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Institute
- Institut für Informatik und Computational Science (839) (remove)
We present a method employing Answer Set Programming in combination with Approximate Model Counting for fast and accurate calculation of error propagation probabilities in digital circuits. By an efficient problem encoding, we achieve an input data format similar to a Verilog netlist so that extensive preprocessing is avoided. By a tight interconnection of our application with the underlying solver, we avoid iterating over fault sites and reduce calls to the solver. Several circuits were analyzed with varying numbers of considered cycles and different degrees of approximation. Our experiments show, that the runtime can be reduced by approximation by a factor of 91, whereas the error compared to the exact result is below 1%.
Instruments for measuring the absorbed dose and dose rate under radiation exposure, known as radiation dosimeters, are indispensable in space missions. They are composed of radiation sensors that generate current or voltage response when exposed to ionizing radiation, and processing electronics for computing the absorbed dose and dose rate. Among a wide range of existing radiation sensors, the Radiation Sensitive Field Effect Transistors (RADFETs) have unique advantages for absorbed dose measurement, and a proven record of successful exploitation in space missions. It has been shown that the RADFETs may be also used for the dose rate monitoring. In that regard, we propose a unique design concept that supports the simultaneous operation of a single RADFET as absorbed dose and dose rate monitor. This enables to reduce the cost of implementation, since the need for other types of radiation sensors can be minimized or eliminated. For processing the RADFET's response we propose a readout system composed of analog signal conditioner (ASC) and a self-adaptive multiprocessing system-on-chip (MPSoC). The soft error rate of MPSoC is monitored in real time with embedded sensors, allowing the autonomous switching between three operating modes (high-performance, de-stress and fault-tolerant), according to the application requirements and radiation conditions.
The radiation-sensitive field-effect transistors (RADFETs) with an oxide thickness of 400 nm are irradiated with gate voltages of 2, 4 and 6 V, and without gate voltage.
A detailed analysis of the mechanisms responsible for the creation of traps during irradiation is performed.
The creation of the traps in the oxide, near and at the silicon/silicon-dioxide (Si/SiO2) interface during irradiation is modelled very well. This modelling can also be used for other MOS transistors containing SiO2.
The behaviour of radiation traps during postirradiation annealing is analysed, and the corresponding functions for their modelling are obtained. The switching traps (STs) do not have significant influence on threshold voltage shift, and two radiation-induced trap types fit the fixed traps (FTs) very well. The fading does not depend on the positive gate voltage applied during irradiation, but it is twice lower in case there is no gate voltage.
A new dosimetric parameter, called the Golden Ratio (GR), is proposed, which represents the ratio between the threshold voltage shift after irradiation and fading after spontaneous annealing. This parameter can be useful for comparing MOS dosimeters.
In the last decades, there was a notable progress in solving the well-known Boolean satisfiability (Sat) problem, which can be witnessed by powerful Sat solvers. One of the reasons why these solvers are so fast are structural properties of instances that are utilized by the solver’s interna. This thesis deals with the well-studied structural property treewidth, which measures the closeness of an instance to being a tree. In fact, there are many problems parameterized by treewidth that are solvable in polynomial time in the instance size when parameterized by treewidth.
In this work, we study advanced treewidth-based methods and tools for problems in knowledge representation and reasoning (KR). Thereby, we provide means to establish precise runtime results (upper bounds) for canonical problems relevant to KR. Then, we present a new type of problem reduction, which we call decomposition-guided (DG) that
allows us to precisely monitor the treewidth when reducing from one problem to another problem. This new reduction type will be the basis for a long-open lower bound result for quantified Boolean formulas and allows us to design a new methodology for establishing runtime lower bounds for problems parameterized by treewidth.
Finally, despite these lower bounds, we provide an efficient implementation of algorithms that adhere to treewidth. Our approach finds suitable abstractions of instances, which are subsequently refined in a recursive fashion, and it uses Sat solvers for solving subproblems. It turns out that our resulting solver is quite competitive for two canonical counting problems related to Sat.
