If My Grandmother Had Wheels She\u27d Be A Trolley Car: The Accumulation of Objects, Encounters and The Passage of Time

The house is the structure. Within the house are rooms, spaces, hallways and corners. In those live the objects.The objects live on surfaces, surfaces that much like the previous layers, are made up of many things, most certainly not one thing. A static object may hold a series of other objects, spaces and events. A static object may also embody the passage of time.Though one may try to hold the object at a constant, that is to slow or even bring a halt to its motion, this task is near impossible. Bird Box House, Bear Box Dresser, Lamp Hat, Macaroni Light Tears, Dresser, Ginger Ale Bottle, Wine Bottle, Beer Bottle, Medicine Bottle, Bagel These objects are anything but singular. They hold many things at once. They are superpositions of everyday objects. My accumulating marks attempt to take a physical account for this motion, the passage of time. When the object begins to grow hair, fur or feathers, in the moment of the drawing, the painting, the bird box, the object, time stands still…bearly

Margaret Weininger, transcript only

Transcript of an interview with Margaret Weininger, née Kulka, by Lucille Brown of Union College. Margaret went by Greta and was born in 1892 in a section of Moravia that was part of Austria at the time she was born and is now in the Czech Republic.https://digitalworks.union.edu/berkoralhistories/1014/thumbnail.jp

Automata Tutor v3

Computer science class enrollments have rapidly risen in the past decade. With current class sizes, standard approaches to grading and providing personalized feedback are no longer possible and new techniques become both feasible and necessary. In this paper, we present the third version of Automata Tutor, a tool for helping teachers and students in large courses on automata and formal languages. The second version of Automata Tutor supported automatic grading and feedback for finite-automata constructions and has already been used by thousands of users in dozens of countries. This new version of Automata Tutor supports automated grading and feedback generation for a greatly extended variety of new problems, including problems that ask students to create regular expressions, context-free grammars, pushdown automata and Turing machines corresponding to a given description, and problems about converting between equivalent models - e.g., from regular expressions to nondeterministic finite automata. Moreover, for several problems, this new version also enables teachers and students to automatically generate new problem instances. We also present the results of a survey run on a class of 950 students, which shows very positive results about the usability and usefulness of the tool

Stochastic Games with Disjunctions of Multiple Objectives (Technical Report)

Stochastic games combine controllable and adversarial non-determinism with stochastic behavior and are a common tool in control, verification and synthesis of reactive systems facing uncertainty. Multi-objective stochastic games are natural in situations where several - possibly conflicting - performance criteria like time and energy consumption are relevant. Such conjunctive combinations are the most studied multi-objective setting in the literature. In this paper, we consider the dual disjunctive problem. More concretely, we study turn-based stochastic two-player games on graphs where the winning condition is to guarantee at least one reachability or safety objective from a given set of alternatives. We present a fine-grained overview of strategy and computational complexity of such \emph{disjunctive queries} (DQs) and provide new lower and upper bounds for several variants of the problem, significantly extending previous works. We also propose a novel value iteration-style algorithm for approximating the set of Pareto optimal thresholds for a given DQ.Comment: Technical report including appendix with detailed proofs, 29 page

Positive association in the fractional fuzzy Potts model

A fractional fuzzy Potts measure is a probability distribution on spin configurations of a finite graph $G$ obtained in two steps: first a subgraph of $G$ is chosen according to a random cluster measure $\phi_{p,q}$, and then a spin ($\pm1$) is chosen independently for each component of the subgraph and assigned to all vertices of that component. We show that whenever $q\geq1$, such a measure is positively associated, meaning that any two increasing events are positively correlated. This generalizes earlier results of H\"{a}ggstr\"{o}m [Ann. Appl. Probab. 9 (1999) 1149--1159] and H\"{a}ggstr\"{o}m and Schramm [Stochastic Process. Appl. 96 (2001) 213--242].Comment: Published in at http://dx.doi.org/10.1214/009117907000000042 the Annals of Probability (http://www.imstat.org/aop/) by the Institute of Mathematical Statistics (http://www.imstat.org

Anytime Guarantees for Reachability in Uncountable Markov Decision Processes

We consider the problem of approximating the reachability probabilities in Markov decision processes (MDP) with uncountable (continuous) state and action spaces. While there are algorithms that, for special classes of such MDP, provide a sequence of approximations converging to the true value in the limit, our aim is to obtain an algorithm with guarantees on the precision of the approximation. As this problem is undecidable in general, assumptions on the MDP are necessary. Our main contribution is to identify sufficient assumptions that are as weak as possible, thus approaching the "boundary" of which systems can be correctly and reliably analyzed. To this end, we also argue why each of our assumptions is necessary for algorithms based on processing finitely many observations. We present two solution variants. The first one provides converging lower bounds under weaker assumptions than typical ones from previous works concerned with guarantees. The second one then utilizes stronger assumptions to additionally provide converging upper bounds. Altogether, we obtain an anytime algorithm, i.e. yielding a sequence of approximants with known and iteratively improving precision, converging to the true value in the limit. Besides, due to the generality of our assumptions, our algorithms are very general templates, readily allowing for various heuristics from literature in contrast to, e.g., a specific discretization algorithm. Our theoretical contribution thus paves the way for future practical improvements without sacrificing correctness guarantees

Additive SMILES-Based Carcinogenicity Models: Probabilistic Principles in the Search for Robust Predictions

Optimal descriptors calculated with the simplified molecular input line entry system (SMILES) have been utilized in modeling of carcinogenicity as continuous values (logTD50). These descriptors can be calculated using correlation weights of SMILES attributes calculated by the Monte Carlo method. A considerable subset of these attributes includes rare attributes. The use of these rare attributes can lead to overtraining. One can avoid the influence of the rare attributes if their correlation weights are fixed to zero. A function, limS, has been defined to identify rare attributes. The limS defines the minimum number of occurrences in the set of structures of the training (subtraining) set, to accept attributes as usable. If an attribute is present less than limS, it is considered “rare”, and thus not used. Two systems of building up models were examined: 1. classic training-test system; 2. balance of correlations for the subtraining and calibration sets (together, they are the original training set: the function of the calibration set is imitation of a preliminary test set). Three random splits into subtraining, calibration, and test sets were analysed. Comparison of abovementioned systems has shown that balance of correlations gives more robust prediction of the carcinogenicity for all three splits (split 1: rtest2=0.7514, stest=0.684; split 2: rtest2=0.7998, stest=0.600; split 3: rtest2=0.7192, stest=0.728)

Enforcing ?-Regular Properties in Markov Chains by Restarting

Restarts are used in many computer systems to improve performance. Examples include reloading a webpage, reissuing a request, or restarting a randomized search. The design of restart strategies has been extensively studied by the performance evaluation community. In this paper, we address the problem of designing universal restart strategies, valid for arbitrary finite-state Markov chains, that enforce a given ?-regular property while not knowing the chain. A strategy enforces a property ? if, with probability 1, the number of restarts is finite, and the run of the Markov chain after the last restart satisfies ?. We design a simple "cautious" strategy that solves the problem, and a more sophisticated "bold" strategy with an almost optimal number of restarts