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Industrial Motor Fault Classification using Deep Learning with IoT Implications

#artificialintelligence

One of my first assignments as a new electrical engineering graduate was to diagnose and troubleshoot an out-of-service induction motor. The first step was to exam the symptoms of the fault and determine the root cause of failure. In order to do this, I required a specific diagnostic tool and a detailed testing procedure. Unfortunately, the diagnostic tool was unavailable in the short-term and the testing procedure required a minimum of 3 weeks to execute. I continue to think about this scenario and I classify it as an opportunity to improve the current standards for motor diagnostics and repair.


Perceived Realism of High-Resolution Generative Adversarial Network–derived Synthetic Mammograms

#artificialintelligence

To explore whether generative adversarial networks (GANs) can enable synthesis of realistic medical images that are indiscernible from real images, even by domain experts. In this retrospective study, progressive growing GANs were used to synthesize mammograms at a resolution of 1280 1024 pixels by using images from 90 000 patients (average age, 56 years 9) collected between 2009 and 2019. To evaluate the results, a method to assess distributional alignment for ultra–high-dimensional pixel distributions was used, which was based on moment plots. This method was able to reveal potential sources of misalignment. A total of 117 volunteer participants (55 radiologists and 62 nonradiologists) took part in a study to assess the realism of synthetic images from GANs.


Towards an AI Coach to Infer Team Mental Model Alignment in Healthcare

arXiv.org Artificial Intelligence

Abstract--Shared mental models are critical to team success; however, in practice, team members may have misaligned models due to a variety of factors. In safety-critical domains (e.g., aviation, healthcare), lack of shared mental models can lead to preventable errors and harm. Towards the goal of mitigating such preventable errors, here, we present a Bayesian approach to infer misalignment in team members' mental models during complex healthcare task execution. As an exemplary application, we demonstrate our approach using two simulated team-based scenarios, derived from actual teamwork in cardiac surgery. In these simulated experiments, our approach inferred model misalignment with over 75% recall, thereby providing a building block for enabling computer-assisted interventions to augment human cognition in the operating room and improve teamwork.


Multi-Principal Assistance Games: Definition and Collegial Mechanisms

arXiv.org Artificial Intelligence

We introduce the concept of a multi-principal assistance game (MPAG), and circumvent an obstacle in social choice theory -- Gibbard's theorem -- by using a sufficiently "collegial" preference inference mechanism. In an MPAG, a single agent assists N human principals who may have widely different preferences. MPAGs generalize assistance games, also known as cooperative inverse reinforcement learning games. We analyze in particular a generalization of apprenticeship learning in which the humans first perform some work to obtain utility and demonstrate their preferences, and then the robot acts to further maximize the sum of human payoffs. We show in this setting that if the game is sufficiently collegial -- i.e., if the humans are responsible for obtaining a sufficient fraction of the rewards through their own actions -- then their preferences are straightforwardly revealed through their work. This revelation mechanism is non-dictatorial, does not limit the possible outcomes to two alternatives, and is dominant-strategy incentive-compatible.


Facilitating the Communication of Politeness through Fine-Grained Paraphrasing

arXiv.org Artificial Intelligence

Aided by technology, people are increasingly able to communicate across geographical, cultural, and language barriers. This ability also results in new challenges, as interlocutors need to adapt their communication approaches to increasingly diverse circumstances. In this work, we take the first steps towards automatically assisting people in adjusting their language to a specific communication circumstance. As a case study, we focus on facilitating the accurate transmission of pragmatic intentions and introduce a methodology for suggesting paraphrases that achieve the intended level of politeness under a given communication circumstance. We demonstrate the feasibility of this approach by evaluating our method in two realistic communication scenarios and show that it can reduce the potential for misalignment between the speaker's intentions and the listener's perceptions in both cases.


