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Recent advances in nonlinear optics have revolutionized integrated photonics, providing on-chip solutions to a wide range of new applications. Currently, state of the art integrated nonlinear photonic devices are mainly based on dielectric material platforms, such as Si 3 N 4 and SiO 2 . While semiconductor materials feature much higher nonlinear coefficients and convenience in active integration, they have suffered from high waveguide losses that prevent the realization of efficient nonlinear processes on-chip. Here, we challenge this status quo and demonstrate a low loss AlGaAs-on-insulator platform with anomalous dispersion and quality ( Q ) factors beyond 1.5 × 10 6 . Such a high quality factor, combined with high nonlinear coefficient and small mode volume, enabled us to demonstrate a Kerr frequency comb threshold of only ∼36 µW in a resonator with a 1 THz free spectral range, ∼100 times lower compared to that in previous semiconductor platforms. Moreover, combs with broad spans (>250 nm) have been generated with a pump power of ∼300 µW, which is lower than the threshold power of state-of the-art dielectric micro combs. A soliton-step transition has also been observed for the first time in an AlGaAs resonator. Despite larger nonlinear coefficients, waveguide losses have prevented using semiconductors instead of dielectric materials for on-chip frequency-comb sources. By significantly reducing waveguide loss, ultra-low-threshold Kerr comb generation is demonstrated in a high- Q AlGaAs-on-insulator microresonator system.

The ability to spectrally translate lightwave signals in a compact, low-power platform is at the heart of the promise of nonlinear nanophotonic technologies. For example, a device to connect the telecommunications band with visible and short near-infrared wavelengths can enable a connection between high-performance chip-integrated lasers based on scalable nanofabrication technology with atomic systems used for time and frequency metrology. Although second-order nonlinear (χ(2)) systems are the natural approach for bridging such large spectral gaps, here we show that third-order nonlinear (χ(3)) systems, despite their typically much weaker nonlinear response, can realize spectral translation with unprecedented performance. By combining resonant enhancement with nanophotonic mode engineering in a silicon nitride microring resonator, we demonstrate efficient spectral translation of a continuous-wave signal from the telecom band (~1,550 nm) to the visible band (~650 nm) through cavity-enhanced four-wave mixing. We achieve such translation over a wide spectral range >250 THz with a translation efficiency of (30.1 ± 2.8)% and using an ultralow pump power of (329 ± 13) μW. The translation efficiency projects to (274 ± 28)% at 1 mW and is more than an order of magnitude larger than what has been achieved in current nanophotonic devices.

Nonlinearity is a powerful determinant of physical systems. Controlling nonlinearity leads to interesting states of matter and new applications. In optics, diverse families of continuous and discrete states arise from balance of nonlinearity and group-velocity dispersion (GVD). Moreover, the dichotomy of states with locally enhanced or diminished field intensity depends critically on the relative sign of nonlinearity and either anomalous or normal GVD. Here, we introduce a resonator with unconditionally normal GVD and a single defect mode that supports both dark, reduced-intensity states and bright, enhanced-intensity states. We access and explore this dark-to-bright pulse continuum by phase-matching with a photonic-crystal resonator, which mediates the competition of nonlinearity and normal GVD. These stationary temporal states are coherent frequency combs, featuring highly designable spectra and ultralow noise repetition-frequency and intensity characteristics. The dark-to-bright continuum illuminates physical roles of Kerr nonlinearity, GVD, and laser propagation in a gapped nanophotonic medium. A Kerr-nonlinear resonator with normal dispersion supports bright and dark pulse states. With photonic crystal ring resonators, this work demonstrates a continuum across these nonlinear states and explores the underlying mechanism.

Nonlinearity in complex systems leads to pattern formation through fundamental interactions between components. With integrated photonics, precision control of nonlinearity explores novel patterns and propels applications. In particular, Kerr-nonlinear resonators support stationary states—including Turing patterns—composed of a few interfering waves, and localized solitons composed of waves across a broad spectrum. Although Turing patterns emerge from an unstable Kerr resonator with sufficiently intense excitation, Kerr solitons do not form spontaneously under constant excitation, making this useful state challenging to access. Here we explore an edgeless photonic crystal resonator (PhCR) that enables spontaneous soliton formation in place of Turing patterns. We design the PhCR nanopattern for single-azimuthal-mode engineering of a group-velocity-dispersion defect that balances Kerr-nonlinear frequency shifts in favour of the soliton state. Our experiments establish PhCR solitons as modelocked pulses through ultraprecise optical-frequency measurements. We show that nanophotonics expand the palette for nonlinear engineering, enabling new phenomena and light sources.Researchers have demonstrated spontaneous soliton formation in an edgeless photonic crystal resonator.

