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Energy-resolved fast-neutron radiography using an event-mode neutron imaging detector
Get PDFEnergy-resolved fast-neutron radiography is a powerful non-destructive technique that can be used to remotely measure the quantity and distribution of elements and isotopes in a sample. This is done by comparing the energy-dependent neutron transmission of a sample with the known cross-sections of individual isotopes. The reconstruction of the composition is possible due to the unique features (e.g. resonances) in the cross-sections of individual isotopes. At short-pulsed (<~ 1 ns) neutron sources, such information is accessible via time-of-flight neutron imaging in principle, but requires a detector with nanosecond temporal resolution. Conventional neutron detectors can meet this requirement only by heavily compromising spatial resolution or efficiency. Here, we present a unique approach on fast neutron resonance radiography using a scintillator-based event-mode imaging detector at a short-pulsed neutron source, including first results on spatially mapped resonance profiles using MeV neutrons. The event mode approach applied in the presented detector allows recording of individual neutron interactions with nanosecond precision in time and sub-mm resolution in space. As a result, the entire available neutron energy spectrum can be measured for each pulse. At the same time, the use of a thick scintillator screen and lenses to focus the produced light results in a highly flexible field of view and a high interaction probability in the sensitive volume of the detector
Arctic plant-fungus interaction networks show major rewiring with environmental variation
Get PDFGlobal environmental change may lead to changes in community structure and in species interactions, ultimately changing ecosystem functioning. Focusing on spatial variation in fungus–plant interactions across the rapidly changing Arctic, we quantified variation in the identity of interaction partners. We then related interaction turnover to variation in the bioclimatic environment by combining network analyses with general dissimilarity modelling. Overall, we found species associations to be highly plastic, with major rewiring among interaction partners across variable environmental conditions. Of this turnover, a major part was attributed to specific environmental properties which are likely to change with progressing climate change. Our findings suggest that the current structure of plant-root associated interactions may be severely altered by rapidly advancing global warming. Nonetheless, flexibility in partner choice may contribute to the resilience of the system
Stability of step size control based on a posteriori error estimates
Get PDFA posteriori error estimates based on residuals can be used for reliable error control of numerical methods. Here, we consider them in the context of ordinary differential equations and Runge-Kutta methods. In particular, we take the approach of Dedner & Giesselmann (2016) and investigate it when used to select the time step size. We focus on step size control stability when combined with explicit Runge-Kutta methods and demonstrate that a standard I controller is unstable while more advanced PI and PID controllers can be designed to be stable. We compare the stability properties of residual-based estimators and classical error estimators based on an embedded Runge-Kutta method both analytically and in numerical experiments
Ag-only inner electrode Na₀.₅Bi₀.₅TiO₃-based X9R MLCC: achieving high performance and cost efficiency
Get PDFThe demand for high-power electronic applications is set to drive the necessity for robust components like multi-layer ceramic capacitors (MLCCs). These MLCCs must endure a broad temperature range and withstand high electric fields. Simultaneously, the production cost of these components is a crucial concern for manufacturers. The regularly used Ag/Pd inner electrodes constitute the most significant cost factor. Hence, this study showcases the fabrication of a sodium bismuth titanate (NBT)-based MLCC using only Ag inner electrodes. This could be achieved by reducing the sintering temperatures with the help of sintering aids, but still maintaining excellent dielectric properties of the ceramic. This MLCC demonstrates an exceptional operational temperature range (− 90 to 310 °C), high energy density (up to 5.1 J/cm³), higher efficiency (92%) at 217 kV/cm, and robust capacitance stability (variation less than 10%) even under high temperatures and electric fields
Biomechanical models in the lower-limb exoskeletons development: a review
