What are Alternatives to Traditional Performance Rating Cycles and Processes?

The dominant format for performance appraisal systems in large U.S. industrial companies continues to be an objective-based approach such as management by objectives (MBO). Most companies conduct formal performance ratings annually or semi-annually. However, the traditional way of performance rating is receiving more and more doubt. With the development of HR theories, practices and technology, many companies are trying to manage employee performance in new ways

BIOCHEMICAL CHARACTERIZATION OF HUMAN MISMATCH RECOGNITION PROTEINS MUTSα AND MUTSβ

The integrity of an organism\u27s genome depends on the fidelity of DNA replication and the efficiency of DNA repair. The DNA mismatch repair (MMR) system, which is highly conserved from prokaryotes to eukaryotes, plays an important role in maintaining genome stability by correcting base-base mismatches and insertion/deletion (ID) mispairs generated during DNA replication and other DNA transactions. Mismatch recognition is a critical step in MMR. Two mismatch recognition proteins, MutSα (MSH2-MSH6 heterodimer) and MutSβ (MSH2-MSH3 heterodimer), have been identified in eukaryotic cells. MutSα and MutSβ have partially overlapping functions, with MutSα recognizing primarily base-base mismatches and 1-2 nt ID mispairs and MutSβ recognizing 2-16-nt ID heteroduplexes. The goal of this dissertation research was to understand the mechanism underlying differential mismatch recognition by human MutSα and MutSβ and to characterize the unique functions of human MutSα and MutSβ in MMR. In this study, recombinant human MutSα and MutSβ were purified. Binding of the proteins to a T-G mispair and a 2-nt ID mispair was analyzed by gel-mobility assay; ATP/ADP binding was characterized using a UV cross-linking assay; ATPase activity was measured using an ATPase assay; MutSα amd MutSβ’s mismatch repair activity was evaluated using a reconstituted in vitro MMR assay. Our studies revealed that the preferential processing of base-base and ID heteroduplexes by MutSα and MutSβ respectively, is determined by the significant differences in the ATPase and ADP binding activities of MutSα and MutSβ, and the high ratio of MutSα:MutSβ in human cells. Our studies also demonstrated that MutSβ interacts similarly with a (CAG)n hairpin and a mismatch, and that excess MutSβ does not inhibit (CAG)n hairpin repair in vitro. These studies provide insight into the determinants of the differential DNA repair specificity of MutSα and MutSβ, the mechanism of mismatch repair initiation, and the mechanism of (CAG)n hairpin processing and repair, which plays a role in the etiology and progression of several human neurological diseases

Deep learning in computational microscopy

We propose to use deep convolutional neural networks (DCNNs) to perform 2D and 3D computational imaging. Specifically, we investigate three different applications. We first try to solve the 3D inverse scattering problem based on learning a huge number of training target and speckle pairs. We also demonstrate a new DCNN architecture to perform Fourier ptychographic Microscopy (FPM) reconstruction, which achieves high-resolution phase recovery with considerably less data than standard FPM. Finally, we employ DCNN models that can predict focused 2D fluorescent microscopic images from blurred images captured at overfocused or underfocused planes.Published versio

Hamiltonian and Phase-Space Representation of Spatial Solitons

We use Hamiltonian ray tracing and phase-space representation to describe the propagation of a single spatial soliton and soliton collisions in a Kerr nonlinear medium. Hamiltonian ray tracing is applied using the iterative nonlinear beam propagation method, which allows taking both wave effects and Kerr nonlinearity into consideration. Energy evolution within a single spatial soliton and the exchange of energy when two solitons collide are interpreted intuitively by ray trajectories and geometrical shearing of the Wigner distribution functions.Comment: 12 pages, 5 figure

3D differential phase contrast microscopy

We demonstrate 3D phase and absorption recovery from partially coherent intensity images captured with a programmable LED array source. Images are captured through-focus with four different illumination patterns. Using first Born and weak object approximations (WOA), a linear 3D differential phase contrast (DPC) model is derived. The partially coherent transfer functions relate the sample's complex refractive index distribution to intensity measurements at varying defocus. Volumetric reconstruction is achieved by a global FFT-based method, without an intermediate 2D phase retrieval step. Because the illumination is spatially partially coherent, the transverse resolution of the reconstructed field achieves twice the NA of coherent systems and improved axial resolution

Deep speckle correlation: a deep learning approach toward scalable imaging through scattering media

Imaging through scattering is an important yet challenging problem. Tremendous progress has been made by exploiting the deterministic input–output “transmission matrix” for a fixed medium. However, this “one-to-one” mapping is highly susceptible to speckle decorrelations – small perturbations to the scattering medium lead to model errors and severe degradation of the imaging performance. Our goal here is to develop a new framework that is highly scalable to both medium perturbations and measurement requirement. To do so, we propose a statistical “one-to-all” deep learning (DL) technique that encapsulates a wide range of statistical variations for the model to be resilient to speckle decorrelations. Specifically, we develop a convolutional neural network (CNN) that is able to learn the statistical information contained in the speckle intensity patterns captured on a set of diffusers having the same macroscopic parameter. We then show for the first time, to the best of our knowledge, that the trained CNN is able to generalize and make high-quality object predictions through an entirely different set of diffusers of the same class. Our work paves the way to a highly scalable DL approach for imaging through scattering media.National Science Foundation (NSF) (1711156); Directorate for Engineering (ENG). (1711156 - National Science Foundation (NSF); Directorate for Engineering (ENG))First author draf

Deep learning approach to scalable imaging through scattering media

We propose a deep learning technique to exploit “deep speckle correlations”. Our work paves the way to a highly scalable deep learning approach for imaging through scattering media.Published versio

Holographic particle localization under multiple scattering

We introduce a novel framework that incorporates multiple scattering for large-scale 3D particle-localization using single-shot in-line holography. Traditional holographic techniques rely on single-scattering models which become inaccurate under high particle-density. We demonstrate that by exploiting multiple-scattering, localization is significantly improved. Both forward and back-scattering are computed by our method under a tractable recursive framework, in which each recursion estimates the next higher-order field within the volume. The inverse scattering is presented as a nonlinear optimization that promotes sparsity, and can be implemented efficiently. We experimentally reconstruct 100 million object voxels from a single 1-megapixel hologram. Our work promises utilization of multiple scattering for versatile large-scale applications