End-to-End Urban Driving by Imitating a Reinforcement Learning Coach

End-to-end approaches to autonomous driving commonly rely on expert demonstrations. Although humans are good drivers, they are not good coaches for end-to-end algorithms that demand dense on-policy supervision. On the contrary, automated experts that leverage privileged information can efficiently generate large scale on-policy and off-policy demonstrations. However, existing automated experts for urban driving make heavy use of hand-crafted rules and perform suboptimally even on driving simulators, where ground-truth information is available. To address these issues, we train a reinforcement learning expert that maps bird's-eye view images to continuous low-level actions. While setting a new performance upper-bound on CARLA, our expert is also a better coach that provides informative supervision signals for imitation learning agents to learn from. Supervised by our reinforcement learning coach, a baseline end-to-end agent with monocular camera-input achieves expert-level performance. Our end-to-end agent achieves a 78% success rate while generalizing to a new town and new weather on the NoCrash-dense benchmark and state-of-the-art performance on the more challenging CARLA LeaderBoard

Thin lenses of asymmetric power

It is generally supposed that thin systems, including refracting surfaces and thin lenses, have powers that are necessarily symmetric.  In other words they have powers which can be represented assymmetric dioptric power matrices and in the familar spherocylindrical form used in optometry and ophthalmology.  This paper shows that this is not correct and that it is indeed possible for a thin system to have a power that is not symmetric and which cannot be expressed in spherocylindrical form.  Thin systems of asymmetric power are illustratedby means of a thin lens that is modelled with small prisms and is chosen to have a dioptric power ma-trix that is antisymmetric.  Similar models can be devised for a thin system whose dioptric power matrix is any  2 2 ×  matrix.  Thus any power, symmetric, asymmetric or antisymmetric, is possible for a thin system.  In this sense our understanding of the power of thin systems is now complete

Foci in ray pencils of general divergency

In generalized optical systems, that is, in systems which may contain thin refracting elements of asymmetric dioptric power, pencils of rays may exhibit phenomena that cannot occur in conventional optical systems.  In conventional optical systems astigmatic pencils have two principal meridians that are necessarily orthogonal; in generalized systems the principal meridians can be at any angle.  In fact in generalized systems a pencil may have only one principal meridian or even none at all.  In contrast to the line foci in the conventional interval of Sturm line foci in generalized systems may be at any angle and there may be only one line focus or no line foci.  A conventional cylindrical pencil has a single line focus at a finite distance but it can be regarded as having a second line focus at infinity.  Only in generalized systems is a single line focus possible without a second at infinity or anywhere else.  The purpose of this paper is to illustrate the types of pencils possible in generalized systems.  Particular attention is paid to the effect of including an antisymmetric component in the divergency of the pencil

{SHIFT}: {A} Synthetic Driving Dataset for Continuous Multi-Task Domain Adaptation

Adapting to a continuously evolving environment is a safety-critical challenge inevitably faced by all autonomous-driving systems. Existing image- and video-based driving datasets, however, fall short of capturing the mutable nature of the real world. In this paper, we introduce the largest multi-task synthetic dataset for autonomous driving, SHIFT. It presents discrete and continuous shifts in cloudiness, rain and fog intensity, time of day, and vehicle and pedestrian density. Featuring a comprehensive sensor suite and annotations for several mainstream perception tasks, SHIFT allows to investigate how a perception systems' performance degrades at increasing levels of domain shift, fostering the development of continuous adaptation strategies to mitigate this problem and assessing the robustness and generality of a model. Our dataset and benchmark toolkit are publicly available at www.vis.xyz/shift

Semantic-Context-Based Augmented Descriptor For Image Feature Matching

Abstract. This paper proposes an augmented version of local features that enhances the discriminative power of the feature without affecting its invariance to image deformations. The idea is about learning local features, aiming to estimate its semantic, which is then exploited in conjunction with the bag of words paradigm to build an augmented feature descriptor. Basically, any local descriptor can be casted in the proposed context, and thus the approach can be easy generalized to fit in with any local approach. The semantic-context signature is a 2D histogram which accumulates the spatial distribution of the visual words around each local feature. The obtained semantic-context component is concatenated with the local feature to generate our proposed feature descriptor. This is expected to handle ambiguities occurring in images with multiple similar motifs and depicting slight complicated non-affine distortions, outliers, and detector errors. The approach is evaluated for two data sets. The first one is intentionally selected with images containing multiple similar regions and depicting slight non-affine distortions. The second is the standard data set of Mikolajczyk. The evaluation results showed our approach performs significantly better than expected results as well as in comparison with other methods.