AI Alignment through Anthropology

#artificialintelligence

AI Alignment through anthropology: How social science can steer AI towards better outcomes Guest article by Anna Leggett, Senior Research Consultant, and Morgan Williams, Junior Consultant, Stripe Partners. If an advanced AI system were instructed to make paper clips, or to fetch coffee, we would not want it to


Bayesian Inverse Reinforcement Learning for Collective Animal Movement

arXiv.org Machine Learning

Agent-based methods allow for defining simple rules that generate complex group behaviors. The governing rules of such models are typically set a priori and parameters are tuned from observed behavior trajectories. Instead of making simplifying assumptions across all anticipated scenarios, inverse reinforcement learning provides inference on the short-term (local) rules governing long term behavior policies by using properties of a Markov decision process. We use the computationally efficient linearly-solvable Markov decision process to learn the local rules governing collective movement for a simulation of the self propelled-particle (SPP) model and a data application for a captive guppy population. The estimation of the behavioral decision costs is done in a Bayesian framework with basis function smoothing. We recover the true costs in the SPP simulation and find the guppies value collective movement more than targeted movement toward shelter.


Sexual dimorphism in body clocks

Science

Circadian rhythms, or the body clock, confer temporal structure on human behavior and physiology to align homeostatic processes with anticipated changes in the environment. Disruption of these rhythms can influence health and well-being. Chronobiological research has often failed to consider how this temporal organization may be affected by sex. The few studies that do consider how these rhythms differ between sexes suggest a dimorphism that warrants further investigation. Recent findings from both humans and animal models illustrate how the systems that generate circadian rhythms diverge between the sexes, which has potential consequences for health and resilience to changes in sleep pattern. Circadian rhythms are generated centrally by a transcription-translation feedback loop in the suprachiasmatic nucleus (SCN) of the hypothalamus. The proteins brain and muscle ARNT-like 1 (BMAL1) and circadian locomotor output cycles kaput (CLOCK) form the positive arm of the molecular clock. This heterodimer promotes the expression of its own repressors period 1 (PER1) and PER2 and cryptochrome 1 (CRY1) and CRY2. Inhibition of the BMAL1-CLOCK heterodimer in turn suppresses PER1/2 and CRY1/2, allowing BMAL1 and CLOCK expression levels to rise once again. This cycle takes roughly 24 hours, forming the core molecular clock. Photic (light) input to the SCN can also induce the expression of PER1/2 and CRY1/2 and represents the main stimulus that entrains the circadian rhythm to the day-night cycle. Estrogen and androgen receptors are expressed in a sexually dimorphic pattern along the central neuronal circuitry regulating circadian rhythms and show rhythmic expression in peripheral tissues ([ 1 ][1]) (see the figure). Early studies of hamsters placed into constant darkness observed shorter free-running periods among females, indicating that their core molecular clock machinery oscillates faster than that of males ([ 2 ][2]). Females also showed significantly earlier onset of activity and responded differently to stimuli that shift the intrinsic timing of the circadian clock. It was subsequently reported that in female mice, behavioral rhythms were more consolidated, had higher amplitudes (difference between the peak and the mean 24-hour activity level), and peaked earlier in the day than in males. Recently, large-scale collections of remote sensing data have enabled the observation of natural activity patterns in humans. A study of 91,105 participants in the UK Biobank revealed that males were more likely to have low-amplitude behavioral rhythms than females ([ 3 ][3]). This can be due to increased activity at night or decreased activity in the daytime, reflecting the conservation of more robust oscillations in activity rhythms among females. Females also spend more time asleep, spend more of their time in slow-wave (deep) sleep, and are more resilient to nocturnal disturbances than males ([ 4 ][4]). A study of chronotype, or day-night preference, in more than 53,000 individuals highlighted how age and sex both substantially affect the timing of circadian rhythms ([ 5 ][5]). Whereas children are typically morning types regardless of sex, after puberty males tend to be more evening oriented than females, mirroring the findings in animal models. The hormonal changes associated with menopause add extra complexity to the aging process for females; chronotypes converge during middle age as both sexes become more morning oriented. New efforts to study rhythmic changes outside the laboratory are characterizing the “chronobiome”—a phenotype incorporating the in-depth