Accelerated Cardiac Diffusion Tensor Imaging Using Joint Low-Rank and Sparsity Constraints

Objective: The purpose of this manuscript is to accelerate cardiac diffusion tensor imaging (CDTI) by integrating low-rankness and compressed sensing. Methods: Diffusion-weighted images exhibit both transform sparsity and low-rankness. These properties can jointly be exploited to accelerate CDTI, especially when a phase map is applied to correct for the phase inconsistency across diffusion directions, thereby enhancing low-rankness. The proposed method is evaluated both ex vivo and in vivo, and is compared to methods using either a low-rank or sparsity constraint alone. Results: Compared to using a low-rank or sparsity constraint alone, the proposed method preserves more accurate helix angle features, the transmural continuum across the myocardium wall, and mean diffusivity at higher acceleration, while yielding significantly lower bias and higher intraclass correlation coefficient. Conclusion: Low-rankness and compressed sensing together facilitate acceleration for both ex vivo and in vivo CDTI, improving reconstruction accuracy compared to employing either constraint alone. Significance: Compared to previous methods for accelerating CDTI, the proposed method has the potential to reach higher acceleration while preserving myofiber architecture features which may allow more spatial coverage, higher spatial resolution and shorter temporal footprint in the future.Comment: 11 pages, 16 figures, published on IEEE Transactions on Biomedical Engineerin

4,7,13,18-Tetra­oxa-1,10-diazo­nia­bicyclo­[8.5.5]icosane bis­(hexa­fluorido­phosphate)

The asymmetric unit of the title structure, C14H30N2O4 2+·2PF6 −, contains the anion and half of the cation, the latter being completed by a crystallographic twofold axis. The cation has a cage structure with the ammonium H atoms pointing into the cage. These H atoms are shielded from inter­molecular inter­actions and form only intra­molecular contacts. There are short inter­molecular C—H⋯F inter­actions in the structure, but no conventional inter­molecular hydrogen bonds

Learning Personalized Models of Human Behavior in Chess

Even when machine learning systems surpass human ability in a domain, there are many reasons why AI systems that capture human-like behavior would be desirable: humans may want to learn from them, they may need to collaborate with them, or they may expect them to serve as partners in an extended interaction. Motivated by this goal of human-like AI systems, the problem of predicting human actions -- as opposed to predicting optimal actions -- has become an increasingly useful task. We extend this line of work by developing highly accurate personalized models of human behavior in the context of chess. Chess is a rich domain for exploring these questions, since it combines a set of appealing features: AI systems have achieved superhuman performance but still interact closely with human chess players both as opponents and preparation tools, and there is an enormous amount of recorded data on individual players. Starting with an open-source version of AlphaZero trained on a population of human players, we demonstrate that we can significantly improve prediction of a particular player's moves by applying a series of fine-tuning adjustments. Furthermore, we can accurately perform stylometry -- predicting who made a given set of actions -- indicating that our personalized models capture human decision-making at an individual level.Comment: The current version of the paper corrects data processing problems present in the previous version. 21 pages, 13 figures, 7 tables (one very long

4,7,13,18-Tetra­oxa-1,10-diazo­nia­bicyclo­[8.5.5]icosane hexa­fluorido­silicate

The asymmetric unit of the title molecular salt, C14H30N2O4 2+·SiF6 2−, contains half of both the anion and the cation, both ions being completed by a crystallographic twofold axis passing through the Si atom. The cation has a cage structure with the ammonium H atoms pointing into the cage. These H atoms are shielded from inter­molecular inter­actions and form only intra­molecular contacts. There are short inter­molecular C—H⋯F inter­actions in the structure, but no conventional inter­molecular hydrogen bonds

Functional interplay between DEAD-box RNA helicases Ded1 and Dbp1 in preinitiation complex attachment and scanning on structured mRNAs in vivo

