Short texts present significant challenges for clustering due to semantic sparsity, limited contextual information, and ambiguous category boundaries. While recent studies incorporating contrastive learning and cluster structure optimization have improved performance, their reliance on augmented samples often introduces noise and weakens the capacity of pretrained language models to capture fine-grained semantics. To address these issues, we propose a Graph-augmented and Over-smoothing-resistant Contrastive Clustering framework (GOCC). Specifically, GOCC constructs sentence-level and cluster-level graphs to capture local semantic similarity and global structural patterns, incorporating these signals into sentence representations to enhance representational quality and clustering suitability. Moreover, we introduce a contrastive mechanism based on intermediate layer representations within graph-augmented contrastive learning to alleviate semantic over-smoothing caused by deep networks. Finally, a target-distribution-driven clustering optimization strategy is employed to leverage high-confidence samples in guiding cluster assignments. Experimental results on several benchmark short text datasets demonstrate that GOCC consistently outperforms state-of-the-art methods in terms of clustering accuracy and normalized mutual information.

Video cameras can only take photographs with limited dynamic range. One method to overcome this is to combine differently exposed images of the same subject matter (i.e. a Wyckoff Set), producing a High Dynamic Range (HDR) result. HDR digital photography started almost 20 years ago. Now, it is possible to produce HDR video in real-time, on both high-power CPU/GPU systems, as well as low-power FPGA boards. However, other FPGA implementations have relied upon methods that are less accurate than current CPU and GPU-based methods. Namely, the earlier FPGA approaches used weighted sum for image compositing. In this paper we provide a novel method for real-time HDR com-positing. As an essential part of an upgraded HDR video production system, the resulting system combines differently exposed video stream (of the same subject matter) in Full HD (1080p at 60fps) on a Kintex-7 FPGA. The proposed work flow, implemented with software written in C, estimates the camera response function according to its quadtree representation and generates the compositing circuit in Verilog HDL from a Wyckoff Set. This circuit consists of parts that perform addressing using multiplexer networks and estimation with bilinear interpolation. It is parameterizable by user-specified error constraints, allowing us to explore the trade-offs in resource usage and precision of the implementation. Here is an MD5 hash function sum generated for the rest of the paper: 07897e61027d15dc3600fadbccfbd67d, citation date: December 18, 2013.

Wearable computing can be used to both extend the range of human perception, and to share sensory experiences with others. For this objective to be made practical, engineering considerations such as form factor, computational power, and power consumption are critical concerns. In this work, we consider the design of a low-power visual seeing aid, and how to implement computationally-intensive computational photography algorithms in a small form factor with low power consumption. We present realtime an FPGA-based HDR (High Dynamic Range) video processing and filtering by integrating tonal and spatial information obtained from multiple different exposures of the same subject matter. In this embodiment the system captures, in rapid succession, sets of three exposures, “dark”, “medium”, and “light”, over and over again, e.g. “dark”, “medium”, “light”, “dark”, “medium”, “light”, and so on, at 60 frames per second. These exposures are used to determine an estimate of the photoquantity every 1/60th of a second (each time a frame comes in, an estimate goes out). This allows us to build a seeing aid that helps people see better in high contrast scenes, for example, while welding, or in outdoor scenes, or scenes where a bright light is shining directly into the eyes of the wearer. Our system is suitable for being built into eyeglasses or small camera-based, lifeglogging, or gesture-sensing pendants, and other miniature wearable devices, with low-power and compact circuits that can be easily mounted on the body.

We present highly parallelizable and computationally efficient High Dynamic Range (HDR) image compositing, reconstruction, and spatotonal mapping algorithms for processing HDR video. We implemented our algorithms in the EyeTap Digital Glass electric seeing aid, for use in everyday life. We also tested the algorithms in extreme dynamic range situations, such as, electric arc welding. Our system runs in real-time, and requires no user intervention, and no fine-tuning of parameters after a one-time calibration, even under a wide variety of very difficult lighting conditions (e.g. electric arc welding, including detailed inspection of the arc, weld puddle, and shielding gas in TIG welding). Our approach can render video at 1920x1080 pixel resolution at interactive frame rates that vary from 24 to 60 frames per second with GPU acceleration. We also implemented our system on FPGAs (Field Programmable Gate Arrays) for being miniaturized and built into eyeglass frames.