/mesotheliomaad.jpg) ] ] --- # Asbestos .pull-left[ - From [Wikipedia](https://en.wikipedia.org/wiki/Asbestos_and_the_law_(United_States)): .quitesmall[ > Asbestos litigation is the longest, most expensive mass tort in U.S. history, involving more than 8,000 defendants and 700,000 claimants. By the early 1990s, "more than half of the 25 largest asbestos manufacturers in the US, including Amatex, Carey-Canada, Celotex, Eagle-Picher, Forty-Eight Insulations, Manville Corporation, National Gypsum, Standard Insulation, Unarco, and UNR Industries had declared bankruptcy. Analysts have estimated that the total costs of asbestos litigation in the U.S. alone will eventually reach $200 to $275 billion." The amounts and method of allocating compensation have been the source of many court cases, and government attempts at resolution of existing and future cases. ] ] .pull-right[ .center[ ![](../images/ambulencechaser.png) ] ]


Patrick LurlayFounder and CTO Nimbaco, Inc I am an engineering manager. I am currenly free lencing. Link to:Contact Info |
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Joel Klatt Says LSU Is The Baker Mayfield-Oklahoma Team
by Tzvi MachlinFOX Sports college football analyst Joel Klatt ruffled some feathers earlier this week when he put LSU at No. 3 in his rankings behind Ohio State and Clemson. Amidst the criticism, Klatt explained his reasoning and made an interesting comparison to the Baker Mayfield-led Oklahoma teams.
On Wednesday’s edition of The Herd, Klatt pointed out that LSU’s defense is ranked a major liability. He noted that during the BCS era, only one team won a national title without a top 25 defense – LSU’s is ranked 53rd.
But in pointing out LSU’s potential path to a national title, Klatt asserted that LSU is doing what Baker Mayfield attempted to do with Oklahoma in 2017: Overwhelm teams with offense so much that defense is a non-factor.
“This is the Baker Mayfield version of Oklahoma. That’s what LSU is right now.”
He did point out that Oklahoma almost beat Georgia in the College Football Playoff that year.
Klatt went on to say that LSU’s defensive struggles don’t mean for sure that they won’t win the national title. But as of now, they’re not as dominant on both sides of the ball as either Ohio State or Clemson.
Is LSU’s defense the biggest thing holding them back from a potential national title?


LOLITA A film review by Steve Rhodes Copyright 1996 Steve Rhodes
RATING (0 TO ****): ***
In 1962 the great director Stanley Kubrick (SPARTACUS, DR. STRANGELOVE, 2001: A SPACE ODYSSEY, A CLOCKWORK ORANGE, and BARRY LYNDON) made a movie of the controversial best seller LOLITA. Although this year Adrian Lyne will be releasing his version with Melanie Griffith as the Charlotte Haze, Jeremy Irons as Humbert Humbert, Frank Langella as Clare Quilty and Dominique Swain as Lolita Haze, this review is of Kubrick's 1962 version.
Censorship was much stronger in 1962. If you wanted to release pictures into places other than art houses, you had to have the Industry's seal on it. In order to get it, Kubrick toned down a story of love between an middle-aged man and a young teen so much that the movie is more about murder and comedy than any sexual deviation. In fact the movie as filmed is so tame that it would almost certainly get a PG-13 rating today. The raunchiest scene is one of Lolita setting on the lawn by herself in a not particularly revealing bikini.
LOLITA is based on the best selling novel by Vladimir Nabokov. The book was labeled pornography by many at the time, and most movie studios did not care to have anything to do with it. Kubrick decided to make it into a movie, but he wanted the seal so he completely reworked the story and got the seal. This was made easier by his throwing away most of the material in the screenplay attempt, more of a tome actually, by the author.
The plot of the book is reasonably straightforward, but Kubrick's change to start off at the ending makes it feel more complicated than it is. Basically the movie is about a 40ish novelist with the strange double name of Humbert Humbert (James Mason) who comes to live one summer in a rented room in Charlotte Haze's (Shelley Winters) house.
Living with single Mrs. Haze is her fourteen year old daughter Lolita (Sue Lyon). For poor Sue Lyon this was the only decent film she ever made. She would latter appear in such classic roles as Linda in EVEL KNIEVEL (1971). In LOLITA she is great as a bored teen who lets her boredom get her into big trouble. Both Mason and Lyon demonstrate the consequences of a lack of morals. Lyon does it with a who-cares attitude; Mason with heavy angst. Both are highly effective.  Notice too how Kubrick has the adults all drinking so heavily you can see that alcoholism is one source of their problems.
Also in the film is a strange screenwriter with an equally unusual name, Clare Quilty (Peter Sellers). Sellers acting in the role is one that many critics like, but I found overboard. He goes too much for the heavy comedy for my taste, and I found scenes with him in it grating on my nerves. Overall, I wish Kubrick had cast someone else. Speaking of casting, Tuesday Weld was considered for the role of Lolita, but she reportedly turned it down. I think she would have been perfect for it.
The dialog is excellent. Typical is a riposte between Humbert and Charlotte. She asks him, "Do you believe in God?" His immediate reply is, "The question is does God believe in me?" Later, in a nice little line, Mason explains how, "The best men all shave twice a day."
If you judge the movie by today's freer standards and ignore most of the actions and contrivances that Kubrick had to go through, the film could seem a bit trite. If you approach it with an open mind, the movie can seen to be a deserving member in the body of work of a master director. Other than Sellers's acting and Mason's silly vaudevillian style routine with a folding bed that had no place in the movie, I liked it all.
Technically the film is interesting as well. The black and white footage by Oswald Morris is quite dramatic. Many scenes have Humbert's car moving down a road with dramatic music (Nelson Riddle) playing to highlight the importance of the journey.
LOLITA runs a bit long at 2:32. I think the material would have been more effect with tighter editing. The picture is unrated, but given there is no sex or nudity and the little violence is not very graphic, I think it would actually get a PG-13 today. If you did not know the story, this version of the movie is so ambiguous that one could easily assume that Lolita's relationship with Humbert is merely platonic. The film should be fine for any teenager. I recommend the picture to you. I look forward to the new Lolita by Lyne and hope I get invited to the press screening. This 1962 version gets *** in my book.
**** = One of the top few films of this or any year. A must see film. *** = Excellent show. Look for it. ** = Average movie. Kind of enjoyable. * = Poor show.  Totally unbearable.
REVIEW WRITTEN ON: July 14, 1996
Opinions expressed are mine and not meant to reflect my employer's.
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
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This interface represents a encryption template, which is the same as the
* desired xml encryption element[ EncryptedType ] but some of the nodes may be
* empty. The empty entities include CipherValue, which is a subset of
* EncryptedData or EncryptedKey. Empty entities are not allowed in a encryption
* template when performing decryption.
*
* In some cases, the encrypter or decrypter can dertermine and locate the
* EncrytedKey from the encryption template by dereference the RetrievalMethod
* inside EncryptedData.
*
* In some cases, the EncryptedKey need to be clearly pointed out by the
* encryption template.
*
* With the help of encryption context, the encrypter or decrypter specifies
* the key from the KeyInfo in the encryption template.
*
*
* It isn't a good method to set the EncryptedKey here. In general, there is
* a RetrievalMethod in EncryptedData by which we can get the EncryptedKey.
*
* And some times, in the low level, it is hard to determine what the key is
* except that the high level application tell the mechanism and parameters.
*
* So I think it will be more simple that the application set the encrypted key
* information. In this case, the application only need to know the xml schema
* or DTD and the encryption device. If so, the high level application takes the
* action of build the EncryptedKey template and references it in the element of
* EncryptedData. And in this case, the calling to set up EncryptedKey template
* and target is not necessary, because the encrypter or decrypter can determine
* and locate the EncrypedKet from the encryption template of EncryptedData by
* dereference the RetrievalMethod.
*
* In some situation, the high level applications do not want to know anything
* about crypto devices( May be he must, because the lower level do not know
* what the key is ). If it gives the key value, it can get EncryptedKey by
* setting the key template and key value target.