Get PDFLower limb exoskeletons serve multiple purposes, like supporting and augmenting movement. Biomechanical models are practical tools to understand human movement, and motor control. This paper provides an overview of these models and a comprehensive review of the current applications of them in assistive device development. It also critically analyzes the existing literature to identify research gaps and suggest future directions. Biomechanical models can be broadly classified as conceptual and detailed models and can be used for the design, control, and assessment of exoskeletons. Also, these models can estimate unmeasurable or hard-to-measure variables, which is also useful within the aforementioned applications. We identified the validation of simulation studies and the enhancement of the accuracy and fidelity of biomechanical models as key future research areas for advancing the development of assistive devices. Additionally, we suggest using exoskeletons as a tool to validate and refine these models. We also emphasize the exploration of model-based design and control approaches for exoskeletons targeting pathological gait, and utilizing biomechanical models for diverse design objectives of exoskeletons. In addition, increasing the availability of open source resources accelerates the advancement of the exoskeleton and biomechanical models. Although biomechanical models are widely applied to improve movement assistance and rehabilitation, their full potential in developing human-compatible exoskeletons remains underexplored and requires further investigation. This review aims to reveal existing needs and cranks new perspectives for developing more effective exoskeletons based on biomechanical models
Crystal structure of decapotassium hexaarsenidodistannate(IV), K₁₀[Sn₂As₆]
Get PDFAs₆Ki₀Sn₂, monoclinic, P12₁/n1 (No. 14), a = 15.1959(9) Å, b = 8.2988(7) Å, c = 9.219(1) Å, β = 90.00(1)°, V = 1162.6 ų, Z = 2, Rgt(F) = 0.041, wRreft(F²) = 0.135, Τ = 293
Unit‐cell parameters determination from a set of independent electron diffraction zonal patterns
No full textDue to the short de Broglie wavelength of electrons compared with X‐rays, the curvature of their Ewald sphere is low, and individual electron diffraction patterns are nearly flat in reciprocal space. As a result, a reliable unit‐cell determination from a set of randomly oriented electron diffraction patterns, an essential step in serial electron diffraction, becomes a non‐trivial task. Here we describe an algorithm for unit‐cell determination from a set of independent electron diffraction patterns, as implemented in the program PIEP (Program for Interpreting Electron diffraction Patterns), written in the early 1990s. We evaluate the performance of the algorithm by unit‐cell determination of two known structures – copper perchlorophthalocyanine (CuPcCl₁₆) and lysozyme, challenging the algorithm by high‐index zone patterns and long crystallographic axes. Finally, we apply the procedure to a new, structurally uncharacterized five amino acid peptide
Inverse Reinforcement Learning for Human Decision-Making Under Uncertainty
No full textHuman decision-making in the real world is characterized by uncertainty, continuous learning, and adaptation. In the past, reinforcement learning and stochastic optimal control have been widely used as normative frameworks to model, reproduce, and predict human behavior. However, interpreting observed behavior requires inverse approaches to infer the underlying decision-making mechanisms. Existing inverse approaches, such as inverse reinforcement learning and inverse optimal control, commonly make assumptions, such as full knowledge of the environment and stationary policies, which often do not align with human behavior in real-world scenarios. This dissertation introduces novel inverse approaches for sequential decision-making that account for the adaptive and dynamic nature of human behavior arising from uncertainty. The contributions are organized into three main parts:
First, we address the problem of inferring local knowledge of human subjects in navigation tasks. Seemingly suboptimal routes taken by humans can be explained by incomplete knowledge of the environment, offering insights into their knowledge and beliefs. We describe a Bayesian inference method for systematically inferring a subject's knowledge of the environmental structure based on their navigation behavior. The approach combines approximate sampling methods with a navigation model based on shortest path reduction with an additional cost for uncertainty for efficient inference. We evaluate the approach using both simulated data and real human trajectories collected in an online experiment.
Second, we consider the problem of inferring time-varying preferences in the form of discount functions, which arise when individuals face uncertainty about risks. These varying preferences can be explained by individuals adapting their risk beliefs over time and manifest as preference inconsistencies and hyperbolic discounting. We derive a normative model of hyperbolic discounting for the discrete-time setting and discuss how beliefs about the risk can be inferred in a human discounting experiment. Additionally, we extend this analysis to continuous-time stochastic optimal control, for which we define a formulation with non-exponential discounting, and present an approach to infer the discount function based on observed decision data.
Finally, we address the problem of inferring latent quantities in sensorimotor control tasks, which can be formulated as partially observable stochastic optimal control problems. In these formulations, subjects receive only partial, noisy observations of their state and are uncertain about the future evolution of the stochastic environment.