We study the concept of reversibility in connection with parallel communicating systems of finite automata (PCFA in short). We define the notion of reversibility in the case of PCFA (also covering the non-deterministic case) and discuss the relationship of the reversibility of the systems and the reversibility of its components. We show that a system can be reversible with non-reversible components, and the other way around, the reversibility of the components does not necessarily imply the reversibility of the system as a whole. We also investigate the computational power of deterministic centralized reversible PCFA. We show that these very simple types of PCFA (returning or non-returning) can recognize regular languages which cannot be accepted by reversible (deterministic) finite automata, and that they can even accept languages that are not context-free. We also separate the deterministic and non-deterministic variants in the case of systems with non-returning communication. We show that there are languages accepted by non-deterministic centralized PCFA, which cannot be recognized by any deterministic variant of the same type.
We introduce a new measure of descriptional complexity on finite automata, called the number of active states. Roughly speaking, the number of active states of an automaton A on input w counts the number of different states visited during the most economic computation of the automaton A for the word w. This concept generalizes to finite automata and regular languages in a straightforward way. We show that the number of active states of both finite automata and regular languages is computable, even with respect to nondeterministic finite automata. We further compare the number of active states to related measures for regular languages. In particular, we show incomparability to the radius of regular languages and that the difference between the number of active states and the total number of states needed in finite automata for a regular language can be of exponential order.
Image feature detection is a key task in computer vision. Scale Invariant Feature Transform (SIFT) is a prevalent and well known algorithm for robust feature detection. However, it is computationally demanding and software implementations are not applicable for real-time performance. In this paper, a versatile and pipelined hardware implementation is proposed, that is capable of computing keypoints and rotation invariant descriptors on-chip. All computations are performed in single precision floating-point format which makes it possible to implement the original algorithm with little alteration. Various rotation resolutions and filter kernel sizes are supported for images of any resolution up to ultra-high definition. For full high definition images, 84 fps can be processed. Ultra high definition images can be processed at 21 fps.
The Internet of Things (IoT) is a system of physical objects that can be discovered, monitored, controlled, or interacted with by electronic devices that communicate over various networking interfaces and eventually can be connected to the wider Internet. [Guinard and Trifa, 2016]. IoT devices are equipped with sensors and/or actuators and may be constrained in terms of memory, computational power, network bandwidth, and energy. Interoperability can help to manage such heterogeneous devices. Interoperability is the ability of different types of systems to work together smoothly. There are four levels of interoperability: physical, network and transport, integration, and data. The data interoperability is subdivided into syntactic and semantic data. Semantic data describes the meaning of data and the common understanding of vocabulary e.g. with the help of dictionaries, taxonomies, ontologies. To achieve interoperability, semantic interoperability is necessary.
Many organizations and companies are working on standards and solutions for interoperability in the IoT. However, the commercial solutions produce a vendor lock-in. They focus on centralized approaches such as cloud-based solutions. This thesis proposes a decentralized approach namely Edge Computing. Edge Computing is based on the concepts of mesh networking and distributed processing. This approach has an advantage that information collection and processing are placed closer to the sources of this information. The goals are to reduce traffic, latency, and to be robust against a lossy or failed Internet connection.
We see management of IoT devices from the network configuration management perspective. This thesis proposes a framework for network configuration management of heterogeneous, constrained IoT devices by using semantic descriptions for interoperability. The MYNO framework is an acronym for MQTT, YANG, NETCONF and Ontology. The NETCONF protocol is the IETF standard for network configuration management. The MQTT protocol is the de-facto standard in the IoT. We picked up the idea of the NETCONF-MQTT bridge, originally proposed by Scheffler and Bonneß[2017], and extended it with semantic device descriptions. These device descriptions provide a description of the device capabilities. They are based on the oneM2M Base ontology and formalized by the Semantic Web Standards.
The novel approach is using a ontology-based device description directly on a constrained device in combination with the MQTT protocol. The bridge was extended in order to query such descriptions. Using a semantic annotation, we achieved that the device capabilities are self-descriptive, machine readable and re-usable.
The concept of a Virtual Device was introduced and implemented, based on semantic device descriptions. A Virtual Device aggregates the capabilities of all devices at the edge network and contributes therefore to the scalability. Thus, it is possible to control all devices via a single RPC call.
The model-driven NETCONF Web-Client is generated automatically from this YANG model which is generated by the bridge based on the semantic device description. The Web-Client provides a user-friendly interface, offers RPC calls and displays sensor values. We demonstrate the feasibility of this approach in different use cases: sensor and actuator scenarios, as well as event configuration and triggering.
