383 research outputs found
Omnidirectional Sensory and Motor Volumes in Electric Fish
Active sensing organisms, such as bats, dolphins, and weakly electric fish, generate a 3-D space for active sensation by emitting self-generated energy into the environment. For a weakly electric fish, we demonstrate that the electrosensory space for prey detection has an unusual, omnidirectional shape. We compare this sensory volume with the animal's motor volume—the volume swept out by the body over selected time intervals and over the time it takes to come to a stop from typical hunting velocities. We find that the motor volume has a similar omnidirectional shape, which can be attributed to the fish's backward-swimming capabilities and body dynamics. We assessed the electrosensory space for prey detection by analyzing simulated changes in spiking activity of primary electrosensory afferents during empirically measured and synthetic prey capture trials. The animal's motor volume was reconstructed from video recordings of body motion during prey capture behavior. Our results suggest that in weakly electric fish, there is a close connection between the shape of the sensory and motor volumes. We consider three general spatial relationships between 3-D sensory and motor volumes in active and passive-sensing animals, and we examine hypotheses about these relationships in the context of the volumes we quantify for weakly electric fish. We propose that the ratio of the sensory volume to the motor volume provides insight into behavioral control strategies across all animals
Compositional Falsification of Cyber-Physical Systems with Machine Learning Components
Cyber-physical systems (CPS), such as automotive systems, are starting to
include sophisticated machine learning (ML) components. Their correctness,
therefore, depends on properties of the inner ML modules. While learning
algorithms aim to generalize from examples, they are only as good as the
examples provided, and recent efforts have shown that they can produce
inconsistent output under small adversarial perturbations. This raises the
question: can the output from learning components can lead to a failure of the
entire CPS? In this work, we address this question by formulating it as a
problem of falsifying signal temporal logic (STL) specifications for CPS with
ML components. We propose a compositional falsification framework where a
temporal logic falsifier and a machine learning analyzer cooperate with the aim
of finding falsifying executions of the considered model. The efficacy of the
proposed technique is shown on an automatic emergency braking system model with
a perception component based on deep neural networks
HySIA: Tool for Simulating and Monitoring Hybrid Automata Based on Interval Analysis
We present HySIA: a reliable runtime verification tool for nonlinear hybrid
automata (HA) and signal temporal logic (STL) properties. HySIA simulates an HA
with interval analysis techniques so that a trajectory is enclosed sharply
within a set of intervals. Then, HySIA computes whether the simulated
trajectory satisfies a given STL property; the computation is performed again
with interval analysis to achieve reliability. Simulation and verification
using HySIA are demonstrated through several example HA and STL formulas.Comment: Appeared in RV'17; the final publication is available at Springe
Robust Online Monitoring of Signal Temporal Logic
Signal Temporal Logic (STL) is a formalism used to rigorously specify
requirements of cyberphysical systems (CPS), i.e., systems mixing digital or
discrete components in interaction with a continuous environment or analog com-
ponents. STL is naturally equipped with a quantitative semantics which can be
used for various purposes: from assessing the robustness of a specification to
guiding searches over the input and parameter space with the goal of falsifying
the given property over system behaviors. Algorithms have been proposed and
implemented for offline computation of such quantitative semantics, but only
few methods exist for an online setting, where one would want to monitor the
satisfaction of a formula during simulation. In this paper, we formalize a
semantics for robust online monitoring of partial traces, i.e., traces for
which there might not be enough data to decide the Boolean satisfaction (and to
compute its quantitative counterpart). We propose an efficient algorithm to
compute it and demonstrate its usage on two large scale real-world case studies
coming from the automotive domain and from CPS education in a Massively Open
Online Course (MOOC) setting. We show that savings in computationally expensive
simulations far outweigh any overheads incurred by an online approach
Learning cover context-free grammars from structural data
We consider the problem of learning an unknown context-free grammar when the
only knowledge available and of interest to the learner is about its structural
descriptions with depth at most The goal is to learn a cover
context-free grammar (CCFG) with respect to , that is, a CFG whose
structural descriptions with depth at most agree with those of the
unknown CFG. We propose an algorithm, called , that efficiently learns
a CCFG using two types of queries: structural equivalence and structural
membership. We show that runs in time polynomial in the number of
states of a minimal deterministic finite cover tree automaton (DCTA) with
