Domain Conditioned Adaptation Network

Tremendous research efforts have been made to thrive deep domain adaptation (DA) by seeking domain-invariant features. Most existing deep DA models only focus on aligning feature representations of task-specific layers across domains while integrating a totally shared convolutional architecture for source and target. However, we argue that such strongly-shared convolutional layers might be harmful for domain-specific feature learning when source and target data distribution differs to a large extent. In this paper, we relax a shared-convnets assumption made by previous DA methods and propose a Domain Conditioned Adaptation Network (DCAN), which aims to excite distinct convolutional channels with a domain conditioned channel attention mechanism. As a result, the critical low-level domain-dependent knowledge could be explored appropriately. As far as we know, this is the first work to explore the domain-wise convolutional channel activation for deep DA networks. Moreover, to effectively align high-level feature distributions across two domains, we further deploy domain conditioned feature correction blocks after task-specific layers, which will explicitly correct the domain discrepancy. Extensive experiments on three cross-domain benchmarks demonstrate the proposed approach outperforms existing methods by a large margin, especially on very tough cross-domain learning tasks.Comment: Accepted by AAAI 202

2,2′-(9,9-Dioctyl-9H-fluorene-2,7-di­yl)bis­(4,4,5,5-tetra­methyl-1,3,2-dioxaborolane)

In the title compound, C41H64B2O4, one of the five-membered rings has an envelope conformation, while the other, which may be affected by disorder, is nearly coplanar with the fluorene ring. The dihedral angle between the fluorene and dioxaborolane rings is 2.29 (1)°. Two of the methyl groups are disordered over two orientations in 0.67 (3):0.33 (3) and 0.568 (10):0.432 (10) ratios

Symmetry Protected Quantum State Renormalization

Symmetry protected topological (SPT) phases with gapless edge excitations have been shown to exist in principle in strongly interacting bosonic/fermionic systems and it is highly desirable to find practical systems to realize such phases through numerical calculation. A central question to be addressed is how to determine the SPT order in the system given the numerical simulation result while no local order parameter can be measured to distinguish the phases from a trivial one. In the tensor network approach to simulate strongly interacting systems, the quantum state renormalization algorithm has been demonstrated to be effective in identifying the intrinsic topological orders. Here we show that a modified algorithm can identify SPT orders by extracting the fixed point entanglement pattern in the ground state wave function which is essential for the existence of SPT order. The key to this approach is to add symmetry protection to the quantum state renormalization process and we demonstrate the effectiveness of this algorithm with the example of AKLT states in both 1D and 2D

Giant Light-Emission Enhancement in Lead Halide Perovskites by Surface Oxygen Passivation.

Surface condition plays an important role in the optical performance of semiconductor materials. As new types of semiconductors, the emerging metal-halide perovskites are promising for next-generation optoelectronic devices. We discover significantly improved light-emission efficiencies in lead halide perovskites due to surface oxygen passivation. The enhancement manifests close to 3 orders of magnitude as the perovskite dimensions decrease to the nanoscale, improving external quantum efficiencies from <0.02% to over 12%. Along with about a 4-fold increase in spontaneous carrier recombination lifetimes, we show that oxygen exposure enhances light emission by reducing the nonradiative recombination channel. Supported by X-ray surface characterization and theoretical modeling, we propose that excess lead atoms on the perovskite surface create deep-level trap states that can be passivated by oxygen adsorption

Caught in the Crossfire: Fears of Chinese-American Scientists

The US leadership in science and technology has greatly benefitted from immigrants from other countries, most notably from China in the recent decades. However, feeling the pressure of potential federal investigation since the 2018 launch of the China Initiative under the Trump administration, Chinese-origin scientists in the US now face higher incentives to leave the US and lower incentives to apply for federal grants. Analyzing data pertaining to institutional affiliations of more than 2.3 million scientific papers, we find a steady increase in the return migration of Chinese-origin scientists from the US back to China. We also conducted a survey of Chinese-origin scientists employed by US universities in tenure or tenure-track positions (n=1300), with results revealing general feelings of fear and anxiety that lead them to consider leaving the US and/or stop applying for federal grants.Comment: 16 pages, 2 figure

Approximating Human-Like Few-shot Learning with GPT-based Compression

In this work, we conceptualize the learning process as information compression. We seek to equip generative pre-trained models with human-like learning capabilities that enable data compression during inference. We present a novel approach that utilizes the Generative Pre-trained Transformer (GPT) to approximate Kolmogorov complexity, with the aim of estimating the optimal Information Distance for few-shot learning. We first propose using GPT as a prior for lossless text compression, achieving a noteworthy compression ratio. Experiment with LLAMA2-7B backbone achieves a compression ratio of 15.5 on enwik9. We justify the pre-training objective of GPT models by demonstrating its equivalence to the compression length, and, consequently, its ability to approximate the information distance for texts. Leveraging the approximated information distance, our method allows the direct application of GPT models in quantitative text similarity measurements. Experiment results show that our method overall achieves superior performance compared to embedding and prompt baselines on challenging NLP tasks, including semantic similarity, zero and one-shot text classification, and zero-shot text ranking

Digital Loop-Mediated Isothermal Amplification on a Commercial Membrane

In this work, we report digital loop-mediated isothermal amplification (LAMP) or reverse-transcription LAMP (RT-LAMP) on a commercial membrane, without the need for complex chip fabrication or use of specialized equipment. Due to the pore size distribution, the theoretical error for digital LAMP on these membranes was analyzed, using a combination of Random Distribution Model and Multi-volume Theory. A facile peel-off process was developed for effective droplets formation on the commercial track-etched polycarbonate (PCTE) membrane. Each pore functions as an individual nanoreactor for single DNA amplification. Absolute quantification of bacteria genomic DNA was realized with a dynamic range from 11 to 1.1 105 copies/µL. One-step digital RT-LAMP was also successfully performed on the membrane for the quantification of MS2 virus in wastewater. With the introduction of new probes, the positive pores can be easily distinguished from negative ones with 100 times difference in fluorescence intensities. Finally, the cost of a disposable membrane is less than $0.1/piece, which, to the best of our knowledge, is the most inexpensive way to perform digital LAMP. The membrane system offers opportunities for point-of-care users or common laboratories to perform digital quantification, single cell analysis, or other bioassays in an inexpensive, flexible and simplified way