Some Formal Implications of Deletion Saltation

In a phonological saltation alternation, a segment or class “skips” a relatively similar category to surface as something less similar, as when /ɡ/ alternates with [x], skipping [k]. White (2013) and Hayes and White (2015) argue that saltation is unnatural—difficult to learn in the laboratory and diachronically unstable. They propose that the phonological grammar includes a learning bias against such unnatural patterns. White and Hayes further demonstrate that Harmonic Grammar (HG; Legendre, Miyata, and Smolensky 1990) cannot model typical saltation without nondefault mechanisms that would require extra steps in acquisition, making HG consistent with their proposed learning bias. I identify deletion saltation as a distinct saltation subtype and show that HG, with faithfulness formalized in standard Correspondence Theory (CT; McCarthy and Prince 1995), can model this pattern. HG/CT thus predicts that deletion saltation, unlike typical (here called segment-scale) saltation, is natural. Other frameworks fail to distinguish the two saltation types—they can either model both types, or neither. Consequently, if future empirical work finds deletion saltation to be more natural than other saltation patterns, this would support weighted-constraint models such as HG over ranked-constraint models such as Optimality Theory (OT; Prince and Smolensky 1993, 2004); would support CT over the *MAP model of faithfulness (Zuraw 2013); and would support formalizing CT featural-faithfulness constraints in terms of IDENT constraints, binary features, or both.

Robust semi-supervised non-negative matrix factorization for binary subspace learning

Non-negative matrix factorization and its extensions were applied to various areas (i.e., dimensionality reduction, clustering, etc.). When the original data are corrupted by outliers and noise, most of non-negative matrix factorization methods cannot achieve robust factorization and learn a subspace with binary codes. This paper puts forward a robust semi-supervised non-negative matrix factorization method for binary subspace learning, called RSNMF, for image clustering. For better clustering performance on the dataset contaminated by outliers and noise, we propose a weighted constraint on the noise matrix and impose manifold learning into non-negative matrix factorization. Moreover, we utilize the discrete hashing learning method to constrain the learned subspace, which can achieve a binary subspace from the original data. Experimental results validate the robustness and effectiveness of RSNMF in binary subspace learning and image clustering on the face dataset corrupted by Salt and Pepper noise and Contiguous Occlusion.

Recognition of Heterogeneous Edges in Multiwavelength Transmission Images Based on the Weighted Constraint Decision Method

Multiwavelength light transmission imaging provides a possibility for early detection of breast cancer. However, due to strong scattering during the transmission process of breast tissue analysis, the transmitted image signal is weak and the image is blurred and this makes heterogeneous edge detection difficult. This paper proposes a method based on the weighted constraint decision (WCD) method to eliminate the erosion and checkerboard effects in image histogram equalization (HE) enhancement and to improve the recognition of heterogeneous edge. Multiwavelength transmission images of phantom are acquired on the designed experimental system and the mask image with high signal-to-noise ratio (SNR) is obtained by frame accumulation and an Otsu thresholding model. Then, during image enhancement the image is divided into low-gray-level (LGL) and high-gray-level (HGL) regions according to the distribution of light intensity in image. And the probability density distribution of gray level in the LGL and HGL regions are redefined respectively according to the WCD method. Finally, the reconstructed image is obtained based on the modified HE. The experimental results show that compared with traditional image enhancement methods, the WCD method proposed in this paper can greatly improve the contrast between heterogeneous region and normal region. Moreover, the correlation between the original image data is maintained to the greatest extent, so that the edge of the heterogeneity can be detected more accurately. In conclusion, the WCD method not only accurately identifies the edge of heterogeneity in multiwavelength transmission images, but it also could improve the clinical application of multiwavelength transmission images in the early detection of breast cancer.

Weighted Constraint Iterative Algorithm for Phase Hologram Generation

A weighted constraint iterative algorithm is presented to calculate phase holograms with quality reconstruction. The image plane is partitioned into two regions where different constraint strategies are implemented during the iteration process. In the image plane, the signal region is constrained directly according to the amplitude distribution of the target image based on an adaptive strategy, whereas the non-signal region is constrained indirectly by total energy control of the hologram plane based on the energy conservation principle. The weighted constraint strategy can improve the reconstruction quality of the phase holograms by broadening the optimizing space of the iterative algorithm, leading to effective convergence of the iteration process. Finally, numerical and optical experiments have been performed to validate the feasibility of our method.

Predicting surface forms in complex inflectional paradigms through phonological constraints

This paper describes a new approach to the 'cell-filling problem' in inflectional paradigms (Ackerman and Malouf 2013) that builds on an account in Rosen (2019) in the framework of Gradient Symbolic Computation ('GSC') (Smolensky and Goldrick 2016). Exponents in a paradigm are derived through (a) Faithfulness to gradiently-weighted input blends of phonological material that occur both as part of a lexeme and on a morphosyntactic feature combination and (b) a gradiently weighted constraint that rewards a candidate for identity with a principal part form (Finkel and Stump 2007) in the paradigm. The analysis is applied to complex paradigms in Ngiti and Kwerba (Finkel and Stump 2007), both of which the previous account was unable to capture, and is presented as evidence in favour of the partially-activated features and weighted constrains in the GSC model.