Adaptive Target Recognition: A Case Study Involving Airport Baggage Screening

This work addresses the question whether it is possible to design a computer-vision based automatic threat recognition (ATR) system so that it can adapt to changing specifications of a threat without having to create a new ATR each time. The changes in threat specifications, which may be warranted by intelligence reports and world events, are typically regarding the physical characteristics of what constitutes a threat: its material composition, its shape, its method of concealment, etc. Here we present our design of an AATR system (Adaptive ATR) that can adapt to changing specifications in materials characterization (meaning density, as measured by its x-ray attenuation coefficient), its mass, and its thickness. Our design uses a two-stage cascaded approach, in which the first stage is characterized by a high recall rate over the entire range of possibilities for the threat parameters that are allowed to change. The purpose of the second stage is to then fine-tune the performance of the overall system for the current threat specifications. The computational effort for this fine-tuning for achieving a desired PD/PFA rate is far less than what it would take to create a new classifier with the same overall performance for the new set of threat specifications

RMPD - A Recursive Mid-Point Displacement Algorithm for Path Planning

Motivated by what is required for real-time path planning, the paper starts out by presenting sRMPD, a new recursive "local" planner founded on the key notion that, unless made necessary by an obstacle, there must be no deviation from the shortest path between any two points, which would normally be a straight line path in the configuration space. Subsequently, we increase the power of sRMPD by using it as a "connect" subroutine call in a higher-level sampling-based algorithm mRMPD that is inspired by multi-RRT. As a consequence, mRMPD spawns a larger number of space exploring trees in regions of the configuration space that are characterized by a higher density of obstacles. The overall effect is a hybrid tree growing strategy with a trade-off between random exploration as made possible by multi-RRT based logic and immediate exploitation of opportunities to connect two states as made possible by sRMPD. The mRMPD planner can be biased with regard to this trade-off for solving different kinds of planning problems efficiently. Based on the test cases we have run, our experiments show that mRMPD can reduce planning time by up to 80% compared to basic RRT

Self-Supervised One-Shot Learning for Automatic Segmentation of StyleGAN Images

We propose a framework for the automatic one-shot segmentation of synthetic images generated by a StyleGAN. Our framework is based on the observation that the multi-scale hidden features in the GAN generator hold useful semantic information that can be utilized for automatic on-the-fly segmentation of the generated images. Using these features, our framework learns to segment synthetic images using a self-supervised contrastive clustering algorithm that projects the hidden features into a compact space for per-pixel classification. This contrastive learner is based on using a novel data augmentation strategy and a pixel-wise swapped prediction loss that leads to faster learning of the feature vectors for one-shot segmentation. We have tested our implementation on five standard benchmarks to yield a segmentation performance that not only outperforms the semi-supervised baselines by an average wIoU margin of 1.02 % but also improves the inference speeds by a factor of 4.5. Finally, we also show the results of using the proposed one-shot learner in implementing BagGAN, a framework for producing annotated synthetic baggage X-ray scans for threat detection. This framework was trained and tested on the PIDRay baggage benchmark to yield a performance comparable to its baseline segmenter based on manual annotations

OUTCOME OF MACHINE REASONING IN A NETWORK MANAGEMENT SYSTEM TOPOLOGY VIEW

A technique is described herein to provide a visualization overlaid on a network topology that illustrates the cascading impact of a network event before it happens. The technique may empower a network administrator to perform one or more steps to mitigate the issue and/or minimize its impact before the issue manifests itself into a critical network condition

Attitude, Linear Velocity and Depth Estimation of a Camera observing a planar target using continuous homography and inertial data

International audienceThis paper revisits the problem of estimating the attitude, linear velocity and depth of an IMU-Camera with respect to a planar target. The considered solution relies on the measurement of the optical flow (extracted from the continuous homography) complemented with gyrometer and accelerometer measurements. The proposed deterministic observer is accompanied with an observability analysis that points out camera's motion excitation conditions whose satisfaction grants stability of the observer and convergence of the estimation errors to zero. The performance of the observer is illustrated by performing experiments on a test-bed IMU-Camera system

RVSPY -- Radial Velocity Survey for Planets around Young Stars. Target characterization and high-cadence survey

We introduce our Radial Velocity Survey for Planets around Young stars (RVSPY), characterise our target stars, and search for substellar companions at orbital separations smaller than a few au from the host star. We use the FEROS spectrograph to obtain high signal-to-noise spectra and time series of precise radial velocities (RVs) of 111 stars most of which are surrounded by debris discs. Our target stars have spectral types between early F and late K, a median age of 400 Myr, and a median distance of 45 pc. We determine for all target stars their basic stellar parameters and present the results of the high-cadence RV survey and activity characterization. We achieve a median single-measurement RV precision of 6 m/s and derive the short-term intrinsic RV scatter of our targets (median 22 m/s), which is mostly caused by stellar activity and decays with age from >100 m/s at 500 Myr. We discover six previously unknown close companions with orbital periods between 10 and 100 days, three of which are low-mass stars, and three are in the brown dwarf mass regime. We detect no hot companion with an orbital period <10 days down to a median mass limit of ~1 M_Jup for stars younger than 500 Myr, which is still compatible with the established occurrence rate of such companions around main-sequence stars. We find significant RV periodicities between 1.3 and 4.5 days for 14 stars, which are, however, all caused by rotational modulation due to starspots. We also analyse the TESS photometric time series data and find significant periodicities for most of the stars. For 11 stars, the photometric periods are also clearly detected in the RV data. We also derive stellar rotation periods ranging from 1 to 10 days for 91 stars, mostly from TESS data. From the intrinsic activity-related short-term RV jitter, we derive the expected mass-detection thresholds for longer-period companions.Comment: 24 pages, 14 figures, 4 tables; Accepted for publication in A&

Radial Velocity Survey for Planets around Young Stars (RVSPY). Target characterisation and high-cadence survey

We introduce our Radial Velocity Survey for Planets around Young stars (RVSPY), characterise our target stars, and search for substellar companions at orbital separations smaller than a few au from the host star. We use the FEROS spectrograph to obtain high signal-to-noise spectra and time series of precise radial velocities (RVs) of 111 stars most of which are surrounded by debris discs. Our target stars have spectral types between early F and late K, a median age of 400 Myr, and a median distance of 45 pc. We determine for all target stars their basic stellar parameters and present the results of the high-cadence RV survey and activity characterization. We achieve a median single-measurement RV precision of 6 m/s and derive the short-term intrinsic RV scatter of our targets (median 22 m/s), which is mostly caused by stellar activity and decays with age from >100 m/s at 500 Myr. We discover six previously unknown close companions with orbital periods between 10 and 100 days, three of which are low-mass stars, and three are in the brown dwarf mass regime. We detect no hot companion with an orbital period <10 days down to a median mass limit of ~1 M_Jup for stars younger than 500 Myr, which is still compatible with the established occurrence rate of such companions around main-sequence stars. We find significant RV periodicities between 1.3 and 4.5 days for 14 stars, which are, however, all caused by rotational modulation due to starspots. We also analyse the TESS photometric time series data and find significant periodicities for most of the stars. For 11 stars, the photometric periods are also clearly detected in the RV data. We also derive stellar rotation periods ranging from 1 to 10 days for 91 stars, mostly from TESS data. From the intrinsic activity-related short-term RV jitter, we derive the expected mass-detection thresholds for longer-period companions