Improved Delay-Dependent Stability Analysis for Neural Networks with Interval Time-Varying Delays

The problem of delay-dependent asymptotic stability analysis for neural networks with interval time-varying delays is considered based on the delay-partitioning method. Some less conservative stability criteria are established in terms of linear matrix inequalities (LMIs) by constructing a new Lyapunov-Krasovskii functional (LKF) in each subinterval and combining with reciprocally convex approach. Moreover, our criteria depend on both the upper and lower bounds on time-varying delay and its derivative, which is different from some existing ones. Finally, a numerical example is given to show the improved stability region of the proposed results

Scones: Towards Conversational Authoring of Sketches

Iteratively refining and critiquing sketches are crucial steps to developing effective designs. We introduce Scones, a mixed-initiative, machine-learning-driven system that enables users to iteratively author sketches from text instructions. Scones is a novel deep-learning-based system that iteratively generates scenes of sketched objects composed with semantic specifications from natural language. Scones exceeds state-of-the-art performance on a text-based scene modification task, and introduces a mask-conditioned sketching model that can generate sketches with poses specified by high-level scene information. In an exploratory user evaluation of Scones, participants reported enjoying an iterative drawing task with Scones, and suggested additional features for further applications. We believe Scones is an early step towards automated, intelligent systems that support human-in-the-loop applications for communicating ideas through sketching in art and design.Comment: Long Paper, IUI '20: Proceedings of the 25th International Conference on Intelligent User Interface

Graph Neural Network with Two Uplift Estimators for Label-Scarcity Individual Uplift Modeling

Uplift modeling aims to measure the incremental effect, which we call uplift, of a strategy or action on the users from randomized experiments or observational data. Most existing uplift methods only use individual data, which are usually not informative enough to capture the unobserved and complex hidden factors regarding the uplift. Furthermore, uplift modeling scenario usually has scarce labeled data, especially for the treatment group, which also poses a great challenge for model training. Considering that the neighbors' features and the social relationships are very informative to characterize a user's uplift, we propose a graph neural network-based framework with two uplift estimators, called GNUM, to learn from the social graph for uplift estimation. Specifically, we design the first estimator based on a class-transformed target. The estimator is general for all types of outcomes, and is able to comprehensively model the treatment and control group data together to approach the uplift. When the outcome is discrete, we further design the other uplift estimator based on our defined partial labels, which is able to utilize more labeled data from both the treatment and control groups, to further alleviate the label scarcity problem. Comprehensive experiments on a public dataset and two industrial datasets show a superior performance of our proposed framework over state-of-the-art methods under various evaluation metrics. The proposed algorithms have been deployed online to serve real-world uplift estimation scenarios

Stability of stope structure under different mining methods

The ore body has a great influence on the stability of surrounding rock and mining safety under different mining modes, and the reasonable selection of mining mode depends on other characteristics, such as ore structure surface feature, rock mass mechanical property, and ground stress distribution. Given the insufficient mining research data, this study establishes a 3D model by using the FLAC3D calculation program. Through numerical simulation and other technical means, a preliminary study on plastic and minimum stress changes during horizontal pillar mining, stress changes under different mining modes, and the effect comparison of full filling mining modes is conducted. Results show that the surrounding rock at the corner of pillar 1 is damaged, the plastic zone decreases, and the minimum stress in each working procedure increases slightly. The area of the plastic zone in alternate mining is smaller to that in continuous mining. This study provides a theoretical basis for ore body mining

Scaling up GANs for Text-to-Image Synthesis

The recent success of text-to-image synthesis has taken the world by storm and captured the general public's imagination. From a technical standpoint, it also marked a drastic change in the favored architecture to design generative image models. GANs used to be the de facto choice, with techniques like StyleGAN. With DALL-E 2, auto-regressive and diffusion models became the new standard for large-scale generative models overnight. This rapid shift raises a fundamental question: can we scale up GANs to benefit from large datasets like LAION? We find that na\"Ively increasing the capacity of the StyleGAN architecture quickly becomes unstable. We introduce GigaGAN, a new GAN architecture that far exceeds this limit, demonstrating GANs as a viable option for text-to-image synthesis. GigaGAN offers three major advantages. First, it is orders of magnitude faster at inference time, taking only 0.13 seconds to synthesize a 512px image. Second, it can synthesize high-resolution images, for example, 16-megapixel pixels in 3.66 seconds. Finally, GigaGAN supports various latent space editing applications such as latent interpolation, style mixing, and vector arithmetic operations.Comment: CVPR 2023. Project webpage at https://mingukkang.github.io/GigaGAN

Perceptions and Barriers of Survivorship Care in Asia: Perceptions From Asian Breast Cancer Survivors.

PurposeWith the long-term goal to optimize post-treatment cancer care in Asia, we conducted a qualitative study to gather in-depth descriptions from multiethnic Asian breast cancer survivors on their perceptions and experiences of cancer survivorship and their perceived barriers to post-treatment follow-up.MethodsTwenty-four breast cancer survivors in Singapore participated in six structured focus group discussions. The focus group discussions were voice recorded, transcribed verbatim, and analyzed by thematic analysis.ResultsBreast cancer survivors were unfamiliar with and disliked the term "survivorship," because it implies that survivors had undergone hardship during their treatment. Cognitive impairment and peripheral neuropathy were physical symptoms that bothered survivors the most, and many indicated that they experienced emotional distress during survivorship, for which they turned to religion and peers as coping strategies. Survivors indicated lack of consultation time and fear of unplanned hospitalization as main barriers to optimal survivorship care. Furthermore, survivors indicated that they preferred receipt of survivorship care at the specialty cancer center.ConclusionBudding survivorship programs in Asia must take survivor perspectives into consideration to ensure that survivorship care is fully optimized within the community