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We introduce a co-designed approach for human portrait relighting that combines a physics-guided architecture with a pre-training framework. Drawing on the Cook-Torrance reflectance model, we have meticulously configured the architecture design to precisely simulate light-surface interactions. Furthermore, to overcome the limitation of scarce high-quality lightstage data, we have developed a self-supervised pre-training strategy. This novel combination of accurate physical modeling and expanded training dataset establishes a new benchmark in relighting realism., Comment: CVPR2024. Live demos available at https://www.beeble.ai/

This study introduces CCNETS (Causal Learning with Causal Cooperative Nets), a novel generative model-based classifier designed to tackle the challenge of generating data for imbalanced datasets in pattern recognition. CCNETS is uniquely crafted to emulate brain-like information processing and comprises three main components: Explainer, Producer, and Reasoner. Each component is designed to mimic specific brain functions, which aids in generating high-quality datasets and enhancing classification performance. The model is particularly focused on addressing the common and significant challenge of handling imbalanced datasets in machine learning. CCNETS's effectiveness is demonstrated through its application to a "fraud dataset," where normal transactions significantly outnumber fraudulent ones (99.83% vs. 0.17%). Traditional methods often struggle with such imbalances, leading to skewed performance metrics. However, CCNETS exhibits superior classification ability, as evidenced by its performance metrics. Specifically, it achieved an F1-score of 0.7992, outperforming traditional models like Autoencoders and Multi-layer Perceptrons (MLP) in the same context. This performance indicates CCNETS's proficiency in more accurately distinguishing between normal and fraudulent patterns. The innovative structure of CCNETS enhances the coherence between generative and classification models, helping to overcome the limitations of pattern recognition that rely solely on generative models. This study emphasizes CCNETS's potential in diverse applications, especially where quality data generation and pattern recognition are key. It proves effective in machine learning, particularly for imbalanced datasets. CCNETS overcomes current challenges in these datasets and advances machine learning with brain-inspired approaches., Comment: 31 pages, authors (3) is Corresponding Author

Chirality is a ubiquitous phenomenon in which a symmetry between left- and right-handed objects is broken, examples in nature ranging from subatomic particles and molecules to living organisms. In particle physics, the weak force is responsible for the symmetry breaking and parity violation in beta decay, but in condensed matter systems interactions that lead to chirality remain poorly understood. Here, we unravel the mechanism of chiral charge density wave formation in the transition-metal dichalcogenide 1T-TiSe2. Using representation analysis, we show that charge density modulations and ionic displacements, which transform as a continuous scalar field and a vector field on a discrete lattice, respectively, follow different irreducible representations of the space group, despite the fact that they propagate with the same wave-vectors and are strongly coupled to each other. This charge-lattice symmetry frustration is resolved by further breaking of all symmetries not common to both sectors through induced lattice distortions, thus leading to chirality. Our theory is verified using Raman spectroscopy and inelastic x-ray scattering, which reveal that all but translation symmetries are broken at a level not resolved by state-of-the-art diffraction techniques., Comment: 10 pages, 3 figures, 1 table

Spin nematic (SN) is a magnetic analog of classical liquid crystals, a fourth state of matter exhibiting characteristics of both liquid and solid. Particularly intriguing is a valence-bond SN, in which spins are quantum entangled to form a multi-polar order without breaking time-reversal symmetry, but its unambiguous experimental realization remains elusive. Here, we establish a SN phase in the square-lattice iridate Sr$_2$IrO$_4$, which approximately realizes a pseudospin one-half Heisenberg antiferromagnet (AF) in the strong spin-orbit coupling limit. Upon cooling, the transition into the SN phase at T$_C$ $\approx$ 263 K is marked by a divergence in the static spin quadrupole susceptibility extracted from our Raman spectra, and concomitant emergence of a collective mode associated with the spontaneous breaking of rotational symmetries. The quadrupolar order persists in the antiferromagnetic (AF) phase below T$_N$ $\approx$ 230 K, and becomes directly observable through its interference with the AF order in resonant x-ray diffraction, which allows us to uniquely determine its spatial structure. Further, we find using resonant inelastic x-ray scattering a complete breakdown of coherent magnon excitations at short-wavelength scales, suggesting a resonating-valence-bond-like quantum entanglement in the AF state. Taken together, our results reveal a quantum order underlying the N\'eel AF that is widely believed to be intimately connected to the mechanism of high temperature superconductivity (HTSC)., Comment: Published in https://www.nature.com/articles/s41586-023-06829-4

Here, we optimized the annealing temperature of HZO/TiN thin film heterostructure via multiscale analysis of remnant polarization, crystallographic phase, minimum ferroelectric domain size, and average grain size. We found that the remnant polarization was closely related to the relative amount of the orthorhombic phase whereas the minimum domain size was to the relative amount of the monoclinic phase. The minimum domain size was obtained at the annealing temperature of 500$^\cird$C while the optimum remnant polarization and capacitance at the annealing temperature of 600$^\circ$C. We conclude that the minimum domain size is more important than the sheer magnitude of remnant polarization considering the retention and fatigue of switchable polarization in nanoscale ferroelectric devices. Our results are expected to contribute to the development of ultra-low-power logic transistors and next-generation non-volatile memory devices., Comment: 28 pages, 11 figures, 1 table