T4F Kit
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A different way to see
Tech4freedom is on a mission to improve the autonomy of blind people through affordable technologies, by providing economic and social value to society. We worked in collaboration from the first concept until serial fabrication to create the company’s first family of products.
The TF4 kit is designed to enhance the everyday life of people with vision disabilities, providing key tools to navigate their daily environments.
Box
A portable device fitted with sensors to obtain and analyze information from the surroundings and transmitting it to the user through his own Smartphone.
Smart Design
The BOX one4all was designed to add comfort in its use and not to force interaction with complex controls. Our goals as their design team were to bring T4F technology to a smart, easy and comfortable way of use. We worked hand in hand with the hardware team to create a unique interface housing where the electronics core fits in a beautiful way.
UFO
The smallest product of the T4F family. Following the BOX line, the UFO is designed to have a perfect fit in hand and in small places such as pockets.
A little device that can find people, define and areas of interest for the user. It has one single button, strategically placed in the middle of the device on a circular gap, which facilitates its allocation intuitively.


The Celebration Directed by: Thomas Vinterberg Starring: Ulrich Thomsen, Henning Moritzen, Thomas Bo Larsen, Paprika Steen, Birthe Neuman Running time: 105 minutes My rating (5 star scale): ****
In Danish with subtitles.
This is the perfect film to see before a family gathering. Whether it's Christmas, an anniversary, a birthday party, whatever; see this movie first. Your dysfuntional family will look perfectly happy and functional in comparison to the folks who make up this movie clan.
Michael, Christian, and Helene have returned home for their father's 60th birthday party. Also at the party, held in a hotel owned by the father and closed for this occasion, are assorted grandparents, uncles, aunts, cousins, children, spouses, and significant others. Christian, the eldest, is asked to say a few words at dinner about his twin sister, who committed suicide earlier that year. Michael is at first not wanted, because he failed to come to his sister's funeral, but his father later offers to put him up for membership in the Freemasons. Also at the party is a German associate of the father who is to be toastmaster.
What follows are a series of revelations that would keep Jerry Springer in business for years. Everything from affairs to abortions to pedophilia to insanity to lesbians. Christian kicks things off by accusing his father of abusing himself and his sister. But that's just the tip of this family iceberg. The partygoers are uncertain how to react. Some think he is drunk, some think he is joking, some want to leave. But they can't because the cook has stolen all their car keys. They are stuck there and they try to make the best of it.
Michael, the youngest of the surviving children, desperately wants to impress his father. He tries to keep the party going. He gets violent with Christian and he drinks far too much, losing what little self control he has.
  First he tries to get rid of the boyfriend, later he gets everyone singing a racist song. Through all this, the poor German toastmaster tries to keep things going.
And that's just touching the surface. I haven't even touched on the strange marriage and stranger sex of Michael and Mette. Or of the mother's bizarre and spiteful speech. Or the maid who seems to have slept with every available male and possibly one female.
Just in case I've misled you, this isn't a comedy. But, while it deals with some pretty heavy stuff, it isn't nearly as bleak as you'd expect. And while it does read like a Springer wish list (I think they even throw chairs), it's a lot more insightful than anything you'll see on tv. It's closer to Ibsen meets Babette's Feast. Well worth paying full price for.
This is the Danish entry for foreign language Oscar and I give it a good chance of making the short list.
-- Allan Jenoff Check out my web page at http://www.interlog.com/~jenoff/home.html
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
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Related links: index of all rec.arts.movies.reviews reviews


Manager — Multiflow project manager.¶
This module defines the ProjectManager class.
-
class
admit.ProjectManager.
ProjectManager
(baseDirs=[])[source]¶ Manages parent projects feeding a multiflow project.
Parameters: baseDirs: list of str
Starting parent project directories.
Notes
Managed projects are numbered sequentially beginning with one. A project ID of zero is reserved for the parent project containing this instance. Individual projects can be accessed by indexing the manager object using the corresponding project ID number.
Attributes
_projects: dict of ADMITs Parent project dictionary, keyed by project ID (a positive integer). _baseDirs: dict of project base directories Project directory dictionary, keyed by project ID (a positive integer). Methods
addProject
(baseDir)Adds a project. findTask
(pid, isMatch)Finds AT(s) by arbitrary matching criterion. findTaskAlias
(pid, alias)Finds AT(s) by alias. getProjectDir
(pid)Retrieves project base directory given its ID number. getProjectId
(baseDir)Retrieves project ID number given the base directory. removeProject
(pid)Removes a project. script
(py, baseDir[, proj])Generates a Python script recreating the current project manager. write
()Writes project XML files. -
addProject
(baseDir)[source]¶ Adds a project.
The project must be completely up to date to be accepted.
Parameters: baseDir : str
ADMIT project base directory.
Returns: int
Project ID number, else -1 if the project was rejected (not up-to-date).
Notes
The up-to-date requirement is a safety feature. Managed projects are assumed to be quasi-static since tasks linked from it must provide valid BDP output at the root of the associated multiflow.
-
findTask
(pid, isMatch)[source]¶ Finds AT(s) by arbitrary matching criterion.
Parameters: pid: int
ADMIT project ID number.
isMatch: bool functor(AT)
Match function (returns True/False given an AT argument).
Returns: list of ATs
ADMIT task reference(s) matching the criterion; may be empty.
-
findTaskAlias
(pid, alias)[source]¶ Finds AT(s) by alias.
Parameters: pid: int
ADMIT project ID number.
alias: str
Matching task alias.
Returns: list of ATs
ADMIT task reference(s) matching the alias; may be empty.
-
getProjectDir
(pid)[source]¶ Retrieves project base directory given its ID number.
Parameters: pid: int
Project ID number.
Returns: str
Project base directory, else None if project not found.
-
getProjectId
(baseDir)[source]¶ Retrieves project ID number given the base directory.
Parameters: baseDir: str
Project base directory.
Returns: int
Project ID number, else -1 if project not found.
-
removeProject
(pid)[source]¶ Removes a project.
The project should not have any tasks still linked in the multiflow. Verifying this is the responsibility of the caller (multiflow project).
Parameters: pid : int
Project ID number.
Returns: bool
True if the project was removed successfully, else False.
Notes
This is a low-level interface not normally called by users.
-
script
(py, baseDir, proj='p')[source]¶ Generates a Python script recreating the current project manager.
Parameters: py : file
Open, writable Python file object.
baseDir : str
Master project base directory (ending in ‘/’).
proj : str, optional
Project variable name; defaults to ‘p’.
Returns: None
Notes
This is a low-level method normally called only by Admit.script().
-