The inverse problem is particularly challenging, as the subjects' beliefs and control signals are usually latent in the observed trajectory data. For linear-quadratic-Gaussian (LQG) systems with multiplicative noise, we derive an approximate likelihood using an assumed density approach to find the most likely parameters given the observed data. Additionally, for general non-linear stochastic systems, we introduce a linearization-based approximation to enable efficient parameter inference. The methods are evaluated on a range of different simulated tasks and on animal reaching data
Grammar-based object representations in a scene parsing task.
No full textThis paper addresses the nature of visual representations associated with complex structured objects, and the role of these representations in perceptual organization. We use a novel experimental paradigm to probe subjects’ intuitions about parsing a scene consisting of overlapping two-dimensional objects. The objects are generated from an abstract 2-dimensional image grammar, which specifies the set of possible configurations of object parts. We show that participants’ performance on the task depends on prior experience with the object class, and is based on structural cues. This indicates that structural representations exerted a top-down influence on parsing. To address the question of representation type, we used a computational model of object matching in conjunction with various probabilistic representational models. Our simulations indicate that grammar-based representations derived from the original grammars are superior to more restrictive exemplar-based representations in explaining human performance on this task, as well as to more inclusive, over-generalizing grammar-based representations
Training AI in Hostile Environments: Adversarially Robust Machine Learning
Get PDFArtificial Intelligence (AI) and particularly Deep Learning (DL)-based techniques have recently achieved numerous remarkable milestones, allowing their application to increasingly complex and critical tasks, including tasks from the security domain. The capability of AI to autonomously process and analyze large amounts of data has enabled the development of advanced security systems that can evaluate large numbers of events within a system while taking into account a wide array of features that would otherwise overwhelm human analysts when performing manual inspections. This scalability and precision make DL a valuable analysis technique to be leveraged in various security-critical applications, allowing the development of sophisticated protection mechanisms.
Adversarial manipulations pose a significant threat in this context, as attackers can exploit the training process to introduce blind spots into the monitoring system. Unlike natural data corruptions, such as biases or mislabeled samples, adversarial manipulations are intentional and capable of adapting their characteristics and intensity in response to deployed defense mechanisms. Adversaries can modify attacks to bypass data-cleaning techniques and systematically compromise the system. Due to DL's fundamental reliance on the underlying training data, the presence of active adversaries in security applications of Deep Neural Networks (DNNs) introduces several unique challenges distinct from those encountered in non-security domains.
First, adversarial manipulations can exploit scenarios where models must dynamically adapt to evolving system behaviors to remain effective. Attackers can take advantage of these adaptive mechanisms by gradually altering behavior in subtle ways, thereby shifting the decision boundaries without triggering alerts in the active monitoring scheme.
The second challenge arises from the scarcity and sensitivity of training data. While traditional DL applications are often constrained by the availability of labeled datasets and the time-intensive nature of the labeling process, security applications face additional restrictions. The data used in such contexts often includes sensitive information, such as users' network traffic or sensor data from smart homes, where privacy concerns or legal constraints may limit access and prevent sharing of the data. Distributed learning paradigms, such as Federated Learning (FL), overcome the need for centralized data collection by outsourcing the training process to individual clients that keep their data locally, sharing only the parameters of the trained DNNs. However, in adversarial environments, this decentralized approach creates an opportunity for attackers to manipulate the trained model. By providing poisoned training contributions, malicious actors can inject blind spots into the aggregated model, undermining its integrity and effectiveness.
The third major challenge is raised by resource constraints in security deployments. While advanced security analyses often necessitate the use of complex DNNs, such models can exceed the computational capacities of resource-constrained devices that are deployed in the real world. To address this limitation, approaches like Split Learning (SL) partition the DNN into client-side layers, which are responsible for processing sensitive input and output data, as well as server-side layers, which handle computation-intensive calculations. This design enables multiple resource-constrained clients to train also large DNNs collaboratively. However, partitioning the model introduces a significant limitation, as the server's restricted access to only a portion of the DNN prevents the server from performing a comprehensive analysis of the model to detect poisoned contributions.