{TADA}: {T}axonomy Adaptive Domain Adaptation

Traditional domain adaptation addresses the task of adapting a model to a novel target domain under limited or no additional supervision. While tackling the input domain gap, the standard domain adaptation settings assume no domain change in the output space. In semantic prediction tasks, different datasets are often labeled according to different semantic taxonomies. In many real-world settings, the target domain task requires a different taxonomy than the one imposed by the source domain. We therefore introduce the more general taxonomy adaptive domain adaptation (TADA) problem, allowing for inconsistent taxonomies between the two domains. We further propose an approach that jointly addresses the image-level and label-level domain adaptation. On the label-level, we employ a bilateral mixed sampling strategy to augment the target domain, and a relabelling method to unify and align the label spaces. We address the image-level domain gap by proposing an uncertainty-rectified contrastive learning method, leading to more domain-invariant and class discriminative features. We extensively evaluate the effectiveness of our framework under different TADA settings: open taxonomy, coarse-to-fine taxonomy, and partially-overlapping taxonomy. Our framework outperforms previous state-of-the-art by a large margin, while capable of adapting to target taxonomies

Formal thought disorder in autism spectrum disorder predicts future symptom severity, but not psychosis prodrome

Formal thought disorder (FTD) is a disruption in the flow of thought, which is inferred from disorganisation of spoken language. FTD in autism spectrum disorders (ASD) might be a precursor of psychotic disorders or a manifestation of ASD symptom severity. The current longitudinal study is a seven-year follow-up of 91 individuals aged 5-12 years with ASD. We tested (1) whether childhood FTD predicted prodromal symptoms of psychosis in adolescence and (2) whether childhood FTD was associated with greater ASD symptom severity in adolescence. ASD symptom severity was assessed in childhood (T1) and 7 years later (T2), using the autism diagnostic observation schedule (ADOS). At T1, the Kiddie-Formal Thought Disorder Rating Scale (KFTDS) was used to measure symptoms of FTD. At T2, the prodromal questionnaire (PQ) was used to assess prodromal symptoms of psychosis. FTD at T1 did not predict prodromal symptoms of psychosis at T2 in children with ASD. FTD symptoms at T1, namely illogical thinking, predicted ASD symptom severity at T2 and this effect remained significant after controlling for T1 ASD symptom severity. In children with ASD, illogical thinking predicts severity of ASD symptoms in adolescence, but FTD does not predict prodromal symptoms of psychosis

The Stability of Comorbid Psychiatric Disorders: A 7 Year Follow Up of Children with Pervasive Developmental Disorder-Not Otherwise Specified

The current study was a 7-year follow-up of 74 6–12 year old children with Pervasive Developmental Disorder-Not Otherwise Specified. We examined the rates and 7 year stability of comorbid psychiatric diagnoses as ascertained with the Diagnostic Interview Schedule for Children: Parent version at ages 6–12 and again at ages 12–20. Also, we examined childhood factors that predicted the stability of comorbid psychiatric disorders. The rate of comorbid psychiatric disorders dropped significantly from childhood (81 %) to adolescence (61 %). Higher levels of parent reported stereotyped behaviors and reduced social interest in childhood significantly predicted the stability of psychiatric comorbidity. Re-evaluation of psychiatric comorbidity should be considered in clinical practice, since several individuals shifted in comorbid diagnoses

The role of pasteurella spp and of Mycoplasma bovis in respiratory diseases in young cattle

Lors des essais terrains d’un traitement antibiotique chez des veaux d’éle vage atteints de maladies respiratoires, la flore trachéobronchique a été inven toriée à différentes périodes : à l’arrivée, avant traitement et après guérison. Le rôle de l’association synergique Mycoplasma bovis - Pasteurella haemoly- tica Al apparaît clairement. Les conditions d’élevage et la participation de certains virus (RSV) sont encore des éléments importants de la pathogénèse et du pronostic des bronchopneumonies infectieuses enzootiques.When we tried an antibiotic treatment on clinical trials on weaner calves with respiratory diseases, tracheobronchial flora was examined at different moments : on the day of the arrival, before treatment and after recovery. The role of the synergistic association of Mycoplasma bovis / Pasteurella haemo- lytica A1 in the development of troubles appears to be confirmed. Bad breeding conditions and the participation of a virus (RSV) are important components of pathogenesis and prognosis of bovine Endemic Infectious Broncho pneumonia