Cross-Inferential Networks for Source-free Unsupervised Domain Adaptation

One central challenge in source-free unsupervised domain adaptation (UDA) is the lack of an effective approach to evaluate the prediction results of the adapted network model in the target domain. To address this challenge, we propose to explore a new method called cross-inferential networks (CIN). Our main idea is that, when we adapt the network model to predict the sample labels from encoded features, we use these prediction results to construct new training samples with derived labels to learn a new examiner network that performs a different but compatible task in the target domain. Specifically, in this work, the base network model is performing image classification while the examiner network is tasked to perform relative ordering of triplets of samples whose training labels are carefully constructed from the prediction results of the base network model. Two similarity measures, cross-network correlation matrix similarity and attention consistency, are then developed to provide important guidance for the UDA process. Our experimental results on benchmark datasets demonstrate that our proposed CIN approach can significantly improve the performance of source-free UDA.Comment: ICIP2023 accepte

Metric Subregularity for Subsmooth Generalized Constraint Equations in Banach Spaces

This paper is devoted to metric subregularity of a kind of generalized constraint equations. In particular, in terms of coderivatives and normal cones, we provide some necessary and sufficient conditions for subsmooth generalized constraint equations to be metrically subregular and strongly metrically subregular in general Banach spaces and Asplund spaces, respectively

Metric Subregularity for Subsmooth Generalized Constraint Equations in Banach Spaces

This paper is devoted to metric subregularity of a kind of generalized constraint equations. In particular, in terms of coderivatives and normal cones, we provide some necessary and sufficient conditions for subsmooth generalized constraint equations to be metrically subregular and strongly metrically subregular in general Banach spaces and Asplund spaces, respectively

Neuro-Modulated Hebbian Learning for Fully Test-Time Adaptation

Fully test-time adaptation aims to adapt the network model based on sequential analysis of input samples during the inference stage to address the cross-domain performance degradation problem of deep neural networks. We take inspiration from the biological plausibility learning where the neuron responses are tuned based on a local synapse-change procedure and activated by competitive lateral inhibition rules. Based on these feed-forward learning rules, we design a soft Hebbian learning process which provides an unsupervised and effective mechanism for online adaptation. We observe that the performance of this feed-forward Hebbian learning for fully test-time adaptation can be significantly improved by incorporating a feedback neuro-modulation layer. It is able to fine-tune the neuron responses based on the external feedback generated by the error back-propagation from the top inference layers. This leads to our proposed neuro-modulated Hebbian learning (NHL) method for fully test-time adaptation. With the unsupervised feed-forward soft Hebbian learning being combined with a learned neuro-modulator to capture feedback from external responses, the source model can be effectively adapted during the testing process. Experimental results on benchmark datasets demonstrate that our proposed method can significantly improve the adaptation performance of network models and outperforms existing state-of-the-art methods.Comment: CVPR2023 accepte

Structure, morphology and magnetic properties of flowerlike gamma-Fe2O3@NiO core/shell nanocomposites synthesized from different precursor concentrations

The flowerlike gamma-Fe2O3@NiO core/shell nanocomposites are synthesized by the two-step method. Their structure and morphology can be controlled by tuning the precursor concentration. Microstructural analysis reveals that all the samples have distinct core/shell structure without impurities, and the NiO shells are built of many irregular nanosheets which enclose the surface of gamma-Fe2O3 core. As the precursor concentration decreases (i.e., more NiO content), the NiO grain grows significantly, and the thickness of NiO shells increases. Magnetic experiments are performed to analyze the influences of different microstructures on magnetic properties of samples and we have the following two results. First, at 5 K, along with increasing thickness of NiO shell, the saturation magnetization increases, while the residual magnetization decreases slightly. Second, the hysteresis loops under cooling field demonstrate that the value of exchange bias effect fluctuates between 13 Oe and 17 Oe. This is mainly because of the NiO shell that (i) is composed of irregular nanosheets with disordered orientations, and (ii) does not form a complete coating around gamma-Fe2O3 core

Pollen source areas of lakes with inflowing rivers: modern pollen influx data from Lake Baiyangdian, China

Comparing pollen influx recorded in traps above the surface and below the surface of Lake Baiyangdian in northern China shows that the average pollen influx in the traps above the surface is much lower, at 1210 grains cm−2 a−1 (varying from 550 to 2770 grains cm−2 a−1), than in the traps below the surface which average 8990 grains cm−2 a−1 (ranging from 430 to 22310 grains cm−2 a−1). This suggests that about 12% of the total pollen influx is transported by air, and 88% via inflowing water. If hydrophyte pollen types are not included, the mean pollen influx in the traps above the surface decreases to 470 grains cm−2 a−1 (varying from 170 to 910 grains cm−2 a−1) and to 5470 grains cm−2 a−1 in the traps below the surface (ranging from 270 to 12820 grains cm−2 a−1), suggesting that the contribution of waterborne pollen to the non-hydrophyte pollen assemblages in Lake Baiyangdian is about 92%. When trap assemblages are compared with sediment–water interface samples from the same location, the differences between pollen assemblages collected using different methods are more significant than differences between assemblages collected at different sample sites in the lake using the same trapping methods. We compare the ratios of terrestrial pollen and aquicolous pollen types (T/A) between traps in the water and aerial traps, and examine pollen assemblages to determine whether proportions of long-distance taxa (i.e. those known to only grow beyond the estimated aerial source radius); these data suggest that the pollen source area of this lake is composed of three parts, an aerial component mainly carried by wind, a fluvial catchment component transported by rivers and another waterborne component transported by surface wash. Where the overall vegetation composition within the ‘aerial catchment’ is different from that of the hydrological catchment, the ratio between aerial and waterborne pollen influx offers a method for estimating the relative importance of these two sources, and therefore a starting point for defining a pollen source area for a lake with inflowing rivers