The semantic approach results in increased memory overhead. Therefore, we evaluated CBOR and RDF HDT for optimization of ontology-based device descriptions for use on constrained devices. The evaluation shows that CBOR is not suitable for long strings and RDF HDT is a promising candidate but is still a W3C Member Submission. Finally, we used an optimized JSON-LD format for the syntax of the device descriptions.
One of the security tasks of network management is the distribution of firmware updates. The MYNO Update Protocol (MUP) was developed and evaluated on constrained devices CC2538dk and 6LoWPAN. The MYNO update process is focused on freshness and authenticity of the firmware. The evaluation shows that it is challenging but feasible to bring the firmware updates to constrained devices using MQTT. As a new requirement for the next MQTT version, we propose to add a slicing feature for the better support of constrained devices. The MQTT broker should slice data to the maximum packet size specified by the device and transfer it slice-by-slice.
For the performance and scalability evaluation of MYNO framework, we setup the High Precision Agriculture demonstrator with 10 ESP-32 NodeMCU boards at the edge of the network. The ESP-32 NodeMCU boards, connected by WLAN, were equipped with six sensors and two actuators. The performance evaluation shows that the processing of ontology-based descriptions on a Raspberry Pi 3B with the RDFLib is a challenging task regarding computational power. Nevertheless, it is feasible because it must be done only once per device during the discovery process.
The MYNO framework was tested with heterogeneous devices such as CC2538dk from Texas Instruments, Arduino Yún Rev 3, and ESP-32 NodeMCU, and IP-based networks such as 6LoWPAN and WLAN.
Summarizing, with the MYNO framework we could show that the semantic approach on constrained devices is feasible in the IoT.
The business problem of having inefficient processes, imprecise process analyses, and simulations as well as non-transparent artificial neuronal network models can be overcome by an easy-to-use modeling concept. With the aim of developing a flexible and efficient approach to modeling, simulating, and optimizing processes, this paper proposes a flexible Concept of Neuronal Modeling (CoNM). The modeling concept, which is described by the modeling language designed and its mathematical formulation and is connected to a technical substantiation, is based on a collection of novel sub-artifacts. As these have been implemented as a computational model, the set of CoNM tools carries out novel kinds of Neuronal Process Modeling (NPM), Neuronal Process Simulations (NPS), and Neuronal Process Optimizations (NPO). The efficacy of the designed artifacts was demonstrated rigorously by means of six experiments and a simulator of real industrial production processes.
Large-scale literature mining to assess the relation between anti-cancer drugs and cancer types
(2021)
Background:
There is a huge body of scientific literature describing the relation between tumor types and anti-cancer drugs. The vast amount of scientific literature makes it impossible for researchers and physicians to extract all relevant information manually.
Methods:
In order to cope with the large amount of literature we applied an automated text mining approach to assess the relations between 30 most frequent cancer types and 270 anti-cancer drugs. We applied two different approaches, a classical text mining based on named entity recognition and an AI-based approach employing word embeddings. The consistency of literature mining results was validated with 3 independent methods: first, using data from FDA approvals, second, using experimentally measured IC-50 cell line data and third, using clinical patient survival data.
Results:
We demonstrated that the automated text mining was able to successfully assess the relation between cancer types and anti-cancer drugs. All validation methods showed a good correspondence between the results from literature mining and independent confirmatory approaches. The relation between most frequent cancer types and drugs employed for their treatment were visualized in a large heatmap. All results are accessible in an interactive web-based knowledge base using the following link: .
Conclusions:
Our approach is able to assess the relations between compounds and cancer types in an automated manner. Both, cancer types and compounds could be grouped into different clusters. Researchers can use the interactive knowledge base to inspect the presented results and follow their own research questions, for example the identification of novel indication areas for known drugs.