respect to . This number is often much smaller than the number of states
of a minimum deterministic finite tree automaton for the structural
descriptions of the unknown grammar
STL-based Analysis of TRAIL-induced Apoptosis Challenges the Notion of Type I/Type II Cell Line Classification
Extrinsic apoptosis is a programmed cell death triggered by external ligands, such as the TNF-related apoptosis inducing ligand (TRAIL). Depending on the cell line, the specific molecular mechanisms leading to cell death may significantly differ. Precise characterization of these differences is crucial for understanding and exploiting extrinsic apoptosis. Cells show distinct behaviors on several aspects of apoptosis, including (i) the relative order of caspases activation, (ii) the necessity of mitochondria outer membrane permeabilization (MOMP) for effector caspase activation, and (iii) the survival of cell lines overexpressing Bcl2. These differences are attributed to the activation of one of two pathways, leading to classification of cell lines into two groups: type I and type II. In this work we challenge this type I/type II cell line classification. We encode the three aforementioned distinguishing behaviors in a formal language, called signal temporal logic (STL), and use it to extensively test the validity of a previously-proposed model of TRAIL-induced apoptosis with respect to experimental observations made on different cell lines. After having solved a few inconsistencies using STL-guided parameter search, we show that these three criteria do not define consistent cell line classifications in type I or type II, and suggest mutants that are predicted to exhibit ambivalent behaviors. In particular, this finding sheds light on the role of a feedback loop between caspases, and reconciliates two apparently-conflicting views regarding the importance of either upstream or downstream processes for cell-type determination. More generally, our work suggests that these three distinguishing behaviors should be merely considered as type I/II features rather than cell-type defining criteria. On the methodological side, this work illustrates the biological relevance of STL-diagrams, STL population data, and STL-guided parameter search implemented in the tool Breach. Such tools are well-adapted to the ever-increasing availability of heterogeneous knowledge on complex signal transduction pathways
Fly-by-Logic: A Tool for Unmanned Aircraft System Fleet Planning using Temporal Logic
Safe planning for fleets of Unmaned Aircraft Systems (UAS) performing complex missions in urban environments has typically been a challenging problem. In the United States of America, the National Aeronautics and Space Administration (NASA) and the Federal Aviation Administration (FAA) have been studying the regulation of the airspace when multiple such fleets of autonomous UAS share the same airspace, outlined in the Concept of Operations document (ConOps). While the focus is on the infrastructure and management of the airspace, the Unmanned Aircraft System (UAS) Traffic Management (UTM) ConOps also outline a potential airspace reservation based system for operation where operators reserve a volume of the airspace for a given time inter- val to operate in, but it makes clear that the safety (separation from other aircraft, terrain, and other hazards) is a responsibility of the drone fleet operators. In this work, we present a tool that allows an operator to plan out missions for fleets of multi-rotor UAS, performing complex time- bound missions. The tool builds upon a correct-by-construction planning method by translating missions to Signal Temporal Logic (STL). Along with a simple user interface, it also has fast and scalable mission planning abilities. We demonstrate our tool for one such mission
On-Line Monitoring for Temporal Logic Robustness
In this paper, we provide a Dynamic Programming algorithm for on-line
monitoring of the state robustness of Metric Temporal Logic specifications with
past time operators. We compute the robustness of MTL with unbounded past and
bounded future temporal operators MTL over sampled traces of Cyber-Physical
Systems. We implemented our tool in Matlab as a Simulink block that can be used
in any Simulink model. We experimentally demonstrate that the overhead of the
MTL robustness monitoring is acceptable for certain classes of practical
specifications
Runtime Verification of Temporal Properties over Out-of-order Data Streams
We present a monitoring approach for verifying systems at runtime. Our
approach targets systems whose components communicate with the monitors over
unreliable channels, where messages can be delayed or lost. In contrast to
prior works, whose property specification languages are limited to
propositional temporal logics, our approach handles an extension of the
real-time logic MTL with freeze quantifiers for reasoning about data values. We
present its underlying theory based on a new three-valued semantics that is
well suited to soundly and completely reason online about event streams in the
presence of message delay or loss. We also evaluate our approach
experimentally. Our prototype implementation processes hundreds of events per
second in settings where messages are received out of order.Comment: long version of the CAV 2017 pape
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