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Vintage Bits: Black Isle Resurfaces
Saturday, October 13, 2012 John ONeill Comments 6 comments
Interplay, publisher of some of the most acclaimed fantasy role playing games of all time – such as the Bard's Tale series, Wasteland, Dragon Wars, Baldur's Gate, Fallout, and Icewind Dale — has announced that the legendary development studio behind many of its most popular titles, Black Isle Studios, has reformed.
Black Isle was owned by Interplay and created in 1996 by Feargus Urquhart. It was formed from the crack team of developers who had created Fallout. Over the next few years, they developed many hits, including Fallout 2, Planescape: Torment, and Icewind Dale. Working with a tiny outfit in Canada named BioWare, they also produced Baldur's Gate and Baldur's Gate II: Shadows of Amn.
Interplay began to experience serious financial difficulties in 2001-2002, after publishing a number of high-budget failures such as Messiah and Kingpin. Black Isle was dissolved as Interplay imploded, a move that shocked the industry. All the employees were laid off on December 8, 2003.
Their last titles were the poorly-received PC game, Lionheart, and Baldur's Gate: Dark Alliance II for the Playstation 2. At the time they were dissolved, they were working on Baldur's Gate III: The Black Hound, Baldur's Gate: Dark Alliance III and Fallout 3, none of which ever saw the light of day. A nearly bankrupt Interplay eventually sold the rights to the Fallout series to Bethesda, who developed and released its own version of Fallout 3 in 2008.
The closing of Black Isle was a serious blow to RPG gamers, and it marked the end of Interplay as a force in the industry. As someone whose all-time favorite games include Dragon Wars and Icewind Dale, I saw it as the end of an era. Interplay survived for most of the next decade by re-packaging its classic games, especially Fallout, Baldur's Gate, and Icewind Dale, as well as their sequels.
Most of the star developers associated with Black Isle, including Feargus Urquhart and Chris Avellone, are not on board for this new iteration. Urquhart and Avellone founded the successful Obsidian Entertainment (Star Wars: Knights of the Old Republic II, Neverwinter Nights 2). But Mark O'Green and Chris Taylor (designer of the original Fallout) have both reportedly returned.
No announcement yet on what titles the resurrected Black Isle is working on. But I'm certainly looking forward to finding out.
Blog Entry, Editor's Blog, Games, News
Smaller is Better
Weird Tales 360 Arrives This Week
darangrissom
The dead god lives!
I have an old lap top that runs Windows 2000 so that I can still play Icewind Dale, Baldur's Gate, and the others. The technical and creative elements that came together to make those games was an event on the same order of magnitude as a cosmic convergence. It took Black Isle, BioWare and TSR to make those games what they were.
I am skeptical that that kind of magic can happen again. Besides, Bethesda has taken good care of Fallout.
At least they re-released the Baldur's Gate: Enhanced Edition, so you can play it on iPad. I can't wait for Icewind Dale.
> At least they re-released the Baldur's Gate: Enhanced Edition, so you can play it on iPad. I can't wait for Icewind Dale.
Daran,
Say what?? I had no idea they'd ported Baldur's Gate to the iPad.
Tell me more. How well does it play?
Wild Ape
I loved those games. I look forward to seeing what new stuff they have to put out.
The Re-release got pushed back to November 30th, so I don't how good it is, but I know some people who got to try the demo, and they can't praise it enough. There's even new content being written.
http://www.baldursgate.com/
I hope they do the other games as well. The adventures of Minsk and Boo must continue.
Black Gate » Blog Archive » Bioshock Creator Irrational Games Shuts its Doors
[…] as Interplay, Origin Systems, SSI, Microprose, Sierra Entertainment, and of course, the brilliant Black Isle. I'm certain there will be plenty of great games on many new platforms in my future, but for […]
Black Gate » Blog Archive » Icewind Dale: Enhanced Edition Available for Pre-Order
[…] Baldur's Gate, Icewind Dale was not actually created by Bioware. Instead, it was written by Black Isle, Interplay's legendary RPG design studio, creators of Fallout 2 (1998), Planescape: Torment […]


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Rouxel est le nom de plusieurs personnalités :
 André Rouxel (1946-), homme politique français,
 Anne-Laure Rouxel (1972-), danseuse et chorégraphe1 française,
 Charles Rouxel, né le  surnommé Charly Rouxel, est un coureur cycliste,
 Christophe Rouxel (?-), metteur en scène français,
 Diane Rouxel (1995-), actrice française,
 Guy Rouxel (1926-2016), joueur de football français,
 Jacques Rouxel (1931-2004), créateur des Shadoks,
 Jean Rouxel (1530-1586) , jurisconsulte, poète et professeur royal en éloquence,
 Jean Rouxel (1935-1998), chimiste français du ,
 Jean-Philippe Rouxel (1966-), coureur cycliste français,
 Laëtitia Rouxel (1979-), dessinatrice de bande dessinée française,
 Lionel Rouxel (1970-), footballeur français,
 Roger Rouxel (1925-1944), résistant français, soldat volontaire de l'armée française de libération,
 Thomas Rouxel (1983-), navigateur et skipper français,
  (1991-), joueur français de badminton.

Famille Rouxel de Grancey
 François Rouxel de Médavy (1604-1691), archevêque de Rouen, frère de Jacques
 Jacques Rouxel de Grancey (1603-1680), maréchal de France
 Pierre Rouxel, comte de Grancey et de Médavy (1626-1704)
 Jacques Eléonor Rouxel de Grancey (1655-1725), maréchal de France, petit-fils du précédent,
 Philippe François Rouxel de Blanchelande (1735-1793), gouverneur français de Saint-Domingue, petit-fils du précédent.
 Louis-François Rouxel, comte de Grancey (1667-1728), chef d'escadre fils de Pierre Rouxel, demi-frère de Jacques Eléonor Rouxel
 François Bénédict de Rouxel, marquis de Grancey (1635-1679), lieutenant général des armées navales, fils de Jacques, frère de Pierre

Lien externe

Voir aussi

Patronyme breton

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Q: Property of $F$ being compact consider the alphabet $\mathcal{A}=\{1,2,\ldots,m\}$ and, given $k\in \mathbb{N}$, let
$\mathcal{A}^k=\{\left(i_1,i_2,\ldots,i_k\right): i_j\in \mathcal{A},\forall\hspace{0.2cm} 1\leq j\leq k\}$
the collection of all words with $k$ characters from the alphabet $\mathcal{A}$. Given any $X\subset \mathbb{R}^n$ and $\left(i_1,\ldots,i_k\right)\in \mathcal{A}^k$, we then define
\begin{equation}X_{i_1i_2\ldots i_k}=\left(S_{i_1}\circ S_{i_2}\circ \ldots\circ S_{i_k}\right)(X).
\end{equation}
where $S_{i_j}$ are similarities.
Since $F$ is an attractor of $\{S_{i_1},\ldots,S_{i_k}\}$ and the union of the images of an application is the application of the union, we can write
\begin{equation}
F=\bigcup_{(i_1,\ldots,i_{k-1},i_k)\in \mathcal{A}^k}F_{i_1,\ldots,i_{k}}.
\end{equation}
Denote by $\mathcal{A}^{\mathbb{N}}=\{(i_1,i_2,i_3,\ldots): 1\leq i_n\leq m, \forall \hspace{0.1cm} n\in \mathbb {N}\}$,that is, the set of all infinite words of the alphabet $\mathcal{A}=\{1,\ldots,m\}$.
Since $F$ is a compact, to each infinite word $(i_1,i_2,i_3,\ldots)$ it is possible to associate a single element
\begin{equation}
x_{i_1i_2i_3\ldots}\in \bigcap_{k=1}^\infty F_{i_1i_2\ldots i_k}
\end{equation}
I would like to understand why if $F$ is compact then to each infinite word $(i_1,i_2,i_3,\ldots)$ it is possible to associate a single element
$x_{i_1i_2i_3\ldots}\in \bigcap_{k=1}^\infty F_{i_1i_2\ldots i_k}$. Why do we have this? Because the property of being compact can we guarantee that? Is there any characterization of a compact space having this property?
Thanks for the help!!

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We repair your vehicle to it's pre-accident condition and any damage that applies to the claim-Additional repairs will be your responsiblity.
Occasionally, in the process of repair we find previous damage to a vehicle unrelated to the accident. In this case we would notify the owner of the vehicle and invite you to come and inspect the previous damage, at this time we would prepare an estimate to repair the previous damage.
There are a couple of choices to make at this time, either pay to repair the prior damage, call your insurance company and ask if they will allow another claim to be opened to repair this damage (another deductible will apply), or not fix it at all.
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If I go to the insurance company's drive-in claims facility, do I have to take my vehicle to one of its preferred shops for repair?
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Who guarantees the workmanship and safety of my vehicle repairs – the insurance company or the body shop?
