Correlator Convolutional Neural Networks: An Interpretable Architecture for Image-like Quantum Matter Data

Machine learning models are a powerful theoretical tool for analyzing data from quantum simulators, in which results of experiments are sets of snapshots of many-body states. Recently, they have been successfully applied to distinguish between snapshots that can not be identified using traditional one and two point correlation functions. Thus far, the complexity of these models has inhibited new physical insights from this approach. Here, using a novel set of nonlinearities we develop a network architecture that discovers features in the data which are directly interpretable in terms of physical observables. In particular, our network can be understood as uncovering high-order correlators which significantly differ between the data studied. We demonstrate this new architecture on sets of simulated snapshots produced by two candidate theories approximating the doped Fermi-Hubbard model, which is realized in state-of-the art quantum gas microscopy experiments. From the trained networks, we uncover that the key distinguishing features are fourth-order spin-charge correlators, providing a means to compare experimental data to theoretical predictions. Our approach lends itself well to the construction of simple, end-to-end interpretable architectures and is applicable to arbitrary lattice data, thus paving the way for new physical insights from machine learning studies of experimental as well as numerical data.Comment: 7 pages, 4 figures + 13 pages of supplemental materia

Machine learning discovery of new phases in programmable quantum simulator snapshots

Machine learning has recently emerged as a promising approach for studying complex phenomena characterized by rich datasets. In particular, data-centric approaches lend to the possibility of automatically discovering structures in experimental datasets that manual inspection may miss. Here, we introduce an interpretable unsupervised-supervised hybrid machine learning approach, the hybrid-correlation convolutional neural network (Hybrid-CCNN), and apply it to experimental data generated using a programmable quantum simulator based on Rydberg atom arrays. Specifically, we apply Hybrid-CCNN to analyze new quantum phases on square lattices with programmable interactions. The initial unsupervised dimensionality reduction and clustering stage first reveals five distinct quantum phase regions. In a second supervised stage, we refine these phase boundaries and characterize each phase by training fully interpretable CCNNs and extracting the relevant correlations for each phase. The characteristic spatial weightings and snippets of correlations specifically recognized in each phase capture quantum fluctuations in the striated phase and identify two previously undetected phases, the rhombic and boundary-ordered phases. These observations demonstrate that a combination of programmable quantum simulators with machine learning can be used as a powerful tool for detailed exploration of correlated quantum states of matter.Comment: 9 pages, 5 figures + 12 pages, 10 figures appendi

Harnessing Interpretable and Unsupervised Machine Learning to Address Big Data from Modern X-ray Diffraction

The information content of crystalline materials becomes astronomical when collective electronic behavior and their fluctuations are taken into account. In the past decade, improvements in source brightness and detector technology at modern x-ray facilities have allowed a dramatically increased fraction of this information to be captured. Now, the primary challenge is to understand and discover scientific principles from big data sets when a comprehensive analysis is beyond human reach. We report the development of a novel unsupervised machine learning approach, XRD Temperature Clustering (X-TEC), that can automatically extract charge density wave (CDW) order parameters and detect intra-unit cell (IUC) ordering and its fluctuations from a series of high-volume X-ray diffraction (XRD) measurements taken at multiple temperatures. We apply X-TEC to XRD data on a quasi-skutterudite family of materials, (Ca$_x$Sr$_{1-x}$)$_3$Rh$_4$Sn$_{13}$, where a quantum critical point arising from charge order is observed as a function of Ca concentration. We further apply X-TEC to XRD data on the pyrochlore metal, Cd$_2$Re$_2$O$_7$, to investigate its two much debated structural phase transitions and uncover the Goldstone mode accompanying them. We demonstrate how unprecedented atomic scale knowledge can be gained when human researchers connect the X-TEC results to physical principles. Specifically, we extract from the X-TEC-revealed selection rule that the Cd and Re displacements are approximately equal in amplitude, but out of phase. This discovery reveals a previously unknown involvement of $5d^2$ Re, supporting the idea of an electronic origin to the structural order. Our approach can radically transform XRD experiments by allowing in-operando data analysis and enabling researchers to refine experiments by discovering interesting regions of phase space on-the-fly

Operational accuracy and comparative persistent antigenicity of HRP2 rapid diagnostic tests for Plasmodium falciparum malaria in a hyperendemic region of Uganda