This cumulative dissertation systematically addresses these challenges to enhance the robustness and security of machine learning training processes in adversarial environments. We address the first challenge of a comprehensive and dynamically adapting but robust security-monitoring system with an autonomously trained anomaly-detection system that adapts to changes in the system's behavior while remaining resilient against manipulations. The system is showcased for detecting attacks on IoT smart homes. While the number of IoT devices continues to grow, many devices still lack even basic security measures. Existing literature for mitigating attacks focuses on network- or host-based intrusion detection of attacks that compromise the IoT device itself. However, these approaches cannot detect attacks that exploit insecure control planes, such as unauthorized commands issued via cloud services without user authentication, where the IoT device is not directly targeted. To address this gap, we propose a scheme that analyzes status changes while considering the device's context, i.e., the states of all other devices in the system. Leveraging DNNs, the scheme evaluates the comprehensive state of the monitored system, models regular behavioral patterns of the smart home, and computes an anomaly score for each triggered action. A significant challenge lies in classifying these scores, as different smart homes exhibit varying levels of behavioral variance, which may also change over time. Conversely, adversaries could exploit adaptive classification boundaries to manipulate the detection. We design a dynamic threshold-tuning scheme that incorporates historical information and the variance in the users' behavior while restricting the impact of short-term deviations, thereby mitigating manipulation attempts and ensuring robust adaptability.
To address the second challenge concerning the availability of potentially sensitive training data, we investigate backdoor-resilient distributed learning schemes. Backdoor attacks introduce well-defined misbehavior for inputs containing a certain activation pattern, making them eligible to intentionally inject a blind spot for certain attacks. To build robust FL systems, we propose a dynamic noising scheme to remove backdoors from the aggregated model, minimizing utility loss and noise magnitude by integrating outlier detection and clipping techniques. Combining these components makes the defense scheme resilient even against adaptive attacks. However, outlier detection may exclude models from benign clients whose datasets are not independently and identically distributed (non-IID) and significantly differ from the data of other clients. In such scenarios, the models trained on these datasets also differ significantly from one another. A critical challenge is to determine whether such discrepancies are caused by benign variations in training data or malicious manipulations. To address this challenge, we build on the first work and design several novel techniques for analyzing model updates, identifying artifacts characteristic of backdoored models, and measuring data similarities using DNN models trained on the clients' datasets. These techniques, combined with similarity estimations for the clients' datasets, are incorporated into a classifier to effectively distinguish between benign and backdoored models. Building on these insights, we design DeepSight, which combines the filtering mechanism with a dynamic clipping scheme to effectively eliminate backdoor attacks, particularly in scenarios where the clients' data show similar complexity. To secure FL in other settings, we introduce CrowdGuard, a scheme that analyzes changes in model behavior using validation data. Addressing the challenge that servers lack validation data and cannot share the models with other clients due to privacy concerns, we propose a novel architecture based on client-side secure enclaves for confidentiality-preserving model validation leveraging the clients' datasets. This architecture enables secure sharing of model updates among clients while isolating applications to prevent privacy breaches. Using this framework, we design an algorithm that detects backdoors by analyzing subtle changes in the behavior of individual neurons and integrates a robust server-side voting mechanism to prevent malicious clients from manipulating the validation result through manipulated validation data. Together, these contributions allow the design of attack-resilient FL systems, advancing defenses against sophisticated adversarial threats.
To address the third challenge, we extend the detection of poisoned training contributions to learning paradigms such as SL, where only certain parts of the DNN can be monitored, and clients train sequentially, preventing direct comparisons of updates. To address these limitations, we inspect the observable parameters using static and dynamic analysis techniques to validate and compare the clients' training objectives. For the dynamic analysis, we design a novel technique that measures the rotational distance between models, capturing subtle changes in updates by considering rotation and orientation. Combined with frequency domain analysis inspecting the models from the static perspective, this ensemble creates a comprehensive fingerprint of the training objectives. Given the inherently sequential structure of SL, we design a circular architecture to analyze each model change, enabling the identification and reversal of malicious training contributions