RNA structures that impede ribosome binding or subsequent scanning of the 5′-untranslated region (5′-UTR) for the AUG initiation codon reduce translation efficiency. Yeast DEAD-box RNA helicase Ded1 appears to promote translation by resolving 5′-UTR structures, but whether its paralog, Dbp1, performs similar functions is unknown. Furthermore, direct in vivo evidence was lacking that Ded1 or Dbp1 resolves 5′-UTR structures that impede attachment of the 43S preinitiation complex (PIC) or scanning. Here, profiling of translating 80S ribosomes reveals that the translational efficiencies of many more mRNAs are reduced in a ded1-ts dbp1Δ double mutant versus either single mutant, becoming highly dependent on Dbp1 or Ded1 only when the other helicase is impaired. Such ‘conditionally hyperdependent’ mRNAs contain unusually long 5′-UTRs with heightened propensity for secondary structure and longer transcript lengths. Consistently, overexpressing Dbp1 in ded1 cells improves the translation of many such Ded1-hyperdependent mRNAs. Importantly, Dbp1 mimics Ded1 in conferring greater acceleration of 48S PIC assembly in a purified system on mRNAs harboring structured 5′-UTRs. Profiling 40S initiation complexes in ded1 and dbp1 mutants provides direct evidence that Ded1 and Dbp1 cooperate to stimulate both PIC attachment and scanning on many Ded1/Dbp1-hyperdependent mRNAs in vivo.Intramural Research Program of the National Institutes of Health; Australian Research Council Discovery Project grant [DP180100111 to T.P.]; National Health and Medical Research Council of Australia Senior Research Fellowship [APP1135928]. Funding for open access charge: Intramural Research Program of the National Institutes of Health

Single-peak and narrow-band mid-infrared thermal emitters driven by mirror-coupled plasmonic quasi-BIC metasurfaces

Wavelength-selective thermal emitters (WS-EMs) hold considerable appeal due to the scarcity of cost-effective, narrow-band sources in the mid-to-long-wave infrared spectrum. WS-EMs achieved via dielectric materials typically exhibit thermal emission peaks with high quality factors (Q factors), but their optical responses are prone to temperature fluctuations. Metallic EMs, on the other hand, show negligible drifts with temperature changes, but their Q factors usually hover around 10. In this study, we introduce and experimentally verify a novel EM grounded in plasmonic quasi-bound states in the continuum (BICs) within a mirror-coupled system. Our design numerically delivers an ultra-narrowband single peak with a Q factor of approximately 64, and near-unity absorptance that can be freely tuned within an expansive band of more than 10 {\mu}m. By introducing air slots symmetrically, the Q factor can be further augmented to around 100. Multipolar analysis and phase diagrams are presented to elucidate the operational principle. Importantly, our infrared spectral measurements affirm the remarkable resilience of our designs' resonance frequency in the face of temperature fluctuations over 300 degrees Celsius. Additionally, we develop an effective impedance model based on the optical nanoantenna theory to understand how further tuning of the emission properties is achieved through precise engineering of the slot. This research thus heralds the potential of applying plasmonic quasi-BICs in designing ultra-narrowband, temperature-stable thermal emitters in mid-infrared. Moreover, such a concept may be adaptable to other frequency ranges, such as near-infrared, Terahertz, and Gigahertz.Comment: 39 pages, 12 figure

A Study on COVID-19 Incidence in Europe through Two SEIR Epidemic Models Which Consider Mixed Contagions from Asymptomatic and Symptomatic Individuals

The impact of the SARS-CoV-2 (COVID-19) on the world has been partially controlled through different measures of social isolation and prophylaxis. Two new SEIR (Susceptible-Exposed-Infected-Recovered) models are proposed in order to describe this spread through different countries of Europe. In both models the infectivity of the asymptomatic period during the exposed stage of the disease will be taken into account. The different transmission rates of the SEIR models are calculated by considering the different locations and, more importantly, the lockdown measures implemented in each region. A new classification of these intervention measures will be set and their influence on the values of the transmission rates will be estimated through regression analysis.The authors are grateful to the institute Carlos III for grant COV20/01213, to the Spanish Government for Grants RTI2018-094336-B-I00 and RTI2018-094902-BC22 (MCIU/AEI/FEDER, UE) and to the Basque Government for Grant IT1207-19

Is My Prediction Arbitrary? The Confounding Effects of Variance in Fair Classification Benchmarks

Variance in predictions across different trained models is a significant, under-explored source of error in fair classification. In practice, the variance on some data examples is so large that decisions can be effectively arbitrary. To investigate this problem, we take an experimental approach and make four overarching contributions: We 1) Define a metric called self-consistency, derived from variance, which we use as a proxy for measuring and reducing arbitrariness; 2) Develop an ensembling algorithm that abstains from classification when a prediction would be arbitrary; 3) Conduct the largest to-date empirical study of the role of variance (vis-a-vis self-consistency and arbitrariness) in fair classification; and, 4) Release a toolkit that makes the US Home Mortgage Disclosure Act (HMDA) datasets easily usable for future research. Altogether, our experiments reveal shocking insights about the reliability of conclusions on benchmark datasets. Most fairness classification benchmarks are close-to-fair when taking into account the amount of arbitrariness present in predictions -- before we even try to apply common fairness interventions. This finding calls into question the practical utility of common algorithmic fairness methods, and in turn suggests that we should fundamentally reconsider how we choose to measure fairness in machine learning