From Reports to Maps

In this paper, we will sketch a project in progress. The project aims at an application of a command and control system. The application is meant to process military reports written in natural language. It exploits computer linguistic techniques, especially information extraction and ontological augmentation. A prototype has already be completed. A real world application of report processing has to go beyond pure syntactic parsing. Semantic analysis is needed and the meaning of the report has to be constructed. Even more, the meaning has to be represented in a format such that it can be visualized within the so called ``common operational picture'' (COP). The COP is an interactive map displaying information. COP standards are provided by NATO. Since military operations of our days -- war operations as well as peace-keeping and nation building ones -- involve forces of many nations, the COP serves as main tool for synchronizing actions and plans. The paper at hand will provide some insights what kind of problems come along if language processing has to result in map visualization. It also will describe some solutions to overcome these problems

Internet Surveys by Direct Mailing: An Innovative Way of Collecting Data

This article describes a new method of collecting data by direct mailing via the Internet. Feasibility and capacities were evaluated through a worldwide opinion poll on global future risks of mankind and potential solutions. Within 1 day, a structured questionnaire was sent to 8,859 randomly selected e-mail addresses. One thousand seven hundred and thirteen were remailed properly completed, 90 within 4 days. Most respondents were residents of North America (64) and Europe (21 ), male (87), and 30 years old on average. Environmental destruction (52) was mentioned as the primary problem, followed by violence (45) and unemployment (45). Education (71 ) was the most frequently proposed solution to future problems. It is obvious that Internet surveys at this time are not repre sentative of the total population. However, they open new dimensions in the interrogation of experts and opinion leaders, especially considering their efficiency and potential for automation

Arbeiten zur enantioselektiven Totalsynthese von Hyperforin

Diese Dissertation beschäftigt sich mit der enantioselektiven Synthese von Hyperforin, einem Naturstoff aus der Familie der polycyclischen polyprenylierten Acylphloroglucinen (PPAPs). Aufgrund der anspruchsvollenen Strukturen und der vielfältigen pharmakologischen Eigenschaften, wurde diese Substanzfamilie in den letzten Jahrzehnten zum herausfordernden Syntheseziel vieler Arbeitsgruppen. Wie bereits in Vorarbeiten unserer Arbeitsgruppe befasst sich diese Dissertation mit einer transannularen Acylierung als Schlüsselschritt zum Aufbau des bicyclischen Grundgerüstes der PPAPs. Die Chemie der Achtring-Verbindungen wird genauer untersucht und wichtige Erkenntnisse hierzu getroffen, die in weiterführenden Arbeiten noch nützlich werden können. Im Zusammenhang mit der entwickelten enantioselektiven Formalsynthese von Hyperforin werden tiefblickende Eindrücke in den Reaktionsmechanismus der asymmetrischen allylischen Alkylierung erhalten, die auf die elektronischen Eigenschaften der verwendeten Liganden zurückzuführen sind. Des Weiteren wurde im Rahmen dieser Arbeit eine von der Baylis-Hillman-Reaktion abgeleitete Lithiumphenylselenid-induzierte Alkoxycarbonylierung entwickelt. α,β- ungesättigte β-Ketoester können somit in einer einstufigen Synthese aus α,β- ungesättigten Ketonen aufgebaut werden.This PhD-thesis deals with the enantioselective synthesis of hyperforin, a natural product of the family of polycyclic polyprenylated acylphloroglucines (PPAPs). Due to its challenging structures and its manifold pharmacological properties, this substance family has become a challenging synthesis target for many research groups in the last decades. As in the previous work of our group, this dissertation deals with transannular acylation as a key step in building the bicyclic backbone of PPAPs. The chemistry of the eight-ring compounds will be investigated in more detail and important findings will be made in this respect, which may be useful in further work. In connection with the developed enantioselective formal synthesis of hyperforin, deep insights into the reaction mechanism of asymmetric allylic alkylation will be obtained, which are due to the electronic properties of the ligands used. In addition, a lithium phenyl selenide-induced alkoxycarbonylation derived from the Baylis-Hillman reaction was developed in this work. α,β-unsaturated β ketoesters can thus be built up in a one-step synthesis from α,β-unsaturated ketones

Immunomodulatory properties and molecular effects in inflammatory diseases of low-dose X-irradiation

Inflammatory diseases are the result of complex and pathologically unbalanced multicellular interactions. For decades, low-dose X-irradiation therapy (LD-RT) has been clinically documented to exert an anti-inflammatory effect on benign diseases and chronic degenerative disorders. By contrast, experimental studies to confirm the effectiveness and to reveal underlying cellular and molecular mechanisms are still at their early stages. During the last decade, however, the modulation of a multitude of immunological processes by LD-RT has been explored in vitro and in vivo. These include leukocyte/endothelial cell adhesion, adhesion molecule and cytokine/chemokine expression, apoptosis induction, and mononuclear/polymorphonuclear cell metabolism and activity. Interestingly, these mechanisms display comparable dose dependences and dose-effect relationships with a maximum effect in the range between 0.3 and 0.7 Gy, already empirically identified to be most effective in the clinical routine. This review summarizes data and models exploring the mechanisms underlying the immunomodulatory properties of LD-RT that may serve as a prerequisite for further systematic analyses to optimize low-dose irradiation procedures in future clinical practice

BioGAP: a 10-Core FP-capable Ultra-Low Power IoT Processor, with Medical-Grade AFE and BLE Connectivity for Wearable Biosignal Processing