The repair shop and in limited circumstances your insurance company. Your insurance company does not accept the liability for the quality and safety of your vehicle's repair – the repair shop does. It is up to you to choose the best collision repair shop. Speak to body shop owner or manager to voice your concerns and ask questions. Once you feel confident the shop is adequately trained and equipped to restore your vehicle to its pre-accident condition, you may then proceed. At that time, any Warranty will be held up by the collision repair shop. You have the legal right and authority to satisfy your "Due Diligence" before signing any repair order.
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text:
Approach to robust multi-objective optimization and probabilistic analysis: the ROPAR algorithm
Oscar O. Marquez-Calvo
IHE Delft Institute for Water Education, Delft, The Netherlands
Delft University of Technology, Delft, The Netherlands
E-mail: o.marquezcalvo@un-ihe.org
Dimitri P. Solomatine
Water Resources Section, Delft University of Technology, Delft, The Netherlands and Water Problems Institute of RAS, Moscow, Russia
Journal of Hydroinformatics (2019) 21 (3): 427-440.
https://doi.org/10.2166/hydro.2019.095
Oscar O. Marquez-Calvo, Dimitri P. Solomatine; Approach to robust multi-objective optimization and probabilistic analysis: the ROPAR algorithm. Journal of Hydroinformatics 1 May 2019; 21 (3): 427–440. doi: https://doi.org/10.2166/hydro.2019.095
This paper considers the problem of robust optimization, and presents the technique called Robust Optimization and Probabilistic Analysis of Robustness (ROPAR). It has been developed for finding robust optimum solutions of a particular class in model-based multi-objective optimization (MOO) problems (i.e. when the objective function is not known analytically), where some of the parameters or inputs to this model are assumed to be uncertain. A Monte Carlo simulation framework is used. It can be straightforwardly implemented in a distributed computing environment which allows the results to be obtained relatively fast. The technique is exemplified in the two case studies: (a) a benchmark problem commonly used to test MOO algorithms (a version of the ZDT1 function); and (b) a design problem of a simple storm drainage system, where the uncertainty is associated with design rainfall events. It is shown that the design found by ROPAR can adequately cope with these uncertainties. The approach can be useful for assisting in a wide range of risk-based decisions.
drainage system, multi-objective optimization, robust optimization, uncertainty
The main focus of this research is on robust multi-objective optimization (RMOO) and the introduction of a new algorithm, with the ultimate aim of testing and using it in water-related problems.
Robust multi-objective optimization in water-related systems
The water sector started to use mathematical optimization algorithms in the 1960s (Karmeli et al. 1968; Schaake & Lai 1969). Nicklow et al. (2009) devoted a paper to review this topic, particularly focusing on the use of genetic algorithms. However, although for many years in typical problem settings the issue of uncertainty was not considered in multi-objective optimization (MOO) of water systems, in the last 10–15 years this aspect has started to be given attention. This type of optimization is often (albeit not always) referred to as robust optimization. The notion of robustness is treated in different studies in various ways and is determined by the particular needs of a case study or the authors' preferences.
Literature sources present various definitions of robustness (Jin & Sendhoff 2003; Gunawan & Azarm 2005; Savic 2005; Deb & Gupta 2006; Beyer & Sendhoff 2007; Gaspar-Cunha & Covas 2008; Petrone et al. 2011; Erfani & Utyuzhnikov 2012). For the purpose of this study, the following definition is adopted, based on the sources just cited: Robust optimization of multiple objectives is an optimization method modified in such a way that it generates a solution or a set of solutions that have minimum (or limited) variability of the objective functions when some elements or parameters of the modelled real system vary due to their uncertainty.
Some of the first works applying RMOO methods were in the field of water distribution systems. In these works (Babayan et al. 2004; Kapelan et al. 2005, 2006; Savic 2005, 2006; Odan et al. 2015), the robustness of the identified solutions was achieved by including a measure of robustness as one of the objective functions, which depends on the type of problem being solved. One example of such a specific objective was used by Kapelan et al. (2006): measuring the probability that at the same time the heads of all the nodes in the network comply with at least the minimum head required for each node. In contrast, in the approach proposed in this paper, the robustness is reached not by requiring an objective function measuring the robustness depending on the problem being solved, but by using criteria of general applicability.
Other research in the water area has dealt with the optimum design of urban drainage networks and wastewater systems. All these works use the smoothing of the objective function as the method to accommodate uncertainty. (This process of smoothing is explained in more detail in the next section.) Andino-Santizo (2012) used two methods to find the robust optimum design of a drainage network considering uncertainty in the population growth. Kang & Lansey (2012), Kebede (2014), Vojinovic et al. (2014), and Yazdi et al. (2014) used a robust optimum design for water, storm- and wastewater infrastructures considering as uncertain the following parameters, respectively: amount of water; climate change, urbanization, population growth, and pipe aging; rainfall; and Manning roughness. Galindo-Calderon et al. (2015) found the robust optimum type and location of the Best Management Practices for a storm drainage network.
Yet another approach of RMOO used for water-related systems follows the notion of deep uncertainty. Walker et al. (2013) classified the uncertainty in two categories: first, uncertainties that can be statistically analysed; and second, uncertainties that cannot be statistically analysed due to the unforeseen future (named deep uncertainty). Beh et al. (2015) solved the problem of the optimal sequencing of additional water supply sources over a 40-year planning horizon. Mortazavi-Naeini et al. (2015) optimized planning and operation of bulk water systems foreseeing conditions of extreme drought. Watson & Kasprzyk (2017) optimized water allocation from multiple market-based supply instruments. In our research, uncertainty is assumed to be such that it allows for probabilistic description and analysis.
In the reviewed literature, the problem formulations used do indeed take uncertainty into account, but it can be seen that uncertainty is in a way 'hidden' and cannot be directly estimated. The problem of robustness is formulated by adding a specific metric of robustness depending on the considered problem type, and therefore varies in different case studies; besides, the process of probabilistic uncertainty propagation to the final solutions is typically not explicit either. This prompted the development of a new algorithm for robust optimization with a general applicability. The novelty of this study also relates to addressing features not fully considered by the approaches previously mentioned. First, it is a method where the robustness is not defined by a metric of robustness depending on the type of problem being optimized. Second, this method does not change an objective function, e.g. by its smoothing, employed earlier. Third, the method characterizes the uncertainty explicitly, using probability density functions.
Methods of robust multi-objective optimization
MOO is a mature technology, and RMOO uses MOO as the basic method. In MOO, when the objective function is not known analytically, the most popular approach is to use a randomized search, and the most widely used group of methods is evolutionary (genetic) optimization (Deb et al. 2002, 2005, 2009; Kollat & Reed 2006; Kukkonen & Deb 2006; Vrugt et al. 2009; Tiwari et al. 2011; Hadka & Reed 2013). For more details and additional literature, the reader is referred to the reviews by Reed et al. (2013) and Maier et al. (2014).
Some of the published literature on MOO techniques which take uncertainty into account (so which could be attributed to a group of robust optimization methods) was analysed, and a possible categorization of these techniques is presented below.
Methods minimizing the mean of the objective function (Zitzler & Kunzli 2004; Kapelan et al. 2005; Limbourg 2005; Savic 2005; Deb & Gupta 2006; Kuzmin 2009; Andino-Santizo 2012; Kebede 2014; Vojinovic et al. 2014). This method minimizes a smoothed version of an objective function f named fμ, which uses the average of the objective function values in the proximity of a given point. This is done instead of directly minimizing the original objective function (i.e. f).
Methods minimizing the mean–variance of the objective function (Jin & Sendhoff 2003; Fieldsend & Everson 2005; Gaspar-Cunha & Covas 2008; Kang & Lansey 2012; Zeferino et al. 2012). This approach is similar to the previous one, but besides calculating the objective function averages fμ in the proximity of the point in question, the standard deviation fσ is also estimated, to end up with their combination fμσ, which is optimized instead of f.
Methods using an additional objective function related to robustness (Babayan et al. 2004; Kapelan et al. 2006; Erfani & Utyuzhnikov 2012; Zeferino et al. 2012; Toloh 2014; Marchi et al. 2016; Roach et al. 2016). In this approach, a vector of objective functions fr which represents some measure of the solution's robustness is added to the original vector of objective functions f. This measure of robustness fr could be the above-mentioned fμ or fσ, or their combination fμσ or another way of measuring the robustness of the system being optimized.
Methods using additional constraints related to robustness (Gunawan & Azarm 2005; Deb & Gupta 2006). In this approach, a vector of constraints is added to the original inequality constraints of the problem, which sets a boundary B to a given measure of robustness.
Method based on comparing the cumulative distribution functions (Petrone et al. 2011). This approach aims at reducing the difference between the CDF characterizing uncertainty of a real solution and that related to an 'ideal' design. For real-life problems when a single CDF is generated as a result of a Monte Carlo simulation, and a substantial number of these have to be generated during optimization, implementation of this approach could be computationally prohibitive.
