CLiC: Concept Learning in Context

This paper addresses the challenge of learning a local visual pattern of an object from one image, and generating images depicting objects with that pattern. Learning a localized concept and placing it on an object in a target image is a nontrivial task, as the objects may have different orientations and shapes. Our approach builds upon recent advancements in visual concept learning. It involves acquiring a visual concept (e.g., an ornament) from a source image and subsequently applying it to an object (e.g., a chair) in a target image. Our key idea is to perform in-context concept learning, acquiring the local visual concept within the broader context of the objects they belong to. To localize the concept learning, we employ soft masks that contain both the concept within the mask and the surrounding image area. We demonstrate our approach through object generation within an image, showcasing plausible embedding of in-context learned concepts. We also introduce methods for directing acquired concepts to specific locations within target images, employing cross-attention mechanisms, and establishing correspondences between source and target objects. The effectiveness of our method is demonstrated through quantitative and qualitative experiments, along with comparisons against baseline techniques

SKED: Sketch-guided Text-based 3D Editing

Text-to-image diffusion models are gradually introduced into computer graphics, recently enabling the development of Text-to-3D pipelines in an open domain. However, for interactive editing purposes, local manipulations of content through a simplistic textual interface can be arduous. Incorporating user guided sketches with Text-to-image pipelines offers users more intuitive control. Still, as state-of-the-art Text-to-3D pipelines rely on optimizing Neural Radiance Fields (NeRF) through gradients from arbitrary rendering views, conditioning on sketches is not straightforward. In this paper, we present SKED, a technique for editing 3D shapes represented by NeRFs. Our technique utilizes as few as two guiding sketches from different views to alter an existing neural field. The edited region respects the prompt semantics through a pre-trained diffusion model. To ensure the generated output adheres to the provided sketches, we propose novel loss functions to generate the desired edits while preserving the density and radiance of the base instance. We demonstrate the effectiveness of our proposed method through several qualitative and quantitative experiments

Maximum Wireless Power Transmission Using Real-Time Single Iteration Adaptive Impedance Matching

Wireless power transfer (WPT) systems’ efficiency is significantly impacted by non-monotonic variations in the coupling coefficient. For very short distances or strong-coupling cases, the WPT efficiency is minimal at the natural resonant frequency, with two peaks around this frequency, known as the frequency splitting phenomenon. On the other hand, WPT capability decreases for long distances or weak coupling cases. Therefore, adaptive matching is required for WPT systems with varying distances, like wireless charging systems for electric vehicles (EVs). This paper first presents a detailed analysis of the frequency splitting phenomenon by studying the root locations of the WPT system’s transfer function. Then, a real-time fixed-frequency adaptive impedance matching (IM) method is proposed, in which the amplitude and phase of the input impedance is estimated using the average active power, the average reactive power, and the amplitude of input voltage. Unlike traditional search-and-find techniques, the proposed method calculates the optimal IM network parameters only in a single iteration, which improves the convergent speed. A scaled-down 20-Watt prototype controlled by the TMSF2812 is fabricated and used to validate the effectiveness of the proposed method over a wide range of coil-to-coil distances

The Prognostic Value of <em>BRAF</em> Mutation in Colorectal Cancer and Melanoma: A Systematic Review and Meta-Analysis

<div><h3>Background</h3><p>Mutation of <em>BRAF</em> is a predominant event in cancers with poor prognosis such as melanoma and colorectal cancer. <em>BRAF</em> mutation leads to a constitutive activation of mitogen activated protein kinase pathway which is essential for cell proliferation and tumor progression. Despite tremendous efforts made to target BRAF for cancer treatment, the correlation between <em>BRAF</em> mutation and patient survival is still a matter of controversy.</p> <h3>Methods/Principal Findings</h3><p>Clinical studies on the correlation between <em>BRAF</em> mutation and patient survival were retrieved from MEDLINE and EMBASE databases between June 2002 and December 2011. One hundred twenty relevant full text studies were categorized based on study design and cancer type. Publication bias was evaluated for each category and pooled hazard ratio (HR) with 95% confidence interval (CI) was calculated using random or fixed effect meta-analysis based on the percentage of heterogeneity. Twenty six studies on colorectal cancer (11,773 patients) and four studies on melanoma (674 patients) were included in our final meta-analysis. The average prevalence of <em>BRAF</em> mutation was 9.6% in colorectal cancer, and 47.8% in melanoma reports. We found that <em>BRAF</em> mutation increases the risk of mortality in colorectal cancer patients for more than two times; HR = 2.25 (95% CI, 1.82–2.83). In addition, we revealed that <em>BRAF</em> mutation also increases the risk of mortality in melanoma patients by 1.7 times (95% CI, 1.37–2.12).</p> <h3>Conclusions</h3><p>We revealed that <em>BRAF</em> mutation is an absolute risk factor for patient survival in colorectal cancer and melanoma.</p> </div

Summary of studies that reported the status of <i>BRAF</i> mutation in papillary thyroid carcinoma with information on patient survival.

<p>DFS, Disease free survival; OR, Odds Ratio; SBiPTC, Synchronous bilateral papillary thyroid carcinoma; UiPTC, Unilateral papillary thyroid carcinoma.</p

Random effect model of Log hazard ratio (LogHR) with 95% confidence interval for studies comparing the effect of <i>BRAF-</i>V600E mutation on overall survival in melanoma patients.

<p>A LogHR <0 implies a survival benefit for patients with <i>BRAF</i> mutation. The square size indicates the power of each study in meta-analysis based on the number of patients in that study. The center of diamond shape at the lowest part indicates the combined LogHR for meta-analysis and its extremities the 95% confidence interval.</p