Curves of genus 3 over small finite fields

We present a table containing the maximal number of rational points on a genus 3 curve over a field of cardinality q, for all q<100. Also, some remarks on Frobenius non-classical quartics over finite fields are given.Comment: 9 page

Self-stabilizing mutual exclusion on a ring, even if K=N

We show that, contrary to common belief, Dijkstra's self-stabilizing mutual exclusion algorithm on a ring [Dij74,Dij82] also stabilizes when the number of states per node is one less than the number of nodes on the ring.Comment: 2 page

Simple Distributed Weighted Matchings

Wattenhofer [WW04] derive a complicated distributed algorithm to compute a weighted matching of an arbitrary weighted graph, that is at most a factor 5 away from the maximum weighted matching of that graph. We show that a variant of the obvious sequential greedy algorithm [Pre99], that computes a weighted matching at most a factor 2 away from the maximum, is easily distributed. This yields the best known distributed approximation algorithm for this problem so far

Mixed hitting-time models

We study a mixed hitting-time (MHT) model that specifies durations as the first time a Levy process - a continuous-time process with stationary and independent increments - crosses a heterogeneous threshold. Such models are of substantial interest because they can be reduced from optimal-stopping models with heterogeneous agents that do not naturally produce a mixed proportional hazards (MPH) structure. We show how strategies for analyzing the MPH model's identifiability can be adapted to prove identifiability of an MHT model with observed regressors and unobserved heterogeneity. We discuss inference from censored data and extensions to time-varying covariates and latent processes with more general time and dependency structures. We conclude by discussing the relative merits of the MHT and MPH models as complementary frameworks for econometric duration analysis.

Learning to Rank from Samples of Variable Quality

Training deep neural networks requires many training samples, but in practice, training labels are expensive to obtain and may be of varying quality, as some may be from trusted expert labelers while others might be from heuristics or other sources of weak supervision such as crowd-sourcing. This creates a fundamental quality-versus quantity trade-off in the learning process. Do we learn from the small amount of high-quality data or the potentially large amount of weakly-labeled data? We argue that if the learner could somehow know and take the label-quality into account when learning the data representation, we could get the best of both worlds. To this end, we introduce "fidelity-weighted learning" (FWL), a semi-supervised student-teacher approach for training deep neural networks using weakly-labeled data. FWL modulates the parameter updates to a student network (trained on the task we care about) on a per-sample basis according to the posterior confidence of its label-quality estimated by a teacher (who has access to the high-quality labels). Both student and teacher are learned from the data. We evaluate FWL on document ranking where we outperform state-of-the-art alternative semi-supervised methods.Comment: Presented at The First International SIGIR2016 Workshop on Learning From Limited Or Noisy Data For Information Retrieval. arXiv admin note: substantial text overlap with arXiv:1711.0279

Clusters and spatial planning - Towards a research program

Land is regarded as an important location factor by business firms. Firms change location due to internal and external factors and these changes should be accommodated by spatial reservations, subsequent land development and finally building of offices, plants or transhipment sheds. Business use of land competes with other uses of land, such as housing, leisure or nature. Spatial planning tries to balance these demands. Its success depends on finding criteria to justify a certain balance between economic and ecological interests or between different economic interests. Land-use should be included into a discussion about environmental sustainability. Spatial optimization would come at the agenda of firms and governments more often than is the case now. Yet, there is the issue of economic dynamics, which could be reduced if not all demands for new land are accommodated, at least according to business. This paper introduces a research program into the linkages between regional-economic-and spatial planning. It discusses the main ideas of this research program.

Separated unity: the East and West German industrial sector in 1936

This paper compares and analyses the East and West German levels of labour productivity in industries in 1936. For this purpose archive-data on the industrial census of 1936 were used. In comparison with earlier studies, which rely directly or indirectly on the official publication of the census, these archive data have the advantage of not being distorted by aggregations for military-strategic reasons. Furthermore a statistical division of what later became East and West Berlin could be made. The present paper confirms the conclusions on the relative productivity in earlier research: in 1936 East Germany realised a lower productivity level in the industrial sector than West Germany. The differences are primarily explained by structural differences due to specialisation resulting in a relatively large "Basic and Fabricated Metal" branch in West Germany and a large branch "Textiles and Wearing Apparel" in East Germany. Furthermore this paper signals a higher aggregate capital intensity in West Germany, which is related to the large share of mining industries. Furthermore the East German level of education was below that of West Germany . Finally institutional differences are likely to have played a role since the major industrial agglomerations of East and West Germany were part of two different "industrial orders".