assessment of thousands of time-of-day signals across diverse physiological readouts that will clarify the array of sexually dimorphic rhythms in humans ([ 6 ][6]). Forced desynchrony protocols, in which the sleep-wake cycle is desynchronized from endogenous circadian rhythms, have also been used to examine how key parameters of circadian rhythmicity differ between the sexes. One such study found that females had higher-amplitude rhythms in their performance on cognitive tests that assess working memory, attention, effort, mood, and sleepiness ([ 7 ][7]). Together with a higher-amplitude rhythm in the sleep-promoting hormone melatonin, this increased amplitude in cognitive processing resulted in females experiencing a greater deficit during the biological night compared with their peak functioning time. Although higher amplitudes (peaks-to-trough ratio) are typically beneficial because they allow for greater compartmentalization of different homeostatic processes, in this example a higher amplitude may be detrimental when females must be awake at night. The expansion of sequencing efforts has revealed how sexual dimorphism in chronobiology extends into oscillations of the transcriptome, metabolome, and microbiome. In a study of transcriptional and metabolic rhythmicity in mice, 71% of liver transcripts showed conserved rhythmicity between males and females, 9% of genes showed rhythmicity only in males, and 16% of genes showed rhythmicity only in females ([ 8 ][8]). A further 4% were rhythmically expressed in both sexes but differed in their phase or amplitude, including several core clock genes that showed higher-amplitude oscillations in females. Only 55% of liver metabolites and 29% of serum metabolites had conserved oscillations between sexes. Germ-free mice, which have no gut microbiota, had diminished sexual dimorphism in their gene expression and circadian rhythms. Female mice have greater diurnal oscillations in their total bacterial load, and genetic disruption of the host clock machinery affects gut microbiota differentially in male and female mice ([ 9 ][9]). Thus, the gut microbiota can contribute to the orchestration of circadian rhythms during development and adulthood; this contribution is likely to differ between the sexes. A clinical study comparing the metabolic response to misalignment between sexes tracked energy consumption and expenditure during 8 days on a normal schedule and then for 8 days featuring a 12-hour phase shift, achieved through an 8-hour wake opportunity followed by a 4-hour sleep opportunity on the fourth day ([ 10 ][10]). Circadian misalignment increased the hunger hormone ghrelin and decreased the satiety hormone leptin in females, which was accompanied by decreased feelings of fullness. Female participants' carbohydrate oxidation rate and respiratory quotients also dropped after misalignment, whereas their energy expenditure and lipid oxidation rate increased. Conversely, males showed an increase in leptin and no change in ghrelin after the phase shift. They reported increased cravings for energy-dense foods, which is inconsistent with their hormone changes, and showed no difference in energy utilization. This switch in energy utilization may reflect an adaptive response to misalignment in females. Studies in animal models have shown that sex hormones are not required to maintain circadian rhythms but can affect their amplitude and response to photic stimuli. Female mice in which the estrogen receptor 1 ( Esr1 ) gene is deleted show fractured behavioral rhythms and blunted phase delays produced by light pulses given in the early active phase of the day. By contrast, light pulses given late in the active phase increase phase advances compared with that of controls ([ 11 ][11]). This suggests that estrogen consolidates behavioral rhythms in female mice and could facilitate the faster entrainment to phase shifts observed in female mice. The same study showed that Esr1 deletion did not affect phase shifting in males, but that wild-type males showed smaller phase shifts than those of females when exposed to light early in the active phase. ![Figure][12] Sex hormone receptors in the circadian network The patterning of estrogen receptors (purple) and androgen receptors (green) throughout the circadian network varies between males and females. The suprachiasmatic nucleus (SCN) signals to peripheral organs, many of which also have circadian oscillations in sex hormone receptors. Circulating estrogen and testosterone are also likely to affect the SCN in a sexually dimorphic, rhythmic fashion. This dimorphism is manifested at the behavioral level through higher-amplitude rhythms in female activity patterns compared with that of males. GRAPHIC: A. KITTERMAN/ SCIENCE Gonadectomy in male mice deconsolidates behavioral rhythms and enhances the phase shift produced by light in the early active but not the late active phase. Thus, testosterone may restrain the phase-shifting effect of light ([ 12 ][13]). Nonreproductive factors are also pertinent to the sexual dimorphism