Do German Welfare-to-Work Programmes Reduce Welfare and Increase Work?

Many Western economies have reformed their welfare systems with the aim of activating welfare recipients by increasing welfare-to-work programmes and job search enforcement. We evaluate the three most important German welfare-to-work programmes implemented after a major reform in January 2005 ("Hartz IV"). Our analysis is based on a unique combination of large scale survey and administrative data that is unusually rich with respect to individual, household, agency level, and regional information. We use this richness to allow for a selection-on-observables approach when doing the econometric evaluation. We find that short-term training programmes on average increase their participants' employment perspectives and that all programmes induce further programme participation. We also show that there is considerable effect heterogeneity across different subgroups of participants that could be exploited to improve the allocation of welfare recipients to the specific programmes and thus increase overall programme effectivenessWelfare-to-work policies, propensity score matching, programme evaluation, panel data, targeting

Strategy for developing a future support system for the Vasa warship and evaluating its mechanical properties

To maintain the integrity of the Vasa warship after salvage in 1961, conservation treatment with polyethylene glycol was carried out to prevent the collapse of cell walls. This treatment had negative effects on the strength and stiffness of the oak and the hull structure has been found to slowly deform over time. It is of interest to construct a three-dimensional numerical computer model to model and predict the deformation of the warship. This creates difficulties related to the complexity of measuring the detailed material properties that are required as input. In this context, a non-destructive methodology to predict the stiffness of the Vasa oak in terms of moduli of elasticity in the three principal directions of timber at critical positions in the ship would be useful. The twofold aim of the paper is to propose a strategy for a support system and to conduct an on-site assessment of the warship to predict the mechanical properties of the Vasa oak material. This paper also contains an up-to-date review of all essential mechanical data measured on the Vasa oak. The preliminary investigation using an X-ray technique to investigate the density properties produced promising results for future use in the evaluation of the mechanical performance. Based on these results, a procedure to establish the stiffness properties of the Vasa oak in terms of MOE was suggested, using a combination of data from previous measurements, in combination with extended tests on Vasa oak specimens and an X-ray-based density calibration procedure. The general complexity of the Vasa warship can be mainly attributed to large variations in the properties of Vasa oak due to surface degradation, chemical treatment and the disintegration of the cell-wall structure originating from centuries of waterlogged conditions. That causes difficulties when assessing mechanical and physical properties on a structural level. A combination of visual inspection together with X-ray investigation is of great importance to evaluate those properties and to obtain more accurate estimates. The results from evaluating mechanical properties can serve as input in a numerical model and serve as a foundation for decision-making relating to the modification of the support system

LNCS

Quantization converts neural networks into low-bit fixed-point computations which can be carried out by efficient integer-only hardware, and is standard practice for the deployment of neural networks on real-time embedded devices. However, like their real-numbered counterpart, quantized networks are not immune to malicious misclassification caused by adversarial attacks. We investigate how quantization affects a network’s robustness to adversarial attacks, which is a formal verification question. We show that neither robustness nor non-robustness are monotonic with changing the number of bits for the representation and, also, neither are preserved by quantization from a real-numbered network. For this reason, we introduce a verification method for quantized neural networks which, using SMT solving over bit-vectors, accounts for their exact, bit-precise semantics. We built a tool and analyzed the effect of quantization on a classifier for the MNIST dataset. We demonstrate that, compared to our method, existing methods for the analysis of real-numbered networks often derive false conclusions about their quantizations, both when determining robustness and when detecting attacks, and that existing methods for quantized networks often miss attacks. Furthermore, we applied our method beyond robustness, showing how the number of bits in quantization enlarges the gender bias of a predictor for students’ grades

Learning Control Policies for Stochastic Systems with Reach-avoid Guarantees

We study the problem of learning controllers for discrete-time non-linear stochastic dynamical systems with formal reach-avoid guarantees. This work presents the first method for providing formal reach-avoid guarantees, which combine and generalize stability and safety guarantees, with a tolerable probability threshold $p\in[0,1]$ over the infinite time horizon. Our method leverages advances in machine learning literature and it represents formal certificates as neural networks. In particular, we learn a certificate in the form of a reach-avoid supermartingale (RASM), a novel notion that we introduce in this work. Our RASMs provide reachability and avoidance guarantees by imposing constraints on what can be viewed as a stochastic extension of level sets of Lyapunov functions for deterministic systems. Our approach solves several important problems -- it can be used to learn a control policy from scratch, to verify a reach-avoid specification for a fixed control policy, or to fine-tune a pre-trained policy if it does not satisfy the reach-avoid specification. We validate our approach on $3$ stochastic non-linear reinforcement learning tasks.Comment: Accepted at AAAI 202

Synthesis and Properties of Bis(nitrocarbamoylethyl) Nitramine - A New Energetic Open-Chain Nitrocarbamate

The nitrocarbamate derivative of the well-known and intensively investigated nitro ester DINA was prepared and studied. Starting with bis(hydroxyethyl) nitramine obtained from DINA, the corresponding carbamate was obtained by treatment with chlorosulfonyl isocyanate (CSI). Using fuming nitric acid only as nitration reagent, the target compound bis(nitrocarbamoylethyl) nitramine was synthesized. Furthermore, a route to the salt bis(nitrocarbamoylethyl)ammonium nitrate by a simple two step synthesis starting from diethanolamine was revealed. The compounds were fully characterized by NMR spectroscopy, X-ray diffraction, differential thermal analysis, vibrational analysis and elemental analysis. The sensitivities towards impact and friction of the energetic compounds were measured, as well as their energetic properties determined by using the energies of formation, calculated on the CBS4-M level of theory, with the EXPLO5 computer code