Inspired by work on Stackelberg security games, we intro- duce Stackelberg planning, where a leader player in a clas- sical planning task chooses a minimum-cost action sequence aimed at maximizing the plan cost of a follower player in the same task. Such Stackelberg planning can provide use- ful analyses not only in planning-based security applications like network penetration testing, but also to measure robust- ness against perturbances in more traditional planning appli- cations (e. g. with a leader sabotaging road network connec- tions in transportation-type domains). To identify all equilib- ria – exhibiting the leader’s own-cost-vs.-follower-cost trade- off – we design leader-follower search, a statespacesearch at the leader level which calls in each state an optimal plan- ner at the follower level. We devise simple heuristic guidance, branch-and-bound style pruning, and partial-order reduction techniques for this setting. We run experiments on Stackel- berg variants of IPC and pentesting benchmarks. In several domains, Stackelberg planning is quite feasible in practice.
The challenge is to store the representation of the transitions efficiently. On the GPU, the Reverse Polish Notation (RPN) 3 (Burks et al., 1954), i. e., a postfix represen- tation of Boolean and arithmetic expressions, was identified as effective. It is used to represent all preconditions and postconditions of the model in one integer array. This array is partitioned into two parts, one for the preconditions, the other for the post- conditions. A prefix assigns the conditions to its processes after creating the array. In addition to the preconditions, each transition indicates the successor state the process will reach after enabling the postconditions. Tokens are used to distinguish different elements of the Boolean formulas. Each entry consists of a pair (token,value) identi- fying the action to take. Consider the precondition my\_place==3-1; starting at position 8 of the array presented in Figure 7.2. It is translated to the RPN as an array of length 10 using tokens for constants, arithmetic operations and variables. Constant tokens, defining also the type of the constant, are followed by the value. Arithmetic to- kens identify the following byte as an operator. One special token is the variable token, there is no need for distinction in arrays or variables, since variables are seen as arrays of length 1, so the token defines the type of the variable and is followed by the index to access it in the state. This yields a pointer-free, compact and flat representation of the transition conditions.
While solving the pricing problem, where we are looking for the schedule for invididual employee, two bounds are commonly used — the lower bound (see Chapter 3.5) and the upper bound (i.e. 0, since we are looking for solutions with negative reduced cost). For branch and bound based solution method it holds (assuming minimization), that each search node (partial individual schedule) has a lower bound associated with it and all the nodes shares the same upper bound. Since we deal with a problem with soft constraints, one of a few ways how to prune nodes in the search tree is to prove that their lower bound has greater value than the best currently known upper bound. These two bounds are getting closer to each other during the run of a pricing algorithm. Naturally, it is desired to have them as close as possible from the start so more nodes can be pruned early.
Alternative disease surveillance systems exist and could be used to potentially alleviate the limitations of ILI+ data. Two alternative sources for surveillance system data include Google Flu Trends (Ginsberg et al., 2009) and Wikipedia (Generous et al., 2014). Both surveillance systems are available in near real-time. Google Flu Trends is available at the municipal level for over 100 municipalities, while it is hoped municipal level data will be possible with Wikipedia. These alternative surveillance systems are not without their own limitations, however. Both Google Flu Trends and Wikipedia serve as proxies for ILINet’s ILI data. That is, neither at- tempts to measure the influenza transmission mechanism. Rather, they both identify search queries that are correlated with ILINet’s ILI data. Furthermore, it is well documented that Google Flu Trends has a tendency to overestimate influenza activity (Lazer et al., 2014). Rather than select one disease surveillance system for influenza forecasting, multiple disease surveil- lance systems could be incorporated into a principled, probabilistic, data-assimilating model. This approach has the potential to leverage the accuracy of traditional surveillance systems and the timeliness and geographic resolution of alternative surveillance systems.
In this paper we review the statespace approach to time series analysis and establish the notation that is adopted in this special volume of the Journal of Statistical Software. We first provide some background on the history of statespace methods for the analysis of time series. This is followed by a concise overview of linear Gaussian statespace analysis including the modelling framework and appropriate estimation methods. We discuss the important class of unobserved component models which incorporate a trend, a seasonal, a cycle, and fixed explanatory and intervention variables for the univariate and multi- variate analysis of time series. We continue the discussion by presenting methods for the computation of different estimates for the unobserved state vector: filtering, prediction, and smoothing. Estimation approaches for the other parameters in the model are also considered. Next, we discuss how the estimation procedures can be used for construct- ing confidence intervals, detecting outlier observations and structural breaks, and testing model assumptions of residual independence, homoscedasticity, and normality. We then show how ARIMA and ARIMA components models fit in the statespace framework to time series analysis. We also provide a basic introduction for non-Gaussian statespace models. Finally, we present an overview of the software tools currently available for the analysis of time series with statespace methods as they are discussed in the other contri- butions to this special volume.