"HEBER "passing over. (1.) Son of Beriah and grandson of Asher (Genesis 46:17; 1 Chronicles 7:31, 32). (2.) The Kenite (Judges 4:11, 17; 5:24), a descendant of Hobab. His wife Jael received Sisera (q.v.) into her tent and then killed him. (3.) 1 Chronicles 4:18. (4.) A Benjamite (1 Chronicles 8:17). (5.) A Gadite (5:13). (See EBER.)
"HEBER "passing over. (1.) Son of Beriah and grandson of Asher (Genesis 46:17; 1 Chronicles 7:31, 32).
(2.) The Kenite (Judges 4:11, 17; 5:24), a descendant of Hobab. His wife Jael received Sisera (q.v.) into her tent and then killed him.
(3.) 1 Chronicles 4:18.
(4.) A Benjamite (1 Chronicles 8:17).
(5.) A Gadite (5:13). (See EBER.)


What are API and Methods/Events ?
An application programming interface (API) specifies how some software components should interact with each other. Slider Revolution use following Methods:
- Pause: revpause()
- Resume: revresume()
- Previous Slide: revprev()
- Next Slide: revnext()
- Show Spec. Slide: revshowslide(slidenr)
- Amounts of Slides: revmaxslide() returns amount of slides
- Current Slide: revmaxslide() returns current slide nr.
- Last Played Slide: revlastslide() returns last played slide nr.
Slider Revolution Triggers following Events
- Slider has been succesfully initialised: revolution.slide.onloaded
- Slider swap between two slides: revolution.slide.onchangereturns as data.slideIndex the next Slide Nr.
- Slider changed to Pause mode: revolution.slide.onpause
- Slider is back from Pause Mode: revolution.slide.onresume
- Video Playing has been started: revolution.slide.onvideoplay
- Video Playing has been stopped: revolution.slide.onvideostop
- Slider stopped: revolution.slide.onstop
- Slider has been called to change the slide (before transition): revolution.slide.onbeforeswap
- Slider has been finnished the Slide Change (after transition): revolution.slide.onafterswap


Hello! My name is Dajah Sky Medina and I would like to say that I am a perserver and very hard working. I love what I do, and I am willing to try anything new. I was born and raised in Arizona anong with my six other sibilings. I am currently a Junior in high school and I have accoplished many things. For instance, I have achevied honor role every year along with maintaining my AP and honors classes. My greatest accoplishment in life was geting into a wonderful school called West-Mec. This is really where I got my start from.


| |
Methods defined here:
- getProportions(self, line_list)
- Returns every second element (=proportion) in a given list starting by 2
@param {List} line_list, list that contains topic indices and proportions
- getTopics(self, line_list, label_list)
- Returns the corresponding label of every second element (=topic) in a given list starting by 1
@param {List} line_list, list that contains topic indices and proportions
@param {List} label_list, label index
- link_document_topic(self, database_connection)
- Links documents and topic labels by processing the label-index.txt
and document-topic-distributions.csv output files of the stanford tmt
and inserts the information into the database
@param {DatabaseConnection} database_connection, connection to the db
- link_topic_term(self, database_connection)
- Links topics (=entities) and terms by processing the summary.txt output file
of the stanford tmt and inserts the information into the database
@param {DatabaseConnection} database_connection, connection to the db
Data descriptors defined here:
- __dict__
- dictionary for instance variables (if defined)
- __weakref__
- list of weak references to the object (if defined)
|


Clickbait is a graphic design event that brings together creative people and promotes the essential nature of design.
Clickbait 2019 will be a graVITas workshop + technical event that will challenge students to explore new opportunities in design.
Who said looks don't matter?