in circadian rhythmicity. For example, female mice entrained to an 8-hour phase shift in 6 days, whereas males took 10 days to adapt ([ 13 ][14]). This difference disappeared in the absence of Liver X Receptor α ( Lxra ), with male mice in which Lxra is deleted showing significantly faster entrainment than that of wild types. This may be due to the impact of Lxra deletion on corticosterone rhythms, which affect reentrainment. It is not well understood how sex hormones influence rhythmicity directly in the SCN because whole-body deletion of the receptors or removal of sex organs obliterates signaling across the entire organism. Sexual dimorphism in the SCN should be revisited by using single-cell transcriptomics and SCN-targeted deletions of estrogen and androgen receptors. The repeated pattern of dimorphic rhythmicity observed in humans and animal models suggest that these differences are not attributable simply to societal pressures on either sex. Consistent with the findings that female mice show enhanced entrainment to phase shifts, studies in rodents have shown that females tend to be more resistant to genetic and environmental circadian disruption. In Clock Δ19/Δ19 mutant mice, in which mutation of the Clock protein interferes with transcriptional regulation by the BMAL1-CLOCK heterodimer and leads to lengthening of the circadian period, females do not develop any detectable cardiac dysfunction until 21 months of age, despite male Clock Δ19/Δ19 mice showing cardiac hypertrophy and dysfunction after 12 months ([ 14 ][15]). However, in ovariectomized Clock Δ19/Δ19 mice, cardiometabolic function was impaired relative to ovariectomized controls by 8 months of age, highlighting the protective effect of estrogen. One possible reason for the resilience to circadian disruption in females relates to their biological imperative. Resistance to the negative consequences of circadian disruption coupled with improved sleep, even when experiencing nocturnal disturbances, may facilitate their adaptation to frequent nocturnal awakenings over a sustained period, given their predominant role in nurturing offspring. The early-activity chronotypes seen in women before menopause also align with those in children. Circadian rhythms are influenced by sex, and this interaction is remolded throughout life. In the healthy state, females often show higher-amplitude oscillations with an earlier peak in gene expression. Dimorphism can also shape the response to circadian misalignment and the downstream consequences of disruptions to normal rhythms. A chronic disruption to human circadian rhythms is shiftwork, which is associated with cardiometabolic disease and cancer. Studies have sought to clarify whether this risk is affected by sex ([ 15 ][16]), but the results are constrained by a lack of longitudinal data. There are large differences in the rhythmic regulation of the liver transcriptome between males and females, but it is unknown whether other organs show similar differences or how faithfully this translates to protein expression and function. In humans, well-controlled, longitudinal analyses of the impact of misalignment will be necessary to address the hypothesis that females are more resilient than males to the disruption of circadian function caused by shiftwork and repeated long-distance travel. 1. [↵][17]1. K. M. Hatcher et al ., Eur. J. Neurosci. 51, 217 (2020). [OpenUrl][18] 2. [↵][19]1. F. C. Davis et al ., Am. J. Physiol. 244, R93 (1983). [OpenUrl][20] 3. [↵][21]1. L. M. Lyall et al ., Lancet Psych. 5, 507 (2018). [OpenUrl][22] 4. [↵][23]1. E. O. Bixler et al ., J. Sleep Res. 18, 221 (2009). [OpenUrl][24][CrossRef][25][PubMed][26][Web of Science][27] 5. [↵][28]1. D. Fischer et al ., PLOS ONE 12, e0178782 (2017). [OpenUrl][29] 6. [↵][30]1. C. Skarke et al ., Sci. Rep. 7, 17141 (2017). [OpenUrl][31][CrossRef][32] 7. [↵][33]1. N. Santhi et al ., Proc. Natl. Acad. Sci. U.S.A. 113, E2730 (2016). [OpenUrl][34][Abstract/FREE Full Text][35] 8. [↵][36]1. B. D. Weger et al ., Cell Metab. 29, 362 (2019). [OpenUrl][37][CrossRef][38] 9. [↵][39]1. X. Liang et al ., Proc. Natl. Acad. Sci. U.S.A. 112, 10479 (2015). [OpenUrl][40][Abstract/FREE Full Text][41] 10. [↵][42]1. J. Qian et al ., Proc. Natl. Acad. Sci. U.S.A. 116, 23806 (2019). [OpenUrl][43][Abstract/FREE Full Text][44] 11. [↵][45]1. M. S. Blattner, 2. M. M. Mahoney , J. Biol. Rhythms 28, 291 (2013). [OpenUrl][46][CrossRef][47][PubMed][48] 12. [↵][49]1. I. N. Karatsoreos et al ., Endocrinology 152, 1970 (2011). [OpenUrl][50][CrossRef][51][PubMed][52][Web of Science][53] 13. [↵][54]1. C. Feillet et al ., PLOS ONE 11, e0150665 (2016). [OpenUrl][55] 14. [↵][56]1. F. J. Alibhai et al ., Cardiovasc. Res. 114, 259 (2018). [OpenUrl][57] 15. [↵][58]1. W. Liu et al ., Dis. Markers 2018, 7925219 (2018). [OpenUrl][59] Acknowledgments: We gratefully acknowledge funding from the Volkswagen Stiftung. G.A.F. is a senior adviser to Calico Laboratories. 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MIT researchers find 'systematic' shortcomings in ImageNet data set

