The state-of-the-art machine learning approaches are based on classical von Neumann computing architectures and have been widely used in many industrial and academic domains. With the recent ...development of quantum computing, researchers and tech-giants have attempted new quantum circuits for machine learning tasks. However, the existing quantum computing platforms are hard to simulate classical deep learning models or problems because of the intractability of deep quantum circuits. Thus, it is necessary to design feasible quantum algorithms for quantum machine learning for noisy intermediate scale quantum (NISQ) devices. This work explores variational quantum circuits for deep reinforcement learning. Specifically, we reshape classical deep reinforcement learning algorithms like experience replay and target network into a representation of variational quantum circuits. Moreover, we use a quantum information encoding scheme to reduce the number of model parameters compared to classical neural networks. To the best of our knowledge, this work is the first proof-of-principle demonstration of variational quantum circuits to approximate the deep Q-value function for decision-making and policy-selection reinforcement learning with experience replay and target network. Besides, our variational quantum circuits can be deployed in many near-term NISQ machines.
The present study investigated changes in autonomic nervous system activity and emotions after a short (2 h) forest bathing program in the Xitou Nature Education Area (XNEA), Taiwan. One hundred and ...twenty-eight (60.0 ± 7.44 years) middle-aged and elderly participants were recruited. Physiological responses, pulse rate, systolic and diastolic blood pressure, heart rate variability (HRV), and psychological indices were measured before and after the program. We observed that pulse rate, systolic and diastolic blood pressure were significantly lower after the program, which indicated physiological benefits from stress recovery. The Profile of Mood States negative mood subscale scores of "tension-anxiety", "anger-hostility", "fatigue-inertia", "depression-dejection", and "confusion-bewilderment" were significantly lower, whereas the positive mood subscale score of "vigor-activity" was higher. Furthermore, participants exhibited significantly lower anxiety levels according to the State-Trait Anxiety Inventory. However, changes in sympathetic and parasympathetic nerve activity were nonsignificant. Our study determined that the short forest bathing program is a promising therapeutic method for enhancing heart rate and blood pressure functions as well as an effective psychological relaxation strategy for middle-aged and elderly individuals.
The de novo design of antimicrobial therapeutics involves the exploration of a vast chemical repertoire to find compounds with broad-spectrum potency and low toxicity. Here, we report an efficient ...computational method for the generation of antimicrobials with desired attributes. The method leverages guidance from classifiers trained on an informative latent space of molecules modelled using a deep generative autoencoder, and screens the generated molecules using deep-learning classifiers as well as physicochemical features derived from high-throughput molecular dynamics simulations. Within 48 days, we identified, synthesized and experimentally tested 20 candidate antimicrobial peptides, of which two displayed high potency against diverse Gram-positive and Gram-negative pathogens (including multidrug-resistant Klebsiella pneumoniae) and a low propensity to induce drug resistance in Escherichia coli. Both peptides have low toxicity, as validated in vitro and in mice. We also show using live-cell confocal imaging that the bactericidal mode of action of the peptides involves the formation of membrane pores. The combination of deep learning and molecular dynamics may accelerate the discovery of potent and selective broad-spectrum antimicrobials.
Microtubules tightly regulate various cellular activities. Our understanding of microtubules is largely based on experiments using microtubule‐targeting agents, which, however, are insufficient to ...dissect the dynamic mechanisms of specific microtubule populations, due to their slow effects on the entire pool of microtubules. To overcome this technological limitation, we have used chemo and optogenetics to disassemble specific microtubule subtypes, including tyrosinated microtubules, primary cilia, mitotic spindles, and intercellular bridges, by rapidly recruiting engineered microtubule‐cleaving enzymes onto target microtubules in a reversible manner. Using this approach, we show that acute microtubule disassembly swiftly halts vesicular trafficking and lysosomal dynamics. It also immediately triggers Golgi and ER reorganization and slows the fusion/fission of mitochondria without affecting mitochondrial membrane potential. In addition, cell rigidity is increased after microtubule disruption owing to increased contractile stress fibers. Microtubule disruption furthermore prevents cell division, but does not cause cell death during interphase. Overall, the reported tools facilitate detailed analysis of how microtubules precisely regulate cellular architecture and functions.
