DeepDriving: Learning Affordance for Direct Perception in Autonomous Driving

Today, there are two major paradigms for vision-based autonomous driving systems: mediated perception approaches that parse an entire scene to make a driving decision, and behavior reflex approaches that directly map an input image to a driving action by a regressor. In this paper, we propose a third paradigm: a direct perception approach to estimate the affordance for driving. We propose to map an input image to a small number of key perception indicators that directly relate to the affordance of a road/traffic state for driving. Our representation provides a set of compact yet complete descriptions of the scene to enable a simple controller to drive autonomously. Falling in between the two extremes of mediated perception and behavior reflex, we argue that our direct perception representation provides the right level of abstraction. To demonstrate this, we train a deep Convolutional Neural Network using recording from 12 hours of human driving in a video game and show that our model can work well to drive a car in a very diverse set of virtual environments. We also train a model for car distance estimation on the KITTI dataset. Results show that our direct perception approach can generalize well to real driving images. Source code and data are available on our project website

Test–Retest Reliability of Mismatch Negativity (MMN) to Emotional Voices

A voice from kin species conveys indispensable social and affective signals with uniquely phylogenetic and ontogenetic standpoints. However, the neural underpinning of emotional voices, beyond low-level acoustic features, activates a processing chain that proceeds from the auditory pathway to the brain structures implicated in cognition and emotion. By using a passive auditory oddball paradigm, which employs emotional voices, this study investigates the test–retest reliability of emotional mismatch negativity (MMN), indicating that the deviants of positively (happily)- and negatively (angrily)-spoken syllables, as compared to neutral standards, can trigger MMN as a response to an automatic discrimination of emotional salience. The neurophysiological estimates of MMN to positive and negative deviants appear to be highly reproducible, irrespective of the subject’s attentional disposition: whether the subjects are set to a condition that involves watching a silent movie or do a working memory task. Specifically, negativity bias is evinced as threatening, relative to positive vocalizations, consistently inducing larger MMN amplitudes, regardless of the day and the time of a day. The present findings provide evidence to support the fact that emotional MMN offers a stable platform to detect subtle changes in current emotional shifts

Atypical Anxiety-Related Amygdala Reactivity and Functional Connectivity in Sant Mat Meditation

While meditation has drawn much attention in cognitive neuroscience, the neural mechanisms underlying its emotional processing remains elusive. Sant Mat meditators were recruited, who adopt a loving-kindness mode of meditation along with a vegetarian diet and an alcohol-restricted lifestyle and novices. We assessed their State-Trait Anxiety Inventory (STAI) and scanned their amygdala reactivity in response to an explicit and implicit (backward masked) perception of fearful and happy faces. In contrast with novices, meditators reported lower STAI scores. Meditators showed stronger amygdala reactivity to explicit happiness than to fear, whereas novices exhibited the opposite pattern. The amygdala reactivity was reduced in meditators regardless of implicit fear or happiness. Those who had more lifetime practice in meditation reported lower STAI and showed a weaker amygdala response to fear. Furthermore, the amygdala in meditators, relative to novices, had a stronger positive functional connectivity with the ventrolateral prefrontal cortex (PFC) to explicit happiness, but a more negative connectivity with the insula and medial orbitofrontal cortex (OFC) to explicit fear. Mediation analysis indicated the amygdala reactivity as the mediator for the linkage between meditation experience and trait anxiety. The findings demonstrate the neural correlates that underpin the beneficial effects of meditation in Sant Mat. Long-term meditation could be functionally coupled with the amygdala reactivity to explicit and implicit emotional processing, which would help reduce anxiety and potentially enhance well-being

The Developmental Origins of the Social Brain: Empathy, Morality, and Justice

The social brain is the cornerstone that effectively negotiates and navigates complex social environments and relationships. When mature, these social abilities facilitate the interaction and cooperation with others. Empathy, morality, and justice, among others, are all closely intertwined, yet the relationships between them are quite complex. They are fundamental components of our human nature, and shape the landscape of our social lives. The various facets of empathy, including affective arousal/emotional sharing, empathic concern, and perspective taking, have unique contributions as subcomponents of morality. This review helps understand how basic forms of empathy, morality, and justice are substantialized in early ontogeny. It provides valuable information as to gain new insights into the underlying neurobiological precursors of the social brain, enabling future translation toward therapeutic and medical interventions

Permian–Triassic evolution of the Bivalvia: Extinction-recovery patterns linked to ecologic and taxonomic selectivity