It can be seen that all the mentioned approaches aggregate uncertainty into the objective functions or constraints and then solve a standard MOO problem resulting in a single Pareto front. This is indeed a valid approach, however, it should be noted that the impact of uncertainty is embedded in this Pareto front, so uncertainty is in a way hidden, 'encoded' in the solutions which are expected to be robust. The uncertainty is encoded because the resulting Pareto front combines the minimization of the objective functions and the robustness in one single piece of information. The problem is that it is not straightforward to 'decode' the level of robustness from the identified solutions and to analyse how Pareto-optimal sets depend on actual realizations of uncertain variables. Decoding this information requires information to be stored for each solution in the Pareto front regarding the relation between the solution and the uncertain parameters. If explicit decoding and presentation of uncertainty are not performed explicitly, this makes it difficult to fully grasp the impact of the uncertainty by the decision makers and to select one of the solutions from the Pareto front.
The presented problem was the main motivation to develop an approach and algorithm entitled 'Robust Optimization and Probabilistic Analysis of Robustness' (ROPAR); in its initial form, it was presented by Solomatine (2012) in a short paper available on the web. In this approach, the uncertainty is not encoded into a single Pareto front, but multiple Pareto fronts are explicitly generated, each corresponding to sampled values of the uncertain variable(s). The multiple Pareto fronts make it possible to analyse the statistical characteristics of resulting uncertainty propagated to solutions, enriching the information available to decision makers. That initial approach by Solomatine (2012) is extended in this paper, to make it capable of finding robust optimum solutions.
The definition of robust optimization of multiple objectives can be formalized mathematically as follows. Consider a system model f, which is an abstraction (either mathematical or computational) of a real system intended to be optimized. The behaviour of the system model is characterized by the value taken by input uncertain variable u, and decision variables x. Then a RMOO problem can be formulated as
subject to:
where f is the vector of the n objective functions (f1, f2, … , fn), gj is the j-th inequality constraint, hj is the j-th equality constraint, k is the number of inequality constraints, q is the number of equality constraints, u is the vector of the uncertain input variables (e.g. rain intensity), umin and umax are the vectors of the minimum and maximum values of uncertain input variables, respectively, x is the vector of the decision variables (e.g. pipe diameter), xmin and xmax are the vectors of the minimum and maximum values of the decision variables, respectively, and fi, gj, hj, x, u ∈ ℝ.
Solutions of a MOO problem are typically represented as a Pareto front F in the space of objectives (criteria) (see the example in Figure 3(a)) which can be defined as
where fq is the qth tuple representing the evaluations of the n objective functions in the Pareto front, F is all the evaluations of the objective functions forming the Pareto front, and ns is the number of tuples comprising the Pareto front.
To summarize, a solution is a configuration or a design that is defined by a specific combination of the values (vector) of the decision variable x. The aim is to find a robust optimum solution x which ensures that the objective function f values remain near the optimal value regardless of the uncertainty that could affect the parameters u.
Robustness metrics
To measure the robustness of the solutions, the two most common metrics for assessing the robustness found in the literature are used (Beyer & Sendhoff 2007). These two metrics of robustness are exemplified with the following function. Assume that the goal is to minimize the objective function f(x). Consider that its robust counterpart is the function F(x).
The first metric of robustness is the 'expected value' of f, which can be represented by
where p(u) is the probability density function of the uncertainty u.
The second metric of robustness is generally known as the 'worst case'. This is represented by
where X(x,u) is the neighbourhood of the solution x.
Objective of the study
The main objective of this study is to propose and test the new technique for RMOO, which would make it possible for decision makers to carry out probabilistic analysis of uncertainty propagation from uncertain factors to solutions, and ultimately to select robust optimum solution(s).
Two case studies are considered: (a) a benchmark problem commonly used to test MOO algorithms (a version of the ZDT1 function); and (b) a design problem of a simple storm drainage system, where uncertainty is associated with design rainfall events.
In this study, we concentrate on presenting the method, testing it in two case studies, and comparing the results to those achieved with deterministic methods. Comparison to other techniques also aimed at finding robust solutions is left for another publication (Marquez-Calvo & Solomatine in preparation-a).
The methodology has three parts:
deterministic optimization with multiple objectives;
robust optimization with multiple objectives;
comparing deterministic and robust solutions.
These three parts are presented in the subsequent sections.
Deterministic optimization with multiple objectives
To assess if, indeed, ROPAR effectively finds robust solutions, the deterministic MOO is carried out first. These deterministic optimum solutions are used as a baseline to compare them with the solutions found using the robust optimization approach, using the robustness metrics presented above. For this study, two MOO algorithms are used: NSGA-II (Deb et al. 2002) and its advanced version, AMGA2 (Tiwari et al. 2011). It is acknowledged that NSGA-II is not the most efficient optimizer nowadays, however, it is very widely used and for that reason, it is employed here as well (its descendant, AMGA2, has been shown to be faster). Furthermore, NSGA-II is used for the storm drainage case and AMGA2 is used for the benchmark function case.
The problem solved using the MOO is the one defined in the section 'Problem statement'. However, given that the problem is solved deterministically, there is no need to sample the input uncertain variable u because it is considered to be fixed at a specific value. In this paper, for the 'fixed u' we use the mean value of 'uncertain u'.
Robust optimization with multiple objectives
ROPAR is the approach used to find solutions that comply with the definition presented in the Introduction. This approach is based on Monte Carlo sampling of the input uncertain parameter u, and allows the robustness of solutions found by MOO algorithms to be probabilistically analysed.
Most methods of robust optimization employ one of the known MOO algorithms as the core method, and we do this as well. As for deterministic optimization, NSGA-II (Deb et al. 2002) and AMGA2 are used as the core optimizers in robust optimization as well.
Before presenting the ROPAR algorithm, it is worthwhile mentioning some facts. Steps 1–7 were first presented by Solomatine (2012). The improved current version has added steps 8–10; these steps aim at finding the robust solutions. In particular, Step 9 defines two criteria (minimize 'expected value', and minimize 'worst case') to search for those solutions with the least variability (in accordance with the definition of robust optimization and the robustness metrics). This extended current version is still named ROPAR.
ROPAR has three main parts. The first part is sampling and generating the Pareto fronts. In this part, a value of an uncertain parameter is sampled, and for each of the samples the system model is instantiated with the sampled parameter and with the rest of the (certain) parameters. This model is used in optimization, resulting in a Pareto front. The number of Pareto fronts is equal to the number of samples generated. The second part of ROPAR is a probabilistic analysis of the Pareto fronts. The third part of ROPAR is finding the single or more robust solution(s).
Algorithm ROPAR
(Sampling and generating the Pareto fronts)
1. for r = 1 to np do
2. Sample ur
3. Find the Pareto-optimal set Fr by solving the deterministic MOO problem for the sampled ur.
(Analysing the Pareto fronts)
4. Pick one objective function fk, say f2. Choose a certain level L2 for f2 (possibly represented by some narrow interval for f2) and form the solution set S with the values of f2 in this interval, taking solutions from all the Pareto sets Fr where r = 1, … , np.
5. Pick another objective function, say f1.
6. Build the empirical distribution of the values of f1 corresponding to the members of set S. This empirical distribution can be approximated by a probability density function characterising the uncertainty of f1 for the solutions corresponding to the chosen level L2 of f2.
7. (optional). Repeat steps 4, 5, and 6 running through a number of various levels L2, until finding a desired level of f2, for example the one with a minimum variance of f1.
(Finding the robust solution)
8. Once a level L2 is chosen, create the set Q containing optimum solutions with a value of f2 approximately equal to L2. Q should include one solution from every Fr where f2 ≈ L2 (the closest one). Optionally Q could include not one but several solutions from every Fr where f2 ≈ L2.
9. The most robust solution xi is found by solving one of the two single-objective optimization problems.
Criterion 1 (the 'minimize expected value' approach):
Criterion 2 (the 'minimize worst case' approach):
where f1 is the objective function 1, ur is the instantiation r of the vector of input variables u with uncertainty.
10. (optional). Repeat steps 8 and 9 running through a number of various levels L2, as many times as the decision maker needs.
Note. In an optional Step 7, the goal is to scan the robustness of the solutions for several values of f2. It is just an initial scan because the robustness of the solutions at different values of f2 is quantitatively defined after carrying out the steps 8, 9, and 10.
Comparing deterministic and robust solutions
To compare the deterministic solution with the robust solutions, the criteria used to determine the robustness in ROPAR are used to measure the robustness of the deterministic solution – these are specified in Step 9 of ROPAR, one for the expected value, and the other one – for the worst case.
