arXiv subjects

Alois Knoll

Publications and source records attributed to Alois Knoll.

269 records · Page 15Linked to original sources

arXiv2019

Guessing Smart: Biased Sampling for Efficient Black-Box Adversarial Attacks

We consider adversarial examples for image classification in the black-box decision-based setting. Here, an attacker cannot access confidence scores, but only the final label. Most attacks for this scenario are either unreliable or inefficient. Focusing on the latter, we show that a specific class of attacks, Boundary Attacks, can be reinterpreted as a biased sampling framework that gains efficiency from domain knowledge. We identify three such biases, image frequency, regional masks and surrogate gradients, and evaluate their performance against an ImageNet classifier. We show that the combination of these biases outperforms the state of the art by a wide margin. We also showcase an efficient way to attack the Google Cloud Vision API, where we craft convincing perturbations with just a few hundred queries. Finally, the methods we propose have also been found to work very well against strong defenses: Our targeted attack won second place in the NeurIPS 2018 Adversarial Vision Challenge.

stat.ML↗

arXiv2019

Energy-Efficient Slithering Gait Exploration for a Snake-like Robot based on Reinforcement Learning

Similar to their counterparts in nature, the flexible bodies of snake-like robots enhance their movement capability and adaptability in diverse environments. However, this flexibility corresponds to a complex control task involving highly redundant degrees of freedom, where traditional model-based methods usually fail to propel the robots energy-efficiently. In this work, we present a novel approach for designing an energy-efficient slithering gait for a snake-like robot using a model-free reinforcement learning (RL) algorithm. Specifically, we present an RL-based controller for generating locomotion gaits at a wide range of velocities, which is trained using the proximal policy optimization (PPO) algorithm. Meanwhile, a traditional parameterized gait controller is presented and the parameter sets are optimized using the grid search and Bayesian optimization algorithms for the purposes of reasonable comparisons. Based on the analysis of the simulation results, we demonstrate that this RL-based controller exhibits very natural and adaptive movements, which are also substantially more energy-efficient than the gaits generated by the parameterized controller. Videos are shown at \textcolor{blue}{\href{https://videoviewsite.wixsite.com/rlsnake}{https://videoviewsite.wixsite.com/rlsnake}}.

cs.RO↗

arXiv2019

A Novel Method for the Absolute Pose Problem with Pairwise Constraints

Absolute pose estimation is a fundamental problem in computer vision, and it is a typical parameter estimation problem, meaning that efforts to solve it will always suffer from outlier-contaminated data. Conventionally, for a fixed dimensionality d and the number of measurements N, a robust estimation problem cannot be solved faster than O(N^d). Furthermore, it is almost impossible to remove d from the exponent of the runtime of a globally optimal algorithm. However, absolute pose estimation is a geometric parameter estimation problem, and thus has special constraints. In this paper, we consider pairwise constraints and propose a globally optimal algorithm for solving the absolute pose estimation problem. The proposed algorithm has a linear complexity in the number of correspondences at a given outlier ratio. Concretely, we first decouple the rotation and the translation subproblems by utilizing the pairwise constraints, and then we solve the rotation subproblem using the branch-and-bound algorithm. Lastly, we estimate the translation based on the known rotation by using another branch-and-bound algorithm. The advantages of our method are demonstrated via thorough testing on both synthetic and real-world data

cs.CV↗

arXiv2019

5G for the Factory of the Future: Wireless Communication in an Industrial Environment

In this paper, the application of 5G communication technology in an industrial environment is discussed. It acts as an enabler for the separation of sensors/actors and resources, like memory and computational power. 5G offers characteristics essential for the proposed approach like robustness, ultra-low latency, high data rates and massive number of devices. A demonstrator of a production line was used as an test environment for 5G in a real-world industrial application. A wide variety of heterogeneous sensor systems is used by a mobile robot platform. The collected data is transmitted via a 5G network to various Cloud systems. The product is treated as a cyber-physical system with a RFID tag in conjunction with the product memory system. The dynamic production flow approach is discussed centered around the robot which is used for transportation and inspection of products. This inspection is performed during the transportation and influences the production flow directly. This is desirable in the scope of Industry 4.0 to have an efficient production down to batch size 1.

cs.NI↗

arXiv2018

Deep Anticipation: Light Weight Intelligent Mobile Sensing in IoT by Recurrent Architecture