Below are the file structures for dBASE table files. Represented are the file structures as used for various versions of dBASE: dBASE III PLUS 1.1, dBASE IV 2.0, dBASE 5.0 for DOS, and dBASE 5.0 for Windows.
| Byte | Contents | Description
| 0 | 1 byte | Valid dBASE III PLUS table file (03h without a memo .DBT file; 83h with a memo).
| 1-3 | 3 bytes | Date of last update; in YYMMDD format.
| 4-7 | 32-bit number | Number of records in the table.
| 8-9 | 16-bit number | Number of bytes in the header.
| 10-11 | 16-bit number | Number of bytes in the record.
| 12-14 | 3 bytes | Reserved bytes.
| 15-27 | 13 bytes | Reserved for dBASE III PLUS on a LAN.
| 28-31 | 4 bytes | Reserved bytes.
| 32-n | 32 bytes | Field descriptor array (the structure of this array is each shown below)
| n+1 | 1 byte | 0Dh stored as the field terminator.
| |
n above is the last byte in the field descriptor array. The size of the array depends on the number of fields in the table file.
| Byte | Contents | Description
| 0-10 | 11 bytes | Field name in ASCII (zero-filled).
| 11 | 1 byte | Field type in ASCII (C, D, L, M, or N).
| 12-15 | 4 bytes | Field data address (address is set in memory; not useful on disk).
| 16 | 1 byte | Field length in binary.
| 17 | 1 byte | Field decimal count in binary.
| 18-19 | 2 bytes | Reserved for dBASE III PLUS on a LAN.
| 20 | 1 byte | Work area ID.
| 21-22 | 2 bytes | Reserved for dBASE III PLUS on a LAN.
| 23 | 1 byte | SET FIELDS flag.
| 24-31 | 1 byte | Reserved bytes.
| |
| Data Type | Data Input
| C (Character) | All OEM code page characters.
| D (Date) | Numbers and a character to separate month, day, and year (stored internally as 8 digits in YYYYMMDD format).
| N (Numeric) | - . 0 1 2 3 4 5 6 7 8 9
| L (Logical) | ? Y y N n T t F f (? when not initialized).
| M (Memo) | All OEM code page characters (stored internally as 10 digits representing a .DBT block number).
| |
Memo field of each record in the .DBF file contains the number of the block (in OEM code page values) where the field's data actually begins. If a field contains no data, the .DBF file contains blanks (20h) rather than a number.
When data is changed in a field, the block numbers may also change and the number in the .DBF may be changed to reflect the new location.
This information is from the Using dBASE III PLUS manual, Appendix C.
| Byte | Contents | Meaning
| 0 | 1byte | Valid dBASE IV file; bits 0-2 indicate version
number, bit 3 the presence of a dBASE IV memo
file, bits 4-6 the presence of an SQL table, bit
7 the presence of any memo file (either dBASE III
PLUS or dBASE IV).
| 1-3 | 3 bytes | Date of last update; formattted as YYMMDD.
| 4-7 | 32-bit number | Number of records in the file.
| 8-9 | 16-bit number | Number of bytes in the header.
| 10-11 | 16-bit number | Number of bytes in the record.
| 12-13 | 2 bytes | Reserved; fill with 0.
| 14 | 1 byte | Flag indicating incomplete transaction.
| 15 | 1 byte | Encryption flag.
| 16-27 | 12 bytes | Reserved for dBASE IV in a multi-user environment.
| 28 | 1 bytes | Production MDX file flag; 01H if there is an MDX, 00H if not.
| 29 | 1 byte | Language driver ID.
| 30-31 | 2 bytes | Reserved; fill with 0.
| 32-n* | 32 bytes each | Field descriptor array (see below).
| n + 1 | 1 byte | 0DH as the field terminator.
| |
* n is the last byte in the field descriptor array. The size of the array depends on the number of fields in the database file.
| Byte | Contents | Meaning
| 0-10 | 11 bytes | Field name in ASCII (zero-filled).
| 11 | 1 byte | Field type in ASCII (C, D, F, L, M, or N).
| 12-15 | 4 bytes | Reserved.
| 16 | 1 byte | Field length in binary.
| 17 | 1 byte | Field decimal count in binary.
| 18-19 | 2 bytes | Reserved.
| 20 | 1 byte | Work area ID.
| 21-30 | 10 bytes | Reserved.
| 31 | 1 byte | Production MDX field flag; 01H if field has an index tag in the production MDX file, 00H if not.
| |
| Data Type | Data Input
| C (Character) | All OEM code page characters.
| D (Date) | Numbers and a character to separate month, day, and year (stored internally as 8 digits in YYYYMMDD format).
| F (Floating point binary numeric) | - . 0 1 2 3 4 5 6 7 8 9
| N (Binary coded decimal numeric) | - . 0 1 2 3 4 5 6 7 8 9
| L (Logical) | ? Y y N n T t F f (? when not initialized).
| M (Memo) | All OEM code page characters (stored internally as 10 digits representing a .DBT block number).
| |
Each memo field of each record in the .DBF file contains the number of the block (in OEM code page values) where the field's data actually begins. If a field contains no data, the .DBF file contains blanks (20h) rather than a number.
When data is changed in a field, the block numbers may also change and the number in the .DBF may be changed to reflect the new location.
This information is from the dBASE IV Language Reference manual, Appendix D.
| Byte | Contents | Description
| 0 | 1 byte | Valid dBASE for Windows table file; bits 0-2 indicate
version number; bit 3 indicates presence of a dBASE IV
or dBASE for Windows memo file; bits 4-6 indicate the
presence of a dBASE IV SQL table; bit 7 indicates the
presence of any .DBT memo file (either a dBASE III PLUS
type or a dBASE IV or dBASE for Windows memo file).
| 1-3 | 3 bytes | Date of last update; in YYMMDD format.
| 4-7 number | 32-bit | Number of records in the table.
| 8-9 number | 16-bit | Number of bytes in the header.
| 10-11 number | 16-bit | Number of bytes in the record.
| 12-13 | 2 bytes | Reserved; filled with zeros.
| 14 | 1 byte | Flag indicating incomplete dBASE transaction.
| 15 | 1 byte | Encryption flag.
| 16-27 | 12 bytes | Reserved for multi-user processing.
| 28 | 1 byte | Production MDX flag; 01h stored in this byte if a production .MDX file exists for this table; 00h if no .MDX file exists.
| 29 | 1 byte | Language driver ID.
| 30-31 | 2 bytes | Reserved; filled with zeros.
| 32-n each | 32 bytes | Field descriptor array (the structure of this array is shown below)
| n+1 | 1 byte | 0Dh stored as the field terminator.
| |
n above is the last byte in the field descriptor array. The size of the array depends on the number of fields in the table file.
| Byte | Contents | Description
| 0-10 | 11 bytes | Field name in ASCII (zero-filled).
| 11 | 1 byte | Field type in ASCII (B, C, D, F, G, L, M, or N).
| 12-15 | 4 bytes | Reserved.
| 16 | 1 byte | Field length in binary.
| 17 | 1 byte | Field decimal count in binary.
| 18-19 | 2 bytes | Reserved.
| 20 | 1 byte | Work area ID.
| 21-30 | 10 bytes | Reserved.
| 31 | 1 byte | Production .MDX field flag; 01h if field has an index
tag in the production .MDX file; 00h if the field is not
indexed.
| |
| Data Type | Data Input
| C (Character) | All OEM code page characters.
| D (Date) | Numbers and a character to separate month, day, and year (stored internally as 8 digits in YYYYMMDD format).
| F (Floating point binary numeric) | - . 0 1 2 3 4 5 6 7 8 9
| N (Numeric) | - . 0 1 2 3 4 5 6 7 8 9
| L (Logical) | ? Y y N n T t F f (? when not initialized).
| M (Memo) | All OEM code page characters (stored internally as 10 digits representing a .DBT block number).
| |
Each memo field of each record in the .DBF file contains the number of the block (in OEM code page values) where the field's data actually begins. If a field contains no data, the .DBF file contains blanks (20h) rather than a number.
When data is changed in a field, the block numbers may also change and the number in the .DBF may be changed to reflect the new location.
Unlike dBASE III PLUS, if you delete text in a memo field, dBASE 5.0 for DOS may reuse the space from the deleted text when you input new text. dBASE III PLUS always appends new text to the end of the .DBT file. In dBASE III PLUS, the .DBT file size grows whenever new text is added, even if other text in the file is deleted.
This information is from the dBASE for DOS Language Reference manual, Appendix C.
| Byte | Contents | Description
| 0 | 1 byte | Valid dBASE for Windows table file; bits 0-2 indicate
version number; bit 3 indicates presence of a dBASE IV
or dBASE for Windows memo file; bits 4-6 indicate the
presence of a dBASE IV SQL table; bit 7 indicates the
presence of any .DBT memo file (either a dBASE III PLUS
type or a dBASE IV or dBASE for Windows memo file).
| 1-3 | 3 bytes | Date of last update; in YYMMDD format.
| 4-7 number | 32-bit | Number of records in the table.
| 8-9 number | 16-bit | Number of bytes in the header.
| 10-11 number | 16-bit | Number of bytes in the record.
| 12-13 | 2 bytes | Reserved; filled with zeros.
| 14 | 1 byte | Flag indicating incomplete dBASE IV transaction.
| 15 | 1 byte | dBASE IV encryption flag.
| 16-27 | 12 bytes | Reserved for multi-user processing.
| 28 | 1 byte | Production MDX flag; 01h stored in this byte if a production .MDX file exists for this table; 00h if no .MDX file exists.
| 29 | 1 byte | Language driver ID.
| 30-31 | 2 bytes | Reserved; filled with zeros.
| 32-n each | 32 bytes | Field descriptor array (the structure of this array is shown below)
| n+1 | 1 byte | 0Dh stored as the field terminator.
| |
n above is the last byte in the field descriptor array. The size of the array depends on the number of fields in the table file.
| Byte | Contents | Description
| 0-10 | 11 bytes | Field name in ASCII (zero-filled).
| 11 | 1 byte | Field type in ASCII (B, C, D, F, G, L, M, or N).
| 12-15 | 4 bytes | Reserved.
| 16 | 1 byte | Field length in binary.
| 17 | 1 byte | Field decimal count in binary.
| 18-19 | 2 bytes | Reserved.
| 20 | 1 byte | Work area ID.
| 21-30 | 10 bytes | Reserved.
| 31 | 1 byte | Production .MDX field flag; 01h if field has an index tag in the production .MDX file; 00h if the field is not indexed.
| |
| Data Type | Data Input
| B (Binary) | All OEM code page characters (stored internally as 10
digits representing a .DBT block number).
| C (Character) | All OEM code page characters.
| D (Date) | Numbers and a character to separate month, day, and year
(stored internally as 8 digits in YYYYMMDD format).
| G (General) | All OEM code page characters (stored internally as 10
digits or OLE) representing a .DBT block number).
| N (Numeric) | - . 0 1 2 3 4 5 6 7 8 9
| L (Logical) | ? Y y N n T t F f (? when not initialized).
| M (Memo) | All OEM code page characters (stored internally as 10
digits representing a .DBT block number).
| |
Each binary, memo, or OLE field of each record in the .DBF file contains the number of the block (in OEM code page values) where the field's data actually begins. If a field contains no data, the .DBF file contains blanks (20h) rather than a number.
When data is changed in a field, the block numbers may also change and the number in the .DBF may be changed to reflect the new location.
Unlike dBASE III PLUS, if you delete text in a memo field (or binary and OLE fields), dBASE for Windows (unlike dBASE IV) may reuse the space from the deleted text when you input new text. dBASE III PLUS always appends new text to the end of the .DBT file. In dBASE III PLUS, the .DBT file size grows whenever new text is added, even if other text in the file is deleted.
This information is from the dBASE for Windows Language Reference manual, Appendix C.


