Identification of Topological Features in Renal Tumor Microenvironment Associated with Patient Survival

Motivation As a highly heterogeneous disease, the progression of tumor is not only achieved by unlimited growth of the tumor cells, but also supported, stimulated, and nurtured by the microenvironment around it. However, traditional qualitative and/or semi-quantitative parameters obtained by pathologist’s visual examination have very limited capability to capture this interaction between tumor and its microenvironment. With the advent of digital pathology, computerized image analysis may provide a better tumor characterization and give new insights into this problem. Results We propose a novel bioimage informatics pipeline for automatically characterizing the topological organization of different cell patterns in the tumor microenvironment. We apply this pipeline to the only publicly available large histopathology image dataset for a cohort of 190 patients with papillary renal cell carcinoma obtained from The Cancer Genome Atlas project. Experimental results show that the proposed topological features can successfully stratify early- and middle-stage patients with distinct survival, and show superior performance to traditional clinical features and cellular morphological and intensity features. The proposed features not only provide new insights into the topological organizations of cancers, but also can be integrated with genomic data in future studies to develop new integrative biomarkers

Exploiting Rich Syntactic Information for Semantic Parsing with Graph-to-Sequence Model

Existing neural semantic parsers mainly utilize a sequence encoder, i.e., a sequential LSTM, to extract word order features while neglecting other valuable syntactic information such as dependency graph or constituent trees. In this paper, we first propose to use the \textit{syntactic graph} to represent three types of syntactic information, i.e., word order, dependency and constituency features. We further employ a graph-to-sequence model to encode the syntactic graph and decode a logical form. Experimental results on benchmark datasets show that our model is comparable to the state-of-the-art on Jobs640, ATIS and Geo880. Experimental results on adversarial examples demonstrate the robustness of the model is also improved by encoding more syntactic information.Comment: EMNLP'1

A comprehensive study on screw design and process optimization of single-screw and twin-screw extrusion of polypropylene

The mechanical properties of the extrudate and the processing conditions of the extrusion must be studied in concert to make the proper recommendations on overall screw design of both single-screw extruders (SSE) and twin-screw extruders (TSE). In this paper, we present a detailed approach to comparing the screw designs of a 30 L/D Brabender SSE and a 32 L/D Japan Steel Works TSE on their effectiveness of polypropylene processing. We observe changes in the torque, pressure, and throughput brought upon by input changes in the extruder screw speed and the material feed rate, which we relate to changes in the mechanical properties of the resulting polypropylene. The first half of our investigation is concerned with the studying of homopolymer polypropylene (HPP) and random-copolymer polypropylene (RCP) and how the haze and flexural modulus of the two are affected by different single-screw designs. By increasing the specific mechanical energy requirement of the screw design by 17±4 kJ/kg, we were able to produce up to a 130±20 MPa increase in the flexural modulus of 2 MFR HPP with no significant increases in haze values. The latter part of our study involves observing how the addition of kneading elements into the design of a TSE can change the mixing behavior and ultimately the impact toughness of impact co-polymers (ICP). By inducing changes in the specific mechanical energy profiles of the screw, we were able to produce increases in the Izod impact resistance of ICPs ≤ 40±10 J/m2 and increases in the flexural modulus ≤ 80±40 MPa. We also study the PP matrix morphology using SEM to make conclusions on the relationship between rubber dispersion and ICP mechanical properties

Continuous Intermediate Token Learning with Implicit Motion Manifold for Keyframe Based Motion Interpolation

Deriving sophisticated 3D motions from sparse keyframes is a particularly challenging problem, due to continuity and exceptionally skeletal precision. The action features are often derivable accurately from the full series of keyframes, and thus, leveraging the global context with transformers has been a promising data-driven embedding approach. However, existing methods are often with inputs of interpolated intermediate frame for continuity using basic interpolation methods with keyframes, which result in a trivial local minimum during training. In this paper, we propose a novel framework to formulate latent motion manifolds with keyframe-based constraints, from which the continuous nature of intermediate token representations is considered. Particularly, our proposed framework consists of two stages for identifying a latent motion subspace, i.e., a keyframe encoding stage and an intermediate token generation stage, and a subsequent motion synthesis stage to extrapolate and compose motion data from manifolds. Through our extensive experiments conducted on both the LaFAN1 and CMU Mocap datasets, our proposed method demonstrates both superior interpolation accuracy and high visual similarity to ground truth motions.Comment: Accepted by CVPR 202

A 3rd-order Continuous-Time Low-Pass Sigma-Delta Analog-to-Digital Converter for Wideband Applications

This thesis presents the design of a 20 MHz bandwidth 3rd-order continuous-time low-pass sigma-delta analog-to-digital converter with low-noise and low-power consumption using TSMC 0.18 μm CMOS technology. The bandwidth of the system is selected to be able to accommodate WiMAX and other wireless network standards. A 3rd-order filter with feed-forward architecture is selected to achieve low-power consumption as well as less complexity. The system uses 3-bit flash quantizer to provide fast data conversion. The current-steering DAC not only achieves low-power and less current sensitivity, but also it helps directly inject the feedback signal without additional circuitries. In order to avoid degradation of the overall performance, cross-coupled transistors are adopted to reduce the current glitches. The proposed system achieves a peak SNDR of 65.9 dB in 20 MHz bandwidth, and consumes 31.735 mW from a 1.8 V supply. The entire circuit is driven by a sampling rate at 500 MHz. The measured in-band IM3 of this thesis is -69 dB with 600 mVp-p two tone signal peak-to-peak voltage