The second function is the objective function to be passed to the maximizer and, as noted above, must have a specific structure (see Section 1.4 ). Before proceeding with the discus- sion of the function, two things need to be emphasized. Firstly, since the MaxBFGS optimizer does not allow constraints on the parameter space, we pass the two variances in their (un- constrained) logarithms, and take the exponents of these log-variances to ensure their non- negativity. This implies that the scores for the log-variances have to be obtained from the scores of the variances. So, if `(σ 2 ) is the log-likelihood and σ 2 = exp(p) is the variance, than
The problem specifically addressed in this research is primarily related to a large number of the irrelevant outcomes of a searching query. In conventional techniques, search engines use pattern matching methodologies and the web contents are searched by matching user given words. This surface matching technique generates results in millions and billions and most of the time irrelative and unrelated results are shown. To make search activity more efficient and effective, the major emphasis of research is contents searching bodies as information agents. Mostly research is being done at backend in the form of multi agents. The preprocessing of user’s query before a search engine processes it is also significant.
, and a continuation o f a building as a space o f dwelling predicated on the tradition that it endows - a present condition contingent on past and future. He concretises the idea that different buildings embody different ways o f being in the world linking ideas o f dwelling to ideas o f nurture, continuity, time and community (which I address in subsequent footnotes). His proposal o f the fourfold embodies conceptions o f situatedness and the acknowledgement o f a unity between the mortal and the immortal (Earth, Sky, Mortals and the Divinities), a recognition o f the limitations o f human existence and the continuity o f nature, expressed as metaphor. The fourfold constitutes continuance in which space becomes place through the human action o f dwelling: being in the world is connected to a sense o f place which is constituted by and endures through human association. Building, constructing, is an action towards dwelling, yet dwelling itself is an outcome o f thinking, a psychological association between persons and their capacity to dwell in an environment (or not) that is particular. For further reading see: Albert Hofstadter, Martin H eidegger: Poetry, Language, Thought, trans. by Albert Hofstadter, N ew York: Harper Colophon Books, 1971 [Bauen, Wohnen, Denken, Munster, Germany: Coppenrath 1951], and Doreen Massey, Space, Place an d Gender, Oxford: Blackwell, 1994.
If both stones are not on the same line or there are opposing stones between them, the threats they create are independent, hence the sum of their threats are the threats created by this full move. The correctness of the sum of threats will only be affected if it will lead to a wrong interpretation of one threat and two threats. If the sum of the threats is greater than or equal to 3, like pattern B in Figure 4.11, it won’t matter if it is wrong, since this kind of threat greater than 3 will end the game. Therefore, we only need to deal with the patterns which the sum of the threats is 2, because if it is wrongly computed as a two threats, and in reality it is a single threat, the opponent actually won’t be forced to move in defense, and thus it may mislead the search.
Mass space is the natural space to search for new particles. Observation of mass peaks above back- ground allows robust discovery using data-driven background estimation from the sidebands. But most important, reconstruction of the unknown masses gives valuable insights to what the new physics is. The search is model independent as the only assumption is the decay topology. As a proof of prin- ciple, mass peaks of both top quark and W boson can be produced from leptonic top pair decays from simulated events as well as using LHC data. It is shown that the method can be used to reconstruct mass peaks from heavy resonances decaying to top pairs as well as next generation heavy quarks decaying with the same event topology. Possible applications to supersymmetric mass reconstruction will be described in a future publication. Other applications include top pair identiﬁcation in order to reject them from the tail of M ET -like observables for supersymmetric searches, as well as use of the
New statistical methods are often demonstrated to produce results that differ from those of previous methods, but it is unclear that these differences are of any importance to the subject matter at hand. In this section, we use state-space methods to examine oceanographic and climate related indices, and show that the use of these models produce significant scientific insight. All of the univariate modeling was done using STAMP, while the subspace identifi- cation procedure was performed in MATLAB on the output from univariate models estimated by STAMP.
Compared to structure theory, which is developed for unit root processes with arbitrary unit structures, statistical theory is in a relatively nascent state with results available only for some cases. Pseudo maximum likelihood estimation theory is developed to some extent for the MFI(1) case and for the I(1) case a computationally simple so-called subspace algorithm for the estimation of the system matrices as well as for order estimation (n) and testing for the number of common trends (d) has been developed. Here we only very briefly discuss the available results and refer the reader to the original papers for details. For pseudo ML estimation see Bauer and Wagner (2006) and for subspace algorithm cointegration analysis see Bauer and Wagner (2002, 2009).
Figure 1. Top: ICARUS T600 detector in the Hall B of LNGS, with a schematic view of diﬀerent parts. The two T300 modules are enlightened, such as the space occupied by the readout electronics and the cryogenics. Bottom: TPC working principle: when a charged particle cross the detector, electrons are drifted through the wire planes, where the charge is collected by the three wire planes. By appropriate voltage biasing, the ﬁrst two , Induction1 and Induction2 planes, provide signals in a non-destructive way; ﬁnally the ionization charge is collected and measured on the last one, the Collection plane. Combining all the information, it is possible to have a 3D reconstruction of the track.