In 6 to 10% of patients with acute myeloid leukemia, mutant isocitrate dehydrogenase 1 is thought to contribute to leukemogenesis. Ivosidenib is an oral inhibitor of mutant IDH1. In a randomized trial, event-free survival was significantly longer with ivosidenib and azacitidine than with placebo and azacitidine.

As smartphones become increasingly prevalent worldwide, the relationship between smartphone addiction and subjective well-being has become a focal point in academic circles. Prior research predominantly delved into the direct correlation between smartphone addiction and subjective well-being, yet there remains a dearth in exploring its underlying mechanisms. This study investigated the mediating role of loneliness in the relationship between smartphone addiction and subjective well-being among Chinese university students. Conducted across 16 universities in eight provinces and municipalities in China, this study encompassed 1527 university students. Data collection utilized scales measuring smartphone addiction, loneliness, and subjective well-being. The findings revealed that: (1) demographic variables such as place of origin, educational level, and family income influenced university students' subjective well-being; (2) a significant negative correlation existed between smartphone addiction and subjective well-being among university students, coupled with a significant positive correlation between smartphone addiction and loneliness, indicating the significant negative predictive effect of smartphone addiction on subjective well-being; (3) loneliness partially mediated the relationship between smartphone addiction and subjective well-being among university students, suggesting that smartphone addiction could directly impact university students' subjective well-being, or indirectly through its effect on loneliness.

Recently, topological semimetals have become a hot topic in condensed matter physics, including Dirac semimetals, Weyl semimetals, and nodal line semimetals (NLSMs). In this paper, a new type of NLSM-type-II NLSM-is proposed based on a two-band cubic lattice model. For type-II NLSM, the zero energy bulk states have a closed loop in momentum space but the (local) Weyl cones on the nodal line become tilted. The effect of the magnetic field and that of the correlation on type-II NLSM are studied. In particular, after considering the repulsive interaction and additional spin degrees of freedom, different types of long range magnetic orders appear in bulk states. In addition, the interaction-induced ferromagnetic (FM) order of surface states may exist. At a critical point between type-I NLSM and type-II NLSM, arbitrary tiny interactions induce FM order due to a flat band at the Fermi surface.

In more than 400 older patients with AML who could not receive myeloablative therapy, the incidence of composite complete remission was higher (66.4% vs. 28.3) and the median overall survival was longer (14.7 vs. 9.6 months) among patients who received azacitidine plus venetoclax (a B-cell lymphoma 2 antagonist) than among those who received azacitidine alone.

Tailoring the interactions between quantum emitters and single photons constitutes one of the cornerstones of quantum optics. Coupling a quantum emitter to the band edge of a photonic crystal waveguide (PCW) provides a unique platform for tuning these interactions. In particular, the cross-over from propagating fields E ( x ) ∝ e ± i k x x outside the bandgap to localized fields E ( x ) ∝ e − κ x | x | within the bandgap should be accompanied by a transition from largely dissipative atom–atom interactions to a regime where dispersive atom–atom interactions are dominant. Here, we experimentally observe this transition by shifting the band edge frequency of the PCW relative to the D₁ line of atomic cesium for N̄ = 3.0 ± 0.5 atoms trapped along the PCW. Our results are the initial demonstration of this paradigm for coherent atom–atom interactions with low dissipation into the guided mode.

Background Recently, automatically extracting biomedical relations has been a significant subject in biomedical research due to the rapid growth of biomedical literature. Since the adaptation to the biomedical domain, the transformer-based BERT models have produced leading results on many biomedical natural language processing tasks. In this work, we will explore the approaches to improve the BERT model for relation extraction tasks in both the pre-training and fine-tuning stages of its applications. In the pre-training stage, we add another level of BERT adaptation on sub-domain data to bridge the gap between domain knowledge and task-specific knowledge. Also, we propose methods to incorporate the ignored knowledge in the last layer of BERT to improve its fine-tuning. Results The experiment results demonstrate that our approaches for pre-training and fine-tuning can improve the BERT model performance. After combining the two proposed techniques, our approach outperforms the original BERT models with averaged F1 score improvement of 2.1% on relation extraction tasks. Moreover, our approach achieves state-of-the-art performance on three relation extraction benchmark datasets. Conclusions The extra pre-training step on sub-domain data can help the BERT model generalization on specific tasks, and our proposed fine-tuning mechanism could utilize the knowledge in the last layer of BERT to boost the model performance. Furthermore, the combination of these two approaches further improves the performance of BERT model on the relation extraction tasks.