Evolution and variability of the Asian monsoon and its potential linkage with uplift of the Himalaya and Tibetan Plateau

© 2016, Tada et al. Uplift of the Himalaya and Tibetan Plateau (HTP) and its linkage with the evolution of the Asian monsoon has been regarded as a typical example of a tectonic–climate linkage. Although this linkage remains unproven because of insufficient data, our understanding has greatly advanced in the past decade. It is thus timely to summarize our knowledge of the uplift history of the HTP, the results of relevant climate simulations, and spatiotemporal changes in the Indian and East Asian monsoons since the late Eocene. Three major pulses of the HTP uplift have become evident: (1) uplift of the southern and central Tibetan Plateau (TP) at ca. 40–35 Ma, (2) uplift of the northern TP at ca. 25–20 Ma, and (3) uplift of the northeastern to eastern TP at ca. 15–10 Ma. Modeling predictions suggest that (i) uplift of the southern and central TP should have intensified the Indian summer monsoon (ISM) and the Somali Jet at 40–35 Ma; (ii) uplift of the northern TP should have intensified the East Asian summer monsoon (EASM) and East Asian winter monsoon (EAWM), as well as the desertification of inland Asia at 25–20 Ma; and (iii) uplift of the northeastern and eastern TP should have further intensified the EASM and EAWM at 15–10 Ma. We tested these predictions by comparing them with paleoclimate data for the time intervals of interest. There are insufficient paleoclimate data to test whether the ISM and Somali Jet intensified with the uplift of the southern and central TP at 40–35 Ma, but it is possible that such uplift enhanced erosion and weathering that drew down atmospheric CO2 and resulted in global cooling. There is good evidence that the EASM and EAWM intensified, and desertification started in inland Asia at 25–20 Ma in association with the uplift of the northern TP. The impact of the uplift of the northeastern and eastern TP on the Asian monsoon at 15–10 Ma is difficult to evaluate because that interval was also a time of global cooling and Antarctic glaciation that might also have influenced the intensity of the Asian monsoon

Yangtse River sediments and erosion rates from source to sink traced with cosmogenic 10 Be: Sediments from major rivers

Estimates of regional erosion and sediment mixing from different sources in the Yangtse River system are presented, based on sand samples collected from major tributaries and the trunk stream, at 23 sites between western Sichuan and the Yangtse Delta. Mixing is estimated from concentrations of Mg, Ca, Sr, Ti, Mn and Fe, which are substantially higher in sand from major tributaries in the western Yangtse River catchment than from tributaries in the eastern catchment. Intermediate concentrations occur in sand from the Yangtse Delta, both for modern samples from the surface and for early Holocene samples from drill holes. Mixing ratios indicate that 35 ± 5% of sand in the delta came from eastern sources. A similar result was obtained using cosmogenic 10Be in quartz grains as a tracer of mixing. Regional erosion rate estimated from 10Be in sand grains from high mountain catchments of the western Yangtse River are mostly similar to rates based on sediment gauging but are sometimes higher, and range to over 700 m Ma- 1, while 10Be measured at upper Yangtse River tributaries on the northeast Tibetan plateau gave rates of 20-30 m Ma- 1. For the eastern catchments, 10Be measurements from quartz sand and sediment gauging both gave rates of 30-70 m Ma- 1. Eroding at this rate, the eastern catchments could not supply more than 20% of the sediment in the delta, in contrast with 35% estimated from geochemical fingerprints. The relative input from eastern sources may have been higher in Late Pleistocene times, under a different climatic regime, and reworking of Pleistocene deposits may still be in progress

Model-Driven Beamforming Neural Networks

Beamforming is evidently a core technology in recent generations of mobile communication networks. Nevertheless, an iterative process is typically required to optimize the parameters, making it ill-placed for real-time implementation due to high complexity and computational delay. Heuristic solutions such as zero-forcing (ZF) are simpler but at the expense of performance loss. Alternatively, deep learning (DL) is well understood to be a generalizing technique that can deliver promising results for a wide range of applications at much lower complexity if it is sufficiently trained. As a consequence, DL may present itself as an attractive solution to beamforming. To exploit DL, this article introduces general data- and model-driven beamforming neural networks (BNNs), presents various possible learning strategies, and also discusses complexity reduction for the DL-based BNNs. We also offer enhancement methods such as training-set augmentation and transfer learning in order to improve the generality of BNNs, accompanied by computer simulation results and testbed results showing the performance of such BNN solutions