The rapid growth of IoT era is shaping the future of mobile services. Advanced communication technology enables a heterogeneous connectivity where mobile devices broadcast information to everything. Mobile applications such as robotics and vehicles connecting to cloud and surroundings transfer the short-range on-board sensor perception system to long-range mobile-sensing perception system. However, the mobile sensing perception brings new challenges for how to efficiently analyze and intelligently interpret the deluge of IoT data in mission- critical services. In this article, we model the challenges as latency, packet loss and measurement noise which severely deteriorate the reliability and quality of IoT data. We integrate the artificial intelligence into IoT to tackle these challenges. We propose a novel architecture that leverages recurrent neural networks (RNN) and Kalman filtering to anticipate motions and interac- tions between objects. The basic idea is to learn environment dynamics by recurrent networks. To improve the robustness of IoT communication, we use the idea of Kalman filtering and deploy a prediction and correction step. In this way, the architecture learns to develop a biased belief between prediction and measurement in the different situation. We demonstrate our approach with synthetic and real-world datasets with noise that mimics the challenges of IoT communications. Our method brings a new level of IoT intelligence. It is also lightweight compared to other state-of-the-art convolutional recurrent architecture and is ideally suitable for the resource-limited mobile applications.

cs.CV↗

arXiv2018

Towards Robotic Eye Surgery: Marker-free, Online Hand-eye Calibration using Optical Coherence Tomography Images

Ophthalmic microsurgery is known to be a challenging operation, which requires very precise and dexterous manipulation. Image guided robot-assisted surgery (RAS) is a promising solution that brings significant improvements in outcomes and reduces the physical limitations of human surgeons. However, this technology must be further developed before it can be routinely used in clinics. One of the problems is the lack of proper calibration between the robotic manipulator and appropriate imaging device. In this work, we developed a flexible framework for hand-eye calibration of an ophthalmic robot with a microscope-integrated Optical Coherence Tomography (MIOCT) without any markers. The proposed method consists of three main steps: a) we estimate the OCT calibration parameters; b) with micro-scale displacements controlled by the robot, we detect and segment the needle tip in 3D-OCT volume; c) we find the transformation between the coordinate system of the OCT camera and the coordinate system of the robot. We verified the capability of our framework in ex-vivo pig eye experiments and compared the results with a reference method (marker-based). In all experiments, our method showed a small difference from the marker based method, with a mean calibration error of 9.2 $μ$m and 7.0 $μ$m, respectively. Additionally, the noise test shows the robustness of the proposed method.

cs.RO↗

arXiv2018

A Survey on Agent-based Simulation using Hardware Accelerators

Due to decelerating gains in single-core CPU performance, computationally expensive simulations are increasingly executed on highly parallel hardware platforms. Agent-based simulations, where simulated entities act with a certain degree of autonomy, frequently provide ample opportunities for parallelisation. Thus, a vast variety of approaches proposed in the literature demonstrated considerable performance gains using hardware platforms such as many-core CPUs and GPUs, merged CPU-GPU chips as well as FPGAs. Typically, a combination of techniques is required to achieve high performance for a given simulation model, putting substantial burden on modellers. To the best of our knowledge, no systematic overview of techniques for agent-based simulations on hardware accelerators has been given in the literature. To close this gap, we provide an overview and categorisation of the literature according to the applied techniques. Since at the current state of research, challenges such as the partitioning of a model for execution on heterogeneous hardware are still a largely manual process, we sketch directions for future research towards automating the hardware mapping and execution. This survey targets modellers seeking an overview of suitable hardware platforms and execution techniques for a specific simulation model, as well as methodology researchers interested in potential research gaps requiring further exploration.

cs.MA↗

arXiv2017

Potentials and Implications of Dedicated Highway Lanes for Autonomous Vehicles

The introduction of autonomous vehicles (AVs) will have far-reaching effects on road traffic in cities and on highways.The implementation of automated highway system (AHS), possibly with a dedicated lane only for AVs, is believed to be a requirement to maximise the benefit from the advantages of AVs. We study the ramifications of an increasing percentage of AVs on the traffic system with and without the introduction of a dedicated AV lane on highways. We conduct an analytical evaluation of a simplified scenario and a macroscopic simulation of the city of Singapore under user equilibrium conditions with a realistic traffic demand. We present findings regarding average travel time, fuel consumption, throughput and road usage. Instead of only considering the highways, we also focus on the effects on the remaining road network. Our results show a reduction of average travel time and fuel consumption as a result of increasing the portion of AVs in the system. We show that the introduction of an AV lane is not beneficial in terms of average commute time. Examining the effects of the AV population only, however, the AV lane provides a considerable reduction of travel time (approx. 25%) at the price of delaying conventional vehicles (approx. 7%). Furthermore a notable shift of travel demand away from the highways towards major and small roads is noticed in early stages of AV penetration of the system. Finally, our findings show that after a certain threshold percentage of AVs the differences between AV and no AV lane scenarios become negligible.

