The Link Between Parental Absence and Poor Reading Comprehension: Evidence From the Left-Behind Children in Rural China

Family environment affects children's reading comprehension ability. In poverty-stricken parts of rural China, some parents have been migrant workers for many years. It is common for left-behind children to have one or both parents permanently missing. This study explores the impact of parental absence on children's reading comprehension. We measured the reading comprehension of 903 children (467 left-behind and 436 parented), using the Chinese Primary School Students' Reading Comprehension Ability Scale. After controlling for the parents' levels of education, reading input, and interest variables, we found that children with absent parents had significantly lower reading comprehension scores than parented children; they struggled to understand chapter layout, the author's intentions, writing technique, evaluation, and appreciation. While children with absent fathers received the same scores as parented children, those with absent mothers received significantly lower scores. Clearly, absent mothers have a greater impact on children's reading comprehension than absent fathers

SiamLST: Learning Spatial and Channel-wise Transform for Visual Tracking

Siamese network based trackers regard visual tracking as a similarity matching task between the target template and search region patches, and achieve a good balance between accuracy and speed in recent years. However, existing trackers do not effectively exploit the spatial and inter-channel cues, which lead to the redundancy of pre-trained model parameters. In this paper, we design a novel visual tracker based on a Learnable Spatial and Channel-wise Transform in Siamese network (SiamLST). The SiamLST tracker includes a powerful feature extraction backbone and an efficient cross-correlation method. The proposed algorithm takes full advantages of CNN and the learnable sparse transform module to represent the template and search patches, which effectively exploit the spatial and channel-wise correlations to deal with complicated scenarios, such as motion blur, in-plane rotation and partial occlusion. Experimental results conducted on multiple tracking benchmarks including OTB2015, VOT2016, GOT-10k and VOT2018 demonstrate that the proposed SiamLST has excellent tracking performances

Highly active air electrode catalysts for Zn‐air batteries: Catalytic mechanism and active center from obfuscation to clearness

Carbon‐based materials have been found to accelerate the sluggish kinetic reaction and are largely subject to the overall Zn‐air batteries (ZABs) property, while their full catalytic mechanism is still not excavated because of the indistinct internal structure and immature in‐situ technology. Up to now, systematic methods have been utilized to study and design promising high‐performance carbon‐based catalysts. To resolve the real active units and catalytic mechanism, developing molecular catalyst is a significant strategy. Herein, the review will initiate to briefly introduce the working principle and composition of ZABs. An important statement is correspondingly provided about the typical structure and catalytic mechanisms for the air cathode material. It also presents the tremendous endeavors on the catalytic performance and stability of carbon‐based material. Furthermore, combined with theoretical calculation, the self‐defined active sites are analyzed to understand the catalytic character, where the molecular catalyst is subsequently summarized and discussed through highlighting the unambiguous and controllable structure, in the hope of surfacing the optimum catalyst. Building on the fundamental understanding of carbon‐based and molecular catalysts, this review is expected to provide guidance and direction toward designing future mechanistic studies and ORR electrocatalysts

Evidence for quasi-one-dimensional charge density wave in CuTe by angle-resolved photoemission spectroscopy

We report the electronic structure of CuTe with a high charge density wave (CDW) transition temperature Tc = 335 K by angle-resolved photoemission spectroscopy (ARPES). An anisotropic charge density wave gap with a maximum value of 190 meV is observed in the quasi-one-dimensional band formed by Te px orbitals. The CDW gap can be filled by increasing temperature or electron doping through in situ potassium deposition. Combining the experimental results with calculated electron scattering susceptibility and phonon dispersion, we suggest that both Fermi surface nesting and electron-phonon coupling play important roles in the emergence of the CDW

Learning to Weight Samples for Dynamic Early-exiting Networks

Early exiting is an effective paradigm for improving the inference efficiency of deep networks. By constructing classifiers with varying resource demands (the exits), such networks allow easy samples to be output at early exits, removing the need for executing deeper layers. While existing works mainly focus on the architectural design of multi-exit networks, the training strategies for such models are largely left unexplored. The current state-of-the-art models treat all samples the same during training. However, the early-exiting behavior during testing has been ignored, leading to a gap between training and testing. In this paper, we propose to bridge this gap by sample weighting. Intuitively, easy samples, which generally exit early in the network during inference, should contribute more to training early classifiers. The training of hard samples (mostly exit from deeper layers), however, should be emphasized by the late classifiers. Our work proposes to adopt a weight prediction network to weight the loss of different training samples at each exit. This weight prediction network and the backbone model are jointly optimized under a meta-learning framework with a novel optimization objective. By bringing the adaptive behavior during inference into the training phase, we show that the proposed weighting mechanism consistently improves the trade-off between classification accuracy and inference efficiency. Code is available at https://github.com/LeapLabTHU/L2W-DEN.Comment: ECCV 202

Widely tunable band gap in a multivalley semiconductor SnSe by potassium doping

SnSe, a group IV-VI monochalcogenide with layered crystal structure similar to black phosphorus, has recently attracted extensive interests due to its excellent thermoelectric properties and potential device applications. Experimental electronic structure of both the valence and conduction bands is critical for understanding the effects of hole versus electron doping on the thermoelectric properties, and to further reveal possible change of the band gap upon doping. Here, we report the multivalley valence bands with a large effective mass on semiconducting SnSe crystals and reveal single-valley conduction bands through electron doping to provide a complete picture of the thermoelectric physics. Moreover, by electron doping through potassium deposition, the band gap of SnSe can be widely tuned from 1.2 eV to 0.4 eV, providing new opportunities for tunable electronic and optoelectronic devices