Direct epitaxial growth of III-Vs on silicon for optical emitters and detectors is an elusive goal. Nanowires enable the local integration of high-quality III-V material, but advanced devices are hampered by their high-aspect ratio vertical geometry. Here, we demonstrate the in-plane monolithic integration of an InGaAs nanostructure p-i-n photodetector on Si. Using free space coupling, photodetectors demonstrate a spectral response from 1200-1700 nm. The 60 nm thin devices, with footprints as low as ~0.06 μm2, provide an ultra-low capacitance which is key for high-speed operation. We demonstrate high-speed optical data reception with a nanostructure photodetector at 32 Gb s-1, enabled by a 3 dB bandwidth exceeding ~25 GHz. When operated as light emitting diode, the p-i-n devices emit around 1600 nm, paving the way for future fully integrated optical links.Ferroaxial materials that exhibit spontaneous ordering of a rotational structural distortion with an axial vector symmetry have gained growing interest, motivated by recent extensive studies on ferroic materials. As in conventional ferroics (e.g., ferroelectrics and ferromagnetics), domain states will be present in the ferroaxial materials. However, the observation of ferroaxial domains is non-trivial due to the nature of the order parameter, which is invariant under both time-reversal and space-inversion operations. Here we propose that NiTiO3 is an order-disorder type ferroaxial material, and spatially resolve its ferroaxial domains by using linear electrogyration effect optical rotation in proportion to an applied electric field. To detect small signals of electrogyration (order of 10-5 deg V-1), we adopt a recently developed difference image-sensing technique. Furthermore, the ferroaxial domains are confirmed on nano-scale spatial resolution with a combined use of scanning transmission electron microscopy and convergent-beam electron diffraction. Our success of the domain visualization will promote the study of ferroaxial materials as a new ferroic state of matter.Urban areas exist in a wide variety of population sizes, from small towns to huge megacities. No proposed form for the statistical distribution of city sizes has received more attention than Zipf's law, a Pareto distribution with power law exponent equal to one. However, this distribution is typically violated by empirical evidence for small and large cities. Moreover, no theory presently exists to derive city size distributions from fundamental demographic choices while also explaining consistent variations. Here we develop a comprehensive framework based on demography to show how the structure of migration flows between cities, together with the differential magnitude of their vital rates, determine a variety of city size distributions. This approach provides a powerful mathematical methodology for deriving Zipf's law as well as other size distributions under specific conditions, and to resolve puzzles associated with their deviations in terms of concepts of choice, symmetry, information, and selection.An amendment to this paper has been published and can be accessed via a link at the top of the paper.We develop an auto-reservoir computing framework, Auto-Reservoir Neural Network (ARNN), to efficiently and accurately make multi-step-ahead predictions based on a short-term high-dimensional time series. Different from traditional reservoir computing whose reservoir is an external dynamical system irrelevant to the target system, ARNN directly transforms the observed high-dimensional dynamics as its reservoir, which maps the high-dimensional/spatial data to the future temporal values of a target variable based on our spatiotemporal information (STI) transformation. Thus, the multi-step prediction of the target variable is achieved in an accurate and computationally efficient manner. ARNN is successfully applied to both representative models and real-world datasets, all of which show satisfactory performance in the multi-step-ahead prediction, even when the data are perturbed by noise and when the system is time-varying. Actually, such ARNN transformation equivalently expands the sample size and thus has great potential in practical applications in artificial intelligence and machine learning.Yes-associated protein 1 (YAP) is a transcriptional regulator with critical roles in mechanotransduction, organ size control, and regeneration. Here, using advanced tools for real-time visualization of native YAP and target gene transcription dynamics, we show that a cycle of fast exodus of nuclear YAP to the cytoplasm followed by fast reentry to the nucleus ("localization-resets") activates YAP target genes. These "resets" are induced by calcium signaling, modulation of actomyosin contractility, or mitosis. Using nascent-transcription reporter knock-ins of YAP target genes, we show a strict association between these resets and downstream transcription. Oncogenically-transformed cell lines lack localization-resets and instead show dramatically elevated rates of nucleocytoplasmic shuttling of YAP, suggesting an escape from compartmentalization-based control. The single-cell localization and transcription traces suggest that YAP activity is not a simple linear function of nuclear enrichment and point to a model of transcriptional activation based on nucleocytoplasmic exchange properties of YAP.Eumelanin is a brown-black biological pigment with sunscreen and radical scavenging functions important to numerous organisms.  https://www.selleckchem.com/products/kpt-8602.html  Eumelanin is also a promising redox-active material for energy conversion and storage, but the chemical structures present in this heterogeneous pigment remain unknown, limiting understanding of the properties of its light-responsive subunits. Here, we introduce an ultrafast vibrational fingerprinting approach for probing the structure and interactions of chromophores in heterogeneous materials like eumelanin. Specifically, transient vibrational spectra in the double-bond stretching region are recorded for subsets of electronic chromophores photoselected by an ultrafast excitation pulse tuned through the UV-visible spectrum. All subsets show a common vibrational fingerprint, indicating that the diverse electronic absorbers in eumelanin, regardless of transition energy, contain the same distribution of IR-active functional groups. Aggregation of chromophores diverse in oxidation state is the key structural property underlying the universal, ultrafast deactivation behavior of eumelanin in response to photoexcitation with any wavelength.