Empathy modulates the temporal structure of social attention

Individuals with low empathy often show reduced attention towards social stimuli. A limitation of this literature is the lack of empirical work that has explicitly characterized how this relationship manifests itself over time. We investigate this issue by analysing data from two large eye-tracking datasets (total n = 176). Via growth-curve analysis, we demonstrate that self-reported empathy (as measured by the empathy quotient—EQ) predicts the temporal evolution of gaze behaviour under conditions where social and non-social stimuli compete for attention. In both datasets, we found that EQ not only predicted a global increase in social attention, but predicted a different temporal profile of social attention. Specifically, we detected a reliable effect of empathy on gaze towards social images after prolonged viewing. An analysis of switch latencies revealed that low-EQ observers switched gaze away from an initially fixated social image more frequently and at earlier latencies than high-EQ observers. Our analyses demonstrate that modelling these temporal components of gaze signals may reveal useful behavioural phenotypes. The explanatory power of this approach may provide enhanced biomarkers for conditions marked by deficits in empathy-related processes

Language Modeling Is Compression

It has long been established that predictive models can be transformed into lossless compressors and vice versa. Incidentally, in recent years, the machine learning community has focused on training increasingly large and powerful self-supervised (language) models. Since these large language models exhibit impressive predictive capabilities, they are well-positioned to be strong compressors. In this work, we advocate for viewing the prediction problem through the lens of compression and evaluate the compression capabilities of large (foundation) models. We show that large language models are powerful general-purpose predictors and that the compression viewpoint provides novel insights into scaling laws, tokenization, and in-context learning. For example, Chinchilla 70B, while trained primarily on text, compresses ImageNet patches to 43.4% and LibriSpeech samples to 16.4% of their raw size, beating domain-specific compressors like PNG (58.5%) or FLAC (30.3%), respectively. Finally, we show that the prediction-compression equivalence allows us to use any compressor (like gzip) to build a conditional generative model

Space station data system analysis/architecture study. Task 2: Options development DR-5. Volume 1: Technology options

The second task in the Space Station Data System (SSDS) Analysis/Architecture Study is the development of an information base that will support the conduct of trade studies and provide sufficient data to make key design/programmatic decisions. This volume identifies the preferred options in the technology category and characterizes these options with respect to performance attributes, constraints, cost, and risk. The technology category includes advanced materials, processes, and techniques that can be used to enhance the implementation of SSDS design structures. The specific areas discussed are mass storage, including space and round on-line storage and off-line storage; man/machine interface; data processing hardware, including flight computers and advanced/fault tolerant computer architectures; and software, including data compression algorithms, on-board high level languages, and software tools. Also discussed are artificial intelligence applications and hard-wire communications

Restoring Application Traffic of Latency-Sensitive Networked Systems using Adversarial Autoencoders

The Internet of Things (IoT), coupled with the edge computing paradigm, is enabling several pervasive networked applications with stringent real-time requirements, such as telemedicine and haptic telecommunications. Recent advances in network virtualization and artificial intelligence are helping solve network latency and capacity problems, learning from several states of the network stack. However, despite such advances, a network architecture able to meet the demands of next-generation networked applications with stringent real-time requirements still has untackled challenges. In this paper, we argue that only using network (or transport) layer information to predict traffic evolution and other network states may be insufficient, and a more holistic approach that considers predictions of application-layer states is needed to repair the inefficiencies of the TCP/IP architecture. Based on this intuition, we present the design and implementation of Reparo. At its core, the design of our solution is based on the detection of a packet loss and its restoration using a Hidden Markov Model (HMM) empowered with adversarial autoencoders. In our evaluation, we considered a telemedicine use case, specifically a telepathology session, in which a microscope is controlled remotely in real-time to assess histological imagery. Our results confirm that the use of adversarial autoencoders enhances the accuracy of the prediction method satisfying our telemedicine application’s requirements with a notable improvement in terms of throughput and latency perceived by the user

Neural architecture search: A contemporary literature review for computer vision applications

Deep Neural Networks have received considerable attention in recent years. As the complexity of network architecture increases in relation to the task complexity, it becomes harder to manually craft an optimal neural network architecture and train it to convergence. As such, Neural Architecture Search (NAS) is becoming far more prevalent within computer vision research, especially when the construction of efficient, smaller network architectures is becoming an increasingly important area of research, for which NAS is well suited. However, despite their promise, contemporary and end-to-end NAS pipeline require vast computational training resources. In this paper, we present a comprehensive overview of contemporary NAS approaches with respect to image classification, object detection, and image segmentation. We adopt consistent terminology to overcome contradictions common within existing NAS literature. Furthermore, we identify and compare current performance limitations in addition to highlighting directions for future NAS research

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 333)

This bibliography lists 122 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during January, 1990. Subject coverage includes: aerospace medicine and psychology, life support systems and controlled environments, safety equipment, exobiology and extraterrestrial life, and flight crew behavior and performance

Examining the Tools Used to Infer Models of Lexical Activation: Eye-tracking, Mouse-tracking, and Reaction Time

Most models of auditory word recognition describe the activation of lexical items in a continuous and graded manner. Much evidence in favor of these models comes from the visual-world paradigm, using either eye fixations or computer cursor trajectories as dependent measures. In particular, Spivey, Grosjean and Knoblich (2005) relied on their observation of unimodality in the distribution of cursor trajectories to argue in favor of a single cognitive process consistent with a continuous model of lexical activation. The present study addresses two questions: (1) whether the logic of inferring the number of cognitive processes from distributional analyses can be extended to a different dependent variable – reaction times, and (2) how robust the distribution of cursor trajectories is to changes in cursor speed (mouse gain). In Experiment 1, eye movements and reaction times were recorded in a visual-world paradigm and reaction times were modeled using ex-Gaussian curve-fitting. Participants responded slower to trials with a phonological competitor presented alongside the target than to trials with a control image presented alongside the target. Crucially, this difference was manifested as a shifting of the distribution rather than as a skewing of the distribution and lends additional support for a continuous model of lexical activation. Experiment 2 measured eye and mouse movements concurrently in a similar visual-world task to investigate the relationship between these two dependent measures at the level of the individual trial. In addition, Experiment 2 manipulated the speed of the cursor (mouse gain) between subjects. The low mouse gain served to reduce the effect of phonological competition. Moreover, the shape of the distribution of cursor trajectories across phonological competitor and control conditions was indistinct with low mouse gain, while the shape of the distributions across the two conditions differed with high mouse gain. This effect of mouse gain shows that the distribution of cursor trajectories is not robust to changes in mouse gain. Moreover, it raises questions about the strength of the linking hypothesis necessary to interpret the distribution of cursor trajectories