Created: 28/07/2015
By: Themography
Email<EMAIL_ADDRESS>Thanks for purchasing Blogio - Minimal Responsive Blog Template. Blogio is an HTML Blog Template built with Bootstrap3, HTML5 and CSS3 that have well-organized valid code. This documentation is to provide you a full guideline for using this template. Feel free to ask any question or contact with us for further question or any kind of solution.Thanks again.
This is a HTML5 Blog Temepalte. It's followed html5 structure. All of the information within the main body area is nested within an html5 tag. You will find all content in specific html5 tags such as ,
,
etc tags. Every section is separated with comments. We followed all coding standard procedure.
Head section structure: Here is the example of
section code.
Navigation section structure: Here is the structure of Navigation. It is very much well organized and well commented.
We are using sevaral CSS files in this template. We always follow the latest trends of coding standard. We make this template with the most popular CSS framework named Bootstrap. This tamplate is build with Bootstarp 3.
CSS File included:# bootstrap.min.css - Currently v3.3.5 is the main bootstrap css file.
# font-awesome.min.css - Version 4.3.0 is used for icon. You can use 519 diffrent Icons in this template by replacing the icon class.
# responsive.css is the custom responsive css media file.
# style.css - Currently v1.0.0 is the main stylesheet. It contains all of the specific stylings for the template. Here is the an example of general style. The file is separated into sections using:
style.css
/* ========================================================== Table of Index ============================================================= 1. GLOBAL CSS 2. HEADER CSS 3. SLIDER POST CSS 4. CONTENT CSS 5. SINGLE PAGE CSS 6. SIDEBAR CSS 7. PAGE: About Me CSS 8. PAGE: 404 Error CSS 9. PAGE: GALLERY POST CSS 10. PAGE: ARCHIVE CSS 11. FOOTER CSS */ /* ================================ 1. GLOBAL ================================ */ some css /* ================================ 2. HEADER ================================ */ some css ------------- ------------- /* ================================ 11. FOOTER ================================ */etc.
If you would like to edit a specific section of the site, simply find the appropriate label in the CSS file, and then scroll down until you find the appropriate style that needs to be edited.
CSS coding standard: We followed css and css3 coding standard. Here is the exampl:.slider .left-arrow .fa-angle-left, .thum-item .left-arrow .fa-angle-left { position: absolute; top: 45%; left: -50px; color: #000000; -webkit-transition: all 0.3s linear; -moz-transition: all 0.3s linear; -o-transition: all 0.3s linear; transition: all 0.3s linear; }
This theme imports Following Javascript files.

You can use your own Instagram photo by adding user ID. Details on Instafeed.js You can also add your location on the google map in the contact page. Just open to the custom.js and replace latitude, longitude of you location.
Once again, thanks for purchasing this Template. As I said you at the beginning, I'd be glad to help you or to solve your problem if you have any questions relating to this Template. No guarantees, but you'll find me trying my best to assist. If you have any more general question relating to this template on Shapebootstrap, you might consider visiting the forums and asking your question in the "Item Discussion" section.


Workshop 2
Angular Architecture Patterns
December 6, 2018 | 07:30 - 17:30
Please bring your own laptop.
07:30
Enjoy the continental breakfast buffet together with the
trainers
08:30
Monorepo-style development
10:30
Break with coffees, teas, juices, fresh yoghurt and
fruits
11:00
Redundancy in large applications
12:30
Lunch time: fresh soup, salads, selections of cold and
warm fish and meat dishes, dessert
14:00
Component architecture + Angular elements
15:30
Break with coffees, teas, juices, energizers and
snacks
16:00
Advanced patterns and state recipes
Workshop 4
Angular & NGRX
December 6, 2018 | 07:30 - 17:30
Learn how to master Redux-style state management with ngrx/store and ngrx/effects.
This workshop covers the concepts of state management and its implementation with Angular. You'll explore architectural patterns and learn how to integrate ngrx to achieve effective and maintainable state management.
Primed with new knowledge, you’ll be able to implement effective state management in Angular applications without thinking twice.
Please bring your own laptop.
07:30
Enjoy the continental breakfast buffet together with the
trainers
08:30
Observable based reactive patterns
10:30
Break with coffees, teas, juices, fresh yoghurt and
fruits
11:00
Actions, reducers and side-effects
12:30
Lunch time: fresh soup, salads, selections of cold and
warm fish and meat dishes, dessert
15:30
Break with coffees, teas, juices, energizers and
snacks
16:00
How to handle immutability


---
title: Contact Ryan Hayden
heading: Let's get your church an awesome new website
description: Send me your name, email and phone number and I'll contact you as soon as I can.
(Or you can reach out to me at<PHONE_NUMBER> or<EMAIL_ADDRESS>---