The Bivalvia is an important benthic clade that was relatively less affected than other benthos during the Permian–Triassic (P–Tr) biotic crisis, reporting losses of 85%, 64%, and 32% at the species, genus and family levels, respectively. This clade proliferated immediately after the P–Tr mass extinction (PTME) to become one of the key elements of the ‘Modern Evolutionary Fauna’ following the P–Tr ‘Great Dying’. Global bivalve occurrence data demonstrate that the initial recovery started in the Griesbachian, a substage immediately after the PTME, and are characterized by relatively high origination and low extinction rates. Thus, unlike other fossil groups, bivalves did not significantly engage in the survival interval. The initial Griesbachian recovery is followed by a stepwise recovery during the Dienerian to Spathian. Then, a remarkably rapid radiation occurred in the Anisian, indicated by extremely high proportional origination and extinction rates. Infaunalization has long been considered the most significant adaptation during the Mesozoic Marine Revolution (MMR), which was thought to have commenced in the Early–Middle Triassic. However, the proportion of infauna in communities remained virtually unchanged before and after the P–Tr biotic crisis; additionally there was no significant difference in proportional extinction/origination rates between infaunal and epifaunal taxa at the genus and family levels through the entire P–Tr transition, implying the absence of ecological selectivity, a conclusion that differs from some previous studies. Therefore, if escalating predatory pressure indeed played a crucial role in driving the initial phases of the MMR, infaunalization was not marked prior to the Ladinian. Alternatively, infaunalization may have played a minor role in facilitating the MMR during the entire era. If so, changes in the physical and chemical environment (‘Court Jester’ model) (i.e. amelioration of marine environments in late Early Triassic), rather than biotic processes (‘Red Queen’ model), may be crucial for the origination and initial phases of the MMR during the early Mesozoic

An fMRI study of affective perspective taking in individuals with psychopathy: imagining another in pain does not evoke empathy

While it is well established that individuals with psychopathy have a marked deficit in affective arousal, emotional empathy, and caring for the well-being of others, the extent to which perspective taking can elicit an emotional response has not yet been studied despite its potential application in rehabilitation. In healthy individuals, affective perspective taking has proven to be an effective means to elicit empathy and concern for others. To examine neural responses in individuals who vary in psychopathy during affective perspective taking, 121 incarcerated males, classified as high (n = 37; Hare psychopathy checklist-revised, PCL-R ≥ 30), intermediate (n = 44; PCL-R between 21 and 29), and low (n = 40; PCL-R ≤ 20) psychopaths, were scanned while viewing stimuli depicting bodily injuries and adopting an imagine-self and an imagine-other perspective. During the imagine-self perspective, participants with high psychopathy showed a typical response within the network involved in empathy for pain, including the anterior insula (aINS), anterior midcingulate cortex (aMCC), supplementary motor area (SMA), inferior frontal gyrus (IFG), somatosensory cortex, and right amygdala. Conversely, during the imagine-other perspective, psychopaths exhibited an atypical pattern of brain activation and effective connectivity seeded in the anterior insula and amygdala with the orbitofrontal cortex (OFC) and ventromedial prefrontal cortex (vmPFC). The response in the amygdala and insula was inversely correlated with PCL-R Factor 1 (interpersonal/affective) during the imagine-other perspective. In high psychopaths, scores on PCL-R Factor 1 predicted the neural response in ventral striatum when imagining others in pain. These patterns of brain activation and effective connectivity associated with differential perspective-taking provide a better understanding of empathy dysfunction in psychopathy, and have the potential to inform intervention programs for this complex clinical problem

Fast Chain-of-Thought: A Glance of Future from Parallel Decoding Leads to Answers Faster

In this work, we propose FastCoT, a model-agnostic framework based on parallel decoding without any further training of an auxiliary model or modification to the LLM itself. FastCoT uses a size-varying context window whose size changes with position to conduct parallel decoding and auto-regressive decoding simultaneously, thus fully utilizing GPU computation resources. In FastCoT, the parallel decoding part provides the LLM with a quick glance of the future composed of approximate tokens, which could lead to faster answers compared to regular autoregressive decoding used by causal transformers. We also provide an implementation of parallel decoding within LLM, which supports KV-cache generation and batch processing. Through extensive experiments, we demonstrate that FastCoT saves inference time by nearly 20% with only a negligible performance drop compared to the regular approach. Additionally, we show that the context window size exhibits considerable robustness for different tasks

Mutual Balancing in State-Object Components for Compositional Zero-Shot Learning

Compositional Zero-Shot Learning (CZSL) aims to recognize unseen compositions from seen states and objects. The disparity between the manually labeled semantic information and its actual visual features causes a significant imbalance of visual deviation in the distribution of various object classes and state classes, which is ignored by existing methods. To ameliorate these issues, we consider the CZSL task as an unbalanced multi-label classification task and propose a novel method called MUtual balancing in STate-object components (MUST) for CZSL, which provides a balancing inductive bias for the model. In particular, we split the classification of the composition classes into two consecutive processes to analyze the entanglement of the two components to get additional knowledge in advance, which reflects the degree of visual deviation between the two components. We use the knowledge gained to modify the model's training process in order to generate more distinct class borders for classes with significant visual deviations. Extensive experiments demonstrate that our approach significantly outperforms the state-of-the-art on MIT-States, UT-Zappos, and C-GQA when combined with the basic CZSL frameworks, and it can improve various CZSL frameworks. Our codes are available on https://anonymous.4open.science/r/MUST_CGE/