Some days I just want to take genetic programming away from the computer scientists. Then I realize I ought to just let them keep the useless, ritualized thing they imagine it is.

"We introduce the concept of attainable sets of payoffs in two-player repeated games with vector payoffs. A set of payoff vectors is called {em attainable} if player 1 can ensure that there is a finite horizon $T$ such that after time $T$ the distance between the set and the cumulative payoff is arbitrarily small, regardless of what strategy player 2 is using. This paper focuses on the case where the attainable set consists of one payoff vector. In this case the vector is called an attainable vector. We study properties of the set of attainable vectors, and characterize when a specific vector is attainable and when every vector is attainable."

Several algorithms are available in the literature for finding the entire set of Pareto-optimal solutions in MultiObjective Linear Programming (MOLP). However, it has not been proposed so far an interior point algorithm that finds all Pareto-optimal solutions of MOLP. We present an explicit construction, based on a transformation of any MOLP into a finite sequence of SemiDefinite Programs (SDP), the solutions of which give the entire set of Pareto-optimal extreme points solutions of MOLP. These SDP problems are solved by interior point methods; thus our approach provides a pseudo-polynomial interior point methodology to find the set of Pareto-optimal solutions of MOLP.

"We present two methods for detecting patterns and clusters in high dimensional time-dependent functional data. Our methods are based on wavelet-based similarity measures, since wavelets are well suited for identifying highly discriminant local time and scale features. The multiresolution aspect of the wavelet transform provides a time-scale decomposition of the signals allowing to visualize and to cluster the functional data into homogeneous groups. For each input function, through its empirical orthogonal wavelet transform the first method uses the distribution of energy across scales generate a handy number of features that can be sufficient to still make the signals well distinguishable. Our new similarity measure combined with an efficient feature selection technique in the wavelet domain is then used within more or less classical clustering algorithms to effectively differentiate among high dimensional populations. The second method uses dissimilarity measures between the whole time-scale representations and are based on wavelet-coherence tools. The clustering is then performed using a k-centroid algorithm starting from these dissimilarities. Practical performance of these methods that jointly designs both the feature selection in the wavelet domain and the classification distance is demonstrated through simulations as well as daily profiles of the French electricity power demand."

It's called the DLR Hand Arm System. It has an anthropomorphic design and packs 52 motors, ultra-miniaturized control electronics, a supercapacitor-based power supply, and a web of high-strength tendons. But what makes it stand out compared to conventional systems is its ability to withstand collisions, thanks to ingeniously designed joints and actuators that can absorb and dissipate energy, much like our own arms and hands do.

"We study the 2-dimensional vector packing problem, which is a generalization of the classical bin packing problem where each item has 2 distinct weights and each bin has 2 corresponding capacities. The goal is to group items into minimum number of bins, without violating the bin capacity constraints.…"

"We describe the structure of the graphs with the smallest average distance and the largest average clustering given their order and size. There is usually a unique graph with the largest average clustering, which at the same time has the smallest possible average distance. In contrast, there are many graphs with the same minimum average distance, ignoring their average clustering. The form of these graphs is shown with analytical arguments. Finally, we measure the sensitivity to rewiring of this architecture with respect to the clustering coefficient, and we devise a method to make these networks more robust with respect to vertex removal."

"For every list of integers x_1, ..., x_m there is some j such that x_1 + ... + x_j - x_{j+1} - ... - x_m \approx 0. So the list can be nearly balanced and for this we only need one alternation between addition and subtraction. But what if the x_i are k-dimensional integer vectors? Using results from topological degree theory we show that balancing is still possible, now with k alternations.
This result is useful in multi-objective optimization, as it allows a polynomial-time computable balance of two alternatives with conflicting costs. The application to two multi-objective optimization problems yields the following results:
- A randomized 1/2-approximation for multi-objective maximum asymmetric traveling salesman, which improves and simplifies the best known approximation for this problem.
- A deterministic 1/2-approximation for multi-objective maximum weighted satisfiability."

"… The aim is to find efficient decompositions that simultaneously minimize the irradiation time, the cardinality of the decomposition and the setup-time to configure the multi-leaf collimator at each step of the decomposition. We propose for this NP-hard multiobjective combinatorial problem a heuristic, based on the adaptation of the two-phase Pareto local search. Experiments are carried out on different size instances and the results are reported."

"The knapsack problem (KP) and its multidimensional version (MKP) are basic problems in combinatorial optimization. In this paper we consider their multiobjective extension (MOKP and MOMKP), for which the aim is to obtain or to approximate the set of efficient solutions. In a first step, we classify and describe briefly the existing works, that are essentially based on the use of metaheuristics. In a second step, we propose the adaptation of the two-phase Pareto local search (2PPLS) to the resolution of the MOMKP. With this aim, we use a very-large scale neighborhood (VLSN) in the second phase of the method, that is the Pareto local search. We compare our results to state-of-the-art results and we show that we obtain results never reached before by heuristics, for the biobjective instances. Finally we consider the extension to three-objective instances."

"The Library of Babel, described by Jorge Luis Borges, stores an enormous amount of information. The Library exists {\it ab aeterno}. Wikipedia, a free online encyclopaedia, becomes a modern analogue of such a Library. Information retrieval and ranking of Wikipedia articles become the challenge of modern society. We analyze the properties of two-dimensional ranking of all Wikipedia English articles and show that it gives their reliable classification with rich and nontrivial features. Detailed studies are done for countries, universities, personalities, physicists, chess players, Dow-Jones companies and other categories."