The process to carry out here is similar to the process known as post-optimization robustness analysis presented in Paton et al. (2014). The robust performance of the solutions from the deterministic Pareto front is evaluated considering all the random samples of rainfall.
Two cases are considered: a benchmark function and a storm drainage network. The cases for this study were deliberately chosen to be simple, since they serve an illustrative purpose to introduce the algorithm. Both of them are optimized deterministically and robustly.
Optimization of a benchmark function
The widely used benchmark function ZDT1 (Zitzler et al. 2000) is employed. This benchmark function is used to test MOO algorithms. The original formulation of ZDT1 is
subject to
Its formulation is modified to add randomness, specifically the modified definition of f2 now is
Here randomfactor is a random variable with a normal distribution with μ = 1 and σ = 0.05 (randomfactor ∼ N(1,0.0025)), which is sampled in the interval [0.8384,1.1789]. A sample within this interval has 99.2% probability of occurrence. The number of samples for this set of experiments is set to 1,000.
The MOO used is AMGA2 (Tiwari et al. 2011), which is set to run until 10,000 function evaluations of ZDT1 are made.
Optimization of a storm drainage network
For this experiment, a model of a simple storm drainage pipe network with 11 pipes is used (see Figure 1) (this is a simplified network in a Latin American town). The network has a fixed layout, where the decision variables are the diameters of pipes. The mathematical formulation of this problem is
where D is the vector (d1, d2, … , d11) representing the diameter of every pipe in the network, CD is the vector (c1, c2, … , c11) representing the cost per length unit of every pipe depending on its diameter, CF is the fixed cost of the project, L is the vector (l1, l2, … , l11) representing the length of every pipe, and P is the amount of precipitation in the basin. In this paper, we are considering the pipe diameters as the only decision variables, and this is, of course, a simplification, since in reality the other variables have to be considered, such as for example, pipe slopes which have an impact on the excavation costs.
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Layout of the storm drainage network with 11 pipes (exported from SWMM software), where lines represent pipes; circles – junctions; squares – subcatchments; and triangle – outfall.
The volume of flood water is determined by running the SWMM modelling software (Rossman 2010). In the remainder of this paper, cost and construction cost are used interchangeably. The details of how the objective functions are calculated are provided in the supplementary data (available with the online version of this paper).
For this case, the only parameter with uncertainty is the rainfall, specifically, the intensity of the rainfall is considered to be random (named irandom). It is assumed that irandom follows a normal distribution with a standard deviation of 7% of the mean (i.e. irandom ∼ ibase*N(1, 0.0049)), where ibase is the intensity of the design rainfall corresponding to the geographical location of the network, considering a return period of 20 years. (The design return period for storm drainage typically varies from 2 to 25 years (Akan & Houghtalen 2003), and for this case 20 years was chosen.)
Two other parameters of the rainfall, duration and pattern, are considered to be fixed, regardless of the intensity (which is, of course, a simplification). With respect to the duration, in the conducted experiments the duration of the rainfall event is taken to be 3 hours. With respect to the pattern of the rainfall, a synthetic hyetograph for design storm was used (Butler & Davies 2004; Haestad Methods & Durrans 2007). The hyetograph resembles the one used by the Chicago method (Keifer & Chu 1957). Despite this type of rainfall profile tending to overestimate peak flows (Marsalek & Watt 1984; Alfieri et al. 2008), here it is used because the intention is to design a robust network able to cope with severe events (Fortunato et al. 2014).
Information describing the main aspects of the network is as follows. Every pipe has the same data: length of 100 m; slope of 0.5%; Manning's roughness coefficient of 0.02. The areas (in ha) of the subcatchments (from upstream to downstream): 5.617, 4.441, 4.441, 4.441, 4.089, 4.089, 6.489, 6.489, 3.245, 3.245, and 3.389. The mean value of rainfall in 3 hours is 360 mm. The system is configured to consider that in the event of flooding, the excess volume is stored atop the junction, in a ponded fashion, and is reintroduced into the system as capacity permits. An SWMM INP file with the values of other parameters can be found in the supplementary data (available with the online version of this paper).
To estimate the required number of samples, the equation developed by Cochran (1977) is used:
where n is the number of samples, z is the abscissa of the normal curve that cuts off an area α at the tails (1 – α equals the desired confidence level, which was set to be 99.90%), e is the aimed level of precision (was set to be 5%), and p is the estimated variability of population (was set to 50%, for details see Israel 1992).
Based on Equation (18), n is estimated to be 949 which was for the sake of simplicity rounded to 1,000, so that the rainfall intensity irandom was sampled 1,000 times. As mentioned before, irandom is the result of the multiplication of ibase with a random number obtained from the normal distribution N (1, 0.0049). The 1,000 samples fell into the interval [0.7738, 1.2505].
The large sample size (1,000) allows the four objectives to be achieved: first, to reach a high confidence level; second, to have a high level of precision; third, to consider the maximum heterogeneity in the population; fourth, to capture those extreme values that have the potential to be the most troublesome. On the other hand, if a high confidence level or a high level of precision is not needed, or the population does not have a high heterogeneity, then the number of samples will be accordingly lower. This number can be also made lower if the computational load is prohibitively high.
In the case of ROPAR, for each of the sampled rainfall intensities the storm drainage network is optimized using MOO NSGA-II (Deb et al. 2002), resulting in 1,000 Pareto fronts, one front for each rainfall intensity. In every optimization, 10,000 function evaluations (i.e. runs of the SWMM model needed to estimate flood volume) are used.
Case 1: The benchmark function
Optimizing the benchmark function using a deterministic approach
As a result of optimizing the benchmark function ZDT1 without uncertainty (i.e. when randomfactor is fixed at 1), the Pareto front in Figure 2(a) is generated.
Optimization of ZDT1 deterministically (a); and robustly (b, c). The ROPAR analysis is based on f2 ≈0.2 (b) and f2 ≈0.6 (c).
Optimizing the benchmark function using ROPAR
As a result of steps 1–3 of the ROPAR algorithm, 1,000 Pareto fronts are generated – see Figures 2(b) and 2(c). In steps 4–7 of ROPAR, these Pareto fronts are analysed to determine which solutions are most robust within the boundaries imposed by the decision makers.
Assume that the decision maker is interested in the values of the objective function 2 in the interval 0.2 ≤ f2 ≤ 0.6 (see Step 7 of ROPAR). To illustrate the analysis of the Pareto fronts, two values of the objective function 2, say 0.2 and 0.6, are picked. Their corresponding PDFs are shown in Figures 2(b) and 2(c), respectively. Comparing these two PDFs, one can say that in the considered interval, the solutions with the values of f2 ≈ 0.6 are the ones with the minimum variability of f1.
Steps 8–10 are not carried out in this case due to the fact that the optimization algorithm finds the ideal (almost exact) solutions for every realization of this random benchmark problem. These solutions are basically the same and there is no reason to use steps 8–10 of the algorithm which has the purpose of finding the most robust solution among them. The ideal solution for this benchmark problem is when xi = 0 for i = 2, 3, … , 11. However, in the experiment with the storm drainage system the solutions are different, and for that reason steps 8–10 are necessary.
Comparing the solutions of the deterministic and robust approaches
As pointed out previously, after carrying out deterministic optimization of ZDT1, there is no more additional information to help to decide which solution (among the 50 solutions forming the Pareto front) to choose. However, if uncertainty in the benchmark function is assumed, and the robust optimization approach is followed, one would have more information enabling one of the solutions to be selected. As shown in Figures 2(b) and 2(c), the robustness of the solution has a direct relation to the value of the objective function 2: the higher value of the objective function 2, the more robust the solution. Given the fact that it was assumed that the decision makers are interested in the solutions within the interval 0.2 ≤ f2 ≤ 0.6, the most robust solutions are those corresponding to f2 ≈ 0.6.
Case 2: The storm drainage network
The problem of constructing (or rehabilitating) a storm drainage network (i.e. the determination of the combination of pipe diameters) is considered, with the two objective functions to minimize: construction cost and flood volume.
Finding the optimum design without uncertainty (deterministic approach)
Assuming fixed rainfall, the optimum deterministic designs of the storm drainage network are found and shown in the Pareto front in Figure 3(a). It can be seen that the cost of the solutions is in the range from 1,000 to 4,000 tmu (thousands of monetary units) and flooding volume of these solutions ranges from 0 to 24 mlw (millions of litres of water). Using the Pareto set aids, the decision maker in the final choice of the single solution for implementation. For example, if a decision maker considers two possible costs, 1,500 and 3,000 tmu, the corresponding flooding volumes given by the optimal solutions (networks) would be 11.2 and 1.7 mlw, correspondingly. However, the single Pareto set does not give the possibility to say anything about the robustness of these solutions against rainfall uncertainty.
Optimizing the storm drainage network: (a) deterministically; (b) using ROPAR with a construction cost ≈ 1,500 tmu; (c) with a construction cost ≈ 3,000 tmu.