As regards decoding, (Jiampojamarn et al., 2007) use a grapheme segmentation module where each grapheme letter can form a chunk with its neighbor or stand alone, a decision that is based on local context and instance-based learning. We hold this approach to be insufficient because (besides the obvious drawback that, as we have shown, larger chunks than two seem appropriate for G2P) it unecessarily restricts the searchspace for grapheme segmentation; once a decision is made to join two letters, it cannot be reversed and alternative segmentations are not considered. The same holds true for the phrasal decoder approach outlined in (Jiampojamarn et al., 2008), the critique of which is already uttered in (Dreyer et al., 2008), namely, that the input string is segmented into substrings which are transduced independently of each other, ignoring context. Contrarily, for decoding x, we compute all possible segmentations of x and score them (in conjunction with the transduced ˆ y strings) with higher order n-gram models, which is clearly superior to the named approaches because it takes both context into account and does not restrict searchspace. Moreover, given an adequate predictor model, we found that enumerating all possible restricted integer compositions is so fast that no further investigation of restricting searchspace is necessary. 9
A typical OSA problem contains three important sets: The E set contains information related to the entities (the space required for the entity, the organisational group it belongs to, constraints associated with it, etc). The R set contains the attributes of each room (the capacity of the room, the floor it is in, the neighbourhood relationships with other rooms, the capacity constraints associated with it, the space that is currently being used, etc). Finally the C set represents all the constraints (hard and soft) associated to entities and the rooms. There are two main goals in the variant of the office space allocation problem  considered in this paper: the minimisation of the space misuse and the minimisation of the soft constraint violations. The space misuse is the summation of the under and over utilisation of the rooms beyond their capacity. Since it is not desirable for the rooms to be overused beyond their capacity, the overuse of a room is penalised twice as much as under-utilisation of a room. In certain cases, it is not even possible to overuse a room at all. On the other hand, it is not desirable for a large number of rooms being under- utilised. In such allocations, it might be preferable to remove these severely under-utilised rooms from the problem, and redo the allocation task with the remaining rooms.
Abstract— This paper deals with the design of optimal mul- tiple gravity assist trajectories with deep space manoeuvres. A pruning method which considers the sequential nature of the problem is presented. The method locates feasible vectors using local optimization and applies a clustering algorithm to find reduced bounding boxes which can be used in a subsequent optimization step. Since multiple local minima remain within the pruned searchspace, the use of a global optimization method, such as Differential Evolution, is suggested for finding solutions which are likely to be close to the global optimum. Two case studies are presented.
Mafia is one of the recent methods proposed by Burdick, D., M. Calimlim and J. Gehrke, for mining the MFI. The search strategy of the algorithm integrates a depth-first traversal of the itemset lattice with effective pruning mechanisms that significantly improve mining performance. Mafia implementation for support counting combines a vertical bitmap representation of the data with an efficient bitmap compression scheme. Mafia uses vertical bit-vector data format, and compression and projection of bitmaps to improve performance. Mafia uses three pruning strategies to remove non-maximal sets. The first is the look-ahead pruning which is introduced in MaxMiner. The second is to check if a new set is subsumed by an existing maximal set. The last technique checks if t(X) ⊆ t(Y). If so X is considered together with Y for extension. Mafia mines a superset of the MFI, and requires a postpruning step to eliminate non-maximal patterns. Max Miner  is another algorithm for finding the maximal elements. It uses efficient pruning techniques to quickly narrow the search. Max Miner employs a breadth first traversal of the searchspace. It reduces database scanning by employing a look ahead pruning strategy. For frequency computation maxminer use a additional technique called support lower bounding. This algorithm also uses dynamic reordering technique to reduce the size of the searchspace. This process produces small number of frequent extension for the next level. The item which occurs in the fewest number of large itemsets should be occurred first and item occurring in the maximum number of large itemset should be occurred last.
StateSpace Models (SSM) is a MATLAB toolbox for time series analysis by statespace methods. The software features fully interactive construction and combination of models, with support for univariate and multivariate models, complex time-varying (dy- namic) models, non-Gaussian models, and various standard models such as ARIMA and structural time-series models. The software includes standard functions for Kalman fil- tering and smoothing, simulation smoothing, likelihood evaluation, parameter estimation, signal extraction and forecasting, with incorporation of exact initialization for filters and smoothers, and support for missing observations and multiple time series input with com- mon analysis structure. The software also includes implementations of TRAMO model selection and Hillmer-Tiao decomposition for ARIMA models. The software will provide a general toolbox for time series analysis on the MATLAB platform, allowing users to take advantage of its readily available graph plotting and general matrix computation capabilities.