Under strong laser fields, electrons in solids radiate high-harmonic fields by travelling through quantum pathways in Bloch bands in the sub-laser-cycle timescales. Understanding these pathways in the momentum space through the high-harmonic radiation can enable an all-optical ultrafast probe to observe coherent lightwave-driven processes and measure electronic structures as recently demonstrated for semiconductors. However, such demonstration has been largely limited for semimetals because the absence of the bandgap hinders an experimental characterization of the exact pathways. In this study, by combining electrostatic control of chemical potentials with HHG measurement, we resolve quantum pathways of massless Dirac fermions in graphene under strong laser fields. Electrical modulation of HHG reveals quantum interference between the multi-photon interband excitation channels. As the light-matter interaction deviates beyond the perturbative regime, elliptically polarized laser fields efficiently drive massless Dirac fermions via an intricate coupling between the interband and intraband transitions, which is corroborated by our theoretical calculations. Our findings pave the way for strong-laser-field tomography of Dirac electrons in various quantum semimetals and their ultrafast electronics with a gate control., Comment: 33 pages, 4 figures, Soonyoung Cha and Minjeong Kim contributed equally to this work, To whom correspondence should be addressed: jonghwankim@postech

Iridates have attracted much interest in the last decade for their novel magnetism emerging in the limit of strong spin-orbit coupling and possible unconventional superconductivity. A standard for growing iridate single crystals has been the flux method using platinum crucibles. Here, we show that this widely used method compromises the sample quality by inclusion of platinum impurities. We find that Sr2IrO4 single crystals grown in iridium crucibles show remarkable differences from those grown in platinum crucibles in their sample characterizations using Raman spectroscopy, resistivity, magnetization, optical third harmonic generation, resonant X-ray diffraction, and resonant inelastic X-ray scattering measurements. In particular, we show that several peaks of sizable intensities disappear in the Raman spectra of samples free of platinum impurities, and a significantly larger activation energy is extracted from the resistivity data compared to previously reported values. Furthermore, we find no evidence of the previously reported glide symmetry breaking structural distortions and confirm the I41/acd space group of the lattice symmetry. Although the platinum impurities are not apparent in the magnetic properties and thus went unnoticed in the stoichiometric insulating phase for a long time, their effects can be much more detrimental to transport properties in chemically doped compounds. Therefore, our result suggests using growth methods that avoid platinum impurities for an investigation of intrinsic physical properties of iridates, and possible superconducting phases., Comment: to be published in Physical Review Materials

Self-supervised learning (SSL) approaches such as wav2vec 2.0 and HuBERT models have shown promising results in various downstream tasks in the speech community. In particular, speech representations learned by SSL models have been shown to be effective for encoding various speech-related characteristics. In this context, we propose a novel automatic pronunciation assessment method based on SSL models. First, the proposed method fine-tunes the pre-trained SSL models with connectionist temporal classification to adapt the English pronunciation of English-as-a-second-language (ESL) learners in a data environment. Then, the layer-wise contextual representations are extracted from all across the transformer layers of the SSL models. Finally, the automatic pronunciation score is estimated using bidirectional long short-term memory with the layer-wise contextual representations and the corresponding text. We show that the proposed SSL model-based methods outperform the baselines, in terms of the Pearson correlation coefficient, on datasets of Korean ESL learner children and Speechocean762. Furthermore, we analyze how different representations of transformer layers in the SSL model affect the performance of the pronunciation assessment task., Comment: Submitted to INTERSPEECH 2022

Spin-orbit entangled pseudospins hold promise for a wide array of exotic magnetism ranging from a Heisenberg antiferromagnet to a Kitaev spin liquid depending on the lattice and bonding geometry, but many of the host materials suffer from lattice distortions and deviate from idealized models in part due to inherent strong pseudospin-lattice coupling. Here, we report on the synthesis of a magnetic superlattice comprising the single ($n$=1) and the double ($n$=2) layer members of the Ruddlesden-Popper series iridates Sr$_{n+1}$Ir$_{n}$O$_{3n+1}$ alternating along the $c$-axis, and provide a comprehensive study of its lattice and magnetic structures using scanning transmission electron microscopy, resonant elastic and inelastic x-ray scattering, third harmonic generation measurements and Raman spectroscopy. The superlattice is free of the structural distortions reported for the parent phases and has a higher point group symmetry, while preserving the magnetic orders and pseudospin dynamics inherited from the parent phases, featuring two magnetic transitions with two symmetry-distinct orders. We infer weaker pseudospin-lattice coupling from the analysis of Raman spectra and attribute it to frustrated magnetic-elastic couplings. Thus, the superlattice expresses a near ideal network of effective spin-one-half moments on a square lattice.