|
||||||||||
| PREV PACKAGE NEXT PACKAGE | FRAMES NO FRAMES |
| Interface Summary | |
|---|---|
| Instantiable<E> |
| Class Summary | |
|---|---|
| OFBarrierReply | Represents an OFPT_BARRIER_REPLY message |
| OFBarrierRequest | Represents an OFPT_BARRIER_REQUEST message |
| OFEchoReply | Represents an ofp_echo_reply message |
| OFEchoRequest | Represents an ofp_echo_request message |
| OFError | Represents an ofp_error_msg |
| OFFeaturesReply | Represents a features reply message |
| OFFeaturesRequest | Represents a features request message |
| OFFlowMod | Represents an ofp_flow_mod message |
| OFFlowRemoved | Represents an ofp_flow_removed message |
| OFGetConfigReply | Represents an OFPT_GET_CONFIG_REPLY type message |
| OFGetConfigRequest | Represents an OFPT_GET_CONFIG_REQUEST type message |
| OFHello | Represents an ofp_hello message |
| OFMatch | Represents an ofp_match structure |
| OFMatchBeanInfo | Extra info for how to treat OFMatch as a JavaBean For some (inane!) reason, using chained setters in OFMatch breaks a lot of the JavaBean defaults. |
| OFMatchWithSwDpid | |
| OFMessage | The base class for all OpenFlow protocol messages. |
| OFMessageContextStore<V> | |
| OFPacketIn | Represents an ofp_packet_in |
| OFPacketOut | Represents an ofp_packet_out message |
| OFPacketQueue | Represents ofp_packet_queue |
| OFPhysicalPort | Represents ofp_phy_port |
| OFPortMod | Represents an ofp_port_mod message |
| OFPortStatus | Represents an ofp_port_status message |
| OFQueueGetConfigReply | Represents an ofp_queue_get_config_request message |
| OFQueueGetConfigRequest | Represents an ofp_queue_get_config_request message |
| OFQueueProp | |
| OFSetConfig | Represents an OFPT_SET_CONFIG type message |
| OFStatisticsMessageBase | Base class for statistics requests/replies |
| OFStatisticsReply | Represents an ofp_stats_reply message |
| OFStatisticsRequest | Represents an ofp_stats_request message |
| OFSwitchConfig | Base class representing ofp_switch_config based messages |
| OFVendor | Represents ofp_vendor_header |
| Wildcards | a more user friendly representation of the wildcards bits in an OpenFlow match. |
| Enum Summary | |
|---|---|
| OFError.OFBadActionCode | |
| OFError.OFBadRequestCode | |
| OFError.OFErrorType | |
| OFError.OFFlowModFailedCode | |
| OFError.OFHelloFailedCode | |
| OFError.OFPortModFailedCode | |
| OFError.OFQueueOpFailedCode | |
| OFFeaturesReply.OFCapabilities | Corresponds to bits on the capabilities field |
| OFFlowRemoved.OFFlowRemovedReason | |
| OFPacketIn.OFPacketInReason | |
| OFPhysicalPort.OFPortConfig | |
| OFPhysicalPort.OFPortFeatures | |
| OFPhysicalPort.OFPortState | |
| OFPort | |
| OFPortStatus.OFPortReason | |
| OFQueueProp.OFQueuePropType | |
| OFStatisticsReply.OFStatisticsReplyFlags | |
| OFSwitchConfig.OFConfigFlags | |
| OFType | List of OpenFlow types and mappings to wire protocol value and derived classes |
| Wildcards.Flag | enum type for the binary flags that can be set in the wildcards field of an OFMatch. |
|
||||||||||
| PREV PACKAGE NEXT PACKAGE | FRAMES NO FRAMES |


How I build this blog with Jeklly and GitHub Pages (Step-by-Step)
All the files to build this blog could be found at my Github repository.
Step 1: Fork Lanyon Theme to my Github repository.
There are lots of Jeklly Themes that we could choose from. I choose Lanyon because it’s the simplest one. Here are the steps to get started.
- Fork the theme repository to my own github.
- Rename the repository to username.github.io
- Change something in the _config.yml file (or any file in the repository). It’ll force GitHub Pages to build the website.
Now, when I go to http://username.github.io, the website is there.
Step 2: Customize the website.
- Know the basic structure of Jeklly directory.
- Install Jeklly.
To customize the website, you need to know some basic ideas about how Jeklly site directory is structured. In Lanyon it looks like this:
The detail of the directory structure is here. Basically, _config.yml stores some configuration data, like the name and url of the blog. _layouts files are templates of the posts. _includes files are partials that can be reused in layouts and posts. _posts files are where your blog posts are stored. index.html is the root directory (home) of your blog.
Every time you change something in the directory, push it to the github repository, GitHub pages will build it automatically. However, a better way of doing this, is to check if everything is what you want it to be before you push it to github. Therefore, I installed Jekyll on my laptop. (Since the os of my laptop is Windows 10, I followed this guide.)
After install Jekyll, navigate into the root directory of the local Jekyll site repository and put jekyll serve command, then the preview of the website can be found at http://localhost:4000 . So that every time you change something, you can review it before push it to the GitHub Pages.
Step 3: Add an archive page to my website.
Create a markdown file in the root directory. Here’s the code to build a archive page:
A note: I’m followed this answer to escape liquid template tags in this blog.
Step 4: Add tags to posts, and a tag page to show posts by tags.
I followed this tutorial to add tags to posts, and alphabetize the tag lists on the tag page.
Step 5: Add comment function to the posts.
I added Disqus to enable comments under the blog. Here are the steps:
- Create a file comments.html in _includes directory, which contains the code below:
- Modify the file _layouts/post.html. Add this line at the end of the file:
- Finally, I can enable comments of a post by adding comments:True in the YAML header of the post.
Step 6: Only show an excerpt of posts in Home page.
Modify the index.html file. Replace the {{ post.content }}
line with {{ post.content | markdownify | strip_html | truncatewords: 50 }}
. It’ll gets the first 50 words and strips any formatting.
Step 6: Write blogs using Markdown.
Finally, I installed MarkdownPad to write posts. I created a directory named _drafts to put draft posts. Normally, they won’t be presented when you do jekyll serve. In order to see them, use jekyll serve –draft instead.


