Specific Heat Study of the Magnetic Superconductor HoNi2B2C

The complex magnetic transitions and superconductivity of HoNi2B2C were studied via the dependence of the heat capacity on temperature and in-plane field angle. We provide an extended, comprehensive magnetic phase diagram for B // [100] and B // [110] based on the thermodynamic measurements. Three magnetic transitions and the superconducting transition were clearly observed. The 5.2 K transition (T_{N}) shows a hysteresis with temperature, indicating the first order nature of the transition at B=0 T. The 6 K transition (T_{M}), namely the onset of the long-range ordering, displays a dramatic in-plane anisotropy: T_{M} increases with increasing magnetic field for B // [100] while it decreases with increasing field for B // [110]. The anomalous anisotropy in T_{M} indicates that the transition is related to the a-axis spiral structure. The 5.5 K transition (T^{*}) shows similar behavior to the 5.2 K transition, i.e., a small in-plane anisotropy and scaling with Ising model. This last transition is ascribed to the change from a^{*} dominant phase to c^{*} dominant phase.Comment: 9 pages, 11 figure

Recent Developments in Algorithmic Teaching

Abstract. The present paper surveys recent developments in algorith-mic teaching. First, the traditional teaching dimension model is recalled. Starting from the observation that the teaching dimension model some-times leads to counterintuitive results, recently developed approaches are presented. Here, main emphasis is put on the following aspects derived from human teaching/learning behavior: the order in which examples are presented should matter; teaching should become harder when the memory size of the learners decreases; teaching should become easier if the learners provide feedback; and it should be possible to teach infinite concepts and/or finite and infinite concept classes. Recent developments in the algorithmic teaching achieving (some) of these aspects are presented and compared.

Teaching Learners with Restricted Mind Changes

Abstract. Within learning theory teaching has been studied in various ways. In a common variant the teacher has to teach all learners that are restricted to output only consistent hypotheses. The complexity of teaching is then measured by the maximum number of mistakes a consis-tent learner can make until successful learning. This is equivalent to the so-called teaching dimension. However, many interesting concept classes have an exponential teaching dimension and it is only meaningful to consider the teachability of finite concept classes. A refined approach of teaching is proposed by introducing a neighbor-hood relation over all possible hypotheses. The learners are then re-stricted to choose a new hypothesis from the neighborhood of their cur-rent one. Teachers are either required to teach finitely or in the limit. Moreover, the variant that the teacher receives the current hypothesis of the learner as feedback is considered. The new models are compared to existing ones and to one another in dependence of the neighborhood relations given. In particular, it is shown that feedback can be very helpful. Moreover, within the new model one can also study the teachability of infinite concept classes with potentially infinite concepts such as languages. Finally, it is shown that in our model teachability and learnability can be rather different. 1

Teaching Memoryless Randomized Learners Without Feedback

The present paper mainly studies the expected teaching time of memoryless randomized learners without feedback. First, a characterization of optimal randomized learners is provided and, based on it, optimal teaching teaching times for certain classes are established. Second, the problem of determining the optimal teaching time is shown to be NP-hard. Third, an algorithm for approximating the optimal teaching time is given. Finally, two heuristics for teaching are studied, i.e., cyclic teachers and greedy teachers