BACKGROUND: Parasite-based diagnosis of malaria by microscopy requires laboratory skills that are generally unavailable at peripheral health facilities. Rapid diagnostic tests (RDTs) require less expertise, but accuracy under operational conditions has not been fully evaluated in Uganda. There are also concerns about RDTs that use the antigen histidine-rich protein 2 (HRP2) to detect Plasmodium falciparum, because this antigen can persist after effective treatment, giving false positive test results in the absence of infection. An assessment of the accuracy of Malaria Pf immuno-chromatographic test (ICT) and description of persistent antigenicity of HRP2 RDTs was undertaken in a hyperendemic area of Uganda. METHODS: Using a cross-sectional design, a total of 357 febrile patients of all ages were tested using ICT, and compared to microscopy as the gold standard reference. Two independent RDT readings were used to assess accuracy and inter-observer reliability. With a longitudinal design to describe persistent antigenicity of ICT and Paracheck, 224 children aged 6-59 months were followed up at 7-day intervals until the HRP2 antigens where undetectable by the RDTs. RESULTS: Of the 357 patients tested during the cross-sectional component, 40% (139) had positive blood smears for asexual forms of P. falciparum. ICT had an overall sensitivity of 98%, a specificity of 72%, a negative predictive value (NPV) of 98% and a positive predictive value (PPV) of 69%. ICT showed a high inter-observer reliability under operational conditions, with 95% of readings having assigned the same results (kappa statistics 0.921, p 50,000/microl, the mean duration of persistent antigenicity was 37 days compared to 26 days for parasitaemia less than 1,000/microl (log rank 21.9, p < 0.001). CONCLUSION: ICT is an accurate and appropriate test for operational use as a diagnostic tool where microscopy is unavailable. However, persistent antigenicity reduces the accuracy of this and other HRP2-based RDTs. The low specificity continues to be of concern, especially in children below five years of age. These pose limitations that need consideration, such as their use for diagnosis of patients returning with symptoms within two to four weeks of treatment. Good clinical skills are essential to interpret test results

Comparison of different methods for delayed post-mortem diagnosis of falciparum malaria

<p>Abstract</p> <p>Background</p> <p>Between 10,000 and 12,000 cases of imported malaria are notified in the European Union each year. Despite an excellent health care system, fatalities do occur. In case of advanced autolysis, the post-mortem diagnostic is impaired. Quicker diagnosis could be achieved by using rapid diagnostic malaria tests.</p> <p>Methods</p> <p>In order to evaluate different methods for the post-mortem diagnosis of <it>Plasmodium falciparum </it>malaria in non-immunes, a study was performed on the basis of forensic autopsies of corpses examined at variable intervals after death in five cases of fatal malaria (with an interval of four hours to five days), and in 20 cases of deaths unrelated to malaria. Detection of parasite DNA by PCR and an immunochromatographic test (ICT) based upon the detection of <it>P. falciparum </it>histidine-rich protein 2 (PfHRP2) were compared with the results of microscopic examination of smears from cadaveric blood, histopathological findings, and autopsy results.</p> <p>Results</p> <p>In all cases of fatal malaria, post-mortem findings were unsuspicious for the final diagnosis, and autoptic investigations, including histopathology, were only performed because of additional information by police officers and neighbours. Macroscopic findings during autopsy were unspecific. Histopathology confirmed sequestration of erythrocytes and pigment in macrophages in most organs in four patients (not evaluable in one patient due to autolysis). Microscopy of cadaveric blood smears revealed remnants of intraerythrocytic parasites, and was compromised or impossible due to autolysis in two cases. PCR and ICT performed with cadaveric blood were positive in all malaria patients and negative in all controls.</p> <p>Conclusion</p> <p>In non-immune fatalities with unclear anamnesis, ICT can be recommended as a sensitive and specific tool for post-mortem malaria diagnosis, which is easier and faster than microscopy, and also applicable when microscopic examination is impossible due to autolysis. PCR is more expensive and time-consuming, but may be used as confirmatory test. In highly endemic areas where asymptomatic parasitaemia is common, confirmation of the diagnosis of malaria as the cause of death has to rely on histopathological findings.</p