Student teachers often struggle to keep track of everything that is happening in the classroom, and particularly to notice and respond when students cause disruptions. The complexity of the classroom environment is a potential contributing factor that has not been empirically tested. In this experimental study, we utilized a virtual reality (VR) classroom to examine whether classroom complexity affects the likelihood of student teachers noticing disruptions and how they react after noticing. Classroom complexity was operationalized as the number of disruptions and the existence of overlapping disruptions (multidimensionality) as well as the existence of parallel teaching tasks (simultaneity). Results showed that student teachers (n = 50) were less likely to notice the scripted disruptions, and also less likely to respond to the disruptions in a comprehensive and effortful manner when facing greater complexity. These results may have implications for both teacher training and the design of VR for training or research purpose. This study contributes to the field from two aspects: 1) it revealed how features of the classroom environment can affect student teachers' noticing of and reaction to disruptions; and 2) it extends the functionality of the VR environment-from a teacher training tool to a testbed of fundamental classroom processes that are difficult to manipulate in real-life.
Background:
Pruritus often accompanies chronic skin diseases, exerting considerable burden on many areas of patient functioning; this burden and the features of pruritus remain insufficiently characterized.
Objective:
To investigate characteristics, including localization patterns, and burden of pruritus in patients with chronic dermatoses.
Methods:
We recruited 800 patients with active chronic skin diseases. We assessed pruritus intensity, localization, and further characteristics. We used validated questionnaires to assess quality of life, work productivity and activity impairment, anxiety, depression, and sleep quality.
Results:
Nine out of every 10 patients had experienced pruritus throughout their disease and 73% in the last 7 days. Pruritus often affected the entire body and was not restricted to skin lesions. Patients with moderate to severe pruritus reported significantly more impairment to their sleep quality and work productivity, and they were more depressed and anxious than control individuals and patients with mild or no pruritus. Suicidal ideations were highly prevalent in patients with chronic pruritus (18.5%) and atopic dermatitis (11.8%).
Conclusions:
Pruritus prevalence and intensity are very high across all dermatoses studied; intensity is linked to impairment in many areas of daily functioning. Effective treatment strategies are urgently required to treat pruritus and the underlying skin disease. ( J Am Acad Dermatol 2021;84:691-700.)
Tropical cyclones range among the costliest disasters on Earth. Their economic repercussions along the supply and trade network also affect remote economies that are not directly affected. We here simulate possible global repercussions on consumption for the example case of Hurricane Sandy in the US (2012) using the shock-propagation model Acclimate. The modeled shock yields a global three-phase ripple: an initial production demand reduction and associated consumption price decrease, followed by a supply shortage with increasing prices, and finally a recovery phase. Regions with strong trade relations to the US experience strong magnitudes of the ripple. A dominating demand reduction or supply shortage leads to overall consumption gains or losses of a region, respectively. While finding these repercussions in historic data is challenging due to strong volatility of economic interactions, numerical models like ours can help to identify them by approaching the problem from an exploratory angle, isolating the effect of interest. For this, our model simulates the economic interactions of over 7000 regional economic sectors, interlinked through about 1.8 million trade relations. Under global warming, the wave-like structures of the economic response to major hurricanes like the one simulated here are likely to intensify and potentially overlap with other weather extremes.
Due to climate change the frequency and character of precipitation are changing as the hydrological cycle intensifies. With regards to snowfall, global warming has two opposing influences; increasing humidity enables intense snowfall, whereas higher temperatures decrease the likelihood of snowfall. Here we show an intensification of extreme snowfall across large areas of the Northern Hemisphere under future warming. This is robust across an ensemble of global climate models when they are bias-corrected with observational data. While mean daily snowfall decreases, both the 99th and the 99.9th percentiles of daily snowfall increase in many regions in the next decades, especially for Northern America and Asia. Additionally, the average intensity of snowfall events exceeding these percentiles as experienced historically increases in many regions. This is likely to pose a challenge to municipalities in mid to high latitudes. Overall, extreme snowfall events are likely to become an increasingly important impact of climate change in the next decades, even if they will become rarer, but not necessarily less intense, in the second half of the century.
Discriminative Models for Biometric Identification using Micro- and Macro-Movements of the Eyes
(2021)
Human visual perception is an active process. Eye movements either alternate between fixations and saccades or follow a smooth pursuit movement in case of moving targets. Besides these macroscopic gaze patterns, the eyes perform involuntary micro-movements during fixations which are commonly categorized into micro-saccades, drift and tremor. Eye movements are frequently studied in cognitive psychology, because they reflect a complex interplay of perception, attention and oculomotor control.
A common insight of psychological research is that macro-movements are highly individual. Inspired by this finding, there has been a considerable amount of prior research on oculomotoric biometric identification. However, the accuracy of known approaches is too low and the time needed for identification is too long for any practical application. This thesis explores discriminative models for the task of biometric identification.