Wearable biosignal processing applications are driving significant progress toward miniaturized, energy-efficient Internet-of-Things solutions for both clinical and consumer applications. However, scaling toward high-density multi-channel front-ends is only feasible by performing data processing and machine Learning (ML) near-sensor through energy-efficient edge processing. To tackle these challenges, we introduce BioGAP, a novel, compact, modular, and lightweight (6g) medical-grade biosignal acquisition and processing platform powered by GAP9, a ten-core ultra-low-power SoC designed for efficient multi-precision (from FP to aggressively quantized integer) processing, as required for advanced ML and DSP. BioGAPs form factor is 16x21x14 mm$^3$ and comprises two stacked PCBs: a baseboard integrating the GAP9 SoC, a wireless Bluetooth Low Energy (BLE) capable SoC, a power management circuit, and an accelerometer; and a shield including an analog front-end (AFE) for ExG acquisition. Finally, the system also includes a flexibly placeable photoplethysmogram (PPG) PCB with a size of 9x7x3 mm$^3$ and a rechargeable battery ($\phi$ 12x5 mm$^2$). We demonstrate BioGAP on a Steady State Visually Evoked Potential (SSVEP)-based Brain-Computer Interface (BCI) application. We achieve 3.6 uJ/sample in streaming and 2.2 uJ/sample in onboard processing mode, thanks to an efficiency on the FFT computation task of 16.7 Mflops/s/mW with wireless bandwidth reduction of 97%, within a power budget of just 18.2 mW allowing for an operation time of 15 h.Comment: 7 pages, 9 figures, 1 table, accepted for IEEE COINS 202

Versatile Skill Control via Self-supervised Adversarial Imitation of Unlabeled Mixed Motions

Learning diverse skills is one of the main challenges in robotics. To this end, imitation learning approaches have achieved impressive results. These methods require explicitly labeled datasets or assume consistent skill execution to enable learning and active control of individual behaviors, which limits their applicability. In this work, we propose a cooperative adversarial method for obtaining single versatile policies with controllable skill sets from unlabeled datasets containing diverse state transition patterns by maximizing their discriminability. Moreover, we show that by utilizing unsupervised skill discovery in the generative adversarial imitation learning framework, novel and useful skills emerge with successful task fulfillment. Finally, the obtained versatile policies are tested on an agile quadruped robot called Solo 8 and present faithful replications of diverse skills encoded in the demonstrations

Learning Agile Skills via Adversarial Imitation of Rough Partial Demonstrations

Learning agile skills is one of the main challenges in robotics. To this end, reinforcement learning approaches have achieved impressive results. These methods require explicit task information in terms of a reward function or an expert that can be queried in simulation to provide a target control output, which limits their applicability. In this work, we propose a generative adversarial method for inferring reward functions from partial and potentially physically incompatible demonstrations for successful skill acquirement where reference or expert demonstrations are not easily accessible. Moreover, we show that by using a Wasserstein GAN formulation and transitions from demonstrations with rough and partial information as input, we are able to extract policies that are robust and capable of imitating demonstrated behaviors. Finally, the obtained skills such as a backflip are tested on an agile quadruped robot called Solo 8 and present faithful replication of hand-held human demonstrations

Diffusion of Macromolecules across the Nuclear Pore Complex

Nuclear pore complexes (NPCs) are very selective filters that monitor the transport between the cytoplasm and the nucleoplasm. Two models have been suggested for the plug of the NPC. They are (i) it is a reversible hydrogel or (ii) it is a polymer brush. We propose a mesoscopic model for the transport of a protein through the plug, that is general enough to cover both. The protein stretches the plug and creates a local deformation. The bubble so created (prtoein+deformation) executes random walk in the plug. We find that for faster relaxation of the gel, the diffusion of the bubble is greater. Further, on using parameters appropriate for the brush, we find that the diffusion coefficient is much lower. Hence the gel model seems to be more likely explanation for the workings of the plug

A Wearable Ultra-Low-Power sEMG-Triggered Ultrasound System for Long-Term Muscle Activity Monitoring

Surface electromyography (sEMG) is a well-established approach to monitor muscular activity on wearable and resource-constrained devices. However, when measuring deeper muscles, its low signal-to-noise ratio (SNR), high signal attenuation, and crosstalk degrade sensing performance. Ultrasound (US) complements sEMG effectively with its higher SNR at high penetration depths. In fact, combining US and sEMG improves the accuracy of muscle dynamic assessment, compared to using only one modality. However, the power envelope of US hardware is considerably higher than that of sEMG, thus inflating energy consumption and reducing the battery life. This work proposes a wearable solution that integrates both modalities and utilizes an EMG-driven wake-up approach to achieve ultra-low power consumption as needed for wearable long-term monitoring. We integrate two wearable state-of-the-art (SoA) US and ExG biosignal acquisition devices to acquire time-synchronized measurements of the short head of the biceps. To minimize power consumption, the US probe is kept in a sleep state when there is no muscle activity. sEMG data are processed on the probe (filtering, envelope extraction and thresholding) to identify muscle activity and generate a trigger to wake-up the US counterpart. The US acquisition starts before muscle fascicles displacement thanks to a triggering time faster than the electromechanical delay (30-100 ms) between the neuromuscular junction stimulation and the muscle contraction. Assuming a muscle contraction of 200 ms at a contraction rate of 1 Hz, the proposed approach enables more than 59% energy saving (with a full-system average power consumption of 12.2 mW) as compared to operating both sEMG and US continuously.Comment: 4 pages, 5 figures, 1 table, 2023 IEEE International Ultrasonics Symposiu