SYNOPSIS
Characterization of microtubules in cells via microtubule‐targeting agents is not ideal for assessing dynamics of specific microtubule populations. Here, a novel approach using an engineered microtubule‐severing enzyme allows spatiotemporal manipulation of microtubule disassembly triggered by either chemicals or illumination.
A triple glutamine mutation in Spastin (dNSpastin3Q) reduces its association with microtubules.
Engineered dNSpastin3Q acts as a microtubule‐severing enzyme in living cells.
Recruitment of dNSpastin3Q to microtubules can be controlled by chemical‐ or light‐induced dimerization.
Induced dimerization of dNSpastin3Q leads to acute disassembly of target microtubule subtypes.
Spatiotemporal manipulation of microtubules by either chemical treatment or light allows assessing their function in cells.
Fluid intelligence (Gf) is the innate ability of an individual to respond to complex and unexpected situations. Although some studies have considered that the multiple-demand (MD) system of the brain ...was the biological foundation for Gf, further characterization of their relationships in the context of aging is limited. The present study hypothesized that the structural metrics of the MD system, including cortical thickness, cortical volumes, and white matter (WM) tract integrity, was the brain correlates for Gf across the adult life span. Partial correlation analysis was performed to investigate whether the MD system could still explain Gf independent of the age effect. Moreover, the partial correlations between Gf and left/right structural metrics within the MD regions were compared to test whether the correlations displayed distinct lateralization.
The participants were recruited from the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) databank, comprising the images of 603 healthy participants aged 18–88 years acquired on a 3-T system. The MRI data included high-resolution T1-weighted and diffusion-weighted images, from which gray matter and WM structural metrics of the MD system were analyzed, respectively. The structural metrics of gray matter were quantified in terms of cortical volume/thickness of five pairs of cortical regions, and those of WM were quantified in terms of the mean axial diffusivity (DA), radial diffusivity (DR), mean diffusivity (DM), and generalized fractional anisotropy (GFA) on five pairs of tracts. Partial correlation controlling for age and sex effects, was performed to investigate the associations of Gf scores with the mean DA, DR, DM and GFA of all tracts in the MD system, those of left and right hemispheric tracts, and those of each tract. Fisher’s exact test was used to compare the partial correlations between left and right MD regions.
The linear relationship between cortical volumes and Gf was evident across all levels of the MD system even after controlling for age and sex. For the WM integrity, diffusion indices including DA, DR, DM and GFA displayed linear relationships with Gf scores at various levels of the MD system. Among the 10 WM tracts connecting the MD regions, bilateral superior longitudinal fasciculus I and bilateral frontal aslant tracts exhibited the strongest and significant associations. Our results did not show significant inter-hemispheric differences in the associations between structural metrics of the MD system and Gf.
Our results demonstrate significant associations between Gf and both cortical volumes and tract integrity of the MD system across the adult lifespan in a population-based cohort. We found that the association remained significant in the entire adult lifespan despite simultaneous decline of Gf and the MD system. Our results suggest that the MD system might be a structural underpinning of Gf and support the fronto-parietal model of cognitive aging. However, we did not find hemispheric differences in the Gf-MD correlations, not supporting the hemi-aging hypothesis.
•The multiple-demand (MD) system links to fluid intelligence (Gf) in lesion brains.•Whether the MD system and Gf are related independent of age is unclear.•Structural metrics of the MD system were correlated with Gf even age was excluded.•Cortical volume and tract integrity revealed significant MD-Gf partial correlations.•The MD system is coupled with Gf in healthy population across adult lifespan.
Consider <inline-formula><tex-math notation="LaTeX">K</tex-math></inline-formula> processes, each generating a sequence of identical and independent random variables. The probability measures of ...these processes have random parameters that must be estimated. Specifically, they share a parameter <inline-formula><tex-math notation="LaTeX">\theta</tex-math></inline-formula> common to all probability measures. Additionally, each process <inline-formula><tex-math notation="LaTeX">i\in \lbrace 1, \dots, K\rbrace</tex-math></inline-formula> has a private parameter <inline-formula><tex-math notation="LaTeX">\alpha _{i}</tex-math></inline-formula>. The objective is to design an active sampling algorithm for sequentially estimating these parameters in order to form reliable estimates for all shared and private parameters with the fewest number of samples. This sampling algorithm has three key components: (i) data-driven sampling decisions, which dynamically over time specifies which of the <inline-formula><tex-math notation="LaTeX">K</tex-math></inline-formula> processes should be selected for sampling; (ii) stopping time for the process, which specifies when the accumulated data is sufficient to form reliable estimates and terminate the sampling process; and (iii) estimators for all shared and private parameters. Owing to the sequential estimation being known to be analytically intractable, this paper adopts conditional estimation cost functions, leading to a sequential estimation approach that was recently shown to render tractable analysis. Asymptotically optimal decision rules (sampling, stopping, and estimation) are delineated, and numerical experiments are provided to compare the efficacy and quality of the proposed procedure with those of the relevant approaches.