Discriminative models optimize a quality measure of the predictions and are usually superior to generative approaches in discriminative tasks. However, using discriminative models requires to select a suitable form of data representation for sequential eye gaze data; i.e., by engineering features or constructing a sequence kernel and the performance of the classification model strongly depends on the data representation. We study two fundamentally different ways of representing eye gaze within a discriminative framework. In the first part of this thesis, we explore the integration of data and psychological background knowledge in the form of generative models to construct representations. To this end, we first develop generative statistical models of gaze behavior during reading and scene viewing that account for viewer-specific distributional properties of gaze patterns. In a second step, we develop a discriminative identification model by deriving Fisher kernel functions from these and several baseline models. We find that an SVM with Fisher kernel is able to reliably identify users based on their eye gaze during reading and scene viewing. However, since the generative models are constrained to use low-frequency macro-movements, they discard a significant amount of information contained in the raw eye tracking signal at a high cost: identification requires about one minute of input recording, which makes it inapplicable for real world biometric systems. In the second part of this thesis, we study a purely data-driven modeling approach. Here, we aim at automatically discovering the individual pattern hidden in the raw eye tracking signal. To this end, we develop a deep convolutional neural network DeepEyedentification that processes yaw and pitch gaze velocities and learns a representation end-to-end. Compared to prior work, this model increases the identification accuracy by one order of magnitude and the time to identification decreases to only seconds. The DeepEyedentificationLive model further improves upon the identification performance by processing binocular input and it also detects presentation-attacks.
We find that by learning a representation, the performance of oculomotoric identification and presentation-attack detection can be driven close to practical relevance for biometric applications. Eye tracking devices with high sampling frequency and precision are expensive and the applicability of eye movement as a biometric feature heavily depends on cost of recording devices.
In the last part of this thesis, we therefore study the requirements on data quality by evaluating the performance of the DeepEyedentificationLive network under reduced spatial and temporal resolution. We find that the method still attains a high identification accuracy at a temporal resolution of only 250 Hz and a precision of 0.03 degrees. Reducing both does not have an additive deteriorating effect.
Stripe rust (Pst) is a major disease of wheat crops leading untreated to severe yield losses. The use of fungicides is often essential to control Pst when sudden outbreaks are imminent. Sensors capable of detecting Pst in wheat crops could optimize the use of fungicides and improve disease monitoring in high-throughput field phenotyping. Now, deep learning provides new tools for image recognition and may pave the way for new camera based sensors that can identify symptoms in early stages of a disease outbreak within the field. The aim of this study was to teach an image classifier to detect Pst symptoms in winter wheat canopies based on a deep residual neural network (ResNet). For this purpose, a large annotation database was created from images taken by a standard RGB camera that was mounted on a platform at a height of 2 m. Images were acquired while the platform was moved over a randomized field experiment with Pst-inoculated and Pst-free plots of winter wheat. The image classifier was trained with 224 x 224 px patches tiled from the original, unprocessed camera images. The image classifier was tested on different stages of the disease outbreak. At patch level the image classifier reached a total accuracy of 90%. To test the image classifier on image level, the image classifier was evaluated with a sliding window using a large striding length of 224 px allowing for fast test performance. At image level, the image classifier reached a total accuracy of 77%. Even in a stage with very low disease spreading (0.5%) at the very beginning of the Pst outbreak, a detection accuracy of 57% was obtained. Still in the initial phase of the Pst outbreak with 2 to 4% of Pst disease spreading, detection accuracy with 76% could be attained. With further optimizations, the image classifier could be implemented in embedded systems and deployed on drones, vehicles or scanning systems for fast mapping of Pst outbreaks.
Machine learning for improvement of thermal conditions inside a hybrid ventilated animal building
(2021)
In buildings with hybrid ventilation, natural ventilation opening positions (windows), mechanical ventilation rates, heating, and cooling are manipulated to maintain desired thermal conditions. The indoor temperature is regulated solely by ventilation (natural and mechanical) when the external conditions are favorable to save external heating and cooling energy. The ventilation parameters are determined by a rule-based control scheme, which is not optimal. This study proposes a methodology to enable real-time optimum control of ventilation parameters. We developed offline prediction models to estimate future thermal conditions from the data collected from building in operation. The developed offline model is then used to find the optimal controllable ventilation parameters in real-time to minimize the setpoint deviation in the building. With the proposed methodology, the experimental building's setpoint deviation improved for 87% of time, on average, by 0.53 degrees C compared to the current deviations.
