Do Start-Ups Pay Less?

The authors analyze Danish registry data from 1991 to 2006 to determine how firm age and firm size influence wages. Unadjusted statistics suggest that smaller firms paid less than larger firms paid, and that firm age had little or no bearing on wages. After adjusting for differences in the characteristics of employees hired by these firms, however, they observe both firm age and firm size effects. Larger firms paid more than did smaller firms for observationally equivalent individuals but, contrary to conventional wisdom, younger firms paid more than older firms. The size effect, however, dominates the age effect. Thus, although the typical start-up — being both young and small — paid less than a more established employer, the largest start-ups paid a wage premium

Neural networks in geophysical applications

Neural networks are increasingly popular in geophysics. Because they are universal approximators, these tools can approximate any continuous function with an arbitrary precision. Hence, they may yield important contributions to finding solutions to a variety of geophysical applications. However, knowledge of many methods and techniques recently developed to increase the performance and to facilitate the use of neural networks does not seem to be widespread in the geophysical community. Therefore, the power of these tools has not yet been explored to their full extent. In this paper, techniques are described for faster training, better overall performance, i.e., generalization,and the automatic estimation of network size and architecture

Formal Analysis of V2X Revocation Protocols

Research on vehicular networking (V2X) security has produced a range of security mechanisms and protocols tailored for this domain, addressing both security and privacy. Typically, the security analysis of these proposals has largely been informal. However, formal analysis can be used to expose flaws and ultimately provide a higher level of assurance in the protocols. This paper focusses on the formal analysis of a particular element of security mechanisms for V2X found in many proposals: the revocation of malicious or misbehaving vehicles from the V2X system by invalidating their credentials. This revocation needs to be performed in an unlinkable way for vehicle privacy even in the context of vehicles regularly changing their pseudonyms. The REWIRE scheme by Forster et al. and its subschemes BASIC and RTOKEN aim to solve this challenge by means of cryptographic solutions and trusted hardware. Formal analysis using the TAMARIN prover identifies two flaws with some of the functional correctness and authentication properties in these schemes. We then propose Obscure Token (OTOKEN), an extension of REWIRE to enable revocation in a privacy preserving manner. Our approach addresses the functional and authentication properties by introducing an additional key-pair, which offers a stronger and verifiable guarantee of successful revocation of vehicles without resolving the long-term identity. Moreover OTOKEN is the first V2X revocation protocol to be co-designed with a formal model.Comment: 16 pages, 4 figure

Nonflammable, antistatic, and heat-sealable film

Antistatic, heat-sealable, nonflammable films prepared from polyvinylidene fluoride and polyvinylidene chloride resin

Network Inference via the Time-Varying Graphical Lasso

Many important problems can be modeled as a system of interconnected entities, where each entity is recording time-dependent observations or measurements. In order to spot trends, detect anomalies, and interpret the temporal dynamics of such data, it is essential to understand the relationships between the different entities and how these relationships evolve over time. In this paper, we introduce the time-varying graphical lasso (TVGL), a method of inferring time-varying networks from raw time series data. We cast the problem in terms of estimating a sparse time-varying inverse covariance matrix, which reveals a dynamic network of interdependencies between the entities. Since dynamic network inference is a computationally expensive task, we derive a scalable message-passing algorithm based on the Alternating Direction Method of Multipliers (ADMM) to solve this problem in an efficient way. We also discuss several extensions, including a streaming algorithm to update the model and incorporate new observations in real time. Finally, we evaluate our TVGL algorithm on both real and synthetic datasets, obtaining interpretable results and outperforming state-of-the-art baselines in terms of both accuracy and scalability

Statistical properties of Klauder-Perelomov coherent states for the Morse potential

We present in this paper a realistic construction of the coherent states for the Morse potential using the Klauder-Perelomov approach . We discuss the statistical properties of these states, by deducing the Q- and P-distribution functions. The thermal expectations for the quantum canonical ideal gas of the Morse oscillators are also calculated

USDA Farm Programs: North Dakota Farmer Participation and Opinions

Agricultural and Food Policy,