Twin Neural Network Regression is a Semi-Supervised Regression Algorithm

Twin neural network regression (TNNR) is a semi-supervised regression algorithm, it can be trained on unlabelled data points as long as other, labelled anchor data points, are present. TNNR is trained to predict differences between the target values of two different data points rather than the targets themselves. By ensembling predicted differences between the targets of an unseen data point and all training data points, it is possible to obtain a very accurate prediction for the original regression problem. Since any loop of predicted differences should sum to zero, loops can be supplied to the training data, even if the data points themselves within loops are unlabelled. Semi-supervised training improves TNNR performance, which is already state of the art, significantly

Twin Neural Network Regression

We introduce twin neural network (TNN) regression. This method predicts differences between the target values of two different data points rather than the targets themselves. The solution of a traditional regression problem is then obtained by averaging over an ensemble of all predicted differences between the targets of an unseen data point and all training data points. Whereas ensembles are normally costly to produce, TNN regression intrinsically creates an ensemble of predictions of twice the size of the training set while only training a single neural network. Since ensembles have been shown to be more accurate than single models this property naturally transfers to TNN regression. We show that TNNs are able to compete or yield more accurate predictions for different data sets, compared to other state-of-the-art methods. Furthermore, TNN regression is constrained by self-consistency conditions. We find that the violation of these conditions provides an estimate for the prediction uncertainty

Laser cavity-soliton microcombs

Microcavity-based frequency combs, or ‘microcombs’1,2, have enabled many fundamental breakthroughs3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21 through the discovery of temporal cavity-solitons. These self-localized waves, described by the Lugiato–Lefever equation22, are sustained by a background of radiation usually containing 95% of the total power23. Simple methods for their efficient generation and control are currently being investigated to finally establish microcombs as out-of-the-lab tools24. Here, we demonstrate microcomb laser cavity-solitons. Laser cavity-solitons are intrinsically background-free and have underpinned key breakthroughs in semiconductor lasers22,25,26,27,28. By merging their properties with the physics of multimode systems29, we provide a new paradigm for soliton generation and control in microcavities. We demonstrate 50-nm-wide bright soliton combs induced at average powers more than one order of magnitude lower than the Lugiato–Lefever soliton power threshold22, measuring a mode efficiency of 75% versus the theoretical limit of 5% for bright Lugiato–Lefever solitons23. Finally, we can tune the repetition rate by well over a megahertz without any active feedback

Seismogenic faults, landslides, and associated tsunamis off southern Italy - Cruise No. M86/2, December 27, 2011 - January 17, 2012, Cartagena (Spain) - Brindisi (Italy)

Summary The continental margins of southern Italy are located along converging plate boundaries, which are affected by intense seismicity and volcanic activity. Most of the coastal areas experienced severe earthquakes, landslides, and tsunamis in historical and/or modern times. The most prominent example is the Messina earthquake of Dec. 28, 1908 (Ms=7.3; 80,000 casualties), which was characterized by the worst tsunami Italy experienced in the historical time (~2000 casualties). It is, however, still unclear, whether this tsunami was triggered by a sudden vertical movement along a major fault during the earthquake or as a result of a giant marine slide initiated by the earthquake. The recurrence rates of major landslides and therefore the risk associated with landslides is also unknown. Based on detailed bathymetric data sets collected by Italian colleagues in the frame of the MaGIC Project (Marine Geohazards along the Italian Coast), we collected seismic data (2D and 3D) and gravity cores in three working areas (The Messina Straits, off Eastern Sicily, the Gioia Basin). A dense grid of new 2D-seismic data in the Messina Straits will allow to map fault patterns in great detail. One interesting outcome in this context is the identification of a set of normal faults striking in an EW-direction, which is almost perpendicular to the previously postulated faults. This EW-striking faults seem to be active. The area off eastern Sicily is characterized by numerous landslides and a complex deformation pattern. A 3D-seismic data set has been collected during the cruise using the so called P-cable in order to investigate these deformation patterns in detail. The new data will be the basis for a risk assessment in the working areas

Impact of Partial Volume Correction on [18F]GE-180 PET Quantification in Subcortical Brain Regions of Patients with Corticobasal Syndrome.

Corticobasal syndrome (CBS) is a rare neurodegenerative condition characterized by four-repeat tau aggregation in the cortical and subcortical brain regions and accompanied by severe atrophy. The aim of this study was to evaluate partial volume effect correction (PVEC) in patients with CBS compared to a control cohort imaged with the 18-kDa translocator protein (TSPO) positron emission tomography (PET) tracer [18F]GE-180. Eighteen patients with CBS and 12 age- and sex-matched healthy controls underwent [18F]GE-180 PET. The cortical and subcortical regions were delineated by deep nuclei parcellation (DNP) of a 3D-T1 MRI. Region-specific subcortical volumes and standardized uptake values and ratios (SUV and SUVr) were extracted before and after region-based voxel-wise PVEC. Regional volumes were compared between patients with CBS and controls. The % group differences and effect sizes (CBS vs. controls) of uncorrected and PVE-corrected SUVr data were compared. Single-region positivity in patients with CBS was assessed by a >2 SD threshold vs. controls and compared between uncorrected and PVE-corrected data. Smaller regional volumes were detected in patients with CBS compared to controls in the right ventral striatum (p = 0.041), the left putamen (p = 0.005), the right putamen (p = 0.038) and the left pallidum (p = 0.015). After applying PVEC, the % group differences were distinctly higher, but the effect sizes of TSPO uptake were only slightly stronger due to the higher variance after PVEC. The single-region positivity of TSPO PET increased in patients with CBS after PVEC (100 vs. 83 regions). PVEC in the cortical and subcortical regions is valuable for TSPO imaging of patients with CBS, leading to the improved detection of elevated [18F]GE-180 uptake, although the effect sizes in the comparison against the controls did not improve strongly

The Astropy Problem

The Astropy Project (http://astropy.org) is, in its own words, "a community effort to develop a single core package for Astronomy in Python and foster interoperability between Python astronomy packages." For five years this project has been managed, written, and operated as a grassroots, self-organized, almost entirely volunteer effort while the software is used by the majority of the astronomical community. Despite this, the project has always been and remains to this day effectively unfunded. Further, contributors receive little or no formal recognition for creating and supporting what is now critical software. This paper explores the problem in detail, outlines possible solutions to correct this, and presents a few suggestions on how to address the sustainability of general purpose astronomical software