Finding the optimum design considering uncertainty (the robust approach)
In the ROPAR algorithm, the objective function f2 is associated with the construction cost (Step 4). Applying ROPAR steps 1, 2, and 3, a set of 1,000 Pareto fronts is generated (Figure 3(b)). In steps 4, 5, and 6, the variability of the flooding volume is analysed for the two construction costs: 1,500 and 3,000 tmu – see Figures 3(b) and 3(c), respectively. As in the case of deterministic optimization, it is easy to see (and it is in a way obvious) that the lower investment leads to more flooding.
However, it is now possible to also see more, e.g. to detect a certain pattern: the lower the network costs, the bigger the variability in the network performance. Despite the relatively small uncertainty of the rainfall that is considered (the standard deviation is equal to 7% of the mean), it is interesting to see the differences in the network performance when less and less is invested in the construction of the network.
Let us assume that at Step 7 of the ROPAR algorithm, the decision maker chooses to build a network with a cost of 3,000 tmu. The decision could be based on the fact that although the 3,000-tmu solution is 100% more expensive than the 1,500-tmu solution, the latter leads to more flooding, but also is 350% more dispersed (measuring dispersion as the range, i.e. the width of the base of the PDF). The range of the 1,500-tmu solution is 14 mlw; the range of the 3,000-tmu solution is 4 mlw; and the ratio of 14 mlw to 4 mlw is 3.5, or 350%. In other words, the 3,000-tmu network has only 29% of the variability of the flooding volume of the 1,500-tmu network, or it can be said that in these terms it is 3.5 times more robust against uncertainty in rainfall. If robustness is seen as an important factor, then a decision maker may find this to be a good additional justification to build a more expensive network – in this case, a 100% more expensive design increases robustness by approximately 3.5 times, and of course leads to less flooding.
Yet another piece of information that can be deduced from these plots is the relation of maximum flooding volume (MFV) to cost under various scenarios. For example, MFV for the network costing 3,000 tmu is 4 mlw, and MFV for the network of 1,500 tmu is 19 mlw, which is almost five times higher.
It should be noted that an increase in robustness for more expensive designs from the engineering point of view is more or less expected: if one invests more in larger pipes, the number of solutions leading to flood is reduced. As investment reaches approximately 4,750 tmu, the pipes would be able to accommodate any amount of storm water and no rainfall would lead to flooding, so there is no uncertainty, and robustness is maximum. In other applications, such a relationship may be much less obvious. In any case, ROPAR makes it possible to identify the sets of solutions with different values of robustness and the corresponding ranges of objective functions, enabling probabilistic analysis.
Steps 8 and 9 of ROPAR identify the robust solution(s), ultimately selecting the most robust of the possible solutions, given the user-defined cost. In this example, for each of the costs 1,500 and 3,000 tmu, there are 1,000 solutions with either less than or equal cost of 1,500 and 3,000 tmu, respectively, that are selected in accordance with Step 8 of the ROPAR algorithm. Next, as specified in Step 9 of ROPAR, there are two criteria to select the most robust solution. Both criteria are applied to exemplify their use.
To apply criterion 1 of Step 9 (minimizing expected value), for every solution the average flood volume (AFV) across the 1,000 rainfall samples is calculated, and the solution corresponding to the minimum AFV is picked. Figures 4(a) and 4(b), corresponding to costs of 1,500 and 3,000, respectively, show the solutions with minimum AFV marked with a red X.
Comparing robustness of solutions: deterministic (black dot), minimum AFV (red X), and minimum MFV (red +); for costs 1,500 (a), 3,000 (b), and those in-between (c) and (d). Please refer to the online version of this paper to see this figure in colour: http://dx.doi.org/10.2166/hydro.2019.095.
The procedure described in the previous paragraph is also used with criterion 2 of Step 9 of ROPAR (minimizing worst case), and here the solution with the minimum MFV is picked. Figures 4(a) and 4(b), corresponding to costs of 1,500 and 3,000, respectively, present the solutions with minimum MFV marked with a red +.
Comparing solutions found by deterministic and robust approaches
To see a more general relationship among the 1,001 solutions (i.e. 1,000 from the robust optimization plus 1 from the deterministic optimization), in Figures 4(a) and 4(b) every solution is plotted in the intersection of its AFV and its MFV. The robust solution with respect to criterion 1 is the one with the smallest AFV, and the robust solution w.r.t. criterion 2 is the one with the smallest MFV. In Figure 4(b), these two solutions are very close to each other; they have almost the same performance, that is, the solution found by criterion 1 could be used as the solution of criterion 2 and vice versa. It should be noted that the proximity of solutions corresponding to criteria 1 and 2 is characteristic only to the considered simple case study, but for more complex cases the results based on using criteria 1 and 2 could be very different (albeit these two criteria obviously correlate).
Furthermore, from Figure 4(b) a Pareto front can be recognized, and it has only two solutions. In Figure 4(b), the red X and the red + form a 'blurred asterisk' because they are pointing to two different solutions (compare with Figure 4(a), where a 'sharp asterisk' is seen because the X and the + are pointing to the same solution). For the decision maker, only these two solutions are worth considering because they dominate all the rest of the solutions. However, for other cases, the Pareto front might have more solutions, and in those cases, all the solutions in the Pareto front should be presented to the decision maker to let him/her decide which solution to select for implementation.
In terms of offering more options to the decision maker, several cost levels can be used. For example, Figures 4(c) and 4(d) show seven robust solutions corresponding to the costs 1,500, 1,750, 2,000, 2,250, 2,500, 2,750, and 3,000 tmu.
Additionally, from Figure 4 it is also possible to see that robust solutions have better performance than the deterministic solutions in terms of the values of AFV and MFV. Lower values for AFV and MFV mean lower variability, and hence higher robustness. This interpretation is in accordance with the definition of robustness used in this study.
Table 1 shows the robust and deterministic solutions with a cost of 3,000 tmu. In the table, the diameters from top to bottom correspond to the pipes in the network from upstream to downstream, respectively.
Pipe diameters (m) for deterministic and robust solutions with a cost of 3,000 tmu
Deterministic
Minimum AFV
Minimum MFV
In this paper, the problem of multi-objective robust optimization taking uncertainty into account is formulated. ROPAR is the algorithm presented and tested to solve this kind of problem. It analyses the robustness of the solutions found and selects the most robust solution(s).
Additionally, a deterministic MOO is compared with a ROPAR MOO. Then the robustness of the deterministic solutions is compared with the robustness of the ROPAR solutions. In every case tested, ROPAR found a more robust solution.
In contrast to the methods already known, ROPAR allows for explicit handling of uncertainty and analysing how uncertainty is propagated to the Pareto-optimal solutions, and allows for the corresponding probabilistic analysis. This makes it possible to identify the sets of solutions with different values of robustness and to select the robust solutions according to the preferences of a decision maker.
The novelty of the presented approach is in two aspects. First, the uncertainty of parameters or inputs is explicitly propagated to the solutions, so that the dispersion of the solutions can be analysed visually and analytically. Second, the new method has general applicability since it uses a quite general definition of robustness, and does not reformulate the objective functions (e.g. by their smoothing). This method can be applied to the optimization of a wide variety of problems, where the parameters are uncertain and can be represented by a PDF. Use of ROPAR for other problems will be demonstrated in the forthcoming papers by Marquez-Calvo & Solomatine (in preparation-a) and Marquez-Calvo & Solomatine (in preparation-b).
One of the limitations of this study is that ROPAR is applied to problems with one source of uncertainty and two objective functions; however, its design allows this algorithm to be employed in problems with more sources of uncertainty and more objective functions. In Marquez-Calvo et al. (submitted), a problem related with water quality in a water distribution network is solved using ROPAR considering 24 sources of uncertainty. In Marquez-Calvo & Solomatine (in preparation-b), the procedure to apply ROPAR to multiple-objective optimization problems is presented, and the robust design of a storm drainage system is identified, taking into account three objective functions and three sources of uncertainty.
Further research will be aimed at developing and testing the visual tools to enable problem representation and analysis in multiple dimensions, and testing various 'trade-off' approaches that make it possible to deal with the computational complexity of ROPAR.
The first author thanks the Mexican Government for its support in funding his PhD programme through the CONACYT programme. Some of the research ideas and components were developed in the framework of the grant No. 17-77-30006 of the Russian Science Foundation, and the Hydroinformatics research fund of IHE Delft. The authors are also grateful for the support of IHE Delft in supplying the computational facilities, and to Dr Gerald Corzo (IHE Delft) for helping with a number of IT-related aspects of carrying out this research.