Indian Ocean Dipole drives malaria resurgence in East African highlands

Malaria resurgence in African highlands in the 1990s has raised questions about the underlying drivers of the increase in disease incidence including the role of El-Niño-Southern Oscillation (ENSO). However, climatic anomalies other than the ENSO are clearly associated with malaria outbreaks in the highlands. Here we show that the Indian Ocean Dipole (IOD), a coupled ocean-atmosphere interaction in the Indian Ocean, affected highland malaria re-emergence. Using cross-wavelet coherence analysis, we found four-year long coherent cycles between the malaria time series and the dipole mode index (DMI) in the 1990s in three highland localities. Conversely, we found a less pronounced coherence between malaria and DMI in lowland localities. The highland/lowland contrast can be explained by the effects of mesoscale systems generated by Lake Victoria on its climate basin. Our results support the need to consider IOD as a driving force in the resurgence of malaria in the East African highlands

Performance and usefulness of the Hexagon rapid diagnostic test in children with asymptomatic malaria living in the Mount Cameroon region

<p>Abstract</p> <p>Background</p> <p>Rapid and correct diagnosis of malaria is considered an important strategy in the control of the disease. However, it remains to be determined how well these tests can perform in those who harbour the parasite, but are asymptomatic, so that rapid diagnostic tests (RDTs) could be used in rapid mass surveillance in malaria control programmes.</p> <p>Methods</p> <p>Microscopic and immunochromatographic diagnosis of malaria were performed on blood samples from the hyperendemic Mount Cameroon region. Thin and thick blood films were stained with Giemsa and examined under light microscopy for malaria parasites. The RDT was performed on the blood samples for the detection of <it>Plasmodium </it>species. In addition, the performance characteristics of the test were determined using microscopy as gold standard.</p> <p>Results</p> <p>Results revealed 40.32% to be positive for microscopy and 34.41% to be positive for the RDT. Parasites were detected in a greater proportion of samples as the parasite density increase. <it>Plasmodium falciparum </it>was the predominant <it>Plasmodium </it>species detected in the study population either by microscopy or by the RDT. Overall, the test recorded a sensitivity and specificity of 85.33% and 95.05% respectively, and an accuracy of 91.40%. The sensitivity and specificity of the RDT increased as parasite densities increased.</p> <p>Conclusion</p> <p>The Hexagon Malaria Combi™ test showed a high sensitivity and specificity in diagnosing malaria in asymptomatic subjects and so could be suitable for use in mass surveillance programmes for the management and control of malaria.</p

Assessing agreement between malaria slide density readings

BACKGROUND: Several criteria have been used to assess agreement between replicate slide readings of malaria parasite density. Such criteria may be based on percent difference, or absolute difference, or a combination. Neither the rationale for choosing between these types of criteria, nor that for choosing the magnitude of difference which defines acceptable agreement, are clear. The current paper seeks a procedure which avoids the disadvantages of these current options and whose parameter values are more clearly justified. METHODS AND RESULTS: Variation of parasite density within a slide is expected, even when it has been prepared from a homogeneous sample. This places lower limits on sensitivity and observer agreement, quantified by the Poisson distribution. This means that, if a criterion of fixed percent difference criterion is used for satisfactory agreement, the number of discrepant readings is over-estimated at low parasite densities. With a criterion of fixed absolute difference, the same happens at high parasite densities. For an ideal slide, following the Poisson distribution, a criterion based on a constant difference in square root counts would apply for all densities. This can be back-transformed to a difference in absolute counts, which, as expected, gives a wider range of acceptable agreement at higher average densities. In an example dataset from Tanzania, observed differences in square root counts correspond to a 95% limits of agreement of -2,800 and +2,500 parasites/microl at average density of 2,000 parasites/microl, and -6,200 and +5,700 parasites/microl at 10,000 parasites/microl. However, there were more outliers beyond those ranges at higher densities, meaning that actual coverage of these ranges was not a constant 95%, but decreased with density. In a second study, a trial of microscopist training, the corresponding ranges of agreement are wider and asymmetrical: -8,600 to +5,200/microl, and -19,200 to +11,700/microl, respectively. By comparison, the optimal limits of agreement, corresponding to Poisson variation, are +/- 780 and +/- 1,800 parasites/microl, respectively. The focus of this approach on the volume of blood read leads to other conclusions. For example, no matter how large a volume of blood is read, some densities are too low to be reliably detected, which in turn means that disagreements on slide positivity may simply result from within-slide variation, rather than reading errors. CONCLUSIONS: The proposed method defines limits of acceptable agreement in a way which allows for the natural increase in variability with parasite density. This includes defining the levels of between-reader variability, which are consistent with random variation: disagreements within these limits should not trigger additional readings. This approach merits investigation in other settings, in order to determine both the extent of its applicability, and appropriate numerical values for limits of agreement