cs.MA↗

arXiv2015

Paired spiking robustly shapes spontaneous activity in neural networks in vitro

In vivo, neurons establish functional connections and preserve information along their synaptic pathways from one information processing stage to the next in a very efficient manner. Paired spiking (PS) enhancement plays a key role by acting as a temporal filter that deletes less informative spikes. We analyzed the spontaneous neural activity evolution in a hippocampal and a cortical network over several weeks exploring whether the same PS coding mechanism appears in neuronal cultures as well. We show that self-organized neural in vitro networks not only develop characteristic bursting activity, but feature robust in vivo-like PS activity. PS activity formed spatiotemporal patterns that started at early days in vitro (DIVs) and lasted until the end of the recording sessions. Initially random-like and sparse PS patterns became robust after three weeks in vitro (WIVs). They were characterized by a high number of occurrences and short inter-paired spike intervals (IPSIs). Spatially, the degree of complexity increased by recruiting new neighboring sites in PS as a culture matured. Moreover, PS activity participated in establishing functional connectivity between different sites within the developing network. Employing transfer entropy (TE) as an information transfer measure, we show that PS activity is robustly involved in establishing effective connectivities. Spiking activity at both individual sites and network level robustly followed each PS within a short time interval. PS may thus be considered a spiking predictor. These findings suggest that PS activity is preserved in spontaneously active in vitro networks as part of a robust coding mechanism as encountered in vivo. We suggest that, presumably in lack of any external sensory stimuli, PS may act as an internal surrogate stimulus to drive neural activity at different developmental stages.

q-bio.NC↗

arXiv2015

A z-Vertex Trigger for Belle II

The Belle II experiment will go into operation at the upgraded SuperKEKB collider in 2016. SuperKEKB is designed to deliver an instantaneous luminosity $\mathcal{L}=8\times10^{35}\,\mathrm{cm}^{-2}\,\mathrm{s}^{-1}$. The experiment will therefore have to cope with a much larger machine background than its predecessor Belle, in particular from events outside of the interaction region. We present the concept of a track trigger, based on a neural network approach, that is able to suppress a large fraction of this background by reconstructing the $z$ (longitudinal) position of the event vertex within the latency of the first level trigger. The trigger uses the hit information from the Central Drift Chamber (CDC) of Belle II within narrow cones in polar and azimuthal angle as well as in transverse momentum ("sectors"), and estimates the $z$-vertex without explicit track reconstruction. The preprocessing for the track trigger is based on the track information provided by the standard CDC trigger. It takes input from the 2D track finder, adds information from the stereo wires of the CDC, and finds the appropriate sectors in the CDC for each track. Within the sector, the $z$-vertex is estimated by a specialized neural network, with the drift times from the CDC as input and a continuous output corresponding to the scaled $z$-vertex. The neural algorithm will be implemented in programmable hardware. To this end a Virtex 7 FPGA board will be used, which provides at present the most promising solution for a fully parallelized implementation of neural networks or alternative multivariate methods. A high speed interface for external memory will be integrated into the platform, to be able to store the $\mathcal{O}(10^9)$ parameters required. The contribution presents the results of our feasibility studies and discusses the details of the envisaged hardware solution.

physics.ins-det↗

arXiv2015

A neural network z-vertex trigger for Belle II

We present the concept of a track trigger for the Belle II experiment, based on a neural network approach, that is able to reconstruct the z (longitudinal) position of the event vertex within the latency of the first level trigger. The trigger will thus be able to suppress a large fraction of the dominating background from events outside of the interaction region. The trigger uses the drift time information of the hits from the Central Drift Chamber (CDC) of Belle II within narrow cones in polar and azimuthal angle as well as in transverse momentum (sectors), and estimates the z-vertex without explicit track reconstruction. The preprocessing for the track trigger is based on the track information provided by the standard CDC trigger. It takes input from the 2D ($r - φ$) track finder, adds information from the stereo wires of the CDC, and finds the appropriate sectors in the CDC for each track in a given event. Within each sector, the z-vertex of the associated track is estimated by a specialized neural network, with a continuous output corresponding to the scaled z-vertex. The input values for the neural network are calculated from the wire hits of the CDC.

physics.ins-det↗

arXiv2012

Dynamic Priority Queue: An SDRAM Arbiter With Bounded Access Latencies for Tight WCET Calculation

