Color-dependent interactions in the three coloring model

Since it was first discussed by Baxter in 1970, the three coloring model has been studied in several contexts, from frustrated magnetism to superconducting devices and glassiness. In presence of interactions, when the model is no longer exactly soluble, it was already observed that the phase diagram is highly nontrivial. Here we discuss the generic case of "color-dependent" nearest-neighbor interactions between the vertex chiralities. We uncover different critical regimes merging into one another: c=1/2 free fermions combining into c=1 free bosons; c=1 free bosons combining into c=2 critical loop models; as well as three separate c=1/2 critical lines merging at a supersymmetric c=3/2 critical point. When the three coupling constants are tuned to equal one another, transfer-matrix calculations highlight a puzzling regime where the central charge appears to vary continuously from 3/2 to 2.This work was supported in part by Engineering and Physical Sciences Research Council (EPSRC) Grant No. GR/R83712/01 and by EPSRC Postdoctoral Research Fellowship EP/G049394/1 (C. Castelnovo), and by EPSRC Grant No. EP/D070643/1 (JJHS). P. Verpoort acknowledges funding by the Studienstiftung des deutschen Volkes

Archetypal landscapes for deep neural networks.

The predictive capabilities of deep neural networks (DNNs) continue to evolve to increasingly impressive levels. However, it is still unclear how training procedures for DNNs succeed in finding parameters that produce good results for such high-dimensional and nonconvex loss functions. In particular, we wish to understand why simple optimization schemes, such as stochastic gradient descent, do not end up trapped in local minima with high loss values that would not yield useful predictions. We explain the optimizability of DNNs by characterizing the local minima and transition states of the loss-function landscape (LFL) along with their connectivity. We show that the LFL of a DNN in the shallow network or data-abundant limit is funneled, and thus easy to optimize. Crucially, in the opposite low-data/deep limit, although the number of minima increases, the landscape is characterized by many minima with similar loss values separated by low barriers. This organization is different from the hierarchical landscapes of structural glass formers and explains why minimization procedures commonly employed by the machine-learning community can navigate the LFL successfully and reach low-lying solutions.A.A.L. was supported by the Winton Program for the Physics of Sustainability. P.C.V. and D.J.W. were supported by the Engineering and Physical Sciences Research Council

Perspective: new insights from loss function landscapes of neural networks

Abstract: We investigate the structure of the loss function landscape for neural networks subject to dataset mislabelling, increased training set diversity, and reduced node connectivity, using various techniques developed for energy landscape exploration. The benchmarking models are classification problems for atomic geometry optimisation and hand-written digit prediction. We consider the effect of varying the size of the atomic configuration space used to generate initial geometries and find that the number of stationary points increases rapidly with the size of the training configuration space. We introduce a measure of node locality to limit network connectivity and perturb permutational weight symmetry, and examine how this parameter affects the resulting landscapes. We find that highly-reduced systems have low capacity and exhibit landscapes with very few minima. On the other hand, small amounts of reduced connectivity can enhance network expressibility and can yield more complex landscapes. Investigating the effect of deliberate classification errors in the training data, we find that the variance in testing AUC, computed over a sample of minima, grows significantly with the training error, providing new insight into the role of the variance-bias trade-off when training under noise. Finally, we illustrate how the number of local minima for networks with two and three hidden layers, but a comparable number of variable edge weights, increases significantly with the number of layers, and as the number of training data decreases. This work helps shed further light on neural network loss landscapes and provides guidance for future work on neural network training and optimisation

Long-lived non-equilibrium superconductivity in a non-centrosymmetric Rashba semiconductor

We report non-equilibrium magnetodynamics in the Rashba-superconductor GeTe, which lacks inversion symmetry in the bulk. We find that at low temperature the system exhibits a non-equilibrium state, which decays on time scales that exceed conventional electronic scattering times by many orders of magnitude. This reveals a non-equilibrium magnetoresponse that is asymmetric under magnetic field reversal and, strikingly, induces a non-equilibrium superconducting state distinct from the equilibrium one. We develop a model of a Rashba system where non-equilibrium configurations relax on a finite timescale which captures the qualitative features of the data. We also obtain evidence for the slow dynamics in another non-superconducting Rashba system. Our work provides novel insights into the dynamics of non-centrosymmetric superconductors and Rashba systems in general