SCARY MOVIE 2 * * *
2001 - USA Director: Keenen Ivory Wayans Starring: Anna Faris, Christopher Masterson, Tim Curry, Marlon Wayans, Regina Hall, Shawn Wayans, Kathleen Robertson, James Woods, Chris Elliott, Tori Spelling, David Cross
Reviewed by Frankie Paiva
The original Scary Movie was released under the radar last year and astounded analysts and its studio, Dimension Films, with its high box office gross. The film spoofed classic horror films using `Scream' as an outline, with plenty of disgusting and raw scatological humor. Scary Movie placed number three on my worst five films of the year list for 2000, but since the film earned $157 million domestically, and because life is unfair, a sequel was inevitable.
The surprise is that it really isn't that bad. The sequel is far more clever than the first, this time adding political references and current events into the mix. There's a nice balance between Firestone tire and Nike TV ad jokes, and a clown being choked to death by a phallus jokes. The film is fun overkill.
Anna Faris (who, in a bizarre side note, was born and raised in the small Seattle suburb of Edmonds, Washington where this author resides) reprises the role of Cindy Campbell. Her horny psychology professor (Tim Curry, who else?) stages some type of psychological experiment for her and all her friends from the original Scary Movie, who all happen to go to the same college. The experiment involves spending the weekend in a haunted mansion. Things, of course, go terribly and hilariously wrong.
Not that the plot really matters. The film moves from one joke to another so quickly it's really a string of punch lines rather than a story. This is forgivable because the material is very funny... but there's a problem.
If you haven't kept up on popular culture lately, you're likely to miss many of the jokes. A very clever spoof of Save the Last Dance flew by even the teenagers at my packed screening, as did a Rocky send-up. Other films are so cleverly woven into the plot that one viewing isn't nearly enough to spot them all. Less obvious jokes spoof films like The Game, The Amityville Horror , Urban Legend, and Hollow Man. All that and a character named Jamie Lee Curtisto.
One of the biggest problems with Scary Movie was that it had to adhere so closely to the storyline of Scream. Scary Movie 2 is a combination of House on Haunted Hill and The Haunting, but even that's a loose structure.
This gives the actors time to roam around, and the result is a more relaxed pace. Regina Hall gives a funny performance that goes against the premise that African-Americans always die first in horror movies. Christopher Masterson and Faris have good chemistry, but the film really belongs to Chris Elliott. As Hanson, the house's caretaker with a deformed, odd-looking left hand, and less than appealing facial features, he is completely wonderful and over-the-top. Tori Spelling is criminally underused.  I was waiting for a `90210' gag or her dad to pop out from somewhere.
The biggest surprise of all may be that Scary Movie 2 seems tamer than the original. Oh sure there's abundant sex humor, but America has become less taboo about these issues since the original's release. Whether tamer or more outrageous, there's plenty of laughs here for a normal person to have a great time, and a Joe-Lieberman-person to start fuming.
Read more reviews at www.moviepie.com
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Scott Doyle /<EMAIL_ADDRESS>If the hidden layer has units indexed by $j$, and the input layer is indexed by $i$, then the net activation is:
$ net_{j} = \sum_{i=1}^{d}x_{i}w_{ji}+w_{j0}=\sum_{i=0}^{d}x_{i}w_{ji}\equiv \mathbf{w}_{j}^{T}\mathbf{x} $
The output layer (indexed by $k$) has a unique set of weights operating on the results of the hidden layer (denoted in total by $\mathbf{y}$):
$ net_{k} = \sum_{j=1}^{n_{H}}y_{i}w_{kj}+w_{k0} = \sum_{j=0}^{n_{H}}y_{j}w_{kj}=\mathbf{w}_{k}^{T}\mathbf{y} $
In addition to the weights, a unit has a nonlinear function it operates on its net activation, which dictates its emitted result:
$ y_{j} = f(net_{j}) \qquad z_{k}=f(net_{k}) $
Combining equations yields our familiar discriminant function:
$ g_{k}(\mathbf{x}) \equiv z_{k} = f\left(\sum_{j=1}^{n_{H}} w_{kj} f\left(\sum_{i=1}^{d}w_{ji}x_{i}+w_{j0}\right)+w_{k0}\right) $
Hidden layers increase the expressive power over and above linear machines or their nonlinear variants.
Any continuous function (i.e. the nonlinear decision regions) should be learnable as a combination of functions; compare our output function with Kolmogorov’s proof:
$ g_{k}(\mathbf{x}) \equiv z_{k} = f\left(\sum_{j=1}^{n_{H}} w_{kj} f\left(\sum_{i=1}^{d}w_{ji}x_{i}+w_{j0}\right)+w_{k0}\right) $
$ g(\mathbf{x}) = \sum_{j=1}^{2n+1}\Xi_{j}\left(\sum_{i=1}^{d}\Psi_{ij}(x_{i})\right) $
… as well as Fourier’s theorem.
Given a set of inputs arranged in opposition, we can take a series of S-shaped curves to approximate a “bump” (may or may not be Gaussian). Given a sufficiently large number of these, we could in theory approximate any function.
Backpropagation is the most general method for setting the weights between units.
Since the hidden layer is an intermediate step, we need an “effective error” we can use to optimize the weights – the credit assignment problem.
This is done during the learning mode (as opposed to the feedforward mode used for classification).
Training error is calculated as the squared difference between the output and the target vector, just as in the LMS algorithm:
$ J(\mathbf{w})=\frac{1}{2}\sum_{k=1}^{c}(t_{k}-z_{k})^{2}=\frac{1}{2}|\mathbf{t}-\mathbf{z}|^{2} $
Backpropagation learning, like most of our learning algorithms, is based on gradient descent:
$ \Delta\mathbf{w}=-\eta\frac{\partial J}{\partial\mathbf{w}} \Delta w_{kj}=-\eta\frac{\partial J}{\partial w_{kj}} $
To solve $\Delta\mathbf{w}$, we must differentiate using the chain rule – first to find the hidden-to-output weights (since we have an output to compare to), and then the input-to-hidden weights.
“Deep Learning” is an extension of regular neural networks.
In general, it refers to stacking many hidden layers between inputs and outputs to model a very complex decision space (MUCH more complex than our simple XOR example).
Thus, deep learning is not new.
What is new is that:
The process of designing architecture, calculating backpropagation, etc. are the same for deep networks as for traditional neural networks.
The added layers provide the ability to describe increasingly nonlinear decision spaces.
The universal approximation theorem postulates that even a single hidden layer, given enough units, can represent almost any continuous function.
The problem is that fully connected deep networks rarely work in practice.
They suffer from a few major drawbacks:
There is a relationship between the depth of the network, the size of the training set, and the computational complexity (i.e. training time) required to fully train the network without overfitting.
To assist in training these systems, we can add layers while at the same time using “tricks” to reduce the number of connections we need to worry about.
The first of these gives rise to Convolutional Neural Nets, which are based around the description and classification of images.
CNNs are focused on “natural” images, but these are the datasets that have had a TON of machine learning research poured into them over the years.
The concepts extend to medical datasets as well, with a few caveats (medical images are often rotationally invariant, etc.).
Practically speaking, what do our input units represent?
$\mathbf{x}=(x_{1},\ldots,x_{n})^{T}$; for each input feature, we have an input node. Imagine we want to build a neural network to identify images of objects – each of the inputs would be a data point in the image (i.e. a pixel).
The CIFAR-10 dataset, a benchmark dataset of natural images, is made of images just 32x32 pixels. For 3-channel color images (R,G,B), this means 3,072 neurons… at the input layer.
The number of inputs balloons very quickly: a 200x200 image would result in 120,000 weights.
Moreover, each of these must be mapped to hidden units – so our weight vector is 120,000 multiplied by some (likely larger) number of hidden units.
And that’s all for one layer, and for a dataset of just 200x200x3 images, which are quite small.
Images have a few characteristics that allow us to constrain the network architecture.
Thus, instead of having a fully-connected network (where every neuron is connected to all the neurons in the previous layer), we constrain each input to a geographic regions surrounding it.
Convolution nets have layers arranged as volumes, which in turn transform the 3D input to a 3D output volume of activations.
There are several types of layers in CNNs which process the input data. The order, number, and size of these layers is the architecture of the network, and is the main difference between LeNet, AlexNet, GoogLeNet, etc.
Here are the most basic layers and their abbreviations:
There are other types of layers, but these are the main ones to know.
Convolutional Layers are the main descriptive element of CNNs. They operate by convolving a filter set across the image, generating a set of outputs for each receptive field. We will cover these in detail in a few slides.
If our input is [32x32x3], and we select a set of 12 space-preserving filters (more on that in a second), then our output will be [32x32x12].
Activation Layers apply a straightforward function to the inputs, leaving the input dimensionality unchanged. There are several types of activation layers, the most commonly-used of which is the Rectified Linear Unit, which applies the function $\max{0,x}$ to the input $x$.
In our example, the [32x32x12] input from the CONV layer is unchanged in size, but any values less than 0 are set to 0.
Pooling Layers perform downsampling operations, which reduce the size of the previous layer’s input in the spatial dimensions. The most common operation is max pooling, where the greatest value in a kernel size is used to determine the output.
Thus an input of [32x32x12] will be turned into a reduced size [16x16x12] layer, assuming we are using a 2x2 kernel (leading to an output that is half the input size).
Fully-Connected Layers are the same kind of layers we saw in classical neural networks, where each neuron is connected fully to the previous layer and fully to the next layer.
In our example, these layers essentially do away with the spatial information in the image, and now we consider each “pixel” as one input like we would for regular nets. If we have 10 output classes, then the [16x16x12] input to this layer would be converted into a [1x1x10] output (after passing it to some large number of hidden fully-connected units).
Pooling and activation layers perform fixed operations, while the convolutional and fully-connected layers have a set of parameters (weights and biases) that are learned as before, with gradient descent and backpropagation.
Convolutional Layers consist of a set of learnable image filters, which are convolved with the image to produce a map of responses at each local neighborhood.
The size of the result of this layer depends on a number of factors:
The Receptive Field, Stride, and Padding affect the “spatial” output dimensions (height and width), while the Depth affects the, well, “depth” output dimension.
The spatial extent of a neuron’s connectivity is called the receptive field of the neuron, which is treated as a hyperparameter. This is equivalent to the convolutional filter’s kernel size.
Each neuron handles a limited spatial location but is connected through the full depth of the input volume; i.e. the neurons corresponding to the color channels of the same receptive field are all connected with one another.
So if we have an input size of [32x32x3], and we set our kernel size (receptive field) of the [CONV] layer to 5, then each neuron in the [CONV] layer will have weights connecting to a 5x5x3 region of the input volume, so each neuron would have 75 weights and one bias term.
The following three parameters controls how the filter operates across the input image:
The size of the output (height and width) is a function of the input size in the spatial dimension ($\mathbf{W}$), the receptive field size ($\mathbf{F}$), the stride ($\mathbf{S}$), and the zero-padding ($\mathbf{P}$):
$ \frac{\mathbf{W}-\mathbf{F}+2\mathbf{P}}{\mathbf{S}}+1 $
So if we designed a CONV layer that operates on our [32x32x3] input ($\mathbf{W}=32$), and we used a filter size of 3 ($\mathbf{F}=3$), a padding of 2 ($\mathbf{P}=2$), and a stride of 3 ($\mathbf{S}=3$), then we would have an output of size:
$ \frac{32 - 3 + (2 \times 2)}{3} + 1 = 12 $
Thus the output volume would have a spatial size of 12x12. Note that the actual volume of the output would be 12x12x$\mathbf{K}$, which includes the depth (each of the filter outputs is 12x12, so stacking up $\mathbf{K}$ filters gives you the depth dimension.
It’s important to note that these parameters are not chosen at random; they have to be selected such that they evenly and smoothly cover the input space.
The use of zero-padding and some other tricks can help you “fit” the layers together.
Research team Krizhevsky, et al. used image sizes of $227\times 227\times 3$, with $\mathbf{F}=11$, $\mathbf{S}=4$, $\mathbf{P}=0$, and depth $\mathbf{K}=96$, yielding an output volume of $55\times 55\times96 = 290,400$ neurons!
With a field size of $11\times11\times3$, this yields $363$ weights (plus one bias term) per neuron, or $290,400 \times (363 + 1)=105,705,600$ parameters… on just the first layer. Each and every one of these parameters has to be estimated through gradient descent and backpropagation!
However, we can reduce this by making a simplifying assumption: Weights are not likely to change dramatically from one region to the next!
Thus, even though the first layer has $55\times55\times96$ neurons, if we say that all of the spatial $55\times55$ neurons can share the same parameter, then we just have $96$ weights, multiplied over the region size: $96\times11\times11\times3=34,848$.
Much better!
If all neurons in a depth slice use the same weight, then the neurons’ weights are convolved with the corresponding input volume.
The result is an activation map of the region (e.g. $55\times55$), and the output volume is just this multiplied by the depth slices (e.g. $96$).
The Pooling layer acts to reduce the spatial size of the representation, which in turn reduces the parameters, computation, and overfitting.
Its primary operation is to take the maximum of a $2\times2$ filter with a stride of $2$, which gets rid of 75% of the least-important activations.
Since this is a single operation, it requires no new parameters! Only hyperparameters $\mathbf{F}$ (spatial extent) and $\mathbf{S}$ (stride).
You can generalize the size of the filter and the stride, but typically $\mathbf{F}=3,\mathbf{S}=2$ or $\mathbf{F}=2,\mathbf{S}=2$.
Note that there’s no “real” reason the maximum operation is used; you could average together (like you do with image resizing), perform a decimation, etc., but the max pooling approach seems to work better.
The fully-connected layers have are connected to all activations in the previous layer and are essentially the same as regular neural network layers.
Since there’s no modulation of the inputs, these can also be optimized / trained the same way.
Since convolutional layers are simply modulated by their inputs (they function the same way), a convolutional layer is a subset of the connections in a fully-connected layer.
For example, setting a convolutional layer with stride equal to 1 and the depth equal to the number of inputs is essentially the same as a fully-connected layer.
Some rules of thumb in terms of size:
Keep downsampling limited to pooling layers by designing convolutional layers such that they preserve the spatial dimensions of the input.
You can impose harsher restrictions if needed: A $224\times224\times3$ image, with three $3\times3$ convolution layers and $64$ filters in each would create three activation volumes of $224\times224\times64$.
This is equivalent to around 10 million activations, or 72 MB of memory – for one image. This is quite rare to see in practice.
Remember: Start Simple! Only move onto more complex approaches if you absolutely need to!
Next class, we will finish up CNNs and discuss some recent departures in the literature from this simple setup. I will also show some examples of CNNs in my own research, and give some software resources where you can play with these networks.