This report introduces a shared resource arbitration scheme "DPQ - Dynamic Priority Queue" which provides bandwidth guarantees and low worst case latency to each master in an MPSoC. Being a non-trivial candidate for timing analysis, SDRAM has been chosen as a showcase, but the approach is valid for any shared resource arbitration. Due to its significant cost, data rate and physical size advantages, SDRAM is a potential candidate for cost sensitive, safety critical and space conserving systems. The variable access latency is a major drawback of SDRAM that induces largely over estimated Worst Case Execution Time (WCET) bounds of applications. In this report we present the DPQ together with an algorithm to predict the shared SDRAM's worst case latencies. We use the approach to calculate WCET bounds of six hardware tasks executing on an Altera Cyclone III FPGA with shared DDR2 memory. The results show that the DPQ is a fair arbitration scheme and produces low WCET bounds.

cs.AR↗

arXiv2012

Distributed Priority Synthesis and its Applications

Given a set of interacting components with non-deterministic variable update and given safety requirements, the goal of priority synthesis is to restrict, by means of priorities, the set of possible interactions in such a way as to guarantee the given safety conditions for all possible runs. In distributed priority synthesis we are interested in obtaining local sets of priorities, which are deployed in terms of local component controllers sharing intended next moves between components in local neighborhoods only. These possible communication paths between local controllers are specified by means of a communication architecture. We formally define the problem of distributed priority synthesis in terms of a multi-player safety game between players for (angelically) selecting the next transition of the components and an environment for (demonically) updating uncontrollable variables; this problem is NP-complete. We propose several optimizations including a solution-space exploration based on a diagnosis method using a nested extension of the usual attractor computation in games together with a reduction to corresponding SAT problems. When diagnosis fails, the method proposes potential candidates to guide the exploration. These optimized algorithms for solving distributed priority synthesis problems have been integrated into our VissBIP framework. An experimental validation of this implementation is performed using a range of case studies including scheduling in multicore processors and modular robotics.

cs.LO↗

arXiv2011

Algorithms for Synthesizing Priorities in Component-based Systems

We present algorithms to synthesize component-based systems that are safe and deadlock-free using priorities, which define stateless-precedence between enabled actions. Our core method combines the concept of fault-localization (using safety-game) and fault-repair (using SAT for conflict resolution). For complex systems, we propose three complementary methods as preprocessing steps for priority synthesis, namely (a) data abstraction to reduce component complexities, (b) alphabet abstraction and #-deadlock to ignore components, and (c) automated assumption learning for compositional priority synthesis.

cs.LO↗

arXiv2010

A Game-theoretic Approach for Synthesizing Fault-Tolerant Embedded Systems

In this paper, we present an approach for fault-tolerant synthesis by combining predefined patterns for fault-tolerance with algorithmic game solving. A non-fault-tolerant system, together with the relevant fault hypothesis and fault-tolerant mechanism templates in a pool are translated into a distributed game, and we perform an incomplete search of strategies to cope with undecidability. The result of the game is translated back to executable code concretizing fault-tolerant mechanisms using constraint solving. The overall approach is implemented to a prototype tool chain and is illustrated using examples.

cs.GT↗

arXiv2009

Modeling and Verification for Timing Satisfaction of Fault-Tolerant Systems with Finiteness

The increasing use of model-based tools enables further use of formal verification techniques in the context of distributed real-time systems. To avoid state explosion, it is necessary to construct verification models that focus on the aspects under consideration. In this paper, we discuss how we construct a verification model for timing analysis in distributed real-time systems. We (1) give observations concerning restrictions of timed automata to model these systems, (2) formulate mathematical representations on how to perform model-to-model transformation to derive verification models from system models, and (3) propose some theoretical criteria how to reduce the model size. The latter is in particular important, as for the verification of complex systems, an efficient model reflecting the properties of the system under consideration is equally important to the verification algorithm itself. Finally, we present an extension of the model-based development tool FTOS, designed to develop fault-tolerant systems, to demonstrate %the benefits of our approach.

cs.DC↗

arXiv2009

FTOS-Verify: Analysis and Verification of Non-Functional Properties for Fault-Tolerant Systems

The focus of the tool FTOS is to alleviate designers' burden by offering code generation for non-functional aspects including fault-tolerance mechanisms. One crucial aspect in this context is to ensure that user-selected mechanisms for the system model are sufficient to resist faults as specified in the underlying fault hypothesis. In this paper, formal approaches in verification are proposed to assist the claim. We first raise the precision of FTOS into pure mathematical constructs, and formulate the deterministic assumption, which is necessary as an extension of Giotto-like systems (e.g., FTOS) to equip with fault-tolerance abilities. We show that local properties of a system with the deterministic assumption will be preserved in a modified synchronous system used as the verification model. This enables the use of techniques known from hardware verification. As for implementation, we develop a prototype tool called FTOS-Verify, deploy it as an Eclipse add-on for FTOS, and conduct several case studies.

cs.DC↗