"A key problem in multiobjective linear programming is to find the set of all efficient extreme points in objective space. In this paper we introduce oriented projective geometry as an efficient and effective framework for solving this problem. The key advantage of oriented projective geometry is that we can work with an "optimally simple" but unbounded efficiency-equivalent polyhedron, yet apply to it the familiar theory and algorithms that are traditionally restricted to bounded polytopes. We apply these techniques to Benson's outer approximation algorithm, using oriented projective geometry to remove an exponentially large complexity from the algorithm and thereby remove a significant burden from the running time."

"… In this paper, we provide a formal introduction to riffled independence and present algorithms for using riffled independence within Fourier-theoretic frameworks which have been explored by a number of recent papers. Additionally, we propose an automated method for discovering sets of items which are riffle independent from a training set of rankings. We show that our clustering-like algorithms can be used to discover meaningful latent coalitions from real preference ranking datasets and to learn the structure of hierarchically decomposable models based on riffled independence."

"We consider the problem of choosing between several models in least-squares regression with heteroscedastic data. We prove that any penalization procedure is suboptimal when the penalty is a function of the dimension of the model, at least for some typical heteroscedastic model selection problems. In particular, Mallows' Cp is suboptimal in this framework. On the contrary, optimal model selection is possible with data-driven penalties such as resampling or $V$-fold penalties. Therefore, it is worth estimating the shape of the penalty from data, even at the price of a higher computational cost. Simulation experiments illustrate the existence of a trade-off between statistical accuracy and computational complexity. As a conclusion, we sketch some rules for choosing a penalty in least-squares regression, depending on what is known about possible variations of the noise-level."

"This article presents a generic method to solve 2D multi-objective placement problem for free-form components. The proposed method is a relaxed placement technique combined with an hybrid algorithm based on a genetic algorithm and a separation algorithm. The genetic algorithm is used as a global optimizer and is in charge of efficiently exploring the search space. The separation algorithm is used to legalize solutions proposed by the global optimizer, so that placement constraints are satisfied. A test case illustrates the application of the proposed method. Extensions for solving the 3D problem are given at the end of the article."

"The performance assessment of algorithms for multiobjective optimization problems is far from being a trivial issue. Recent results indicate that unary performance measures, i.e. performance measures which assign a single value to each non-dominated point set, are inherently limited in their inferential power. Despite these limitations, the hypervolume indicator (also known as Lebesgue measure or S metric) is still considered to possess some reasonable properties, having also been proposed as a guidance criterion for accepting solutions in Multiobjective Evolutionary Algorithms. Therefore, the computational time taken for computing the hypervolume indicator is a crucial factor for the performance of such algorithms.…"

"Here's why I don't think of "too big" as a myth for resolving a firm. In my mind, the farther you are from the origin in that graph, the harder it is for the government to detect problems and properly deter large firms under resolution authority. (This is why I draw our "safe" resolution as a circle, instead of a square.) Holding for a liquidity risk, the larger the firm, the more vicious the effects of having a shadow banking run on the rest of the financial sector and on the real economy. It is possible that the green circle here will be cast out far, and that size and pressures of campaign donations won't play a major part. But why take the chance?"

Nonplussed (but satisfying) to see somebody do such a thorough job discussing something I was planning on doing almost 20 years ago…

"Every human likes choices. But today's fast route planning algorithms usually compute just a single route between source and target. There are beginnings to compute alternative routes, but this topic has not been studied thoroughly. Often, the aspect of meaningful alternative routes is neglected from a human point of view. We fill in this gap by suggesting mathematical definitions for such routes. As a second contribution we propose heuristics to compute them, as this is NP-hard in general."

"It was also realised that had the layout optimization solver been incorporated within a user-friendly interactive software package, then it would have been very much easier to rapidly change the design problem in response to feedback from other members of the design team..."

"The skyline problem is to compute the best tuples from a set of ordered d-tuples. The name is originated from what the solution represented on 2d plane resembles the scene that urban buildings comprise. Skyline is one of the recommendation queries, and it is considering multi criteria. It is very interesting problem as well as very useful query. This problem has been being intensively studied for recent years. Today, I’m going to present the problem definition of skyline. Next time, I’ll describe several algorithms for the skyline problem."

"In this paper, we will present a novel R-tree based algorithm, which adopts a depth- first search technique. In the algorithm, we also developed a “forward checking” tech- nique based on a “region dominance” relation to reduce space complexity. We can show that the new algorithm is I/O optimal and requires a logarithmic space in the worst case in the 2D space if there are not many overlapping in R-tree. Further, our experiment demonstrated that the new algorithm requires a much smaller space than the existing I/O optimal techniques, and is progressive in a way sensitive to user’s requirements."

Stalin is extremely slow to compile. In principle this isn’t a big deal: you can debug using a different scheme compiler. Still, Stalin seems to be somewhat less robust to edge cases, than at least chicken scheme.
It is amazing that Scheme code with no type declarations can beat C by almost a factor of 2.
Though in principle Stalin produces intermediate c code, it is utterly alien and low-level. I have not been able to determine exactly what options Stalin is using when it calls gcc on the source code. That could account for some of the difference.