The neutrophil-to-lymphocyte ratio (NLR) is a crucial prognosis predictor following several major operations. However, the association between NLR and the outcome after hip fracture surgery is ...unclear. In this meta-analysis, we investigated the correlation between NLR and postoperative mortality in geriatric patients following hip surgery.
PubMed, Embase, Cochrane library, and Google Scholar were searched for studies up to June 2021 reporting the correlation between NLR and postoperative mortality in elderly patients undergoing surgery for hip fracture. Data from studies reporting the mean of NLR and its 95% confidence interval (CI) were pooled. Both long-term (≥ 1 year) and short-term (≤ 30 days) mortality rates were included for analysis.
Eight retrospective studies comprising a total of 1563 patients were included. Both preoperative and postoperative NLRs (mean difference MD: 2.75, 95% CI: 0.23-5.27; P = 0.03 and MD: 2.36, 95% CI: 0.51-4.21; P = 0.01, respectively) were significantly higher in the long-term mortality group than in the long-term survival group. However, no significant differences in NLR were noted between the short-term mortality and survival groups (MD: - 1.02, 95% CI: - 3.98 to 1.93; P = 0.5).
Higher preoperative and postoperative NLRs were correlated with a higher risk of long-term mortality following surgery for hip fracture in the geriatric population, suggesting the prognostic value of NLR for long-term survival. Further studies with well-controlled confounders are warranted to clarify the predictive value of NLR in clinical practice in geriatric patients with hip fracture.
In this article, we carve out artificial intelligence (AI) maintenance from the robustness perspective. Our proposal for AI maintenance facilitates robustness assessment, status tracking, risk ...scanning, model hardening, and regulation throughout the AI lifecycle, which is an essential milestone toward building sustainable and trustworthy AI ecosystems.
Deep Community Detection Pin-Yu Chen; Hero, Alfred O.
IEEE transactions on signal processing,
11/2015, Letnik:
63, Številka:
21
Journal Article
Recenzirano
Odprti dostop
A deep community in a graph is a connected component that can only be seen after removal of nodes or edges from the rest of the graph. This paper formulates the problem of detecting deep communities ...as multi-stage node removal that maximizes a new centrality measure, called the local Fiedler vector centrality (LFVC), at each stage. The LFVC is associated with the sensitivity of algebraic connectivity to node or edge removals. We prove that a greedy node/edge removal strategy, based on successive maximization of LFVC, has bounded performance loss relative to the optimal, but intractable, combinatorial batch removal strategy. Under a stochastic block model framework, we show that the greedy LFVC strategy can extract deep communities with probability one as the number of observations becomes large. We apply the greedy LFVC strategy to real-world social network datasets. Compared with conventional community detection methods we demonstrate improved ability to identify important communities and key members in the network.
The rapid spread of the COVID-19 pandemic has resulted in an unprecedented amount of sequence data of the SARS-CoV-2 genome-millions of sequences and counting. This amount of data, while being orders ...of magnitude beyond the capacity of traditional approaches to understanding the diversity, dynamics, and evolution of viruses, is nonetheless a rich resource for machine learning (ML) approaches as alternatives for extracting such important information from these data. It is of hence utmost importance to design a framework for testing and benchmarking the robustness of these ML models. This paper makes the first effort (to our knowledge) to benchmark the robustness of ML models by simulating biological sequences with errors. In this paper, we introduce several ways to perturb SARS-CoV-2 genome sequences to mimic the error profiles of common sequencing platforms such as Illumina and PacBio. We show from experiments on a wide array of ML models that some simulation-based approaches with different perturbation budgets are more robust (and accurate) than others for specific embedding methods to certain noise simulations on the input sequences. Our benchmarking framework may assist researchers in properly assessing different ML models and help them understand the behavior of the SARS-CoV-2 virus or avoid possible future pandemics.
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