Green synthesis and photocatalytic dye degradation activity of CuO Nanoparticles

This project was supported by Researchers Supporting Project Number (RSP-2023R7) King Saud University, Riyadh, Saudi Arabia. S.A.C.C. acknowledges support from FCT/MCTES (Fundação para a Ciência e Tecnologia and Ministério da Ciência, Tecnologia e Ensino Superior) through projects UIDB/50006/2020 and UIDP/50006/2020 and for the Scientific Employment Stimulus—Institutional Call (CEECINST/00102/2018). The authors thank The Islamia University of Bahawalpur for providing basic facilities.The degradation of dyes is a difficult task due to their persistent and stable nature; therefore, developing materials with desirable properties to degrade dyes is an important area of research. In the present study, we propose a simple, one-pot mechanochemical approach to synthesize CuO nanoparticles (NPs) using the leaf extract of Seriphidium oliverianum, as a reducing and stabilizing agent. The CuO NPs were characterized via X-ray diffraction (XRD), scanning electron microscopy (SEM), photoluminescence (PL) and Fourier-transform infrared spectroscopy (FTIR). The photocatalytic activity of CuO NPs was monitored using ultraviolet-visible (UV-Vis) spectroscopy. The CuO NPs exhibited high potential for the degradation of water-soluble industrial dyes. The degradation rates for methyl green (MG) and methyl orange (MO) were 65.231% ± 0.242 and 65.078% ± 0.392, respectively. Bio-mechanochemically synthesized CuO NPs proved to be good candidates for efficiently removing dyes from waterpublishersversionpublishe

Anti-CCP2 Antibodies: An Overview and Perspective of the Diagnostic Abilities of this Serological Marker for Early Rheumatoid Arthritis

The literature of the last 4 years confirms that the anti-CCP2 test is a very useful marker for the early and specific diagnosis of rheumatoid arthritis (RA). The anti-CCP2 test is very specific for RA (95–99%) and has sensitivity comparable to that of the rheumatoid factor (70–75%). The antibodies can be detected very early in the disease and can be used as an indicator for the progression and prognosis of RA. In this review, these interesting properties and some future possibilities of this diagnostic test are discussed

Antibodies to citrullinated proteins and differences in clinical progression of rheumatoid arthritis

Antibodies to citrullinated proteins (anti-cyclic-citrullinated peptide [anti-CCP] antibodies) are highly specific for rheumatoid arthritis (RA) and precede the onset of disease symptoms, indicating a pathogenetic role for these antibodies in RA. We recently showed that distinct genetic risk factors are associated with either anti-CCP-positive disease or anti-CCP-negative disease. These data are important as they indicate that distinct pathogenic mechanisms are underlying anti-CCP-positive disease or anti-CCP-negative disease. Likewise, these observations raise the question of whether anti-CCP-positive RA and anti-CCP-negative RA are clinically different disease entities. We therefore investigated whether RA patients with anti-CCP antibodies have a different clinical presentation and disease course compared with patients without these autoantibodies. In a cohort of 454 incident patients with RA, 228 patients were anti-CCP-positive and 226 patients were anti-CCP-negative. The early symptoms, tender and swollen joint count, and C-reactive protein level at inclusion, as well as the swollen joint count and radiological destruction during 4 years of follow-up, were compared for the two groups. There were no differences in morning stiffness, type, location and distribution of early symptoms, patients' rated disease activity and C-reactive protein at inclusion between RA patients with and without anti-CCP antibodies. The mean tender and swollen joint count for the different joints at inclusion was similar. At follow-up, patients with anti-CCP antibodies had more swollen joints and more severe radiological destruction. Nevertheless, the distribution of affected joints, for swelling, bone erosions and joint space narrowing, was similar. In conclusion, the phenotype of RA patients with or without anti-CCP antibodies is similar with respect to clinical presentation but differs with respect to disease course

Anti-citrullinated peptide antibody-negative RA is a genetically distinct subset: a definitive study using only bone-erosive ACPA-negative rheumatoid arthritis

Objectives. ACPA is a highly specific marker for RA. It was recently reported that ACPA can be used to classify RA into two disease subsets, ACPA-positive and ACPA-negative RA. ACPA-positive RA was found to be associated with the HLA-DR shared epitope (SE), but ACPA negative was not. However, the suspicion remained that this result was caused by the ACPA-negative RA subset containing patients with non-RA diseases. We examined whether this is the case even when possible non-RA ACPA-negative RA patients were excluded by selecting only patients with bone erosion