Income effects from labor market training programs in Sweden during the 80's and 90's

Swedish labor market programs appear large from an international perspective, yet their consequences are not fully investigated and understood. In this paper we estimate a switching regression model with training effect modeled as a random coefficient, partitioned in an observed and unobserved component. We investigate labor market training for three cohorts during the 80s and the beginning of the 90s on its effect on earnings. We separate the analysis between Swedish-born and foreign-born individuals to identify differences in their responses to training. The results indicate that there is positive sorting into training. We find that the proportion of trainees having positive rewards from training was not very different from the proportion having negative rewards. This means that the results do not support the view that from efficiency considerations, too few persons were enrolled in labor market training during this period. Differences in results across cohorts can be interpreted as being caused by rapid changes in the labor market. Further, consistent with results from several previous studies we find that being young often means no positive pay-off from training, and the same is found for persons with only primary education. In conflict with what earlier studies have shown, we found that males have a better pay-off from training than females. Rewards from training were higher for foreign-born than for natives and rewards among the former vary by place of birth.Labor market training; non-experimental estimator; positive sorting; unobserved heterogeneity to training reward; random coefficient model

Habitual Criminal Statutes: Shield or Sword

An essential part of future collision avoidance systems is to be able to predict road curvature. This can be based on vision data, but the lateral movement of leading vehicles can also be used to support road geometry estimation. This paper presents a method for detecting lane departures, including lane changes, of leading vehicles. This information is used to adapt the dynamic models used in the estimation algorithm in order to accommodate for the fact that a lane departure is in progress. The goal is to improve the accuracy of the road geometry estimates, which is affected by the motion of leading vehicles. The significantly improved performance is demonstrated using sensor data from authentic traffic environments

New Search for Monochromatic Neutrinos from Dark Matter Decay

From data recently reported from the IceCube telescope, we derive new bounds on the monochromatic neutrino signal produced from dark matter particle decays. In the few TeV to tens of TeV energy range, these bounds turn out to be better than previous limits by more than an order of magnitude. As a result, intensity constraints on neutrino lines at energies above a few TeV are now comparable to those on gamma-ray lines. From the same data sample, we also perform a detailed search for a neutrino line, showing that there is no significant hint for such a signal.Comment: 12 pages, 13 figures. Tabulated results attached among the source files (Appendix_D_Table.dat

Legal Statutes of Arab Refugees

The recently developed particle filter offers a general numerical tool to approximate the state a posteriori density in nonlinear and non-Gaussian filtering problems with arbitrary accuracy. Because the particle filter is fairly easy to implement and tune, it has quickly become a popular tool in signal processing applications. Its main drawback is that it is quite computer intensive. For a given filtering accuracy, the computational complexity increases quickly with the state dimension. One remedy to this problem is what in statistics is called Rao-Blackwellization, where states appearing linearly in the dynamics are marginalized out. This leads to that a Kalman filter is attached to each particle. Our main contribution here is to sort out when marginalization is possible for state space models, and to point out the implications in some typical signal processing applications. The methodology and impact in practice is illustrated on terrain navigation for aircrafts. The marginalized particle filter for a state-space model with nine states is evaluated on real aircraft data, and the result is that very good accuracy is achieved with quite reasonable complexity

The Abandoned Side of the Internet: Hijacking Internet Resources When Domain Names Expire

The vulnerability of the Internet has been demonstrated by prominent IP prefix hijacking events. Major outages such as the China Telecom incident in 2010 stimulate speculations about malicious intentions behind such anomalies. Surprisingly, almost all discussions in the current literature assume that hijacking incidents are enabled by the lack of security mechanisms in the inter-domain routing protocol BGP. In this paper, we discuss an attacker model that accounts for the hijacking of network ownership information stored in Regional Internet Registry (RIR) databases. We show that such threats emerge from abandoned Internet resources (e.g., IP address blocks, AS numbers). When DNS names expire, attackers gain the opportunity to take resource ownership by re-registering domain names that are referenced by corresponding RIR database objects. We argue that this kind of attack is more attractive than conventional hijacking, since the attacker can act in full anonymity on behalf of a victim. Despite corresponding incidents have been observed in the past, current detection techniques are not qualified to deal with these attacks. We show that they are feasible with very little effort, and analyze the risk potential of abandoned Internet resources for the European service region: our findings reveal that currently 73 /24 IP prefixes and 7 ASes are vulnerable to be stealthily abused. We discuss countermeasures and outline research directions towards preventive solutions.Comment: Final version for TMA 201

The Paradox of Free Market Democracy: Rethinking Development Policy

The Kalman filter computes the maximum a posteriori (MAP) estimate of the states for linear state space models with Gaussian noise. We interpret the Kalman filter as the solution to a convex optimization problem, and show that we can generalize the MAP state estimator to any noise with a log-concave density function and any combination of linear equality and convex inequality constraints on the states. We illustrate the principle on a hidden Markov model, where the state vector contains probabilities that are positive and sum to one

Possible methods for the determination of the PP-parity of the Θ+\Theta^+-pentaquark in NN-collisions

We present two possibilities to determine the P-parity of the pentaquark $\Theta^+$, in a model independent way, via the measurement of polarization observables in $p+p\to \Theta^+ +\Sigma^+$, or $n+p\to \Theta^+ +\Lambda^0$, in the near threshold region. Besides the measurement of the spin correlation coefficient, $A_{xx}=A_{yy}$, (in collisions of transversally polarized nucleons), the coefficient $D_{xx}$ of polarization transfer from the initial proton to the final $\Sigma^+(\Lambda^0)$ hyperon is also unambiguously related to the $\Theta^+$ parity.Comment: 7 pages, 1 figur

Propagating phonons coupled to an artificial atom

Quantum information can be stored in micromechanical resonators, encoded as quanta of vibration known as phonons. The vibrational motion is then restricted to the stationary eigenmodes of the resonator, which thus serves as local storage for phonons. In contrast, we couple propagating phonons to an artificial atom in the quantum regime, and reproduce findings from quantum optics with sound taking over the role of light. Our results highlight the similarities between phonons and photons, but also point to new opportunities arising from the unique features of quantum mechanical sound. The low propagation speed of phonons should enable new dynamic schemes for processing quantum information, and the short wavelength allows regimes of atomic physics to be explored which cannot be reached in photonic systems.Comment: 30 pages, 6 figures, 1 tabl

Accurate 3D Object Detection using Energy-Based Models

Accurate 3D object detection (3DOD) is crucial for safe navigation of complex environments by autonomous robots. Regressing accurate 3D bounding boxes in cluttered environments based on sparse LiDAR data is however a highly challenging problem. We address this task by exploring recent advances in conditional energy-based models (EBMs) for probabilistic regression. While methods employing EBMs for regression have demonstrated impressive performance on 2D object detection in images, these techniques are not directly applicable to 3D bounding boxes. In this work, we therefore design a differentiable pooling operator for 3D bounding boxes, serving as the core module of our EBM network. We further integrate this general approach into the state-of-the-art 3D object detector SA-SSD. On the KITTI dataset, our proposed approach consistently outperforms the SA-SSD baseline across all 3DOD metrics, demonstrating the potential of EBM-based regression for highly accurate 3DOD. Code is available at https://github.com/fregu856/ebms_3dod.Comment: Code is available at https://github.com/fregu856/ebms_3do