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Visual analysis of three-dimensional flow field based on WebVR
Groundwater flow simulation through the application of the FREEWAT modeling platform
Application of GIS-based models of weights of evidence, weighting factor, and statistical index in spatial modeling of groundwater
A web-based decision support system for smart dam operations using weather forecasts


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January Peak Month For Divorce Proceedings
More people start divorce proceedings in January than at any other time of the year, with many couples realising over the Christmas period that they're no longer happy with their spouse.
The Independent highlighted figures from relationship charity Relate, which recorded a 24 per cent spike in calls about divorce in January last year, compared to the average month, with a similar jump expected over the coming weeks in 2018.
Figures from divorce support service Amicable also show that online searches for the term 'divorce' are expected to be high in January. After analysing data the organisation expects more than 40,500 people to search for this term in the coming weeks, the newspaper added.
Chris Sherwood, chief executive at Relate, told the publication why the charity gets so many more calls at the start of the year.
"The reality is that many of the calls we receive in January are from couples who are already at crisis point after existing relationship tensions have come to a head over Christmas," he stated.
Mr Sherwood went on to urge any couples who are struggling to get on with their partner to seek help sooner rather than later.
In December, divorce expert Sara Davison shared advice with readers of the Express about how to avoid a relationship ending at this time of the year. Among her suggestions were to talk openly about how you'd like to celebrate Christmas, to be careful not to overspend and to make time to spend with each other.
If you are looking for divorce solicitors in Surrey, contact us today to find out how we can support you in this difficult time.


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Update on Shared Parental Leave in Northern Ireland
The Work and Families (Northern Ireland) Act 2015 ("WFA") will come into full legal effect in Northern Ireland in early April 2015, having received Royal Assent earlier this month.
The purpose of the WFA is to provide a legislative framework in Northern Ireland which enables leave and pay entitlements for working parents in Northern Ireland to match those available to working parents in Great Britain following the introduction of similar legislation in GB in December 2014.
In summary, the WFA
sets out a legislative framework allowing for the voluntary sharing of leave and pay entitlement between eligible parents following the birth or adoption of a child (shared parental leave and pay). It also abolishes the right to additional paternity leave and pay, which the new system replaces and builds upon and makes consequential adjustments to other rights for working parents to ensure that they appropriately interface with the WFA provisions;
allows foster parents who are a child's intended adoptive parents to access adoption pay and leave, and intended parents in surrogacy arrangements to avail of paternity and adoption leave and pay;
provides for statutory adoption pay to be paid at 90% of earnings for the first six weeks. It also facilitates paid time off work for primary adopters to attend up to five introductory meetings before a child is placed with them for adoption; and unpaid time off for secondary adopters to attend two such meetings. A similar right to take unpaid time off work is provided for the partners of new mothers to attend ante-natal appointments; and
facilitates extension of the current right to request flexible working to all employees having an appropriate length of service with their employer.
Draft guidance has been published by the Department of Employment and Learning.
We will be hosting an Employment: In focus breakfast briefing on the new shared parental leave rights and implications for businesses in Northern Ireland on 5 March 2015. If you would like to attend please contact Kirsty Branagh at kbranagh@algoodbody.com.
Date published: 26 January 2015


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Bubble Banter: Saturday's winners and losers
By Rob DausterMar 1, 2014, 11:53 PM EST
There are 15 days left until Selection Sunday. Every morning from now until the bracket comes out, we'll help you get caught up on the happenings with impact on the bubble from the night before.
You can see NBCSports.com's latest bracket here.
(This post will be updated throughout the day)
Oklahoma State: The Pokes beat Kansas, which should just about lock up a bid.
Xavier: We wrote about the Musketeer's here.
Dayton: The Flyers landed a massive win on Saturday, hanging on to knock off UMass at home in a game they desperately needed. Dayton had played their way back into bubble contention at losing four straight games in January, but with three sub-100 losses on their resume, a 3-5 record against the top 50 wasn't enough. Based on the RPI, UMass is now Dayton's best win on the season. Don't be surprised to see the Flyers hosting a game in the First Four when brackets are re-released on Monday.
Tennessee and Missouri: The Vols notched their eighth top 100 win on Saturday by blowing out Vanderbilt. Tennessee was out the tournament as of today, according to our Dave Ommen, and beating Vanderbilt certainly doesn't change that fact. The Tigers, on the other hand, were in the dance, and they handled their business against Mississippi State. Tennessee's finale against Missouri could end up being a play-in game.
Arkansas: There was no hangover for the Razorbacks, as they followed up a win at Kentucky by knocking off Georgia, who was third in the SEC, at home. The Razorbacks are looking like they will end up sitting right around the cutline on Selection Sunday, meaning that any loss that they suffer will hurt.
Baylor: The Bears bounced back from a loss to Texas this week by beating Texas Tech in Waco on Saturday. It's their fifth win in the last six games and likely keeps Scott Drew's team on the right side of the bubble as we head into the final week of the regular season. The Bears host Iowa State and visit Kansas State next week. I'd suggest at least a split.
Pitt: The Panthers won in overtime at Notre Dame, which means that Pitt is still comfortably in the NCAA tournament for now. Jame Dixon will be dancing as long as they don't do anything silly like lose to N.C. State or Clemson.
Oregon: The Ducks followed up Thursday night's win at UCLA by knocking off USC on the road. At this point, they are probably safely in the tournament, but with Arizona and Arizona State coming to Eugene next week, two straight losses and an early exit in the Pac-12 tournament could put them into an ugly position on Selection Sunday.
Gonzaga: The Bulldogs knocked off Saint Mary's in impressive fashion in Moraga. That's a good win. But the Zags are still in a precarious position should they lose early in the WCC tournament. They have one top 50 win (BYU) and two sub-150 losses. An 8-4 record against the top 100 does look good, however.
St. Joseph's: The Hawks keep themselves in a good spot with their 17th win in the last 20 games, this one coming on the road against St. Bonaventure. It's their seventh top 100 win this season. Split George Washington on the road and La Salle at home and St. Joe's should be dancing.
Nebraska: The Huskers bounced back from a loss to Illinois last week to knock off Northwestern at home on Saturday. It was a loss Tim Miles' team couldn't afford. Right now, they are probably on the wrong side of the bubble, but they have two key games next week to change that. Beating Indiana at Indiana is important, but picking up a win at home over Wisconsin in the season finale would likely lock up a tournament bid.
BYU: The Cougars avoided getting tripped up at San Diego on Saturday, meaning that they will head into the WCC on a four-game winning streak and having won eight of their last nine game. They probably need to make a run to the title game simply because an earlier exit will likely result in an ugly looking loss.
Utah: The Utes are a perfect example of why it's important to play a tough non-conference schedule. Their win over Colorado on Saturday was their fourth top-35 win. They have just one loss outside the top 80. But they're not all that close to a bid thanks to their atrocious non-conference schedule. They need to win at Cal and at Stanford next weekend to have a chance.
West Virginia: The tournament hopes of the Mountaineers' are a bit of a pipe dream at this point, but they knocked off TCU on Saturday which means that, with wins at Oklahoma and over Kansas to close out the regular season, WVU will have a chance. It's a long shot, but they still have a shot.
Colorado: The Buffaloes dropped a potentially costly game at Utah. It was their second straight loss after winning five of six, and with road trips to Stanford and Cal next week, that streak could realistically get extended to four straight losses. That could be a problem, as Colorado is going to be judged based on what they've done since Spencer Dinwiddie went down with an ACL injury. One win next week should be enough.
Minnesota: The Golden Gophers couldn't build on a massive win over Iowa during the week, falling at Michigan on Saturday evening. Minnesota finishes their season next Saturday at home against Penn State. With a 3-8 record against the top 50, including a win over Wisconsin, and a 6-10 record against the top 100 — along with two sub-100 losses — Rick Pitino's club looks like they might need to do some damage in the Big Ten tournament to feel safe.
Cal: The Bears didn't have much fun on their trip to the Arizona schools, getting worked by both the Wildcats and, on Saturday, the Sun Devils. They've lost three of their last four and are now just 3-8 against the top 50. They have a nice win over Arizona, but that's balanced out by a pair of sub-100 losses. They get Utah and Colorado at home next week. One would help. They might want both to feel safe.
LSU: The Tigers had a chance to pick up a statement win at Florida, but they got smacked around for 40 minutes. It's their fourth loss in the last six games, and they simply have too many bad losses and no more chances for good wins. LSU looks destined for the NIT.
Richmond: If getting blown out by George Mason didn't cost the Spiders an at-large bid, getting blown out by Rhode Island did.
N.C. State: I'm not even sure that the Wolfpack were still technically on the bubble, but after losing by 15 to Miami at home, they're now on that 'auto-bid or bust' game plan.
Games left to be played:
7:00 Iowa State at Kansas State
10:00 Gonzaga at Saint Mary's
Tags: Bubble Banter
The four most important questions after Kansas-Kansas State fight
Screengrab via ESPN
Wvery other sport can treat brawls like comedy, and I think it's about time that we did the same for basketball.
