1.Smallgoods Pty. Ltd. is a manufacturer of processed meat products including salami. During the manufacturing process, the salami is put through a treatment process that kills bacteria which is known in the small goods industry to form in the salami during production. The bacteriaisvery small and cannot be seen with the human eye. During the manufacture of a particular batch of salami marked “use by 31 July 2018”, Smallgoods Pty Ltd omitted to put the batch through the bacteria treatment process. The salami was then packaged in air-tight plastic packaging which allows the purchaser to see the salami.

This batch of salami was sold to Supermarkets Pty. Ltd.for sale in its supermarket in Melbourne. On 20 July 2018, Ann purchased a package of salami which was marked “use by 31July2018” from the supermarket. Above the smallgoods section of the supermarket was a large sign which read “The Liability of Supermarkets Pty. Ltd.” for any loss or damage caused by any product it sells is limited to the cost of replacing the product”.

Ann properly stored the salami in the refrigerator but did not look at the “use by” date. On 7 August 2018, she consumed part of the salami which contained bacteria andshebecame very ill. She required hospitalisation, incurring large medical expenses and was unable to go to work for two months.

(a)Compare Ann’s rights against the salami manufacturer under the tort of negligence with her rights under ss54 and 138 of the ACL (10 marks).(b)Aside from the rights discussed above, explainwhether Ann has any (and if so, what) rights against Supermarkets Pty. Ltd. under the ACL in relation to the contaminated salami (10 marks).

2.For some years, Shanti had worked night shift at the U-Bewt Shoes factory, where she was the cutting-machine operator. Her shift started at 3pm and finished at 11pm. She travelled to work by car, and the factory had a car park in which she parked. When she left work at 11pm, the car park was fairly empty, and quite dark. She was the only one leaving at that time, and she had to walk over 100 metres to reach her car. Shanti had, in the past, caught a glimpse of a person in the bushes surrounding the car park, but had never been able to identify the person, since the carpark was unlit.

Shanti had mentioned herconcerns about walking in the carpark at night to her manager, Mr Collins, and suggested that the factory erect some lights, but he had told her that there was nothing to worry about. The carpark was fenced, and there was a security guard at the gateway to the carpark.Shantireplied that the security guard was not of much help to her, since he was 500 metres from the actual carpark.Shanti also told Mr Collins that everyone knew that some cars had been brokeninto. Mr Collins said that he had heard about the problem but said that nothing more needed to be done, since the carpark was secure.

A week after this conversation, Shanti was walking to her car when she saw a figure at one of the other cars. When she caught a clearer view she saw that it was a man, and that he was clearly breaking into her car. She yelled out, and the man ran towards her and punched herand knockedherto the ground. He then kicked her, and grabbed her handbag before running off.

As a result of the injuries that Shanti suffered during the attack she was unable to work for several months. She blames the factory for the attack, and wants to sue.AdviseShanti about the likely success of her action (15 marks)

 

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(a) For any state s containing the item A—> a • 13, ItemPollow((s,A—) a • 13)) c Follow(A)
03)
U U ItemFollow((s,A—)al • 130). Follow(A)
s A —> cri • pies

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1. Show that the access link in an activation record (the “static” link) isn’t static.

2. Suppose that we tried to avoid adding an access link to each activation record in a language with nested procedures by devising a mechanism for counting our way through dynamic links until the appropriate activation was reached (dynamic links will always lead eventually to the correct lexical environment). Why can’t this work?

 

Q

 

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1. Show that the counting numbers defined by equation (11.26) are convex.

2. In this exercise, we will derive another message passing algorithm, called the two-way algorithm, for finding fixed points of region-based energy functionals; this algorithm allows for more general region graphs than in exercise. It uses two messages along each link r → r’: one from r to r’ and one from r’ to r. Consider a region-based free energy as in equation (11.27). For any region r, let pr = |Up(r)| be the number of regions that are directly upward of r. Assume that for any top region (so that pr = 0), we have that κr = 1. We now define qr = (1 − κr)/pregion, taking qr = 1 when pr = 0 (so that κr = 1, as per our assumption). Assume furthermore that qr ≠ 2, and define βr = 1/(2 − qr).

The following equalities define the messages and the potentials in this algorithm:

Note that the messages  are as we would expect: the message sent from r to r’ is simply the product of r’s initial potential with all of its incoming messages except the one from r’ (and similarly for the message sent from r’ to r). However, as we discussed in our derivation of the region graph algorithm, this computation will double-count the information that arrived at r from r’ via an indirect path. The final computation of the messages δ  is intended to correct for that double-counting. In this exercise, you will show that the fixed points are precisely the same as the fixed points of the update equations equation (11.71)–equation (11.76).

a. We begin by defining the messages in terms of the beliefs and the Lagrange multipliers:

Show that these messages satisfy the fixed-point equations in equation (11.33).

b. Show that

Conclude that equation (11.76) holds.

c. Show that

Show that the only solution to these equations is given by equation (11.73) and equation (11.74).

d. Show by direct derivation that theorem 11.6 holds for the potentials defined by equation (11.71)–11.76.

 

 

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1. Show that if Q = i Q(Xi|Ui), then

2. Develop a variational approximation using Bayesian networks. Assume that Q is represented by a Bayesian network of a given structure G. Derive the fixed-point characterization of parameters that maximize the energy functional for this type of approximation.

 

 

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