Optimized deep learning model as a basis for fast UAV mapping of weed species in winter wheat crops
(2021)
Weed maps should be available quickly, reliably, and with high detail to be useful for site-specific management in crop protection and to promote more sustainable agriculture by reducing pesticide use. Here, the optimization of a deep residual convolutional neural network (ResNet-18) for the classification of weed and crop plants in UAV imagery is proposed. The target was to reach sufficient performance on an embedded system by maintaining the same features of the ResNet-18 model as a basis for fast UAV mapping. This would enable online recognition and subsequent mapping of weeds during UAV flying operation. Optimization was achieved mainly by avoiding redundant computations that arise when a classification model is applied on overlapping tiles in a larger input image. The model was trained and tested with imagery obtained from a UAV flight campaign at low altitude over a winter wheat field, and classification was performed on species level with the weed species Matricaria chamomilla L., Papaver rhoeas L., Veronica hederifolia L., and Viola arvensis ssp. arvensis observed in that field. The ResNet-18 model with the optimized image-level prediction pipeline reached a performance of 2.2 frames per second with an NVIDIA Jetson AGX Xavier on the full resolution UAV image, which would amount to about 1.78 ha h(-1) area output for continuous field mapping. The overall accuracy for determining crop, soil, and weed species was 94%. There were some limitations in the detection of species unknown to the model. When shifting from 16-bit to 32-bit model precision, no improvement in classification accuracy was observed, but a strong decline in speed performance, especially when a higher number of filters was used in the ResNet-18 model. Future work should be directed towards the integration of the mapping process on UAV platforms, guiding UAVs autonomously for mapping purpose, and ensuring the transferability of the models to other crop fields.
In control theory, to solve a finite-horizon sequential decision problem (SDP) commonly means to find a list of decision rules that result in an optimal expected total reward (or cost) when taking a given number of decision steps. SDPs are routinely solved using Bellman's backward induction. Textbook authors (e.g. Bertsekas or Puterman) typically give more or less formal proofs to show that the backward induction algorithm is correct as solution method for deterministic and stochastic SDPs. Botta, Jansson and Ionescu propose a generic framework for finite horizon, monadic SDPs together with a monadic version of backward induction for solving such SDPs. In monadic SDPs, the monad captures a generic notion of uncertainty, while a generic measure function aggregates rewards. In the present paper, we define a notion of correctness for monadic SDPs and identify three conditions that allow us to prove a correctness result for monadic backward induction that is comparable to textbook correctness proofs for ordinary backward induction. The conditions that we impose are fairly general and can be cast in category-theoretical terms using the notion of Eilenberg-Moore algebra. They hold in familiar settings like those of deterministic or stochastic SDPs, but we also give examples in which they fail. Our results show that backward induction can safely be employed for a broader class of SDPs than usually treated in textbooks. However, they also rule out certain instances that were considered admissible in the context of Botta et al. 's generic framework. Our development is formalised in Idris as an extension of the Botta et al. framework and the sources are available as supplementary material.
In recent years, many efforts have been made to apply image processing techniques for plant leaf identification. However, categorizing leaf images at the cultivar/variety level, because of the very low inter-class variability, is still a challenging task. In this research, we propose an automatic discriminative method based on convolutional neural networks (CNNs) for classifying 12 different cultivars of common beans that belong to three various species. We show that employing advanced loss functions, such as Additive Angular Margin Loss and Large Margin Cosine Loss, instead of the standard softmax loss function for the classification can yield better discrimination between classes and thereby mitigate the problem of low inter-class variability. The method was evaluated by classifying species (level I), cultivars from the same species (level II), and cultivars from different species (level III), based on images from the leaf foreside and backside. The results indicate that the performance of the classification algorithm on the leaf backside image dataset is superior. The maximum mean classification accuracies of 95.86, 91.37 and 86.87% were obtained at the levels I, II and III, respectively. The proposed method outperforms the previous relevant works and provides a reliable approach for plant cultivars identification.