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| | Builder (TDDProxy component) |
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| virtual TBuilder | CenterFrame (bool centerFrame) |
| | By default the frame is positioned exactly where the drag element is, so we use the cursor offset provided by Ext.dd.DD. Another option that works only if you do not have constraints on the obj is to have the drag frame centered around the cursor. Set centerFrame to true for this effect. Defaults to: false More...
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| virtual TBuilder | ResizeFrame (bool resizeFrame) |
| | By default we resize the drag frame to be the same size as the element we want to drag (this is to get the frame effect). We can turn it off if we want a different behavior.  Defaults to true. More...
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- Type Constraints
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| TDDProxy | : | DDProxy | |
| TBuilder | : | Builder | |
| TBuilder | : | TDDProxy | |
| TBuilder | : | TBuilder | |
◆ Builder() [1/4]
◆ Builder() [2/4]
◆ Builder() [3/4]
◆ Builder() [4/4]
◆ AfterDrag()
Abstract method runs on drag end
- Parameters
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| action | The action delegate |
- Returns
- An instance of TBuilder
◆ CenterFrame()
By default the frame is positioned exactly where the drag element is, so we use the cursor offset provided by Ext.dd.DD. Another option that works only if you do not have constraints on the obj is to have the drag frame centered around the cursor. Set centerFrame to true for this effect. Defaults to: false
◆ operator Builder()
◆ ResizeFrame()
By default we resize the drag frame to be the same size as the element we want to drag (this is to get the frame effect). We can turn it off if we want a different behavior.
The documentation for this class was generated from the following file:


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