So let's take a look at the four funniest moments from last night's Kansas-Kansas State fight. Shouts to Jomboy:
1. IS THE KANSAS MASCOT OK?
Throughout the entire fight, the mascot is just in utter disbelief. He cannot believe what he just saw, and he certainly cannot be consoled:
2. CAN JEREMY CASE START AT LINEBACKER FOR KU'S FOOTBALL TEAM?
Case is the video coordinator for Kansas. He's also a former Kansas point guard. He knows what this rivalry is all about, and he also is not going to be afraid to get in the middle of it.
Case starts out on the wrong side of the melee:
But when he sees De Sousa and Love squaring up and throwing punches, he intervenes by throwing himself into a player six inches taller than him:
Silvio De Sousa picked up a chair and was ready to smash that thing over someone's head WWE style. pic.twitter.com/QMHupzi86i
— Kyle Boone (@Kyle__Boone) January 22, 2020
3. WHAT HAPPENED TO JAMES LOVE III'S SHOE?
James Love the third has played in exactly one game this season. He has spent more time on the court fighting that he has actually playing, but he still found a way to get into the middle of this fight and, in the process, lost his shoe:
Holy smokes. Here's another angle of the KU-Kansas State brawl that just broke out. Wow. pic.twitter.com/Mqm7Lbv4tX
He's not dressed for the game.
Did he bring an extra pair of shoes? Did he have to head back onto the bus without a shoe on this right foot? So many questions, so few answers.
4. WHO IS THE MAN IN THE ORANGE HAT?
He's some kind of photographer.
He got his shot, that's for sure:
Kansas-Kansas State fight: Nuance, context the key in Silvio De Sousa discussion
So I wanted to elaborate on a point that I made on twitter this morning because 280 characters just is not enough to be able to parse through the nuance of this situation.
If you missed it, the thread is here.
First and foremost, everyone involved in this needs to be punished. Silvio De Sousa needs to be suspended. Antonio Gordon needs to be suspended. James Love III needs to be suspended. David McCormack, and potentially Marcus Garrett, probably need to be suspended, although I'm not sure either of them actually through a punch. Point being, anyone else that threw a punch needs to be suspended.
Full stop.
I am not saying otherwise.
But I think that it is important to add some context to the conversation, and I also think that it is important to say this: This doesn't make any of the young men involved in this fight bad people. Silvio De Sousa is not inherently a bad person because he picked up a stool, and the faux-trage of people calling for him to get booted out of school, arrested or even deported are, at best, completely over-reacting and, at worst, showing off a bit of their racial bias.
Before I get into this, one more thing: I am not condoning any of it. Fights like this should not happen.
But the reality of hyper-competitive athletics is that in emotionally charged situations, fights are going to happen. And if you've ever been in a fight like this, you know that things happen incredibly quickly. You're not thinking, you're reacting. You can't call a 20 second time out to come up with a way to defend yourself when someone is throwing haymakers, you just do what you can in the moment.
So let's talk about the moment, shall we?
De Sousa is the guy that set this entire thing in motion with the way that he reacted to DaJuan Gordon's steal and layup attempt. The reason the Kansas State bench rushes over to the scene is because De Sousa is towering over one of their freshman teammates, and the reason the Kansas sideline runs over is because the Kansas State sideline does. What turned this incident into a full-fledged brawl was Antonio Gordon flying in and shoving De Sousa over the back of the basket stanchion. De Sousa reacts by throwing punches at two different Kansas State players when a third player — James Love III, in the black polo — comes flying in and squares up with him. They both throw a few punches at each other, knocking De Sousa back over the stanchion again as Kansas staffer Jeremy Case comes flying in to break them up.
Put yourself in De Sousa's shoes here. In the span of 10 seconds, he's fought three different Kansas State players, sees nothing but purple in front of him and just got knocked to the ground. Is he getting jumped? Does he have to fight them 1-on-3? That's when he grabs the stool, to defend himself, and when he sees that no one is coming after him anymore, he drops it:
After seeing this video from several different angles, this one in particular makes me think Silvio De Sousa was reacting to being engaged as opposed to initiating the brawl. pic.twitter.com/2IGIay4HsO
He should be suspended for 8-10 games.
He set this entire thing in motion.
But maybe, just maybe, tone down the rhetoric.
The four most important questions after Kansas-Kansas State fight January 22, 2020 2:40 pm Kansas-Kansas State fight: Nuance, context the key in Silvio De Sousa discussion January 22, 2020 2:30 pm Kansas suspends Silvio De Sousa 'indefinitely' following brawl January 22, 2020 1:06 pm Women's Wednesday: A new column dedicated to the women of college basketball January 22, 2020 11:00 am Kansas, Kansas State both taking blame for massive fight January 22, 2020 9:59 am Bill Self on Kansas-Kansas State brawl: 'It's an embarrassment' January 22, 2020 1:11 am Kansas and Kansas State end rivalry game in fight January 21, 2020 11:59 pm Tuesday's Things to Know: Kansas and K-State Brawl, TCU's signature win, Villanova beats Butler January 21, 2020 11:54 pm TCU upends No. 18 Texas Tech January 21, 2020 11:14 pm No. 21 Illinois runs away in 2nd half to sweep Purdue January 21, 2020 11:04 pm Illinois' Alan Griffin ejected for stepping on Purdue player January 21, 2020 8:30 pm Walter McCarty dismissed as Evansville head coach January 21, 2020 7:32 pm Player of the Year Power Rankings: Powell vs. Pritchard vs. Howard vs. Toppin January 21, 2020 2:10 pm Three Things To Know: Shaka's seat heats up, Baylor survives, Virginia doesn't January 21, 2020 12:22 am Chris Mack: David Johnson's shoulder 'is fine' January 20, 2020 2:08 pm Bracketology: Welcome to the top line, San Diego State January 20, 2020 1:30 pm AP Poll: Baylor leapfrogs Gonzaga, seventh No. 1 team this season January 20, 2020 12:45 pm Monday's Overreaction: Myles Powell, Payton Pritchard, David Johnson and the two worst chokes of the year January 20, 2020 12:14 pm Monday's Overreactions: Louisville beat Duke, Auburn lost, Butler lost, Get The Gat January 20, 2020 7:05 am College Basketball Top 25 Power Rankings: Baylor and Gonzaga lead the way January 19, 2020 12:23 pm Ten Things You Need To Know: Catch up on all of Saturday's college hoops action January 18, 2020 11:50 pm David Johnson shines as No. 11 Louisville beats No. 3 Duke, 79-73 January 18, 2020 8:39 pm Diarra, Gordon lift Kansas State over No. 12 West Virginia January 18, 2020 6:40 pm Payton Pritchard three caps come-from-behind win for No. 8 Oregon at Washington (VIDEO) January 18, 2020 6:30 pm Dotson and Azubuike lead No. 6 Kansas over Texas 66-57 January 18, 2020 6:30 pm John Calipari gets ejected, Kentucky survives to beat Arkansas (VIDEO) January 18, 2020 5:46 pm Payne's career game helps Florida beat No. 4 Auburn 69-47 January 18, 2020 5:06 pm No. 5 Butler loses second straight as DePaul gets first Big East win January 18, 2020 3:21 pm No. 2 Baylor pushes winning streak to 15 with come-from-behind win at Oklahoma State January 18, 2020 2:53 pm No. 14 Villanova survives UConn, 61-55 January 18, 2020 2:47 pm Gators guard Andrew Nembhard has flu, could miss Auburn game January 17, 2020 5:04 pm Best Bets: Where is the value in college basketball this weekend? January 17, 2020 2:14 pm Elleby's 25 points leads Washington State in upset of No. 8 Oregon January 17, 2020 12:24 am Three Things To Know: No. 8 Oregon lost, the Pac-12 is dunk, Memphis won January 17, 2020 12:17 am CBT Podcast: Duke, Auburn, Butler and Kentucky all lose, so are we worried yet? January 16, 2020 2:37 pm Why does San Francisco intentionally foul at the end of the first half? January 16, 2020 11:31 am Three Things To Know: Auburn lost, Seton Hall won, Kentucky choked January 16, 2020 12:29 am Rose leads Temple to upset of No. 16 Wichita State January 15, 2020 11:50 pm Alabama knocks off unbeaten No. 4 Auburn January 15, 2020 11:24 pm Couisnard's three gives South Carolina buzzer-beating win over No. 10 Kentucky January 15, 2020 9:05 pm


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The FBI's Domestic "War on Terror" Is an Authoritarian Power Grab
Branko Marcetic
Buzzfeed has revealed the FBI played a leading role in orchestrating last year's far-right terrorist plot against Michigan governor Gretchen Whitmer — which the bureau then foiled, to great fanfare. The incident has since been used to hand the FBI even more power.
