Impossibility of Growing Quantum Bit Commitments

Quantum key distribution (QKD) is often, more correctly, called key growing. Given a short key as a seed, QKD enables two parties, connected by an insecure quantum channel, to generate a secret key of arbitrary length. Conversely, no key agreement is possible without access to an initial key. Here, we consider another fundamental cryptographic task, commitments. While, similar to key agreement, commitments cannot be realized from scratch, we ask whether they may be grown. That is, given the ability to commit to a fixed number of bits, is there a way to augment this to commitments to strings of arbitrary length? Using recently developed information-theoretic techniques, we answer this question to the negative.Comment: 10 pages, minor change

Rapid pre-gel visualization of proteins with mass spectrometry compatibility

Despite all of the prophecies of doom, gel electrophoresis is still prevalent in modern proteomic workflows. However, the currently used protein staining methods represent a serious bottleneck for a quick subsequent protein analysis using mass spectrometry. Substituting traditional protein stains by pre-gel derivatization with visible and mass spectrometry compatible reagents eliminates several processing steps and drastically reduces the sample preparation time. A defined chemistry permits seamless integration of such covalent protein staining methods into standardized bioinformatic pipelines. Using Uniblue A we could covalently stain simple to complex protein samples within 1 minute. Protein profiles on the gels were not compromised and MS/MS based sequence coverages higher than 80% could be obtained. In addition, the visual tracking of covalently stained proteins and peptides facilitates method development and validation. Altogether, this new chemo-proteomic approach enables true "at-line" analysis of proteins

Soft sensor development for real-time process monitoring of multidimensional fractionation in tubular centrifuges

High centrifugal acceleration and throughput rates of tubular centrifuges enable the solid–liquid size separation and fractionation of nanoparticles on a bench scale. Nowadays, advantageous product properties are defined by precise specifications regarding particle size and material composition. Hence, there is a demand for innovative and efficient downstream processing of complex particle suspensions. With this type of centrifuge working in a semi-continuous mode, an online observation of the separation quality is needed for optimization purposes. To analyze the composition of fines downstream of the centrifuge, a UV/vis soft sensor is developed to monitor the sorting of polymer and metal oxide nanoparticles by their size and density. By spectroscopic multi-component analysis, a measured UV/vis signal is translated into a model based prediction of the relative solids volume fraction of the fines. High signal stability and an adaptive but mandatory calibration routine enable the presented setup to accurately predict the product’s composition at variable operating conditions. It is outlined how this software-based UV/vis sensor can be utilized effectively for challenging real-time process analytics in multi-component suspension processing. The setup provides insight into the underlying process dynamics and assists in optimizing the outcome of separation tasks on the nanoscale

Controlling a Vacuum Suction Cup Cluster using Simulation-Trained Reinforcement Learning Agents

Using compressed air in industrial processes is often accompanied by a poor cost-benefit ratio and a negative impact on the environmental footprint due to usual distribution inefficiencies. Compressed air-based systems are expensive regarding installation and lead to high running costs due to pricey maintenance requirements and low energy efficiency due to leakage. However, compressed air-based systems are indispensable for various industrial processes, like handling parts with Class A surface requirements such as outer skin sheets in automobile production. Most of those outer skin parts are solely handled by vacuum-based grippers to minimize any visible effect on the finished car. Fulfilling customer expectations and simultaneously reducing the running costs of decisive systems requires finding innovative strategies focused on using the precious resource of compressed air as efficiently as possible. This work presents a sim2real reinforcement learning approach to efficiently hold a workpiece attached to a vacuum suction cup cluster. In addition to pure energy-saving, reinforcement learning enables those agents to be trained without collecting extensive data beforehand. Furthermore, the sim2real approach makes it easy and parallelizable to examine numerous agents by training them in a simulation of the testing rig rather than at the testing rig itself. The possibility to train various agents fast additionally facilitates focusing on the robustness and simplicity of the found agents instead of only searching for strategies that work, making training an intelligent system scalable and effective. The resulting agents reduce the amount of energy necessary to hold the workpiece attached by more than 15% compared to a reference strategy without machine learning and by more than 99% compared to a conventional strategy