#artificialintelligence

MIT researchers have concluded that the well-known ImageNet data set has "systematic annotation issues" and is misaligned with ground truth or direct observation when used as a benchmark data set. "Our analysis pinpoints how a noisy data collection pipeline can lead to a systematic misalignment between the resulting benchmark and the real-world task it serves as a proxy for," the researchers write in a paper titled "From ImageNet to Image Classification: Contextualizing Progress on Benchmarks." "We believe that developing annotation pipelines that better capture the ground truth while remaining scalable is an important avenue for future research." When the Stanford University Vision Lab introduced ImageNet at the Conference on Computer Vision and Pattern Recognition (CVPR) in 2009, it was much larger than many previously existing image data sets. The ImageNet data set contains millions of photos and was assembled over the span of more than two years. ImageNet uses the WordNet hierarchy for data labels and is widely used as a benchmark for object recognition models.


From Discrete to Continuous Convolution Layers

arXiv.org Machine Learning

A basic operation in Convolutional Neural Networks (CNNs) is spatial resizing of feature maps. This is done either by strided convolution (donwscaling) or transposed convolution (upscaling). Such operations are limited to a fixed filter moving at predetermined integer steps (strides). Spatial sizes of consecutive layers are related by integer scale factors, predetermined at architectural design, and remain fixed throughout training and inference time. We propose a generalization of the common Conv-layer, from a discrete layer to a Continuous Convolution (CC) Layer. CC Layers naturally extend Conv-layers by representing the filter as a learned continuous function over sub-pixel coordinates. This allows learnable and principled resizing of feature maps, to any size, dynamically and consistently across scales. Once trained, the CC layer can be used to output any scale/size chosen at inference time. The scale can be non-integer and differ between the axes. CC gives rise to new freedoms for architectural design, such as dynamic layer shapes at inference time, or gradual architectures where the size changes by a small factor at each layer. This gives rise to many desired CNN properties, new architectural design capabilities, and useful applications. We further show that current Conv-layers suffer from inherent misalignments, which are ameliorated by CC layers.