DeepSketchHair: Deep Sketch-based 3D Hair Modeling

We present sketchhair, a deep learning based tool for interactive modeling of 3D hair from 2D sketches. Given a 3D bust model as reference, our sketching system takes as input a user-drawn sketch (consisting of hair contour and a few strokes indicating the hair growing direction within a hair region), and automatically generates a 3D hair model, which matches the input sketch both globally and locally. The key enablers of our system are two carefully designed neural networks, namely, S2ONet, which converts an input sketch to a dense 2D hair orientation field; and O2VNet, which maps the 2D orientation field to a 3D vector field. Our system also supports hair editing with additional sketches in new views. This is enabled by another deep neural network, V2VNet, which updates the 3D vector field with respect to the new sketches. All the three networks are trained with synthetic data generated from a 3D hairstyle database. We demonstrate the effectiveness and expressiveness of our tool using a variety of hairstyles and also compare our method with prior art

MUSE-Fi: Contactless MUti-person SEnsing Exploiting Near-field Wi-Fi Channel Variation

Having been studied for more than a decade, Wi-Fi human sensing still faces a major challenge in the presence of multiple persons, simply because the limited bandwidth of Wi-Fi fails to provide a sufficient range resolution to physically separate multiple subjects. Existing solutions mostly avoid this challenge by switching to radars with GHz bandwidth, at the cost of cumbersome deployments. Therefore, could Wi-Fi human sensing handle multiple subjects remains an open question. This paper presents MUSE-Fi, the first Wi-Fi multi-person sensing system with physical separability. The principle behind MUSE-Fi is that, given a Wi-Fi device (e.g., smartphone) very close to a subject, the near-field channel variation caused by the subject significantly overwhelms variations caused by other distant subjects. Consequently, focusing on the channel state information (CSI) carried by the traffic in and out of this device naturally allows for physically separating multiple subjects. Based on this principle, we propose three sensing strategies for MUSE-Fi: i) uplink CSI, ii) downlink CSI, and iii) downlink beamforming feedback, where we specifically tackle signal recovery from sparse (per-user) traffic under realistic multi-user communication scenarios. Our extensive evaluations clearly demonstrate that MUSE-Fi is able to successfully handle multi-person sensing with respect to three typical applications: respiration monitoring, gesture detection, and activity recognition.Comment: 15 pages. Accepted by ACM MobiCom 202

One-shot ultraspectral imaging with reconfigurable metasurfaces

One-shot spectral imaging that can obtain spectral information from thousands of different points in space at one time has always been difficult to achieve. Its realization makes it possible to get spatial real-time dynamic spectral information, which is extremely important for both fundamental scientific research and various practical applications. In this study, a one-shot ultraspectral imaging device fitting thousands of micro-spectrometers (6336 pixels) on a chip no larger than 0.5 cm$^2$, is proposed and demonstrated. Exotic light modulation is achieved by using a unique reconfigurable metasurface supercell with 158400 metasurface units, which enables 6336 micro-spectrometers with dynamic image-adaptive performances to simultaneously guarantee the density of spectral pixels and the quality of spectral reconstruction. Additionally, by constructing a new algorithm based on compressive sensing, the snapshot device can reconstruct ultraspectral imaging information ($\Delta\lambda$/$\lambda$~0.001) covering a broad (300-nm-wide) visible spectrum with an ultra-high center-wavelength accuracy of 0.04-nm standard deviation and spectral resolution of 0.8 nm. This scheme of reconfigurable metasurfaces makes the device can be directly extended to almost any commercial camera with different spectral bands to seamlessly switch the information between image and spectral image, and will open up a new space for the application of spectral analysis combining with image recognition and intellisense

A New Framework for Fast Homomorphic Matrix Multiplication

Homomorphic Encryption (HE) is one of the mainstream cryptographic tools used to enable secure outsourced computation. A typical task is secure matrix computation. Popular HE schemes are all based on the problem of Ring Learning with Errors (RLWE), where the messages are encrypted in a ring. In general, the ring dimension should be large to ensure security, which is often larger than the matrix size. Hence, exploiting the ring structure to make fast homomorphic matrix computation has been an important topic in HE. In this paper, we present a new framework for encoding a matrix and performing multiplication on encrypted matrices. The new framework requires fewer basic homomorphic operations for matrix multiplication. Suppose that the ring dimension is $n$ and the matrix size is $d\times d$ with $d= n^{\rho}$. (1) In the compact case where $\rho \leq \frac{1}{3}$, the multiplication of two encrypted matrices requires $\tilde{O}(1)$ basic homomorphic operations, which include plaintext-ciphertext multiplications, ciphertext-ciphertext multiplications, and homomorphic Galois automorphisms. (2) In the large sized case where $\rho> \frac{1}{3}$, our new method requires $O\big(d^{(1 - \frac{1}{3\rho})\cdot \log_2 7 }\big)$ basic homomorphic operations, which is better than all existing methods. In addition, the new framework reduces the communication cost, since it requires fewer key-switching keys. The number of key-switching keys is reduced from $O(d)$ to $O(\log d)$