FBI investigators in Thousand Oaks, California, 2018. (Robyn Beck / AFP via Getty Images)
There are many ways to undermine democracy. In the United States, one of the most persistent threats to democratic rights has been the hoarding of power by largely unaccountable security forces that, in a single-minded drive to protect what they define as national security, have tended to run roughshod over civil liberties and treat activists and dissidents as criminals.
It was the case in the early twentieth century, when the FBI rounded up radicals, foreigners, and draft-evaders. It was the case mid-century, when the COINTELPRO program harassed Matin Luther King Jr and other civil rights protesters. It was the case under George W. Bush, when a newly launched "war on terror" became an excuse to go after law-abiding Muslims and government critics. And it was the case just last year, as a panoply of federal agencies were used by a desperate president Trump to go after protesters, journalists, and immigration lawyers, to name a few.
All of this is particularly relevant in light of a major new investigation by Buzzfeed published last week, examining the details of last year's highly publicized FBI thwarting of a plot by a Michigan militia to kidnap the state's Democratic governor, Gretchen Whitmer. At the time, I made a basic point about the whole situation: we should be careful about simply accepting the FBI's account of the events at face value, not least because the sparse details included in its affidavit called to mind an old and controversial Bureau practice. That would be the FBI's use of informants and undercover agents to effectively manufacture their own terrorist plots — typically by entrapping down-on-their-luck or mentally unwell Muslim men — which they then foiled and publicized, thereby justifying more money, resources, and powers for the "war on terror."
While at the time I cautioned we didn't have enough information yet to make a definitive call either way, the extensive reporting done by Buzzfeed all but confirms this was the case. According to the outlet, FBI informants "had a hand in nearly every aspect of the alleged plot, starting with its inception," from organizing and funding nationwide meetings where those involved in the eventual plot first met, to supplying, encouraging, and even helping lead the entire affair. Buzzfeed reporters Jessica Garrison and Ken Bensinger previously reported that the Bureau used a whopping twelve informants in the case, double the number of actual plotters charged.
One of those, known only as Dan, was paid nearly $55,000 by the FBI for seven months of work, during which time he became second-in-command of the whole operation. His handiwork included encouraging the plot's chief, non-FBI ringleader — the literally basement-dwelling Adam Fox — in his increasingly unhinged fantasies, and using his sway to include Fox in meetings despite other militia members' concerns about Fox's stability. Dan's FBI handlers, meanwhile, encouraged him to draw in as many people as possible into the plot, and provided the money he used to fund a national training exercise early on in the affair.
It should go without saying that none of this makes any of the individuals involved in the plot good guys, or any less noxious in their politics. This should be a basic point for anyone committed to basic democratic freedoms or opposed to mass incarceration: believing in due process isn't supporting rape, murder, or other crimes; and defending the rights of the accused doesn't necessarily mean you'd invite them over for dinner with the family. Or to use a closer analogy: sticking up for the civil liberties of accused Islamic terrorists doesn't mean that you sympathized with their ideology or their alleged crimes.
But this news is significant beyond just the principle involved here. Even news outlets that were well aware of the FBI's history of doing this exact thing with Islamic terrorist plots opted for sensationalistic coverage of the kidnapping scheme, uncritically reproducing the FBI's preferred portrayal of the events. This plot was then, post-January 6, often cited together with the Capitol riot as a dramatic, visceral example of the growing threat of Far Right terrorism that required the failed and abuse-filled "war on terror" to be expanded to the home front.
Speaking of January 6, there remain serious questions about law enforcement agencies' conduct on that day. The security and intelligence failure which was the only reason the "Stop the Steal" protest was able to get out of hand and charge into the Capitol still hasn't been adequately explained. Besides contradictory testimony from officials about the failure of their response, we know the FBI and others warned the Capitol police in advance about the protests. We now also know there was at least one undercover agent among the rioters, on top of the fact that the leader of the Proud Boys — one of the far-right groups who took part in the incident — was a "prolific" law enforcement informant, in the words of his own lawyer, and that at least four Proud Boy leaders in total were feeding information to the Bureau since 2019, directly contradicting the FBI director's sworn testimony earlier this year.
Why, given all this, did law enforcement fail so spectacularly to keep a mostly unarmed crowd of protesters out of the Capitol, particularly after the police's militarized and heavy-handed response to anti-police brutality protests over the past six years? How were they taken by surprise when the entire event was planned in the open anyway? Why have law enforcement officials offered inconsistent, contradictory, and even misleading testimony? All good questions the national press has been largely uninterested in, obsessed instead with debunking overcooked claims that the FBI "organized" the riot.
None of this points to a deliberate conspiracy. But it certainly seems like the two principal events upon which a dangerous new domestic "war on terror" has been launched off of are, first, a kidnapping plot that only existed because the FBI made it happen, and second, a completely avoidable security failure by law enforcement that was asleep at the wheel. In other words, it points to a classic national security dynamic: agencies overzealously prosecuting national security threats, or royally screwing up, and instead of admitting to mistakes, using the incidents to justify more power and resources for themselves.
(Not that we should reflexively dismiss more sinister possibilities: after all, we just found out that as part of its pursuit of Julian Assange, the FBI stood by (or worse) as one of its informants carried out a cyberattack on the Icelandic government, which the Bureau used as a pretext to enter the country to go after the WikiLeaks founder.)
With the image of an archetypal terrorist shifting under Trump's tenure as president, from dark-skinned Muslim men to burly white right-wing militiamen and racists, the broad left is currently in danger of letting its antipathy toward the this new face of terrorism put it in the position of facilitating an alarming concentration of police power and an eventual crackdown on political dissent. Post-January 6, far too many progressives who previously fretted about abuse of the criminal justice system suddenly turned into a mix of Rudy Giuliani and George W. Bush, calling for harsh prosecution of those who had committed only property damage on January 6, or even just walked around not doing very much. As Tom Cotton noted gleefully after the incident: "Some liberals appear to have shed their reservations about the use of force now that the mob carries different signs and chants different slogans."
Left-wing lawmakers facilitated the passage of a bill funneling enormous money and resources to the Capitol police, which has now, alarmingly, been expanded into a national anti-terrorism police force, one that's conveniently exempt from the Freedom of Information Act. After a disgraceful 2020 that saw public confidence in the police dip below 50 percent thanks to shocking, unabashed brutality and authoritarianism, the orgy of police propaganda that followed January 6 has now made them one of only three institutions that a majority of Americans still believe in. And prosecutors are trying to throw the book at entirely nonviolent January 6 protesters simply for having stood nearby as acts of property damage and violence were committed — all to nothing but crickets from civil libertarians, only a few years after similar prosecutorial overreach against the J20 defendants inspired sustained outrage and alarm.
Whether it's a conscious strategy isn't clear, but it's a plain fact that national security agencies are now increasingly leaning on right-wing terrorists as the go-to justification for the same demands they were making when Islamic terror was the horror of the day, from internet censorship, to weakening encryption. In the process, they've tapped into a more liberal-minded audience that has switched its position on all these issues now that the ostensible targets have changed.
What's particularly frustrating is that we were all just handed a walking, talking cautionary tale about the perils of this sort of thing in the form of Donald Trump. The tail-end of Trump's presidency showed exactly how dangerous a sprawling national security apparatus could be in the wrong hands, and yet not even a year later, liberals are pushing to vest the federal government with even more authoritarian powers, ones that will be handed down to the next extreme Republican who wins the presidency… who very well might be Donald Trump. Meanwhile, already the FBI has used the Capitol riot as a justification to go after left-wing protesters and dissidents, a sure sign of what's to come, as Biden's domestic "war on terror" strategy has made clear.
While right-wing mobs and white supremacists are nothing to scoff at, the potential for American fascism has always been most rooted in those institutions of authority that prize "law and order" and property rights over justice and civil liberties, the very same ones we watched last year brutalizing unarmed protesters and journalists, kidnapping people off the street, and turning American cities into foreign war zones — and who are still attacking nonviolent activists without a care in the world. The Left should be at the front lines opposing a startling push to feed them more power and resources. Instead, far too many are silent, or even cheering it on.
Branko Marcetic is a Jacobin staff writer and the author of Yesterday's Man: The Case Against Joe Biden . He lives in Toronto, Canada.
2021 US Capitol riot
There are many ways to undermine democracy. In the United States, one of the most persistent threats to democratic rights has been the hoarding of power by largely unaccountable security forces that, in a single-minded drive to protect what they define as national security, have tended to run roughshod over civil liberties and treat activists […]
To See the Danger of a Domestic "War on Terror," Look No Further Than This Florida Case
Here Are 4 Better Responses to the Capitol Riot Than Expanding "Domestic Terror" Crackdowns
We Shouldn't Trust the FBI's Narrative on the Gretchen Whitmer Kidnapping Scheme
Don't Expand the War on Terror in the Name of Antiracism
Arun Kundnani
Jeanne Theoharis